Surface temperature comparison

import act
import numpy as np
import xarray as xr
import pandas as pd
import matplotlib.pyplot as plt
from datetime import timedelta
import xwrf
import cmweather
import glob
from bokeh.models.formatters import DatetimeTickFormatter
import hvplot.xarray
import holoviews as hv
hv.extension("bokeh")

# Set your username and token here!
username = '---------'
token = '##########'

# Set the datastream and start/enddates
datastream_snow = 'gucmetM1.b1' # snow depth, also precipitation rate
# datastream_metprep = 'gucmetM1.b1' # surface temperature+precipitation
startdate1 = '2022-01-02'
enddate1 = '2022-01-07'
startdate2 = '2022-01-25'
enddate2 = '2022-01-30'
startdate3 = '2023-04-03'
enddate3 = '2023-04-08'

# wrf: snow depth()
# SNOWNC: ACCUMULATED TOTAL GRID SCALE SNOW AND ICE
# SNOWC: FLAG INDICATING SNOW COVERAGE (1 FOR SNOW COVER)

# Use ACT to easily download the data.  Watch for the data citation!  Show some support
# for ARM's instrument experts and cite their data if you use it in a publication
datasnow1 = act.discovery.download_arm_data(username, token, datastream_snow, startdate1, enddate1)
datasnow2 = act.discovery.download_arm_data(username, token, datastream_snow, startdate2, enddate2)
datasnow3 = act.discovery.download_arm_data(username, token, datastream_snow, startdate3, enddate3)
# datamet = act.discovery.download_arm_data(username, token, datastream_metprep, startdate, enddate)

[DOWNLOADING] gucmetM1.b1.20220105.000000.cdf
[DOWNLOADING] gucmetM1.b1.20220103.000000.cdf
[DOWNLOADING] gucmetM1.b1.20220104.000000.cdf
[DOWNLOADING] gucmetM1.b1.20220106.000000.cdf
[DOWNLOADING] gucmetM1.b1.20220102.000000.cdf
[DOWNLOADING] gucmetM1.b1.20220107.000000.cdf

If you use these data to prepare a publication, please cite:

Kyrouac, J., Shi, Y., & Tuftedal, M. Surface Meteorological Instrumentation
(MET). Atmospheric Radiation Measurement (ARM) User Facility.
https://doi.org/10.5439/1786358

[DOWNLOADING] gucmetM1.b1.20220126.000000.cdf
[DOWNLOADING] gucmetM1.b1.20220127.000000.cdf
[DOWNLOADING] gucmetM1.b1.20220128.000000.cdf
[DOWNLOADING] gucmetM1.b1.20220129.000000.cdf
[DOWNLOADING] gucmetM1.b1.20220125.000000.cdf
[DOWNLOADING] gucmetM1.b1.20220130.000000.cdf

If you use these data to prepare a publication, please cite:

Kyrouac, J., Shi, Y., & Tuftedal, M. Surface Meteorological Instrumentation
(MET). Atmospheric Radiation Measurement (ARM) User Facility.
https://doi.org/10.5439/1786358

[DOWNLOADING] gucmetM1.b1.20230405.000000.cdf
[DOWNLOADING] gucmetM1.b1.20230407.000000.cdf
[DOWNLOADING] gucmetM1.b1.20230406.000000.cdf
[DOWNLOADING] gucmetM1.b1.20230403.000000.cdf
[DOWNLOADING] gucmetM1.b1.20230404.000000.cdf
[DOWNLOADING] gucmetM1.b1.20230408.000000.cdf

If you use these data to prepare a publication, please cite:

Kyrouac, J., Shi, Y., & Tuftedal, M. Surface Meteorological Instrumentation
(MET). Atmospheric Radiation Measurement (ARM) User Facility.
https://doi.org/10.5439/1786358

# Let's read in the data using ACT and check out the data
ds_snow1 = act.io.read_arm_netcdf(datasnow1) # 1-min
ds_snow2 = act.io.read_arm_netcdf(datasnow2) # 1-min
ds_snow3 = act.io.read_arm_netcdf(datasnow3) # 1-min

ds_snow1

<xarray.Dataset> Size: 2MB
Dimensions:                       (time: 8640, bound: 2)
Coordinates:
  * time                          (time) datetime64[ns] 69kB 2022-01-02 ... 2...
Dimensions without coordinates: bound
Data variables: (12/51)
    base_time                     (time) datetime64[ns] 69kB 2022-01-02 ... 2...
    time_offset                   (time) datetime64[ns] 69kB 2022-01-02 ... 2...
    time_bounds                   (time, bound) object 138kB dask.array<chunksize=(1440, 2), meta=np.ndarray>
    atmos_pressure                (time) float32 35kB dask.array<chunksize=(1440,), meta=np.ndarray>
    qc_atmos_pressure             (time) int32 35kB dask.array<chunksize=(1440,), meta=np.ndarray>
    temp_mean                     (time) float32 35kB dask.array<chunksize=(1440,), meta=np.ndarray>
    ...                            ...
    qc_logger_volt                (time) int32 35kB dask.array<chunksize=(1440,), meta=np.ndarray>
    logger_temp                   (time) float32 35kB dask.array<chunksize=(1440,), meta=np.ndarray>
    qc_logger_temp                (time) int32 35kB dask.array<chunksize=(1440,), meta=np.ndarray>
    lat                           (time) float32 35kB 38.96 38.96 ... 38.96
    lon                           (time) float32 35kB -107.0 -107.0 ... -107.0
    alt                           (time) float32 35kB 2.886e+03 ... 2.886e+03
Attributes: (12/25)
    command_line:                met_ingest -s guc -f M1 -RD --max-runtime 0
    Conventions:                 ARM-1.3
    process_version:             ingest-met-4.53-0.el7
    dod_version:                 met-b1-11.2
    input_source:                /data/reproc/D221103.2/collection/guc/gucmet...
    site_id:                     guc
    ...                          ...
    tbrg_precip_corr_info:       0.000000 * tbrg_precip_total^2 + 0.000000 * ...
    history:                     created by user yahmad on machine prod-proc5...
    _file_dates:                 ['20220102', '20220103', '20220104', '202201...
    _file_times:                 ['000000', '000000', '000000', '000000', '00...
    _datastream:                 gucmetM1.b1
    _arm_standards_flag:         1

xarray.Dataset

• Dimensions:
  • time: 8640
  • bound: 2
• Coordinates: (1)
  • time
    (time)
    datetime64[ns]
    2022-01-02 ... 2022-01-07T23:59:00

    long_name :

    Time offset from midnight

    bounds :

    time_bounds

    standard_name :

    time

    array(['2022-01-02T00:00:00.000000000', '2022-01-02T00:01:00.000000000',
           '2022-01-02T00:02:00.000000000', ..., '2022-01-07T23:57:00.000000000',
           '2022-01-07T23:58:00.000000000', '2022-01-07T23:59:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (51)
  • base_time
    (time)
    datetime64[ns]
    2022-01-02 ... 2022-01-07

    string :

    2022-01-02 00:00:00 0:00

    long_name :

    Base time in Epoch

    ancillary_variables :

    time_offset

    array(['2022-01-02T00:00:00.000000000', '2022-01-02T00:00:00.000000000',
           '2022-01-02T00:00:00.000000000', ...,
           '2022-01-07T00:00:00.000000000', '2022-01-07T00:00:00.000000000',
           '2022-01-07T00:00:00.000000000'], dtype='datetime64[ns]')

  • time_offset
    (time)
    datetime64[ns]
    2022-01-02 ... 2022-01-07T23:59:00

    long_name :

    Time offset from base_time

    ancillary_variables :

    base_time

    array(['2022-01-02T00:00:00.000000000', '2022-01-02T00:01:00.000000000',
           '2022-01-02T00:02:00.000000000', ...,
           '2022-01-07T23:57:00.000000000', '2022-01-07T23:58:00.000000000',
           '2022-01-07T23:59:00.000000000'], dtype='datetime64[ns]')

  • time_bounds
    (time, bound)
    object
    dask.array<chunksize=(1440, 2), meta=np.ndarray>

    long_name :

    Time cell bounds

    bound_offsets :

    [-60. 0.]

    Array Chunk Bytes 135.00 kiB 22.50 kiB Shape (8640, 2) (1440, 2) Dask graph 6 chunks in 13 graph layers Data type object numpy.ndarray

  • atmos_pressure
    (time)
    float32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Atmospheric pressure

    units :

    kPa

    valid_min :

    60.0

    valid_max :

    110.0

    valid_delta :

    1.0

    ancillary_variables :

    qc_atmos_pressure

    standard_name :

    surface_air_pressure

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • qc_atmos_pressure
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: Atmospheric pressure

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    bit_4_description :

    Difference between current and previous values exceeds valid_delta.

    bit_4_assessment :

    Indeterminate

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • temp_mean
    (time)
    float32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Temperature mean

    units :

    degC

    valid_min :

    -40.0

    valid_max :

    50.0

    valid_delta :

    20.0

    ancillary_variables :

    qc_temp_mean

    standard_name :

    air_temperature

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • qc_temp_mean
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: Temperature mean

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    bit_4_description :

    Difference between current and previous values exceeds valid_delta.

    bit_4_assessment :

    Indeterminate

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • temp_std
    (time)
    float32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Temperature standard deviation

    units :

    degC

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • rh_mean
    (time)
    float32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Relative humidity mean

    units :

    %

    valid_min :

    0.0

    valid_max :

    104.0

    valid_delta :

    30.0

    ancillary_variables :

    qc_rh_mean

    standard_name :

    relative_humidity

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • qc_rh_mean
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: Relative humidity mean

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    bit_4_description :

    Difference between current and previous values exceeds valid_delta.

    bit_4_assessment :

    Indeterminate

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • rh_std
    (time)
    float32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Relative humidity standard deviation

    units :

    %

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • vapor_pressure_mean
    (time)
    float32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Vapor pressure mean, calculated

    units :

    kPa

    valid_min :

    0.0

    valid_max :

    10.0

    valid_delta :

    1.0

    ancillary_variables :

    qc_vapor_pressure_mean

    standard_name :

    water_vapor_partial_pressure_in_air

    comment :

    The calculation is done with respect to ice or water, depending on the measured temperature being below or above 0 degC, respectively

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • qc_vapor_pressure_mean
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: Vapor pressure mean, calculated

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    bit_4_description :

    Difference between current and previous values exceeds valid_delta.

    bit_4_assessment :

    Indeterminate

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • vapor_pressure_std
    (time)
    float32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Vapor pressure standard deviation

    units :

    kPa

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • wspd_arith_mean
    (time)
    float32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Wind speed arithmetic mean

    units :

    m/s

    valid_min :

    0.0

    valid_max :

    60.0

    valid_delta :

    20.0

    ancillary_variables :

    qc_wspd_arith_mean

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • qc_wspd_arith_mean
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: Wind speed arithmetic mean

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    bit_4_description :

    Difference between current and previous values exceeds valid_delta.

    bit_4_assessment :

    Indeterminate

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • wspd_vec_mean
    (time)
    float32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Wind speed vector mean

    units :

    m/s

    valid_min :

    0.0

    valid_max :

    60.0

    valid_delta :

    20.0

    ancillary_variables :

    qc_wspd_vec_mean

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • qc_wspd_vec_mean
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: Wind speed vector mean

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    bit_4_description :

    Difference between current and previous values exceeds valid_delta.

    bit_4_assessment :

    Indeterminate

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • wdir_vec_mean
    (time)
    float32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Wind direction vector mean

    units :

    degree

    valid_min :

    0.0

    valid_max :

    360.0

    ancillary_variables :

    qc_wdir_vec_mean

    standard_name :

    wind_from_direction

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • qc_wdir_vec_mean
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: Wind direction vector mean

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • wdir_vec_std
    (time)
    float32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Wind direction vector mean standard deviation

    units :

    degree

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • pwd_err_code
    (time)
    float64
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    PWD alarm

    units :

    1

    Array Chunk Bytes 67.50 kiB 11.25 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float64 numpy.ndarray

  • pwd_mean_vis_1min
    (time)
    float64
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    PWD 1 minute mean visibility

    units :

    m

    valid_min :

    0

    valid_max :

    20000

    ancillary_variables :

    qc_pwd_mean_vis_1min

    standard_name :

    visibility_in_air

    Array Chunk Bytes 67.50 kiB 11.25 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float64 numpy.ndarray

  • qc_pwd_mean_vis_1min
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: PWD 1 minute mean visibility

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • pwd_mean_vis_10min
    (time)
    float64
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    PWD 10 minute mean visibility

    units :

    m

    valid_min :

    0

    valid_max :

    20000

    ancillary_variables :

    qc_pwd_mean_vis_10min

    standard_name :

    visibility_in_air

    Array Chunk Bytes 67.50 kiB 11.25 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float64 numpy.ndarray

  • qc_pwd_mean_vis_10min
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: PWD 10 minute mean visibility

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • pwd_pw_code_inst
    (time)
    float64
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    PWD instantaneous present weather code

    units :

    1

    valid_min :

    0

    valid_max :

    99

    ancillary_variables :

    qc_pwd_pw_code_inst

    Array Chunk Bytes 67.50 kiB 11.25 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float64 numpy.ndarray

  • qc_pwd_pw_code_inst
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: PWD instantaneous present weather code

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • pwd_pw_code_15min
    (time)
    float64
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    PWD 15 minute present weather code

    units :

    1

    valid_min :

    0

    valid_max :

    99

    ancillary_variables :

    qc_pwd_pw_code_15min

    Array Chunk Bytes 67.50 kiB 11.25 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float64 numpy.ndarray

  • qc_pwd_pw_code_15min
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: PWD 15 minute present weather code

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • pwd_pw_code_1hr
    (time)
    float64
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    PWD 1 hour present weather code

    units :

    1

    valid_min :

    0

    valid_max :

    99

    ancillary_variables :

    qc_pwd_pw_code_1hr

    Array Chunk Bytes 67.50 kiB 11.25 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float64 numpy.ndarray

  • qc_pwd_pw_code_1hr
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: PWD 1 hour present weather code

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • pwd_precip_rate_mean_1min
    (time)
    float32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    PWD 1 minute mean precipitation rate

    units :

    mm/hr

    valid_min :

    0.0

    valid_max :

    999.99

    valid_delta :

    100.0

    ancillary_variables :

    qc_pwd_precip_rate_mean_1min

    standard_name :

    lwe_precipitation_rate

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • qc_pwd_precip_rate_mean_1min
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: PWD 1 minute mean precipitation rate

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    bit_4_description :

    Difference between current and previous values exceeds valid_delta.

    bit_4_assessment :

    Indeterminate

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • pwd_cumul_rain
    (time)
    float32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    PWD cumulative liquid precipitation

    units :

    mm

    valid_min :

    0.0

    valid_max :

    99.99

    valid_delta :

    50.0

    ancillary_variables :

    qc_pwd_cumul_rain

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • qc_pwd_cumul_rain
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: PWD cumulative liquid precipitation

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    bit_4_description :

    Difference between current and previous values exceeds valid_delta.

    bit_4_assessment :

    Indeterminate

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • pwd_cumul_snow
    (time)
    float32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    PWD cumulative snow

    units :

    mm

    valid_min :

    0.0

    valid_max :

    999.0

    valid_delta :

    100.0

    ancillary_variables :

    qc_pwd_cumul_snow

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • qc_pwd_cumul_snow
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: PWD cumulative snow

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    bit_4_description :

    Difference between current and previous values exceeds valid_delta.

    bit_4_assessment :

    Indeterminate

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • org_precip_rate_mean
    (time)
    float32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    ORG precipitation rate mean

    units :

    mm/hr

    valid_min :

    0.0

    valid_max :

    500.0

    ancillary_variables :

    qc_org_precip_rate_mean

    standard_name :

    lwe_precipitation_rate

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • qc_org_precip_rate_mean
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: ORG precipitation rate mean

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • tbrg_precip_total
    (time)
    float32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    TBRG precipitation total

    units :

    mm

    valid_min :

    0.0

    valid_max :

    10.0

    ancillary_variables :

    qc_tbrg_precip_total

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • qc_tbrg_precip_total
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: TBRG precipitation total

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • tbrg_precip_total_corr
    (time)
    float32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    TBRG precipitation total, corrected

    units :

    mm

    valid_min :

    0.0

    valid_max :

    10.0

    ancillary_variables :

    qc_tbrg_precip_total_corr

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • qc_tbrg_precip_total_corr
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: TBRG precipitation total, corrected

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • logger_volt
    (time)
    float32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Logger voltage

    units :

    V

    valid_min :

    10.0

    valid_max :

    15.0

    valid_delta :

    5.0

    ancillary_variables :

    qc_logger_volt

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • qc_logger_volt
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: Logger voltage

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    bit_4_description :

    Difference between current and previous values exceeds valid_delta.

    bit_4_assessment :

    Indeterminate

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • logger_temp
    (time)
    float32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Logger temperature

    units :

    degC

    valid_min :

    -25.0

    valid_max :

    50.0

    valid_delta :

    10.0

    ancillary_variables :

    qc_logger_temp

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • qc_logger_temp
    (time)
    int32
    dask.array<chunksize=(1440,), meta=np.ndarray>

    long_name :

    Quality check results on variable: Logger temperature

    units :

    1

    standard_name :

    quality_flag

    description :

    This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Value is equal to missing_value.

    bit_1_assessment :

    Bad

    bit_2_description :

    Value is less than valid_min.

    bit_2_assessment :

    Bad

    bit_3_description :

    Value is greater than valid_max.

    bit_3_assessment :

    Bad

    bit_4_description :

    Difference between current and previous values exceeds valid_delta.

    bit_4_assessment :

    Indeterminate

    Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 13 graph layers Data type int32 numpy.ndarray

  • lat
    (time)
    float32
    38.96 38.96 38.96 ... 38.96 38.96

    long_name :

    North latitude

    units :

    degree_N

    valid_min :

    -90.0

    valid_max :

    90.0

    standard_name :

    latitude

    array([38.956158, 38.956158, 38.956158, ..., 38.956158, 38.956158,
           38.956158], dtype=float32)

  • lon
    (time)
    float32
    -107.0 -107.0 ... -107.0 -107.0

    long_name :

    East longitude

    units :

    degree_E

    valid_min :

    -180.0

    valid_max :

    180.0

    standard_name :

    longitude

    array([-106.987854, -106.987854, -106.987854, ..., -106.987854,
           -106.987854, -106.987854], dtype=float32)

  • alt
    (time)
    float32
    2.886e+03 2.886e+03 ... 2.886e+03

    long_name :

    Altitude above mean sea level

    units :

    m

    standard_name :

    altitude

    array([2886., 2886., 2886., ..., 2886., 2886., 2886.], dtype=float32)

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2022-01-02 00:00:00', '2022-01-02 00:01:00',
                   '2022-01-02 00:02:00', '2022-01-02 00:03:00',
                   '2022-01-02 00:04:00', '2022-01-02 00:05:00',
                   '2022-01-02 00:06:00', '2022-01-02 00:07:00',
                   '2022-01-02 00:08:00', '2022-01-02 00:09:00',
                   ...
                   '2022-01-07 23:50:00', '2022-01-07 23:51:00',
                   '2022-01-07 23:52:00', '2022-01-07 23:53:00',
                   '2022-01-07 23:54:00', '2022-01-07 23:55:00',
                   '2022-01-07 23:56:00', '2022-01-07 23:57:00',
                   '2022-01-07 23:58:00', '2022-01-07 23:59:00'],
                  dtype='datetime64[ns]', name='time', length=8640, freq=None))

• Attributes: (25)

  command_line :

  met_ingest -s guc -f M1 -RD --max-runtime 0

  Conventions :

  ARM-1.3

  process_version :

  ingest-met-4.53-0.el7

  dod_version :

  met-b1-11.2

  input_source :

  /data/reproc/D221103.2/collection/guc/gucmetM1.00/MET_Table1.20220102000000.dat

  site_id :

  guc

  platform_id :

  met

  facility_id :

  M1

  data_level :

  b1

  location_description :

  Surface Atmosphere Integrated Field Laboratory (SAIL), Mt Crested Butte, Colorado

  datastream :

  gucmetM1.b1

  serial_number :

  0

  sampling_interval :

  variable, see instrument handbook

  averaging_interval :

  60 seconds

  doi :

  10.5439/1786358

  averaging_interval_comment :

  The time assigned to each data point indicates the end of the averaging interval.

  org :

  Optical Rain Gauge

  tbrg :

  Tipping Bucket Rain Gauge

  pwd :

  Present Weather Detector

  tbrg_precip_corr_info :

  0.000000 * tbrg_precip_total^2 + 0.000000 * tbrg_precip_total

  history :

  created by user yahmad on machine prod-proc5.adc.arm.gov at 2023-11-16 19:52:14, using ingest-met-4.53-0.el7

  _file_dates :

  ['20220102', '20220103', '20220104', '20220105', '20220106', '20220107']

  _file_times :

  ['000000', '000000', '000000', '000000', '000000', '000000']

  _datastream :

  gucmetM1.b1

  _arm_standards_flag :

  1

ds_snow1["temp_mean"] = ds_snow1["temp_mean"] + 273.15
ds_snow2["temp_mean"] = ds_snow2["temp_mean"] + 273.15
ds_snow3["temp_mean"] = ds_snow3["temp_mean"] + 273.15

ds_snow1["temp_mean"]

<xarray.DataArray 'temp_mean' (time: 8640)> Size: 35kB
dask.array<add, shape=(8640,), dtype=float32, chunksize=(1440,), chunktype=numpy.ndarray>
Coordinates:
  * time     (time) datetime64[ns] 69kB 2022-01-02 ... 2022-01-07T23:59:00

xarray.DataArray

'temp_mean'

• time: 8640

• dask.array<chunksize=(1440,), meta=np.ndarray>

  Array Chunk Bytes 33.75 kiB 5.62 kiB Shape (8640,) (1440,) Dask graph 6 chunks in 14 graph layers Data type float32 numpy.ndarray

• Coordinates: (1)
  • time
    (time)
    datetime64[ns]
    2022-01-02 ... 2022-01-07T23:59:00

    long_name :

    Time offset from midnight

    bounds :

    time_bounds

    standard_name :

    time

    array(['2022-01-02T00:00:00.000000000', '2022-01-02T00:01:00.000000000',
           '2022-01-02T00:02:00.000000000', ..., '2022-01-07T23:57:00.000000000',
           '2022-01-07T23:58:00.000000000', '2022-01-07T23:59:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2022-01-02 00:00:00', '2022-01-02 00:01:00',
                   '2022-01-02 00:02:00', '2022-01-02 00:03:00',
                   '2022-01-02 00:04:00', '2022-01-02 00:05:00',
                   '2022-01-02 00:06:00', '2022-01-02 00:07:00',
                   '2022-01-02 00:08:00', '2022-01-02 00:09:00',
                   ...
                   '2022-01-07 23:50:00', '2022-01-07 23:51:00',
                   '2022-01-07 23:52:00', '2022-01-07 23:53:00',
                   '2022-01-07 23:54:00', '2022-01-07 23:55:00',
                   '2022-01-07 23:56:00', '2022-01-07 23:57:00',
                   '2022-01-07 23:58:00', '2022-01-07 23:59:00'],
                  dtype='datetime64[ns]', name='time', length=8640, freq=None))

• Attributes: (0)

variable_snow = "tbrg_precip_total_corr"
variable_temp = "temp_mean"

# Jan 2-7 2022
# Create a plotting display object with 2 plots
display = act.plotting.TimeSeriesDisplay(ds_snow1,subplot_shape=(2,), figsize=(15,10))

# Plot up the variable in the first plot - Surface precipitation corrected (tbrg_precip_total_corr)
display.plot(variable_snow, subplot_index=(0,),day_night_background=True,
             set_title="Control case: Total precipitation")
# display.day_night_background(subplot_index=(0,))

# Plot up the variable in the second plot - Temperature: temp_mean
display.plot(variable_temp, subplot_index=(1,),day_night_background=True,
             set_title="Control case: Surface Temperature")
# display.day_night_background(subplot_index=(1,))
plt.ylabel('(K)')
plt.show() 

# Jan 25-30 2022
# Create a plotting display object with 2 plots
display = act.plotting.TimeSeriesDisplay(ds_snow2,subplot_shape=(2,), figsize=(15,10))

# Plot up the variable in the first plot - Surface precipitation corrected (tbrg_precip_total_corr)
display.plot(variable_snow, subplot_index=(0,),day_night_background=True,
             set_title="Black carbon case: Total precipitation")
# display.day_night_background(subplot_index=(0,))

# Plot up the variable in the second plot - Temperature: temp_mean
display.plot(variable_temp, subplot_index=(1,),day_night_background=True,
             set_title="Black carbon case: Surface Temperature")
# display.day_night_background(subplot_index=(1,))

plt.show() 

# Apr 3-8 2022
# Create a plotting display object with 2 plots
display = act.plotting.TimeSeriesDisplay(ds_snow3,subplot_shape=(2,), figsize=(15,10))

# Plot up the variable in the first plot - Surface precipitation corrected (tbrg_precip_total_corr)
display.plot(variable_snow, subplot_index=(0,),day_night_background=True,
             set_title="Dust case: Total precipitation")
# display.day_night_background(subplot_index=(0,))

# Plot up the variable in the second plot - Temperature: temp_mean
display.plot(variable_temp, subplot_index=(1,),day_night_background=True,
             set_title="Dust case: Surface Temperature")
# display.day_night_background(subplot_index=(1,))

plt.show() 

## WRF
files_ctrl=sorted(glob.glob("/data/home/mqzhang/sail-cookbook/notebooks/downloaded_files/control/*"))
files_bc=sorted(glob.glob("/data/home/mqzhang/sail-cookbook/notebooks/downloaded_files/bc/*"))
files_dust=sorted(glob.glob("/data/home/mqzhang/sail-cookbook/notebooks/downloaded_files/dust/*"))

ds_ctrl = xr.open_mfdataset(files_ctrl,concat_dim="Time",combine="nested").xwrf.postprocess().squeeze()
ds_ctrl

<xarray.Dataset> Size: 543MB
Dimensions:         (Time: 168, y: 201, x: 201)
Coordinates:
  * Time            (Time) datetime64[ns] 1kB 2022-01-02 ... 2022-01-08T23:00:00
  * x               (x) float64 2kB -5.344e+04 -5.294e+04 ... 4.656e+04
  * y               (y) float64 2kB -5.6e+04 -5.55e+04 ... 4.35e+04 4.4e+04
Data variables: (12/22)
    Times           (Time) |S19 3kB dask.array<chunksize=(24,), meta=np.ndarray>
    Q2              (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    T2              (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    PSFC            (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    U10             (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    V10             (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    ...              ...
    EMISS           (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    HFX             (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    QFX             (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    LH              (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    SNOWC           (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    wrf_projection  object 8B +proj=lcc +x_0=0 +y_0=0 +a=6370000 +b=6370000 +...
Attributes: (12/88)
    TITLE:                            OUTPUT FROM WRF V4.4 MODEL
    START_DATE:                      2021-12-31_00:00:00
    WEST-EAST_GRID_DIMENSION:        202
    SOUTH-NORTH_GRID_DIMENSION:      202
    BOTTOM-TOP_GRID_DIMENSION:       50
    DX:                              500.0
    ...                              ...
    ISURBAN:                         13
    ISOILWATER:                      14
    HYBRID_OPT:                      2
    ETAC:                            0.2
    history:                         Sat Mar 25 11:56:03 2023: ncrcat /global...
    NCO:                             netCDF Operators version 5.0.1 (Homepage...

xarray.Dataset

• Dimensions:
  • Time: 168
  • y: 201
  • x: 201
• Coordinates: (3)
  • Time
    (Time)
    datetime64[ns]
    2022-01-02 ... 2022-01-08T23:00:00

    long_name :

    Time

    standard_name :

    time

    array(['2022-01-02T00:00:00.000000000', '2022-01-02T01:00:00.000000000',
           '2022-01-02T02:00:00.000000000', '2022-01-02T03:00:00.000000000',
           '2022-01-02T04:00:00.000000000', '2022-01-02T05:00:00.000000000',
           '2022-01-02T06:00:00.000000000', '2022-01-02T07:00:00.000000000',
           '2022-01-02T08:00:00.000000000', '2022-01-02T09:00:00.000000000',
           '2022-01-02T10:00:00.000000000', '2022-01-02T11:00:00.000000000',
           '2022-01-02T12:00:00.000000000', '2022-01-02T13:00:00.000000000',
           '2022-01-02T14:00:00.000000000', '2022-01-02T15:00:00.000000000',
           '2022-01-02T16:00:00.000000000', '2022-01-02T17:00:00.000000000',
           '2022-01-02T18:00:00.000000000', '2022-01-02T19:00:00.000000000',
           '2022-01-02T20:00:00.000000000', '2022-01-02T21:00:00.000000000',
           '2022-01-02T22:00:00.000000000', '2022-01-02T23:00:00.000000000',
           '2022-01-03T00:00:00.000000000', '2022-01-03T01:00:00.000000000',
           '2022-01-03T02:00:00.000000000', '2022-01-03T03:00:00.000000000',
           '2022-01-03T04:00:00.000000000', '2022-01-03T05:00:00.000000000',
           '2022-01-03T06:00:00.000000000', '2022-01-03T07:00:00.000000000',
           '2022-01-03T08:00:00.000000000', '2022-01-03T09:00:00.000000000',
           '2022-01-03T10:00:00.000000000', '2022-01-03T11:00:00.000000000',
           '2022-01-03T12:00:00.000000000', '2022-01-03T13:00:00.000000000',
           '2022-01-03T14:00:00.000000000', '2022-01-03T15:00:00.000000000',
           '2022-01-03T16:00:00.000000000', '2022-01-03T17:00:00.000000000',
           '2022-01-03T18:00:00.000000000', '2022-01-03T19:00:00.000000000',
           '2022-01-03T20:00:00.000000000', '2022-01-03T21:00:00.000000000',
           '2022-01-03T22:00:00.000000000', '2022-01-03T23:00:00.000000000',
           '2022-01-04T00:00:00.000000000', '2022-01-04T01:00:00.000000000',
           '2022-01-04T02:00:00.000000000', '2022-01-04T03:00:00.000000000',
           '2022-01-04T04:00:00.000000000', '2022-01-04T05:00:00.000000000',
           '2022-01-04T06:00:00.000000000', '2022-01-04T07:00:00.000000000',
           '2022-01-04T08:00:00.000000000', '2022-01-04T09:00:00.000000000',
           '2022-01-04T10:00:00.000000000', '2022-01-04T11:00:00.000000000',
           '2022-01-04T12:00:00.000000000', '2022-01-04T13:00:00.000000000',
           '2022-01-04T14:00:00.000000000', '2022-01-04T15:00:00.000000000',
           '2022-01-04T16:00:00.000000000', '2022-01-04T17:00:00.000000000',
           '2022-01-04T18:00:00.000000000', '2022-01-04T19:00:00.000000000',
           '2022-01-04T20:00:00.000000000', '2022-01-04T21:00:00.000000000',
           '2022-01-04T22:00:00.000000000', '2022-01-04T23:00:00.000000000',
           '2022-01-05T00:00:00.000000000', '2022-01-05T01:00:00.000000000',
           '2022-01-05T02:00:00.000000000', '2022-01-05T03:00:00.000000000',
           '2022-01-05T04:00:00.000000000', '2022-01-05T05:00:00.000000000',
           '2022-01-05T06:00:00.000000000', '2022-01-05T07:00:00.000000000',
           '2022-01-05T08:00:00.000000000', '2022-01-05T09:00:00.000000000',
           '2022-01-05T10:00:00.000000000', '2022-01-05T11:00:00.000000000',
           '2022-01-05T12:00:00.000000000', '2022-01-05T13:00:00.000000000',
           '2022-01-05T14:00:00.000000000', '2022-01-05T15:00:00.000000000',
           '2022-01-05T16:00:00.000000000', '2022-01-05T17:00:00.000000000',
           '2022-01-05T18:00:00.000000000', '2022-01-05T19:00:00.000000000',
           '2022-01-05T20:00:00.000000000', '2022-01-05T21:00:00.000000000',
           '2022-01-05T22:00:00.000000000', '2022-01-05T23:00:00.000000000',
           '2022-01-06T00:00:00.000000000', '2022-01-06T01:00:00.000000000',
           '2022-01-06T02:00:00.000000000', '2022-01-06T03:00:00.000000000',
           '2022-01-06T04:00:00.000000000', '2022-01-06T05:00:00.000000000',
           '2022-01-06T06:00:00.000000000', '2022-01-06T07:00:00.000000000',
           '2022-01-06T08:00:00.000000000', '2022-01-06T09:00:00.000000000',
           '2022-01-06T10:00:00.000000000', '2022-01-06T11:00:00.000000000',
           '2022-01-06T12:00:00.000000000', '2022-01-06T13:00:00.000000000',
           '2022-01-06T14:00:00.000000000', '2022-01-06T15:00:00.000000000',
           '2022-01-06T16:00:00.000000000', '2022-01-06T17:00:00.000000000',
           '2022-01-06T18:00:00.000000000', '2022-01-06T19:00:00.000000000',
           '2022-01-06T20:00:00.000000000', '2022-01-06T21:00:00.000000000',
           '2022-01-06T22:00:00.000000000', '2022-01-06T23:00:00.000000000',
           '2022-01-07T00:00:00.000000000', '2022-01-07T01:00:00.000000000',
           '2022-01-07T02:00:00.000000000', '2022-01-07T03:00:00.000000000',
           '2022-01-07T04:00:00.000000000', '2022-01-07T05:00:00.000000000',
           '2022-01-07T06:00:00.000000000', '2022-01-07T07:00:00.000000000',
           '2022-01-07T08:00:00.000000000', '2022-01-07T09:00:00.000000000',
           '2022-01-07T10:00:00.000000000', '2022-01-07T11:00:00.000000000',
           '2022-01-07T12:00:00.000000000', '2022-01-07T13:00:00.000000000',
           '2022-01-07T14:00:00.000000000', '2022-01-07T15:00:00.000000000',
           '2022-01-07T16:00:00.000000000', '2022-01-07T17:00:00.000000000',
           '2022-01-07T18:00:00.000000000', '2022-01-07T19:00:00.000000000',
           '2022-01-07T20:00:00.000000000', '2022-01-07T21:00:00.000000000',
           '2022-01-07T22:00:00.000000000', '2022-01-07T23:00:00.000000000',
           '2022-01-08T00:00:00.000000000', '2022-01-08T01:00:00.000000000',
           '2022-01-08T02:00:00.000000000', '2022-01-08T03:00:00.000000000',
           '2022-01-08T04:00:00.000000000', '2022-01-08T05:00:00.000000000',
           '2022-01-08T06:00:00.000000000', '2022-01-08T07:00:00.000000000',
           '2022-01-08T08:00:00.000000000', '2022-01-08T09:00:00.000000000',
           '2022-01-08T10:00:00.000000000', '2022-01-08T11:00:00.000000000',
           '2022-01-08T12:00:00.000000000', '2022-01-08T13:00:00.000000000',
           '2022-01-08T14:00:00.000000000', '2022-01-08T15:00:00.000000000',
           '2022-01-08T16:00:00.000000000', '2022-01-08T17:00:00.000000000',
           '2022-01-08T18:00:00.000000000', '2022-01-08T19:00:00.000000000',
           '2022-01-08T20:00:00.000000000', '2022-01-08T21:00:00.000000000',
           '2022-01-08T22:00:00.000000000', '2022-01-08T23:00:00.000000000'],
          dtype='datetime64[ns]')

  • x
    (x)
    float64
    -5.344e+04 -5.294e+04 ... 4.656e+04

    units :

    m

    standard_name :

    projection_x_coordinate

    axis :

    X

    array([-53443.825943, -52943.825943, -52443.825943, ...,  45556.174057,
            46056.174057,  46556.174057])

  • y
    (y)
    float64
    -5.6e+04 -5.55e+04 ... 4.4e+04

    units :

    m

    standard_name :

    projection_y_coordinate

    axis :

    Y

    array([-55999.217634, -55499.217634, -54999.217634, ...,  43000.782366,
            43500.782366,  44000.782366])

• Data variables: (22)
  • Times
    (Time)
    |S19
    dask.array<chunksize=(24,), meta=np.ndarray>

    cell_methods :

    Time: mean

    Array Chunk Bytes 3.12 kiB 456 B Shape (168,) (24,) Dask graph 7 chunks in 15 graph layers Data type |S19 numpy.ndarray

  • Q2
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    QV at 2 M

    units :

    kg kg-1

    stagger :

    cell_methods :

    Time: mean

    standard_name :

    humidity_mixing_ratio

    long_name :

    humidity_mixing_ratio_at_2m_agl

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • T2
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    TEMP at 2 M

    units :

    K

    stagger :

    cell_methods :

    Time: mean

    standard_name :

    air_temperature

    long_name :

    air_temperature_at_2m_agl

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • PSFC
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    SFC PRESSURE

    units :

    Pa

    stagger :

    cell_methods :

    Time: mean

    standard_name :

    air_pressure

    long_name :

    air_pressure_at_surface

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • U10
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    U at 10 M

    units :

    m s-1

    stagger :

    cell_methods :

    Time: mean

    standard_name :

    x_wind

    long_name :

    x_wind_at_10m_agl

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • V10
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    V at 10 M

    units :

    m s-1

    stagger :

    cell_methods :

    Time: mean

    standard_name :

    y_wind

    long_name :

    y_wind_at_10m_agl

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • SNOW
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    SNOW WATER EQUIVALENT

    units :

    kg m-2

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • SNOWH
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    PHYSICAL SNOW DEPTH

    units :

    m

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • HGT_SHAD
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Height of orographic shadow

    units :

    m

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • TSK
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    SURFACE SKIN TEMPERATURE

    units :

    K

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • RAINC
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED TOTAL CUMULUS PRECIPITATION

    units :

    mm

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • RAINNC
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED TOTAL GRID SCALE PRECIPITATION

    units :

    mm

    stagger :

    cell_methods :

    Time: mean

    standard_name :

    integral_of_lwe_precipitation_rate_wrt_time

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • SWDOWN
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    DOWNWARD SHORT WAVE FLUX AT GROUND SURFACE

    units :

    W m-2

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • GLW
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    DOWNWARD LONG WAVE FLUX AT GROUND SURFACE

    units :

    W m-2

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • SWNORM
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    NORMAL SHORT WAVE FLUX AT GROUND SURFACE (SLOPE-DEPENDENT)

    units :

    W m-2

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • ALBEDO
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ALBEDO

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • EMISS
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    SURFACE EMISSIVITY

    units :

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • HFX
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    UPWARD HEAT FLUX AT THE SURFACE

    units :

    W m-2

    stagger :

    cell_methods :

    Time: mean

    standard_name :

    surface_upward_heat_flux_in_air

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • QFX
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    UPWARD MOISTURE FLUX AT THE SURFACE

    units :

    kg m-2 s-1

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • LH
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    LATENT HEAT FLUX AT THE SURFACE

    units :

    W m-2

    stagger :

    cell_methods :

    Time: mean

    standard_name :

    surface_upward_latent_heat_flux_in_air

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • SNOWC
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    FLAG INDICATING SNOW COVERAGE (1 FOR SNOW COVER)

    units :

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • wrf_projection
    ()
    object
    +proj=lcc +x_0=0 +y_0=0 +a=63700...

    crs_wkt :

    PROJCRS["unknown",BASEGEOGCRS["unknown",DATUM["unknown",ELLIPSOID["unknown",6370000,0,LENGTHUNIT["metre",1,ID["EPSG",9001]]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8901]]],CONVERSION["unknown",METHOD["Lambert Conic Conformal (2SP)",ID["EPSG",9802]],PARAMETER["Latitude of false origin",38.900016784668,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8821]],PARAMETER["Longitude of false origin",-107,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8822]],PARAMETER["Latitude of 1st standard parallel",30,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8823]],PARAMETER["Latitude of 2nd standard parallel",50,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8824]],PARAMETER["Easting at false origin",0,LENGTHUNIT["metre",1],ID["EPSG",8826]],PARAMETER["Northing at false origin",0,LENGTHUNIT["metre",1],ID["EPSG",8827]]],CS[Cartesian,2],AXIS["(E)",east,ORDER[1],LENGTHUNIT["metre",1,ID["EPSG",9001]]],AXIS["(N)",north,ORDER[2],LENGTHUNIT["metre",1,ID["EPSG",9001]]]]

    semi_major_axis :

    6370000.0

    semi_minor_axis :

    6370000.0

    inverse_flattening :

    0.0

    reference_ellipsoid_name :

    unknown

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    unknown

    horizontal_datum_name :

    unknown

    projected_crs_name :

    unknown

    grid_mapping_name :

    lambert_conformal_conic

    standard_parallel :

    (30.0, 50.0)

    latitude_of_projection_origin :

    38.90001678466797

    longitude_of_central_meridian :

    -107.0

    false_easting :

    0.0

    false_northing :

    0.0

    array(<Projected CRS: +proj=lcc +x_0=0 +y_0=0 +a=6370000 +b=6370000 +lat ...>
    Name: unknown
    Axis Info [cartesian]:
    - E[east]: Easting (metre)
    - N[north]: Northing (metre)
    Area of Use:
    - undefined
    Coordinate Operation:
    - name: unknown
    - method: Lambert Conic Conformal (2SP)
    Datum: unknown
    - Ellipsoid: unknown
    - Prime Meridian: Greenwich
    , dtype=object)

• Indexes: (3)
  • Time
    PandasIndex

    PandasIndex(DatetimeIndex(['2022-01-02 00:00:00', '2022-01-02 01:00:00',
                   '2022-01-02 02:00:00', '2022-01-02 03:00:00',
                   '2022-01-02 04:00:00', '2022-01-02 05:00:00',
                   '2022-01-02 06:00:00', '2022-01-02 07:00:00',
                   '2022-01-02 08:00:00', '2022-01-02 09:00:00',
                   ...
                   '2022-01-08 14:00:00', '2022-01-08 15:00:00',
                   '2022-01-08 16:00:00', '2022-01-08 17:00:00',
                   '2022-01-08 18:00:00', '2022-01-08 19:00:00',
                   '2022-01-08 20:00:00', '2022-01-08 21:00:00',
                   '2022-01-08 22:00:00', '2022-01-08 23:00:00'],
                  dtype='datetime64[ns]', name='Time', length=168, freq=None))

  • x
    PandasIndex

    PandasIndex(Index([-53443.82594293087, -52943.82594293087, -52443.82594293087,
           -51943.82594293087, -51443.82594293087, -50943.82594293087,
           -50443.82594293087, -49943.82594293087, -49443.82594293087,
           -48943.82594293087,
           ...
            42056.17405706913,  42556.17405706913,  43056.17405706913,
            43556.17405706913,  44056.17405706913,  44556.17405706913,
            45056.17405706913,  45556.17405706913,  46056.17405706913,
            46556.17405706913],
          dtype='float64', name='x', length=201))

  • y
    PandasIndex

    PandasIndex(Index([-55999.21763437279, -55499.21763437279, -54999.21763437279,
           -54499.21763437279, -53999.21763437279, -53499.21763437279,
           -52999.21763437279, -52499.21763437279, -51999.21763437279,
           -51499.21763437279,
           ...
            39500.78236562721,  40000.78236562721,  40500.78236562721,
            41000.78236562721,  41500.78236562721,  42000.78236562721,
            42500.78236562721,  43000.78236562721,  43500.78236562721,
            44000.78236562721],
          dtype='float64', name='y', length=201))

• Attributes: (88)

  TITLE :

  OUTPUT FROM WRF V4.4 MODEL

  START_DATE :

  2021-12-31_00:00:00

  WEST-EAST_GRID_DIMENSION :

  202

  SOUTH-NORTH_GRID_DIMENSION :

  202

  BOTTOM-TOP_GRID_DIMENSION :

  50

  DX :

  500.0

  DY :

  500.0

  AERCU_OPT :

  0

  AERCU_FCT :

  1.0

  IDEAL_CASE :

  0

  DIFF_6TH_SLOPEOPT :

  0

  AUTO_LEVELS_OPT :

  2

  DIFF_6TH_THRESH :

  0.1

  DZBOT :

  50.0

  DZSTRETCH_S :

  1.3

  DZSTRETCH_U :

  1.1

  GRIDTYPE :

  C

  DIFF_OPT :

  1

  KM_OPT :

  4

  DAMP_OPT :

  0

  DAMPCOEF :

  0.2

  KHDIF :

  0.0

  KVDIF :

  0.0

  MP_PHYSICS :

  8

  RA_LW_PHYSICS :

  3

  RA_SW_PHYSICS :

  3

  SF_SFCLAY_PHYSICS :

  2

  SF_SURFACE_PHYSICS :

  4

  BL_PBL_PHYSICS :

  2

  CU_PHYSICS :

  0

  SF_LAKE_PHYSICS :

  0

  SURFACE_INPUT_SOURCE :

  1

  SST_UPDATE :

  1

  GHG_INPUT :

  1

  GRID_FDDA :

  0

  GFDDA_INTERVAL_M :

  0

  GFDDA_END_H :

  0

  GRID_SFDDA :

  0

  SGFDDA_INTERVAL_M :

  0

  SGFDDA_END_H :

  0

  HYPSOMETRIC_OPT :

  2

  USE_THETA_M :

  1

  GWD_OPT :

  0

  SF_URBAN_PHYSICS :

  0

  SF_SURFACE_MOSAIC :

  0

  SF_OCEAN_PHYSICS :

  0

  WEST-EAST_PATCH_START_UNSTAG :

  1

  WEST-EAST_PATCH_END_UNSTAG :

  201

  WEST-EAST_PATCH_START_STAG :

  1

  WEST-EAST_PATCH_END_STAG :

  202

  SOUTH-NORTH_PATCH_START_UNSTAG :

  1

  SOUTH-NORTH_PATCH_END_UNSTAG :

  201

  SOUTH-NORTH_PATCH_START_STAG :

  1

  SOUTH-NORTH_PATCH_END_STAG :

  202

  BOTTOM-TOP_PATCH_START_UNSTAG :

  1

  BOTTOM-TOP_PATCH_END_UNSTAG :

  49

  BOTTOM-TOP_PATCH_START_STAG :

  1

  BOTTOM-TOP_PATCH_END_STAG :

  50

  GRID_ID :

  3

  PARENT_ID :

  2

  I_PARENT_START :

  67

  J_PARENT_START :

  67

  PARENT_GRID_RATIO :

  3

  DT :

  2.6666667

  CEN_LAT :

  38.84523

  CEN_LON :

  -107.040375

  TRUELAT1 :

  30.0

  TRUELAT2 :

  50.0

  MOAD_CEN_LAT :

  38.900017

  STAND_LON :

  -107.0

  POLE_LAT :

  90.0

  POLE_LON :

  0.0

  GMT :

  0.0

  JULYR :

  2021

  JULDAY :

  365

  MAP_PROJ :

  1

  MAP_PROJ_CHAR :

  Lambert Conformal

  MMINLU :

  MODIFIED_IGBP_MODIS_NOAH

  NUM_LAND_CAT :

  21

  ISWATER :

  17

  ISLAKE :

  21

  ISICE :

  15

  ISURBAN :

  13

  ISOILWATER :

  14

  HYBRID_OPT :

  2

  ETAC :

  0.2

  history :

  Sat Mar 25 11:56:03 2023: ncrcat /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_00:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_01:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_02:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_03:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_04:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_05:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_06:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_07:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_08:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_09:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_10:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_11:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_12:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_13:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_14:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_15:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_16:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_17:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_18:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_19:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_20:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_21:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_22:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-02_23:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_shared/hourly/wrfhourly_d03_2022-01-02.nc

  NCO :

  netCDF Operators version 5.0.1 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)

formatter = DatetimeTickFormatter(hours="%d %b %Y \n %H:%M UTC")
ds_ctrl["T2"].mean(dim=['x', 'y']).hvplot(xformatter=formatter)

ds_bc = xr.open_mfdataset(files_bc,concat_dim="Time",combine="nested").xwrf.postprocess().squeeze()
ds_bc

<xarray.Dataset> Size: 543MB
Dimensions:         (Time: 168, y: 201, x: 201)
Coordinates:
  * Time            (Time) datetime64[ns] 1kB 2022-01-25 ... 2022-01-31T23:00:00
  * x               (x) float64 2kB -5.344e+04 -5.294e+04 ... 4.656e+04
  * y               (y) float64 2kB -5.6e+04 -5.55e+04 ... 4.35e+04 4.4e+04
Data variables: (12/22)
    Times           (Time) |S19 3kB dask.array<chunksize=(24,), meta=np.ndarray>
    Q2              (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    T2              (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    PSFC            (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    U10             (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    V10             (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    ...              ...
    EMISS           (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    HFX             (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    QFX             (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    LH              (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    SNOWC           (Time, y, x) float32 27MB dask.array<chunksize=(24, 201, 201), meta=np.ndarray>
    wrf_projection  object 8B +proj=lcc +x_0=0 +y_0=0 +a=6370000 +b=6370000 +...
Attributes: (12/88)
    TITLE:                            OUTPUT FROM WRF V4.4 MODEL
    START_DATE:                      2022-01-17_00:00:00
    WEST-EAST_GRID_DIMENSION:        202
    SOUTH-NORTH_GRID_DIMENSION:      202
    BOTTOM-TOP_GRID_DIMENSION:       50
    DX:                              500.0
    ...                              ...
    ISURBAN:                         13
    ISOILWATER:                      14
    HYBRID_OPT:                      2
    ETAC:                            0.2
    history:                         Sat Mar 25 11:57:59 2023: ncrcat /global...
    NCO:                             netCDF Operators version 5.0.1 (Homepage...

xarray.Dataset

• Dimensions:
  • Time: 168
  • y: 201
  • x: 201
• Coordinates: (3)
  • Time
    (Time)
    datetime64[ns]
    2022-01-25 ... 2022-01-31T23:00:00

    long_name :

    Time

    standard_name :

    time

    array(['2022-01-25T00:00:00.000000000', '2022-01-25T01:00:00.000000000',
           '2022-01-25T02:00:00.000000000', '2022-01-25T03:00:00.000000000',
           '2022-01-25T04:00:00.000000000', '2022-01-25T05:00:00.000000000',
           '2022-01-25T06:00:00.000000000', '2022-01-25T07:00:00.000000000',
           '2022-01-25T08:00:00.000000000', '2022-01-25T09:00:00.000000000',
           '2022-01-25T10:00:00.000000000', '2022-01-25T11:00:00.000000000',
           '2022-01-25T12:00:00.000000000', '2022-01-25T13:00:00.000000000',
           '2022-01-25T14:00:00.000000000', '2022-01-25T15:00:00.000000000',
           '2022-01-25T16:00:00.000000000', '2022-01-25T17:00:00.000000000',
           '2022-01-25T18:00:00.000000000', '2022-01-25T19:00:00.000000000',
           '2022-01-25T20:00:00.000000000', '2022-01-25T21:00:00.000000000',
           '2022-01-25T22:00:00.000000000', '2022-01-25T23:00:00.000000000',
           '2022-01-26T00:00:00.000000000', '2022-01-26T01:00:00.000000000',
           '2022-01-26T02:00:00.000000000', '2022-01-26T03:00:00.000000000',
           '2022-01-26T04:00:00.000000000', '2022-01-26T05:00:00.000000000',
           '2022-01-26T06:00:00.000000000', '2022-01-26T07:00:00.000000000',
           '2022-01-26T08:00:00.000000000', '2022-01-26T09:00:00.000000000',
           '2022-01-26T10:00:00.000000000', '2022-01-26T11:00:00.000000000',
           '2022-01-26T12:00:00.000000000', '2022-01-26T13:00:00.000000000',
           '2022-01-26T14:00:00.000000000', '2022-01-26T15:00:00.000000000',
           '2022-01-26T16:00:00.000000000', '2022-01-26T17:00:00.000000000',
           '2022-01-26T18:00:00.000000000', '2022-01-26T19:00:00.000000000',
           '2022-01-26T20:00:00.000000000', '2022-01-26T21:00:00.000000000',
           '2022-01-26T22:00:00.000000000', '2022-01-26T23:00:00.000000000',
           '2022-01-27T00:00:00.000000000', '2022-01-27T01:00:00.000000000',
           '2022-01-27T02:00:00.000000000', '2022-01-27T03:00:00.000000000',
           '2022-01-27T04:00:00.000000000', '2022-01-27T05:00:00.000000000',
           '2022-01-27T06:00:00.000000000', '2022-01-27T07:00:00.000000000',
           '2022-01-27T08:00:00.000000000', '2022-01-27T09:00:00.000000000',
           '2022-01-27T10:00:00.000000000', '2022-01-27T11:00:00.000000000',
           '2022-01-27T12:00:00.000000000', '2022-01-27T13:00:00.000000000',
           '2022-01-27T14:00:00.000000000', '2022-01-27T15:00:00.000000000',
           '2022-01-27T16:00:00.000000000', '2022-01-27T17:00:00.000000000',
           '2022-01-27T18:00:00.000000000', '2022-01-27T19:00:00.000000000',
           '2022-01-27T20:00:00.000000000', '2022-01-27T21:00:00.000000000',
           '2022-01-27T22:00:00.000000000', '2022-01-27T23:00:00.000000000',
           '2022-01-28T00:00:00.000000000', '2022-01-28T01:00:00.000000000',
           '2022-01-28T02:00:00.000000000', '2022-01-28T03:00:00.000000000',
           '2022-01-28T04:00:00.000000000', '2022-01-28T05:00:00.000000000',
           '2022-01-28T06:00:00.000000000', '2022-01-28T07:00:00.000000000',
           '2022-01-28T08:00:00.000000000', '2022-01-28T09:00:00.000000000',
           '2022-01-28T10:00:00.000000000', '2022-01-28T11:00:00.000000000',
           '2022-01-28T12:00:00.000000000', '2022-01-28T13:00:00.000000000',
           '2022-01-28T14:00:00.000000000', '2022-01-28T15:00:00.000000000',
           '2022-01-28T16:00:00.000000000', '2022-01-28T17:00:00.000000000',
           '2022-01-28T18:00:00.000000000', '2022-01-28T19:00:00.000000000',
           '2022-01-28T20:00:00.000000000', '2022-01-28T21:00:00.000000000',
           '2022-01-28T22:00:00.000000000', '2022-01-28T23:00:00.000000000',
           '2022-01-29T00:00:00.000000000', '2022-01-29T01:00:00.000000000',
           '2022-01-29T02:00:00.000000000', '2022-01-29T03:00:00.000000000',
           '2022-01-29T04:00:00.000000000', '2022-01-29T05:00:00.000000000',
           '2022-01-29T06:00:00.000000000', '2022-01-29T07:00:00.000000000',
           '2022-01-29T08:00:00.000000000', '2022-01-29T09:00:00.000000000',
           '2022-01-29T10:00:00.000000000', '2022-01-29T11:00:00.000000000',
           '2022-01-29T12:00:00.000000000', '2022-01-29T13:00:00.000000000',
           '2022-01-29T14:00:00.000000000', '2022-01-29T15:00:00.000000000',
           '2022-01-29T16:00:00.000000000', '2022-01-29T17:00:00.000000000',
           '2022-01-29T18:00:00.000000000', '2022-01-29T19:00:00.000000000',
           '2022-01-29T20:00:00.000000000', '2022-01-29T21:00:00.000000000',
           '2022-01-29T22:00:00.000000000', '2022-01-29T23:00:00.000000000',
           '2022-01-30T00:00:00.000000000', '2022-01-30T01:00:00.000000000',
           '2022-01-30T02:00:00.000000000', '2022-01-30T03:00:00.000000000',
           '2022-01-30T04:00:00.000000000', '2022-01-30T05:00:00.000000000',
           '2022-01-30T06:00:00.000000000', '2022-01-30T07:00:00.000000000',
           '2022-01-30T08:00:00.000000000', '2022-01-30T09:00:00.000000000',
           '2022-01-30T10:00:00.000000000', '2022-01-30T11:00:00.000000000',
           '2022-01-30T12:00:00.000000000', '2022-01-30T13:00:00.000000000',
           '2022-01-30T14:00:00.000000000', '2022-01-30T15:00:00.000000000',
           '2022-01-30T16:00:00.000000000', '2022-01-30T17:00:00.000000000',
           '2022-01-30T18:00:00.000000000', '2022-01-30T19:00:00.000000000',
           '2022-01-30T20:00:00.000000000', '2022-01-30T21:00:00.000000000',
           '2022-01-30T22:00:00.000000000', '2022-01-30T23:00:00.000000000',
           '2022-01-31T00:00:00.000000000', '2022-01-31T01:00:00.000000000',
           '2022-01-31T02:00:00.000000000', '2022-01-31T03:00:00.000000000',
           '2022-01-31T04:00:00.000000000', '2022-01-31T05:00:00.000000000',
           '2022-01-31T06:00:00.000000000', '2022-01-31T07:00:00.000000000',
           '2022-01-31T08:00:00.000000000', '2022-01-31T09:00:00.000000000',
           '2022-01-31T10:00:00.000000000', '2022-01-31T11:00:00.000000000',
           '2022-01-31T12:00:00.000000000', '2022-01-31T13:00:00.000000000',
           '2022-01-31T14:00:00.000000000', '2022-01-31T15:00:00.000000000',
           '2022-01-31T16:00:00.000000000', '2022-01-31T17:00:00.000000000',
           '2022-01-31T18:00:00.000000000', '2022-01-31T19:00:00.000000000',
           '2022-01-31T20:00:00.000000000', '2022-01-31T21:00:00.000000000',
           '2022-01-31T22:00:00.000000000', '2022-01-31T23:00:00.000000000'],
          dtype='datetime64[ns]')

  • x
    (x)
    float64
    -5.344e+04 -5.294e+04 ... 4.656e+04

    units :

    m

    standard_name :

    projection_x_coordinate

    axis :

    X

    array([-53443.825943, -52943.825943, -52443.825943, ...,  45556.174057,
            46056.174057,  46556.174057])

  • y
    (y)
    float64
    -5.6e+04 -5.55e+04 ... 4.4e+04

    units :

    m

    standard_name :

    projection_y_coordinate

    axis :

    Y

    array([-55999.217634, -55499.217634, -54999.217634, ...,  43000.782366,
            43500.782366,  44000.782366])

• Data variables: (22)
  • Times
    (Time)
    |S19
    dask.array<chunksize=(24,), meta=np.ndarray>

    cell_methods :

    Time: mean

    Array Chunk Bytes 3.12 kiB 456 B Shape (168,) (24,) Dask graph 7 chunks in 15 graph layers Data type |S19 numpy.ndarray

  • Q2
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    QV at 2 M

    units :

    kg kg-1

    stagger :

    cell_methods :

    Time: mean

    standard_name :

    humidity_mixing_ratio

    long_name :

    humidity_mixing_ratio_at_2m_agl

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • T2
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    TEMP at 2 M

    units :

    K

    stagger :

    cell_methods :

    Time: mean

    standard_name :

    air_temperature

    long_name :

    air_temperature_at_2m_agl

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • PSFC
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    SFC PRESSURE

    units :

    Pa

    stagger :

    cell_methods :

    Time: mean

    standard_name :

    air_pressure

    long_name :

    air_pressure_at_surface

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • U10
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    U at 10 M

    units :

    m s-1

    stagger :

    cell_methods :

    Time: mean

    standard_name :

    x_wind

    long_name :

    x_wind_at_10m_agl

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • V10
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    V at 10 M

    units :

    m s-1

    stagger :

    cell_methods :

    Time: mean

    standard_name :

    y_wind

    long_name :

    y_wind_at_10m_agl

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • SNOW
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    SNOW WATER EQUIVALENT

    units :

    kg m-2

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • SNOWH
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    PHYSICAL SNOW DEPTH

    units :

    m

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • HGT_SHAD
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Height of orographic shadow

    units :

    m

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • TSK
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    SURFACE SKIN TEMPERATURE

    units :

    K

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • RAINC
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED TOTAL CUMULUS PRECIPITATION

    units :

    mm

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • RAINNC
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED TOTAL GRID SCALE PRECIPITATION

    units :

    mm

    stagger :

    cell_methods :

    Time: mean

    standard_name :

    integral_of_lwe_precipitation_rate_wrt_time

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • SWDOWN
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    DOWNWARD SHORT WAVE FLUX AT GROUND SURFACE

    units :

    W m-2

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • GLW
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    DOWNWARD LONG WAVE FLUX AT GROUND SURFACE

    units :

    W m-2

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • SWNORM
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    NORMAL SHORT WAVE FLUX AT GROUND SURFACE (SLOPE-DEPENDENT)

    units :

    W m-2

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • ALBEDO
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ALBEDO

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • EMISS
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    SURFACE EMISSIVITY

    units :

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • HFX
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    UPWARD HEAT FLUX AT THE SURFACE

    units :

    W m-2

    stagger :

    cell_methods :

    Time: mean

    standard_name :

    surface_upward_heat_flux_in_air

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • QFX
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    UPWARD MOISTURE FLUX AT THE SURFACE

    units :

    kg m-2 s-1

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • LH
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    LATENT HEAT FLUX AT THE SURFACE

    units :

    W m-2

    stagger :

    cell_methods :

    Time: mean

    standard_name :

    surface_upward_latent_heat_flux_in_air

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • SNOWC
    (Time, y, x)
    float32
    dask.array<chunksize=(24, 201, 201), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    FLAG INDICATING SNOW COVERAGE (1 FOR SNOW COVER)

    units :

    stagger :

    cell_methods :

    Time: mean

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 25.89 MiB 3.70 MiB Shape (168, 201, 201) (24, 201, 201) Dask graph 7 chunks in 15 graph layers Data type float32 numpy.ndarray

  • wrf_projection
    ()
    object
    +proj=lcc +x_0=0 +y_0=0 +a=63700...

    crs_wkt :

    PROJCRS["unknown",BASEGEOGCRS["unknown",DATUM["unknown",ELLIPSOID["unknown",6370000,0,LENGTHUNIT["metre",1,ID["EPSG",9001]]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8901]]],CONVERSION["unknown",METHOD["Lambert Conic Conformal (2SP)",ID["EPSG",9802]],PARAMETER["Latitude of false origin",38.900016784668,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8821]],PARAMETER["Longitude of false origin",-107,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8822]],PARAMETER["Latitude of 1st standard parallel",30,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8823]],PARAMETER["Latitude of 2nd standard parallel",50,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8824]],PARAMETER["Easting at false origin",0,LENGTHUNIT["metre",1],ID["EPSG",8826]],PARAMETER["Northing at false origin",0,LENGTHUNIT["metre",1],ID["EPSG",8827]]],CS[Cartesian,2],AXIS["(E)",east,ORDER[1],LENGTHUNIT["metre",1,ID["EPSG",9001]]],AXIS["(N)",north,ORDER[2],LENGTHUNIT["metre",1,ID["EPSG",9001]]]]

    semi_major_axis :

    6370000.0

    semi_minor_axis :

    6370000.0

    inverse_flattening :

    0.0

    reference_ellipsoid_name :

    unknown

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    unknown

    horizontal_datum_name :

    unknown

    projected_crs_name :

    unknown

    grid_mapping_name :

    lambert_conformal_conic

    standard_parallel :

    (30.0, 50.0)

    latitude_of_projection_origin :

    38.90001678466797

    longitude_of_central_meridian :

    -107.0

    false_easting :

    0.0

    false_northing :

    0.0

    array(<Projected CRS: +proj=lcc +x_0=0 +y_0=0 +a=6370000 +b=6370000 +lat ...>
    Name: unknown
    Axis Info [cartesian]:
    - E[east]: Easting (metre)
    - N[north]: Northing (metre)
    Area of Use:
    - undefined
    Coordinate Operation:
    - name: unknown
    - method: Lambert Conic Conformal (2SP)
    Datum: unknown
    - Ellipsoid: unknown
    - Prime Meridian: Greenwich
    , dtype=object)

• Indexes: (3)
  • Time
    PandasIndex

    PandasIndex(DatetimeIndex(['2022-01-25 00:00:00', '2022-01-25 01:00:00',
                   '2022-01-25 02:00:00', '2022-01-25 03:00:00',
                   '2022-01-25 04:00:00', '2022-01-25 05:00:00',
                   '2022-01-25 06:00:00', '2022-01-25 07:00:00',
                   '2022-01-25 08:00:00', '2022-01-25 09:00:00',
                   ...
                   '2022-01-31 14:00:00', '2022-01-31 15:00:00',
                   '2022-01-31 16:00:00', '2022-01-31 17:00:00',
                   '2022-01-31 18:00:00', '2022-01-31 19:00:00',
                   '2022-01-31 20:00:00', '2022-01-31 21:00:00',
                   '2022-01-31 22:00:00', '2022-01-31 23:00:00'],
                  dtype='datetime64[ns]', name='Time', length=168, freq=None))

  • x
    PandasIndex

    PandasIndex(Index([-53443.82594293087, -52943.82594293087, -52443.82594293087,
           -51943.82594293087, -51443.82594293087, -50943.82594293087,
           -50443.82594293087, -49943.82594293087, -49443.82594293087,
           -48943.82594293087,
           ...
            42056.17405706913,  42556.17405706913,  43056.17405706913,
            43556.17405706913,  44056.17405706913,  44556.17405706913,
            45056.17405706913,  45556.17405706913,  46056.17405706913,
            46556.17405706913],
          dtype='float64', name='x', length=201))

  • y
    PandasIndex

    PandasIndex(Index([-55999.21763437279, -55499.21763437279, -54999.21763437279,
           -54499.21763437279, -53999.21763437279, -53499.21763437279,
           -52999.21763437279, -52499.21763437279, -51999.21763437279,
           -51499.21763437279,
           ...
            39500.78236562721,  40000.78236562721,  40500.78236562721,
            41000.78236562721,  41500.78236562721,  42000.78236562721,
            42500.78236562721,  43000.78236562721,  43500.78236562721,
            44000.78236562721],
          dtype='float64', name='y', length=201))

• Attributes: (88)

  TITLE :

  OUTPUT FROM WRF V4.4 MODEL

  START_DATE :

  2022-01-17_00:00:00

  WEST-EAST_GRID_DIMENSION :

  202

  SOUTH-NORTH_GRID_DIMENSION :

  202

  BOTTOM-TOP_GRID_DIMENSION :

  50

  DX :

  500.0

  DY :

  500.0

  AERCU_OPT :

  0

  AERCU_FCT :

  1.0

  IDEAL_CASE :

  0

  DIFF_6TH_SLOPEOPT :

  0

  AUTO_LEVELS_OPT :

  2

  DIFF_6TH_THRESH :

  0.1

  DZBOT :

  50.0

  DZSTRETCH_S :

  1.3

  DZSTRETCH_U :

  1.1

  GRIDTYPE :

  C

  DIFF_OPT :

  1

  KM_OPT :

  4

  DAMP_OPT :

  0

  DAMPCOEF :

  0.2

  KHDIF :

  0.0

  KVDIF :

  0.0

  MP_PHYSICS :

  8

  RA_LW_PHYSICS :

  3

  RA_SW_PHYSICS :

  3

  SF_SFCLAY_PHYSICS :

  2

  SF_SURFACE_PHYSICS :

  4

  BL_PBL_PHYSICS :

  2

  CU_PHYSICS :

  0

  SF_LAKE_PHYSICS :

  0

  SURFACE_INPUT_SOURCE :

  1

  SST_UPDATE :

  1

  GHG_INPUT :

  1

  GRID_FDDA :

  0

  GFDDA_INTERVAL_M :

  0

  GFDDA_END_H :

  0

  GRID_SFDDA :

  0

  SGFDDA_INTERVAL_M :

  0

  SGFDDA_END_H :

  0

  HYPSOMETRIC_OPT :

  2

  USE_THETA_M :

  1

  GWD_OPT :

  0

  SF_URBAN_PHYSICS :

  0

  SF_SURFACE_MOSAIC :

  0

  SF_OCEAN_PHYSICS :

  0

  WEST-EAST_PATCH_START_UNSTAG :

  1

  WEST-EAST_PATCH_END_UNSTAG :

  201

  WEST-EAST_PATCH_START_STAG :

  1

  WEST-EAST_PATCH_END_STAG :

  202

  SOUTH-NORTH_PATCH_START_UNSTAG :

  1

  SOUTH-NORTH_PATCH_END_UNSTAG :

  201

  SOUTH-NORTH_PATCH_START_STAG :

  1

  SOUTH-NORTH_PATCH_END_STAG :

  202

  BOTTOM-TOP_PATCH_START_UNSTAG :

  1

  BOTTOM-TOP_PATCH_END_UNSTAG :

  49

  BOTTOM-TOP_PATCH_START_STAG :

  1

  BOTTOM-TOP_PATCH_END_STAG :

  50

  GRID_ID :

  3

  PARENT_ID :

  2

  I_PARENT_START :

  67

  J_PARENT_START :

  67

  PARENT_GRID_RATIO :

  3

  DT :

  2.6666667

  CEN_LAT :

  38.84523

  CEN_LON :

  -107.040375

  TRUELAT1 :

  30.0

  TRUELAT2 :

  50.0

  MOAD_CEN_LAT :

  38.900017

  STAND_LON :

  -107.0

  POLE_LAT :

  90.0

  POLE_LON :

  0.0

  GMT :

  0.0

  JULYR :

  2022

  JULDAY :

  17

  MAP_PROJ :

  1

  MAP_PROJ_CHAR :

  Lambert Conformal

  MMINLU :

  MODIFIED_IGBP_MODIS_NOAH

  NUM_LAND_CAT :

  21

  ISWATER :

  17

  ISLAKE :

  21

  ISICE :

  15

  ISURBAN :

  13

  ISOILWATER :

  14

  HYBRID_OPT :

  2

  ETAC :

  0.2

  history :

  Sat Mar 25 11:57:59 2023: ncrcat /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_00:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_01:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_02:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_03:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_04:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_05:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_06:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_07:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_08:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_09:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_10:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_11:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_12:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_13:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_14:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_15:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_16:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_17:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_18:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_19:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_20:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_21:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_22:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_data/HOURLY_OUTPUT/wrfhourly_d03_2022-01-25_23:00:00 /global/cfs/projectdirs/m4098/WRF_WY22/bsu_cfsr22_shared/hourly/wrfhourly_d03_2022-01-25.nc

  NCO :

  netCDF Operators version 5.0.1 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)

ds_bc["T2"].mean(dim=['x', 'y']).hvplot(xformatter=formatter)

ds_dust = xr.open_mfdataset(files_dust,concat_dim="Time",combine="nested").xwrf.postprocess().squeeze()
ds_dust

<xarray.Dataset> Size: 6GB
Dimensions:                    (Time: 28, y: 201, x: 201, soil_layers_stag: 4,
                                z: 49, x_stag: 202, y_stag: 202, z_stag: 50,
                                seed_dim_stag: 2, snow_layers_stag: 3,
                                snso_layers_stag: 7)
Coordinates: (12/15)
    CLAT                       (y, x) float32 162kB dask.array<chunksize=(101, 101), meta=np.ndarray>
    XLAT                       (y, x) float32 162kB dask.array<chunksize=(101, 101), meta=np.ndarray>
    XLONG                      (y, x) float32 162kB dask.array<chunksize=(101, 101), meta=np.ndarray>
    XTIME                      (Time) datetime64[ns] 224B dask.array<chunksize=(1,), meta=np.ndarray>
    XLAT_U                     (y, x_stag) float32 162kB dask.array<chunksize=(101, 101), meta=np.ndarray>
    XLONG_U                    (y, x_stag) float32 162kB dask.array<chunksize=(101, 101), meta=np.ndarray>
    ...                         ...
  * z_stag                     (z_stag) float32 200B 1.0 0.9935 ... 0.006181 0.0
  * Time                       (Time) datetime64[ns] 224B 2023-04-03 ... 2023...
  * x                          (x) float64 2kB -5.344e+04 ... 4.656e+04
  * y                          (y) float64 2kB -5.6e+04 -5.55e+04 ... 4.4e+04
  * y_stag                     (y_stag) float64 2kB -5.625e+04 ... 4.425e+04
  * x_stag                     (x_stag) float64 2kB -5.369e+04 ... 4.681e+04
Dimensions without coordinates: soil_layers_stag, seed_dim_stag,
                                snow_layers_stag, snso_layers_stag
Data variables: (12/352)
    Times                      (Time) |S19 532B dask.array<chunksize=(1,), meta=np.ndarray>
    LU_INDEX                   (Time, y, x) float32 5MB dask.array<chunksize=(1, 101, 101), meta=np.ndarray>
    ZS                         (Time, soil_layers_stag) float32 448B dask.array<chunksize=(1, 4), meta=np.ndarray>
    DZS                        (Time, soil_layers_stag) float32 448B dask.array<chunksize=(1, 4), meta=np.ndarray>
    VAR_SSO                    (Time, y, x) float32 5MB dask.array<chunksize=(1, 101, 101), meta=np.ndarray>
    BATHYMETRY_FLAG            (Time) int32 112B dask.array<chunksize=(1,), meta=np.ndarray>
    ...                         ...
    geopotential_height        (Time, z_stag, y, x) float32 226MB dask.array<chunksize=(1, 50, 101, 101), meta=np.ndarray>
    wind_east                  (Time, z, y, x) float32 222MB dask.array<chunksize=(1, 49, 100, 100), meta=np.ndarray>
    wind_north                 (Time, z, y, x) float32 222MB dask.array<chunksize=(1, 49, 100, 100), meta=np.ndarray>
    wind_east_10               (Time, y, x) float32 5MB dask.array<chunksize=(1, 101, 101), meta=np.ndarray>
    wind_north_10              (Time, y, x) float32 5MB dask.array<chunksize=(1, 101, 101), meta=np.ndarray>
    wrf_projection             object 8B +proj=lcc +x_0=0 +y_0=0 +a=6370000 +...
Attributes: (12/153)
    TITLE:                            OUTPUT FROM WRF V4.4 MODEL
    START_DATE:                      2023-03-25_00:00:00
    SIMULATION_START_DATE:           2021-09-15_00:00:00
    WEST-EAST_GRID_DIMENSION:        202
    SOUTH-NORTH_GRID_DIMENSION:      202
    BOTTOM-TOP_GRID_DIMENSION:       50
    ...                              ...
    ISLAKE:                          21
    ISICE:                           15
    ISURBAN:                         13
    ISOILWATER:                      14
    HYBRID_OPT:                      2
    ETAC:                            0.2

xarray.Dataset

• Dimensions:
  • Time: 28
  • y: 201
  • x: 201
  • soil_layers_stag: 4
  • z: 49
  • x_stag: 202
  • y_stag: 202
  • z_stag: 50
  • seed_dim_stag: 2
  • snow_layers_stag: 3
  • snso_layers_stag: 7
• Coordinates: (15)
  • CLAT
    (y, x)
    float32
    dask.array<chunksize=(101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    COMPUTATIONAL GRID LATITUDE, SOUTH IS NEGATIVE

    units :

    degree_north

    stagger :

    Array Chunk Bytes 157.82 kiB 39.85 kiB Shape (201, 201) (101, 101) Dask graph 4 chunks in 58 graph layers Data type float32 numpy.ndarray

  • XLAT
    (y, x)
    float32
    dask.array<chunksize=(101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    LATITUDE, SOUTH IS NEGATIVE

    units :

    degree_north

    stagger :

    Array Chunk Bytes 157.82 kiB 39.85 kiB Shape (201, 201) (101, 101) Dask graph 4 chunks in 58 graph layers Data type float32 numpy.ndarray

  • XLONG
    (y, x)
    float32
    dask.array<chunksize=(101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    LONGITUDE, WEST IS NEGATIVE

    units :

    degree_east

    stagger :

    Array Chunk Bytes 157.82 kiB 39.85 kiB Shape (201, 201) (101, 101) Dask graph 4 chunks in 58 graph layers Data type float32 numpy.ndarray

  • XTIME
    (Time)
    datetime64[ns]
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    minutes since 2021-09-15 00:00:00

    stagger :

    Array Chunk Bytes 224 B 8 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type datetime64[ns] numpy.ndarray

  • XLAT_U
    (y, x_stag)
    float32
    dask.array<chunksize=(101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    LATITUDE, SOUTH IS NEGATIVE

    units :

    degree_north

    stagger :

    X

    Array Chunk Bytes 158.60 kiB 39.85 kiB Shape (201, 202) (101, 101) Dask graph 4 chunks in 58 graph layers Data type float32 numpy.ndarray

  • XLONG_U
    (y, x_stag)
    float32
    dask.array<chunksize=(101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    LONGITUDE, WEST IS NEGATIVE

    units :

    degree_east

    stagger :

    X

    Array Chunk Bytes 158.60 kiB 39.85 kiB Shape (201, 202) (101, 101) Dask graph 4 chunks in 58 graph layers Data type float32 numpy.ndarray

  • XLAT_V
    (y_stag, x)
    float32
    dask.array<chunksize=(101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    LATITUDE, SOUTH IS NEGATIVE

    units :

    degree_north

    stagger :

    Y

    Array Chunk Bytes 158.60 kiB 39.85 kiB Shape (202, 201) (101, 101) Dask graph 4 chunks in 58 graph layers Data type float32 numpy.ndarray

  • XLONG_V
    (y_stag, x)
    float32
    dask.array<chunksize=(101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    LONGITUDE, WEST IS NEGATIVE

    units :

    degree_east

    stagger :

    Y

    Array Chunk Bytes 158.60 kiB 39.85 kiB Shape (202, 201) (101, 101) Dask graph 4 chunks in 58 graph layers Data type float32 numpy.ndarray

  • z
    (z)
    float32
    0.9967 0.9893 ... 0.009443 0.003091

    FieldType :

    104

    MemoryOrder :

    Z

    description :

    eta values on half (mass) levels

    units :

    stagger :

    axis :

    Z

    standard_name :

    atmosphere_hybrid_sigma_pressure_coordinate

    array([0.996736, 0.989321, 0.979926, 0.968107, 0.953371, 0.935198, 0.913083,
           0.886598, 0.855468, 0.819651, 0.779403, 0.735318, 0.690713, 0.648459,
           0.608432, 0.570514, 0.534594, 0.500567, 0.468333, 0.437798, 0.408872,
           0.38147 , 0.355512, 0.330922, 0.307628, 0.285562, 0.264658, 0.244856,
           0.226097, 0.208327, 0.191494, 0.175547, 0.160441, 0.146131, 0.132575,
           0.119733, 0.107568, 0.096044, 0.085128, 0.074786, 0.06499 , 0.05571 ,
           0.046919, 0.038591, 0.030702, 0.023229, 0.01615 , 0.009443, 0.003091],
          dtype=float32)

  • z_stag
    (z_stag)
    float32
    1.0 0.9935 0.9852 ... 0.006181 0.0

    FieldType :

    104

    MemoryOrder :

    Z

    description :

    eta values on full (w) levels

    units :

    stagger :

    Z

    axis :

    Z

    c_grid_axis_shift :

    0.5

    standard_name :

    atmosphere_hybrid_sigma_pressure_coordinate

    array([1.      , 0.993471, 0.98517 , 0.974682, 0.961533, 0.945209, 0.925186,
           0.90098 , 0.872217, 0.83872 , 0.800582, 0.758224, 0.712412, 0.669015,
           0.627904, 0.58896 , 0.552068, 0.51712 , 0.484014, 0.452652, 0.422943,
           0.3948  , 0.36814 , 0.342884, 0.31896 , 0.296296, 0.274827, 0.254489,
           0.235223, 0.216972, 0.199683, 0.183305, 0.16779 , 0.153092, 0.139169,
           0.12598 , 0.113486, 0.10165 , 0.090438, 0.079817, 0.069756, 0.060225,
           0.051196, 0.042642, 0.03454 , 0.026865, 0.019594, 0.012706, 0.006181,
           0.      ], dtype=float32)

  • Time
    (Time)
    datetime64[ns]
    2023-04-03 ... 2023-04-09T18:00:00

    long_name :

    Time

    standard_name :

    time

    array(['2023-04-03T00:00:00.000000000', '2023-04-03T06:00:00.000000000',
           '2023-04-03T12:00:00.000000000', '2023-04-03T18:00:00.000000000',
           '2023-04-04T00:00:00.000000000', '2023-04-04T06:00:00.000000000',
           '2023-04-04T12:00:00.000000000', '2023-04-04T18:00:00.000000000',
           '2023-04-05T00:00:00.000000000', '2023-04-05T06:00:00.000000000',
           '2023-04-05T12:00:00.000000000', '2023-04-05T18:00:00.000000000',
           '2023-04-06T00:00:00.000000000', '2023-04-06T06:00:00.000000000',
           '2023-04-06T12:00:00.000000000', '2023-04-06T18:00:00.000000000',
           '2023-04-07T00:00:00.000000000', '2023-04-07T06:00:00.000000000',
           '2023-04-07T12:00:00.000000000', '2023-04-07T18:00:00.000000000',
           '2023-04-08T00:00:00.000000000', '2023-04-08T06:00:00.000000000',
           '2023-04-08T12:00:00.000000000', '2023-04-08T18:00:00.000000000',
           '2023-04-09T00:00:00.000000000', '2023-04-09T06:00:00.000000000',
           '2023-04-09T12:00:00.000000000', '2023-04-09T18:00:00.000000000'],
          dtype='datetime64[ns]')

  • x
    (x)
    float64
    -5.344e+04 -5.294e+04 ... 4.656e+04

    units :

    m

    standard_name :

    projection_x_coordinate

    axis :

    X

    array([-53443.825943, -52943.825943, -52443.825943, ...,  45556.174057,
            46056.174057,  46556.174057])

  • y
    (y)
    float64
    -5.6e+04 -5.55e+04 ... 4.4e+04

    units :

    m

    standard_name :

    projection_y_coordinate

    axis :

    Y

    array([-55999.217634, -55499.217634, -54999.217634, ...,  43000.782366,
            43500.782366,  44000.782366])

  • y_stag
    (y_stag)
    float64
    -5.625e+04 -5.575e+04 ... 4.425e+04

    units :

    m

    standard_name :

    projection_y_coordinate

    axis :

    Y

    c_grid_axis_shift :

    0.5

    array([-56249.217634, -55749.217634, -55249.217634, ...,  43250.782366,
            43750.782366,  44250.782366])

  • x_stag
    (x_stag)
    float64
    -5.369e+04 -5.319e+04 ... 4.681e+04

    units :

    m

    standard_name :

    projection_x_coordinate

    axis :

    X

    c_grid_axis_shift :

    0.5

    array([-53693.825943, -53193.825943, -52693.825943, ...,  45806.174057,
            46306.174057,  46806.174057])

• Data variables: (352)
  • Times
    (Time)
    |S19
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 532 B 19 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type |S19 numpy.ndarray

  • LU_INDEX
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    LAND USE CATEGORY

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ZS
    (Time, soil_layers_stag)
    float32
    dask.array<chunksize=(1, 4), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    Z

    description :

    DEPTHS OF CENTERS OF SOIL LAYERS

    units :

    m

    stagger :

    Z

    Array Chunk Bytes 448 B 16 B Shape (28, 4) (1, 4) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • DZS
    (Time, soil_layers_stag)
    float32
    dask.array<chunksize=(1, 4), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    Z

    description :

    THICKNESSES OF SOIL LAYERS

    units :

    m

    stagger :

    Z

    Array Chunk Bytes 448 B 16 B Shape (28, 4) (1, 4) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • VAR_SSO
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Variance of Subgrid Scale Orography MSL

    units :

    m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • BATHYMETRY_FLAG
    (Time)
    int32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    106

    MemoryOrder :

    0

    description :

    Flag for bathymetry in the global attributes for metgrid data

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type int32 numpy.ndarray

  • U
    (Time, z, y, x_stag)
    float32
    dask.array<chunksize=(1, 49, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    x-wind component

    units :

    m s-1

    stagger :

    X

    standard_name :

    x_wind

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 212.50 MiB 1.91 MiB Shape (28, 49, 201, 202) (1, 49, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • V
    (Time, z, y_stag, x)
    float32
    dask.array<chunksize=(1, 49, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    y-wind component

    units :

    m s-1

    stagger :

    Y

    standard_name :

    y_wind

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 212.50 MiB 1.91 MiB Shape (28, 49, 202, 201) (1, 49, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • W
    (Time, z_stag, y, x)
    float32
    dask.array<chunksize=(1, 50, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    z-wind component

    units :

    m s-1

    stagger :

    Z

    standard_name :

    upward_air_velocity

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 215.76 MiB 1.95 MiB Shape (28, 50, 201, 201) (1, 50, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • THM
    (Time, z, y, x)
    float32
    dask.array<chunksize=(1, 49, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    either 1) pert moist pot temp=(1+Rv/Rd Qv)*(theta)-T0, or 2) pert dry pot temp=t

    units :

    K

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 211.45 MiB 1.91 MiB Shape (28, 49, 201, 201) (1, 49, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • HFX_FORCE
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    SCM ideal surface sensible heat flux

    units :

    W m-2

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • LH_FORCE
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    SCM ideal surface latent heat flux

    units :

    W m-2

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • TSK_FORCE
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    SCM ideal surface skin temperature

    units :

    W m-2

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • HFX_FORCE_TEND
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    SCM ideal surface sensible heat flux tendency

    units :

    W m-2 s-1

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • LH_FORCE_TEND
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    SCM ideal surface latent heat flux tendency

    units :

    W m-2 s-1

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • TSK_FORCE_TEND
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    SCM ideal surface skin temperature tendency

    units :

    W m-2 s-1

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • MU
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    perturbation dry air mass in column

    units :

    Pa

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • MUB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    base state dry air mass in column

    units :

    Pa

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • NEST_POS
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    -

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • FNM
    (Time, z)
    float32
    dask.array<chunksize=(1, 49), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    Z

    description :

    upper weight for vertical stretching

    units :

    stagger :

    Array Chunk Bytes 5.36 kiB 196 B Shape (28, 49) (1, 49) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • FNP
    (Time, z)
    float32
    dask.array<chunksize=(1, 49), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    Z

    description :

    lower weight for vertical stretching

    units :

    stagger :

    Array Chunk Bytes 5.36 kiB 196 B Shape (28, 49) (1, 49) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • RDNW
    (Time, z)
    float32
    dask.array<chunksize=(1, 49), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    Z

    description :

    inverse d(eta) values between full (w) levels

    units :

    stagger :

    Array Chunk Bytes 5.36 kiB 196 B Shape (28, 49) (1, 49) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • RDN
    (Time, z)
    float32
    dask.array<chunksize=(1, 49), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    Z

    description :

    inverse d(eta) values between half (mass) levels

    units :

    stagger :

    Array Chunk Bytes 5.36 kiB 196 B Shape (28, 49) (1, 49) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • DNW
    (Time, z)
    float32
    dask.array<chunksize=(1, 49), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    Z

    description :

    d(eta) values between full (w) levels

    units :

    stagger :

    Array Chunk Bytes 5.36 kiB 196 B Shape (28, 49) (1, 49) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • DN
    (Time, z)
    float32
    dask.array<chunksize=(1, 49), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    Z

    description :

    d(eta) values between half (mass) levels

    units :

    stagger :

    Array Chunk Bytes 5.36 kiB 196 B Shape (28, 49) (1, 49) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • CFN
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    extrapolation constant

    units :

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • CFN1
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    extrapolation constant

    units :

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • THIS_IS_AN_IDEAL_RUN
    (Time)
    int32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    106

    MemoryOrder :

    0

    description :

    T/F flag: this is an ARW ideal simulation

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type int32 numpy.ndarray

  • P_HYD
    (Time, z, y, x)
    float32
    dask.array<chunksize=(1, 49, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    hydrostatic pressure

    units :

    Pa

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 211.45 MiB 1.91 MiB Shape (28, 49, 201, 201) (1, 49, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • Q2
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    QV at 2 M

    units :

    kg kg-1

    stagger :

    standard_name :

    humidity_mixing_ratio

    long_name :

    humidity_mixing_ratio_at_2m_agl

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • T2
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    TEMP at 2 M

    units :

    K

    stagger :

    standard_name :

    air_temperature

    long_name :

    air_temperature_at_2m_agl

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • TH2
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    POT TEMP at 2 M

    units :

    K

    stagger :

    standard_name :

    air_potential_temperature

    long_name :

    air_potential_temperature_at_2m_agl

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • PSFC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    SFC PRESSURE

    units :

    Pa

    stagger :

    standard_name :

    air_pressure

    long_name :

    air_pressure_at_surface

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • U10
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    U at 10 M

    units :

    m s-1

    stagger :

    standard_name :

    x_wind

    long_name :

    x_wind_at_10m_agl

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • V10
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    V at 10 M

    units :

    m s-1

    stagger :

    standard_name :

    y_wind

    long_name :

    y_wind_at_10m_agl

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • RDX
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    INVERSE X GRID LENGTH

    units :

    m-1

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • RDY
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    INVERSE Y GRID LENGTH

    units :

    m-1

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • AREA2D
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Horizontal grid cell area, using dx, dy, and map factors

    units :

    m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • DX2D
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Horizontal grid distance: sqrt(area2d)

    units :

    m

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • RESM
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    TIME WEIGHT CONSTANT FOR SMALL STEPS

    units :

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ZETATOP
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    ZETA AT MODEL TOP

    units :

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • CF1
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    2nd order extrapolation constant

    units :

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • CF2
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    2nd order extrapolation constant

    units :

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • CF3
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    2nd order extrapolation constant

    units :

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ITIMESTEP
    (Time)
    int32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    106

    MemoryOrder :

    0

    description :

    units :

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type int32 numpy.ndarray

  • QVAPOR
    (Time, z, y, x)
    float32
    dask.array<chunksize=(1, 49, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    Water vapor mixing ratio

    units :

    kg kg-1

    stagger :

    standard_name :

    humidity_mixing_ratio

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 211.45 MiB 1.91 MiB Shape (28, 49, 201, 201) (1, 49, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • QCLOUD
    (Time, z, y, x)
    float32
    dask.array<chunksize=(1, 49, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    Cloud water mixing ratio

    units :

    kg kg-1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 211.45 MiB 1.91 MiB Shape (28, 49, 201, 201) (1, 49, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • QRAIN
    (Time, z, y, x)
    float32
    dask.array<chunksize=(1, 49, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    Rain water mixing ratio

    units :

    kg kg-1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 211.45 MiB 1.91 MiB Shape (28, 49, 201, 201) (1, 49, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • QICE
    (Time, z, y, x)
    float32
    dask.array<chunksize=(1, 49, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    Ice mixing ratio

    units :

    kg kg-1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 211.45 MiB 1.91 MiB Shape (28, 49, 201, 201) (1, 49, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • QSNOW
    (Time, z, y, x)
    float32
    dask.array<chunksize=(1, 49, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    Snow mixing ratio

    units :

    kg kg-1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 211.45 MiB 1.91 MiB Shape (28, 49, 201, 201) (1, 49, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • QGRAUP
    (Time, z, y, x)
    float32
    dask.array<chunksize=(1, 49, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    Graupel mixing ratio

    units :

    kg kg-1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 211.45 MiB 1.91 MiB Shape (28, 49, 201, 201) (1, 49, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • QNICE
    (Time, z, y, x)
    float32
    dask.array<chunksize=(1, 49, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    Ice Number concentration

    units :

    kg-1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 211.45 MiB 1.91 MiB Shape (28, 49, 201, 201) (1, 49, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • QNRAIN
    (Time, z, y, x)
    float32
    dask.array<chunksize=(1, 49, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    Rain Number concentration

    units :

    kg-1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 211.45 MiB 1.91 MiB Shape (28, 49, 201, 201) (1, 49, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SHDMAX
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ANNUAL MAX VEG FRACTION

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SHDMIN
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ANNUAL MIN VEG FRACTION

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SNOALB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ANNUAL MAX SNOW ALBEDO IN FRACTION

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • TSLB
    (Time, soil_layers_stag, y, x)
    float32
    dask.array<chunksize=(1, 4, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    SOIL TEMPERATURE

    units :

    K

    stagger :

    Z

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 17.26 MiB 159.39 kiB Shape (28, 4, 201, 201) (1, 4, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SMOIS
    (Time, soil_layers_stag, y, x)
    float32
    dask.array<chunksize=(1, 4, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    SOIL MOISTURE

    units :

    m3 m-3

    stagger :

    Z

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 17.26 MiB 159.39 kiB Shape (28, 4, 201, 201) (1, 4, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SH2O
    (Time, soil_layers_stag, y, x)
    float32
    dask.array<chunksize=(1, 4, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    SOIL LIQUID WATER

    units :

    m3 m-3

    stagger :

    Z

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 17.26 MiB 159.39 kiB Shape (28, 4, 201, 201) (1, 4, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SEAICE
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    SEA ICE FLAG

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • XICEM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    SEA ICE FLAG (PREVIOUS STEP)

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SFROFF
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    SURFACE RUNOFF

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • UDROFF
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    UNDERGROUND RUNOFF

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • IVGTYP
    (Time, y, x)
    int32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    106

    MemoryOrder :

    XY

    description :

    DOMINANT VEGETATION CATEGORY

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type int32 numpy.ndarray

  • ISLTYP
    (Time, y, x)
    int32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    106

    MemoryOrder :

    XY

    description :

    DOMINANT SOIL CATEGORY

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type int32 numpy.ndarray

  • VEGFRA
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    VEGETATION FRACTION

    units :

    stagger :

    standard_name :

    vegetation_area_fraction

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • GRDFLX
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    GROUND HEAT FLUX

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACGRDFLX
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED GROUND HEAT FLUX

    units :

    J m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSNOM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED MELTED SNOW

    units :

    kg m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SNOW
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    SNOW WATER EQUIVALENT

    units :

    kg m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SNOWH
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    PHYSICAL SNOW DEPTH

    units :

    m

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • CANWAT
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    CANOPY WATER

    units :

    kg m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SSTSK
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    SKIN SEA SURFACE TEMPERATURE

    units :

    K

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • WATER_DEPTH
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    global water depth

    units :

    m

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • COSZEN
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    COS of SOLAR ZENITH ANGLE

    units :

    1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • LAI
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    LEAF AREA INDEX

    units :

    m-2/m-2

    stagger :

    standard_name :

    leaf_area_index

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • U10E
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Special U at 10 M from MYJSFC

    units :

    m s-1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • V10E
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Special V at 10 M from MYJSFC

    units :

    m s-1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • VAR
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    STANDARD DEVIATION OF SUBGRID-SCALE OROGRAPHY

    units :

    m

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • TKE_PBL
    (Time, z_stag, y, x)
    float32
    dask.array<chunksize=(1, 50, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    TKE from PBL

    units :

    m2 s-2

    stagger :

    Z

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 215.76 MiB 1.95 MiB Shape (28, 50, 201, 201) (1, 50, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • EL_PBL
    (Time, z_stag, y, x)
    float32
    dask.array<chunksize=(1, 50, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    Length scale from PBL

    units :

    m

    stagger :

    Z

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 215.76 MiB 1.95 MiB Shape (28, 50, 201, 201) (1, 50, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • MAPFAC_M
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Map scale factor on mass grid

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • MAPFAC_U
    (Time, y, x_stag)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Map scale factor on u-grid

    units :

    stagger :

    X

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.34 MiB 39.85 kiB Shape (28, 201, 202) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • MAPFAC_V
    (Time, y_stag, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Map scale factor on v-grid

    units :

    stagger :

    Y

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.34 MiB 39.85 kiB Shape (28, 202, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • MAPFAC_MX
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Map scale factor on mass grid, x direction

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • MAPFAC_MY
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Map scale factor on mass grid, y direction

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • MAPFAC_UX
    (Time, y, x_stag)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Map scale factor on u-grid, x direction

    units :

    stagger :

    X

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.34 MiB 39.85 kiB Shape (28, 201, 202) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • MAPFAC_UY
    (Time, y, x_stag)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Map scale factor on u-grid, y direction

    units :

    stagger :

    X

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.34 MiB 39.85 kiB Shape (28, 201, 202) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • MAPFAC_VX
    (Time, y_stag, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Map scale factor on v-grid, x direction

    units :

    stagger :

    Y

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.34 MiB 39.85 kiB Shape (28, 202, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • MF_VX_INV
    (Time, y_stag, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Inverse map scale factor on v-grid, x direction

    units :

    stagger :

    Y

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.34 MiB 39.85 kiB Shape (28, 202, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • MAPFAC_VY
    (Time, y_stag, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Map scale factor on v-grid, y direction

    units :

    stagger :

    Y

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.34 MiB 39.85 kiB Shape (28, 202, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • F
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Coriolis sine latitude term

    units :

    s-1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • E
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Coriolis cosine latitude term

    units :

    s-1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SINALPHA
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Local sine of map rotation

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • COSALPHA
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Local cosine of map rotation

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • HGT
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Terrain Height

    units :

    m

    stagger :

    standard_name :

    surface_altitude

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • TSK
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    SURFACE SKIN TEMPERATURE

    units :

    K

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • P_TOP
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    PRESSURE TOP OF THE MODEL

    units :

    Pa

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • GOT_VAR_SSO
    (Time)
    int32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    106

    MemoryOrder :

    0

    description :

    whether VAR_SSO was included in WPS output (beginning V3.4)

    units :

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type int32 numpy.ndarray

  • T00
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    BASE STATE TEMPERATURE

    units :

    K

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • P00
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    BASE STATE PRESSURE

    units :

    Pa

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • TLP
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    BASE STATE LAPSE RATE

    units :

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • TISO
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    TEMP AT WHICH THE BASE T TURNS CONST

    units :

    K

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • TLP_STRAT
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    BASE STATE LAPSE RATE (DT/D(LN(P)) IN STRATOSPHERE

    units :

    K

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • P_STRAT
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    BASE STATE PRESSURE AT BOTTOM OF STRATOSPHERE

    units :

    Pa

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • MAX_MSFTX
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    Max map factor in domain

    units :

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • MAX_MSFTY
    (Time)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    0

    description :

    Max map factor in domain

    units :

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • RAINC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED TOTAL CUMULUS PRECIPITATION

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • RAINSH
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED SHALLOW CUMULUS PRECIPITATION

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • RAINNC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED TOTAL GRID SCALE PRECIPITATION

    units :

    mm

    stagger :

    standard_name :

    integral_of_lwe_precipitation_rate_wrt_time

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SNOWNC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED TOTAL GRID SCALE SNOW AND ICE

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • GRAUPELNC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED TOTAL GRID SCALE GRAUPEL

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • HAILNC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED TOTAL GRID SCALE HAIL

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • CLDFRA
    (Time, z, y, x)
    float32
    dask.array<chunksize=(1, 49, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    CLOUD FRACTION

    units :

    stagger :

    standard_name :

    cloud_area_fraction_in_atmosphere_layer

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 211.45 MiB 1.91 MiB Shape (28, 49, 201, 201) (1, 49, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SWDOWN
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    DOWNWARD SHORT WAVE FLUX AT GROUND SURFACE

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • GLW
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    DOWNWARD LONG WAVE FLUX AT GROUND SURFACE

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SWNORM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    NORMAL SHORT WAVE FLUX AT GROUND SURFACE (SLOPE-DEPENDENT)

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSWUPT
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED UPWELLING SHORTWAVE FLUX AT TOP

    units :

    J m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSWUPTC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED UPWELLING CLEAR SKY SHORTWAVE FLUX AT TOP

    units :

    J m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSWDNT
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED DOWNWELLING SHORTWAVE FLUX AT TOP

    units :

    J m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSWDNTC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED DOWNWELLING CLEAR SKY SHORTWAVE FLUX AT TOP

    units :

    J m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSWUPB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED UPWELLING SHORTWAVE FLUX AT BOTTOM

    units :

    J m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSWUPBC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED UPWELLING CLEAR SKY SHORTWAVE FLUX AT BOTTOM

    units :

    J m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSWDNB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED DOWNWELLING SHORTWAVE FLUX AT BOTTOM

    units :

    J m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSWDNBC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED DOWNWELLING CLEAR SKY SHORTWAVE FLUX AT BOTTOM

    units :

    J m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACLWUPT
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED UPWELLING LONGWAVE FLUX AT TOP

    units :

    J m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACLWUPTC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED UPWELLING CLEAR SKY LONGWAVE FLUX AT TOP

    units :

    J m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACLWDNT
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED DOWNWELLING LONGWAVE FLUX AT TOP

    units :

    J m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACLWDNTC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED DOWNWELLING CLEAR SKY LONGWAVE FLUX AT TOP

    units :

    J m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACLWUPB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED UPWELLING LONGWAVE FLUX AT BOTTOM

    units :

    J m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACLWUPBC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED UPWELLING CLEAR SKY LONGWAVE FLUX AT BOTTOM

    units :

    J m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACLWDNB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED DOWNWELLING LONGWAVE FLUX AT BOTTOM

    units :

    J m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACLWDNBC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED DOWNWELLING CLEAR SKY LONGWAVE FLUX AT BOTTOM

    units :

    J m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SWUPT
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    INSTANTANEOUS UPWELLING SHORTWAVE FLUX AT TOP

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SWUPTC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    INSTANTANEOUS UPWELLING CLEAR SKY SHORTWAVE FLUX AT TOP

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SWDNT
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    INSTANTANEOUS DOWNWELLING SHORTWAVE FLUX AT TOP

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SWDNTC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    INSTANTANEOUS DOWNWELLING CLEAR SKY SHORTWAVE FLUX AT TOP

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SWUPB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    INSTANTANEOUS UPWELLING SHORTWAVE FLUX AT BOTTOM

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SWUPBC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    INSTANTANEOUS UPWELLING CLEAR SKY SHORTWAVE FLUX AT BOTTOM

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SWDNB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    INSTANTANEOUS DOWNWELLING SHORTWAVE FLUX AT BOTTOM

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SWDNBC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    INSTANTANEOUS DOWNWELLING CLEAR SKY SHORTWAVE FLUX AT BOTTOM

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • LWUPT
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    INSTANTANEOUS UPWELLING LONGWAVE FLUX AT TOP

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • LWUPTC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    INSTANTANEOUS UPWELLING CLEAR SKY LONGWAVE FLUX AT TOP

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • LWDNT
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    INSTANTANEOUS DOWNWELLING LONGWAVE FLUX AT TOP

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • LWDNTC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    INSTANTANEOUS DOWNWELLING CLEAR SKY LONGWAVE FLUX AT TOP

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • LWUPB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    INSTANTANEOUS UPWELLING LONGWAVE FLUX AT BOTTOM

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • LWUPBC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    INSTANTANEOUS UPWELLING CLEAR SKY LONGWAVE FLUX AT BOTTOM

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • LWDNB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    INSTANTANEOUS DOWNWELLING LONGWAVE FLUX AT BOTTOM

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • LWDNBC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    INSTANTANEOUS DOWNWELLING CLEAR SKY LONGWAVE FLUX AT BOTTOM

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • OLR
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    TOA OUTGOING LONG WAVE

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ALBEDO
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ALBEDO

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ALBBCK
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    BACKGROUND ALBEDO

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • EMISS
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    SURFACE EMISSIVITY

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • NOAHRES
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    RESIDUAL OF THE NOAH SURFACE ENERGY BUDGET

    units :

    W m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • TMN
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    SOIL TEMPERATURE AT LOWER BOUNDARY

    units :

    K

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • XLAND
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    LAND MASK (1 FOR LAND, 2 FOR WATER)

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • UST
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    U* IN SIMILARITY THEORY

    units :

    m s-1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • PBLH
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    PBL HEIGHT

    units :

    m

    stagger :

    standard_name :

    atmosphere_boundary_layer_thickness

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • HFX
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    UPWARD HEAT FLUX AT THE SURFACE

    units :

    W m-2

    stagger :

    standard_name :

    surface_upward_heat_flux_in_air

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • QFX
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    UPWARD MOISTURE FLUX AT THE SURFACE

    units :

    kg m-2 s-1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • LH
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    LATENT HEAT FLUX AT THE SURFACE

    units :

    W m-2

    stagger :

    standard_name :

    surface_upward_latent_heat_flux_in_air

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACHFX
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED UPWARD HEAT FLUX AT THE SURFACE

    units :

    J m-2

    stagger :

    standard_name :

    integral_of_surface_upward_heat_flux_in_air_wrt_time

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACLHF
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ACCUMULATED UPWARD LATENT HEAT FLUX AT THE SURFACE

    units :

    J m-2

    stagger :

    standard_name :

    integral_of_surface_upward_latent_heat_flux_in_air_wrf_time

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SNOWC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    FLAG INDICATING SNOW COVERAGE (1 FOR SNOW COVER)

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SR
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    fraction of frozen precipitation

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SAVE_TOPO_FROM_REAL
    (Time)
    int32
    dask.array<chunksize=(1,), meta=np.ndarray>

    FieldType :

    106

    MemoryOrder :

    0

    description :

    1=original topo from real/0=topo modified by WRF

    stagger :

    Array Chunk Bytes 112 B 4 B Shape (28,) (1,) Dask graph 28 chunks in 57 graph layers Data type int32 numpy.ndarray

  • ISEEDARR_SPPT
    (Time, seed_dim_stag)
    int32
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    FieldType :

    106

    MemoryOrder :

    Z

    description :

    Array to hold seed for restart, SPPT

    units :

    stagger :

    Z

    Array Chunk Bytes 224 B 8 B Shape (28, 2) (1, 2) Dask graph 28 chunks in 57 graph layers Data type int32 numpy.ndarray

  • ISEEDARR_SKEBS
    (Time, seed_dim_stag)
    int32
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    FieldType :

    106

    MemoryOrder :

    Z

    description :

    Array to hold seed for restart, SKEBS

    units :

    stagger :

    Z

    Array Chunk Bytes 224 B 8 B Shape (28, 2) (1, 2) Dask graph 28 chunks in 57 graph layers Data type int32 numpy.ndarray

  • ISEEDARR_RAND_PERTURB
    (Time, seed_dim_stag)
    int32
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    FieldType :

    106

    MemoryOrder :

    Z

    description :

    Array to hold seed for restart, RAND_PERT

    units :

    stagger :

    Z

    Array Chunk Bytes 224 B 8 B Shape (28, 2) (1, 2) Dask graph 28 chunks in 57 graph layers Data type int32 numpy.ndarray

  • ISEEDARRAY_SPP_CONV
    (Time, seed_dim_stag)
    int32
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    FieldType :

    106

    MemoryOrder :

    Z

    description :

    Array to hold seed for restart, RAND_PERT2

    units :

    stagger :

    Z

    Array Chunk Bytes 224 B 8 B Shape (28, 2) (1, 2) Dask graph 28 chunks in 57 graph layers Data type int32 numpy.ndarray

  • ISEEDARRAY_SPP_PBL
    (Time, seed_dim_stag)
    int32
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    FieldType :

    106

    MemoryOrder :

    Z

    description :

    Array to hold seed for restart, RAND_PERT3

    units :

    stagger :

    Z

    Array Chunk Bytes 224 B 8 B Shape (28, 2) (1, 2) Dask graph 28 chunks in 57 graph layers Data type int32 numpy.ndarray

  • ISEEDARRAY_SPP_LSM
    (Time, seed_dim_stag)
    int32
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    FieldType :

    106

    MemoryOrder :

    Z

    description :

    Array to hold seed for restart, RAND_PERT4

    units :

    stagger :

    Z

    Array Chunk Bytes 224 B 8 B Shape (28, 2) (1, 2) Dask graph 28 chunks in 57 graph layers Data type int32 numpy.ndarray

  • ISNOW
    (Time, y, x)
    int32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    106

    MemoryOrder :

    XY

    description :

    no. of snow layer

    units :

    m3 m-3

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type int32 numpy.ndarray

  • TV
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    vegetation leaf temperature

    units :

    K

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • TG
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    bulk ground temperature

    units :

    K

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • CANICE
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    intercepted ice mass

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • CANLIQ
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    intercepted liquid water

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • EAH
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    canopy air vapor pressure

    units :

    pa

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • TAH
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    canopy air temperature

    units :

    K

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • CM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    surf. exchange coeff. for momentum

    units :

    m/s

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • CH
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    surf. exchange coeff. for heat

    units :

    m/s

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • FWET
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    wetted or snowed canopy fraction

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SNEQVO
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    snow mass at last time step

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ALBOLD
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    snow albedo at last timestep

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • QSNOWXY
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    snowfall on the ground

    units :

    mm/s

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • QRAINXY
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    rainfall on the ground

    units :

    mm/s

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • WSLAKE
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    lake water storage

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ZWT
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    water table depth

    units :

    m

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • WA
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    water in the acquifer

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • WT
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    groundwater storage

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • TSNO
    (Time, snow_layers_stag, y, x)
    float32
    dask.array<chunksize=(1, 3, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    snow temperature

    units :

    K

    stagger :

    Z

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 12.95 MiB 119.54 kiB Shape (28, 3, 201, 201) (1, 3, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ZSNSO
    (Time, snso_layers_stag, y, x)
    float32
    dask.array<chunksize=(1, 7, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    layer-bottom depth from snow surf

    units :

    m

    stagger :

    Z

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 30.21 MiB 278.93 kiB Shape (28, 7, 201, 201) (1, 7, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SNICE
    (Time, snow_layers_stag, y, x)
    float32
    dask.array<chunksize=(1, 3, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    snow layer ice

    units :

    mm

    stagger :

    Z

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 12.95 MiB 119.54 kiB Shape (28, 3, 201, 201) (1, 3, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SNLIQ
    (Time, snow_layers_stag, y, x)
    float32
    dask.array<chunksize=(1, 3, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XYZ

    description :

    snow layer liquid

    units :

    mm

    stagger :

    Z

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 12.95 MiB 119.54 kiB Shape (28, 3, 201, 201) (1, 3, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • LFMASS
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    leaf mass

    units :

    g/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • RTMASS
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    mass of fine roots

    units :

    g/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • STMASS
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    stem mass

    units :

    g/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • WOOD
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    mass of wood

    units :

    g/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • STBLCP
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    stable carbon pool

    units :

    g/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • FASTCP
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    short-lived carbon

    units :

    g/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • XSAI
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    stem area index

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • TAUSS
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    non-dimensional snow age

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • T2V
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    2 meter temperature over canopy

    units :

    K

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • T2B
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    2 meter temperature over bare ground

    units :

    K

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • Q2V
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    2 meter mixing ratio over canopy

    units :

    kg kg-1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • Q2B
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    2 meter mixing ratio over bare ground

    units :

    kg kg-1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • TRAD
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    surface radiative temperature

    units :

    K

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • NEE
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    net ecosystem exchange

    units :

    g m-2 s-1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • GPP
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    gross primary productivity

    units :

    g m-2 s-1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • NPP
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    net primary productivity

    units :

    g m-2 s-1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • FVEG
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Noah-MP vegetation fraction

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • QIN
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    groundwater recharge

    units :

    mm/s

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • RUNSF
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    surface runoff

    units :

    mm/s

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • RUNSB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    subsurface runoff

    units :

    mm/s

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ECAN
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    evaporation of intercepted water

    units :

    mm/s

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • EDIR
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ground surface evaporation rate

    units :

    mm/s

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ETRAN
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    transpiration rate

    units :

    mm/s

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • FSA
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    total absorbed solar radiation

    units :

    W/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • FIRA
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    total net longwave rad

    units :

    W/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • APAR
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    photosyn active energy by canopy

    units :

    W/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • PSN
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    total photosynthesis

    units :

    umol m-2 s-1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SAV
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    solar rad absorbed by veg

    units :

    W/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SAG
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    solar rad absorbed by ground

    units :

    W/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • RSSUN
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    sunlit stomatal resistance

    units :

    s/m

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • RSSHA
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    shaded stomatal resistance

    units :

    s/m

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • BGAP
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    between canopy gap

    units :

    1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • WGAP
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    within canopy gap

    units :

    1

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • TGV
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    ground temp. under canopy

    units :

    K

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • TGB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    bare ground temperature

    units :

    K

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • CHV
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    vegetated heat exchange coefficient

    units :

    m/s

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • CHB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    bare-ground heat exchange coefficient

    units :

    m/s

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SHG
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    sensible heat flux: ground to canopy

    units :

    W/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SHC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    sensible heat flux: leaf to canopy

    units :

    W/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SHB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    sensible heat flux: bare grnd to atmo

    units :

    W/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • EVG
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    latent heat flux: ground to canopy

    units :

    W/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • EVB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    latent heat flux: bare grnd to atmo

    units :

    W/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • GHV
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    heat flux into soil: under canopy

    units :

    W/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • GHB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    heat flux into soil: bare fraction

    units :

    W/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • IRG
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    net longwave at below canopy surface

    units :

    W/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • IRC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    net longwave in canopy

    units :

    W/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • IRB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    net longwave at bare fraction surface

    units :

    W/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • TR
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    transpiration

    units :

    W/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • EVC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    canopy evaporation

    units :

    W/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • CHLEAF
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    leaf exchange coefficient

    units :

    m/s

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • CHUC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    under canopy exchange coefficient

    units :

    m/s

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • CHV2
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    leaf exchange coefficient

    units :

    m/s

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • CHB2
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    under canopy exchange coefficient

    units :

    m/s

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • CHSTAR
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    dummy exchange coefficient

    units :

    m/s

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SMCWTD
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    deep soil moisture

    units :

    m3 m-3

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • RECH
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    water table recharge

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • QRFS
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    sum baseflow

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • QSPRINGS
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    sum seeping water

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • QSLAT
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    sum lateral flow

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACINTS
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated canopy snow interception

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACINTR
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated canopy rain interception

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACDRIPR
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated canopy rain drip

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACTHROR
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated rain throughfall

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACEVAC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated canopy evaporation

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACDEWC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated canopy dew

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • FORCTLSM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    lowest model T into LSM

    units :

    K

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • FORCQLSM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    lowest model Q into LSM

    units :

    kg/kg

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • FORCPLSM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    lowest model P into LSM

    units :

    Pa

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • FORCZLSM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    lowest model Z into LSM

    units :

    m

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • FORCWLSM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    lowest model wind speed into LSM

    units :

    m/s

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACRAINLSM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated liquid precipitation into LSM

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACRUNSB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated subsurface runoff

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACRUNSF
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated surface runoff

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACECAN
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated evaporation of intercepted water

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACETRAN
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated transpiration

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACEDIR
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated ground surface evaporation

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACQLAT
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    accumulated lateral flow

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACQRF
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    accumulated baseflow

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACETLSM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated evaporation

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSNOWLSM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated frozen precipitation into LSM

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSUBC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated canopy sublimation

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACFROC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated canopy frost

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACFRZC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated canopy liquid freeze

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACMELTC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated canopy snow melt

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSNBOT
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated total liquid water (melt + rain through pack) out of snowpack bottom

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSNMELT
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated snowmelt due to phase change

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACPONDING
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated surface ponding from complete pack melt

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSNSUB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated snow pack sublimation

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSNFRO
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated snow pack frost

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACRAINSNOW
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    accumulated rain on snow pack

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACDRIPS
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated canopy snow drip

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACTHROS
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated snow throughfall

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSAGB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated solar radiation absorbed at bare fraction

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACIRB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated net longwave at bare fraction

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSHB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated sensible heat flux at bare fraction

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACEVB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated latent heat flux at bare fraction

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACGHB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated heat flux into soil at bare fraction

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACPAHB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated precipitation advected energy to bare ground

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSAGV
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated solar radiation absorbed at vegetated fraction

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACIRG
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated net longwave below canopy surface

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSHG
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated sensible heat flux: ground to canopy

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACEVG
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated latent heat flux: ground to canopy

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACGHV
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated heat flux into soil under canopy

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACPAHG
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated precipitation advected energy to below canopy

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSAV
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated solar rad absorbed by vegetated fraction

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACIRC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated net longwave in canopy

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSHC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated sensible heat flux: canopy to atmosphere

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACEVC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated canopy evaporation

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACTR
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated transpiration

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACPAHV
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated precipitation advected energy to vegetation

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSWDNLSM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated shortwave down at LSM

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSWUPLSM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated shortwave up at LSM

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACLWDNLSM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated longwave down at LSM

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACLWUPLSM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated longwave up at LSM

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACSHFLSM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    accumulated total sensible heat flux

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACLHFLSM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    accumulated total latent heat flux

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACGHFLSM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    accumulated total ground heat flux

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACPAHLSM
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    accumulated total precip heat flux

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACCANHS
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    accumulated canopy storage change

    units :

    kJ m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SOILENERGY
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    energy content in soil relative to 273.16

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SNOWENERGY
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    energy content in snow relative to 273.16

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • ACEFLXB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    accumulated bottom soil heat flux

    units :

    kJ/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • GRAIN
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    mass of grain

    units :

    g/m2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • GDD
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    growing degree days

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • CROPCAT
    (Time, y, x)
    int32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    106

    MemoryOrder :

    XY

    description :

    dominant crop category

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type int32 numpy.ndarray

  • PGS
    (Time, y, x)
    int32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    106

    MemoryOrder :

    XY

    description :

    pgs

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type int32 numpy.ndarray

  • QTDRAIN
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Accumulated tile drainage flux

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • IRNUMSI
    (Time, y, x)
    int32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    106

    MemoryOrder :

    XY

    description :

    Sprinkler Irrigation Event Count

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type int32 numpy.ndarray

  • IRNUMMI
    (Time, y, x)
    int32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    106

    MemoryOrder :

    XY

    description :

    Micro Irrigation Event Count

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type int32 numpy.ndarray

  • IRNUMFI
    (Time, y, x)
    int32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    106

    MemoryOrder :

    XY

    description :

    Flood Irrigation Event Count

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type int32 numpy.ndarray

  • IRSIVOL
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Sprinkler Irrigation Water Accumulated

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • IRMIVOL
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Micro Irrigation Water Accumulated

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • IRFIVOL
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Flood Irrigation Water Accumulated

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • IRELOSS
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Sprinkler Evaporation Loss Accumulated

    units :

    mm

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • IRRSPLH
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    Sprinkler Evaporation Loss Accumulated

    units :

    J m-2

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • C1H
    (Time, z)
    float32
    dask.array<chunksize=(1, 49), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    Z

    description :

    half levels, c1h = d bf / d eta, using znw

    units :

    1

    stagger :

    Array Chunk Bytes 5.36 kiB 196 B Shape (28, 49) (1, 49) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • C2H
    (Time, z)
    float32
    dask.array<chunksize=(1, 49), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    Z

    description :

    half levels, c2h = (1-c1h)*(p0-pt)

    units :

    Pa

    stagger :

    Array Chunk Bytes 5.36 kiB 196 B Shape (28, 49) (1, 49) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • C1F
    (Time, z_stag)
    float32
    dask.array<chunksize=(1, 50), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    Z

    description :

    full levels, c1f = d bf / d eta, using znu

    units :

    1

    stagger :

    Z

    Array Chunk Bytes 5.47 kiB 200 B Shape (28, 50) (1, 50) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • C2F
    (Time, z_stag)
    float32
    dask.array<chunksize=(1, 50), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    Z

    description :

    full levels, c2f = (1-c1f)*(p0-pt)

    units :

    Pa

    stagger :

    Z

    Array Chunk Bytes 5.47 kiB 200 B Shape (28, 50) (1, 50) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • C3H
    (Time, z)
    float32
    dask.array<chunksize=(1, 49), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    Z

    description :

    half levels, c3h = bh

    units :

    1

    stagger :

    Array Chunk Bytes 5.36 kiB 196 B Shape (28, 49) (1, 49) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • C4H
    (Time, z)
    float32
    dask.array<chunksize=(1, 49), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    Z

    description :

    half levels, c4h = (eta-bh)*(p0-pt), using znu

    units :

    Pa

    stagger :

    Array Chunk Bytes 5.36 kiB 196 B Shape (28, 49) (1, 49) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • C3F
    (Time, z_stag)
    float32
    dask.array<chunksize=(1, 50), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    Z

    description :

    full levels, c3f = bf

    units :

    1

    stagger :

    Z

    Array Chunk Bytes 5.47 kiB 200 B Shape (28, 50) (1, 50) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • C4F
    (Time, z_stag)
    float32
    dask.array<chunksize=(1, 50), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    Z

    description :

    full levels, c4f = (eta-bf)*(p0-pt), using znw

    units :

    Pa

    stagger :

    Z

    Array Chunk Bytes 5.47 kiB 200 B Shape (28, 50) (1, 50) Dask graph 28 chunks in 57 graph layers Data type float32 numpy.ndarray

  • PCB
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    base state dry air mass in column

    units :

    Pa

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • PC
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    perturbation dry air mass in column

    units :

    Pa

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • LANDMASK
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    LAND MASK (1 FOR LAND, 0 FOR WATER)

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • LAKEMASK
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    LAKE MASK (1 FOR LAKE, 0 FOR NON-LAKE)

    units :

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SST
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    SEA SURFACE TEMPERATURE

    units :

    K

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • SST_INPUT
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    FieldType :

    104

    MemoryOrder :

    XY

    description :

    SEA SURFACE TEMPERATURE FROM WRFLOWINPUT FILE

    units :

    K

    stagger :

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 57 graph layers Data type float32 numpy.ndarray

  • air_potential_temperature
    (Time, z, y, x)
    float32
    dask.array<chunksize=(1, 49, 101, 101), meta=np.ndarray>

    units :

    K

    standard_name :

    air_potential_temperature

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 211.45 MiB 1.91 MiB Shape (28, 49, 201, 201) (1, 49, 101, 101) Dask graph 112 chunks in 58 graph layers Data type float32 numpy.ndarray

  • air_pressure
    (Time, z, y, x)
    float32
    dask.array<chunksize=(1, 49, 101, 101), meta=np.ndarray>

    units :

    Pa

    standard_name :

    air_pressure

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 211.45 MiB 1.91 MiB Shape (28, 49, 201, 201) (1, 49, 101, 101) Dask graph 112 chunks in 115 graph layers Data type float32 numpy.ndarray

  • geopotential
    (Time, z_stag, y, x)
    float32
    dask.array<chunksize=(1, 50, 101, 101), meta=np.ndarray>

    units :

    m**2 s**-2

    standard_name :

    geopotential

    stagger :

    Z

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 215.76 MiB 1.95 MiB Shape (28, 50, 201, 201) (1, 50, 101, 101) Dask graph 112 chunks in 115 graph layers Data type float32 numpy.ndarray

  • geopotential_height
    (Time, z_stag, y, x)
    float32
    dask.array<chunksize=(1, 50, 101, 101), meta=np.ndarray>

    units :

    m

    standard_name :

    geopotential_height

    stagger :

    Z

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 215.76 MiB 1.95 MiB Shape (28, 50, 201, 201) (1, 50, 101, 101) Dask graph 112 chunks in 116 graph layers Data type float32 numpy.ndarray

  • wind_east
    (Time, z, y, x)
    float32
    dask.array<chunksize=(1, 49, 100, 100), meta=np.ndarray>

    description :

    earth-relative x-wind component

    standard_name :

    eastward_wind

    units :

    m s-1

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 211.45 MiB 1.87 MiB Shape (28, 49, 201, 201) (1, 49, 100, 100) Dask graph 252 chunks in 251 graph layers Data type float32 numpy.ndarray

  • wind_north
    (Time, z, y, x)
    float32
    dask.array<chunksize=(1, 49, 100, 100), meta=np.ndarray>

    description :

    earth-relative y-wind component

    standard_name :

    northward_wind

    units :

    m s-1

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 211.45 MiB 1.87 MiB Shape (28, 49, 201, 201) (1, 49, 100, 100) Dask graph 252 chunks in 251 graph layers Data type float32 numpy.ndarray

  • wind_east_10
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    description :

    earth-relative 10m x-wind component

    units :

    m s-1

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 231 graph layers Data type float32 numpy.ndarray

  • wind_north_10
    (Time, y, x)
    float32
    dask.array<chunksize=(1, 101, 101), meta=np.ndarray>

    description :

    earth-relative 10m y-wind component

    units :

    m s-1

    grid_mapping :

    wrf_projection

    Array Chunk Bytes 4.32 MiB 39.85 kiB Shape (28, 201, 201) (1, 101, 101) Dask graph 112 chunks in 231 graph layers Data type float32 numpy.ndarray

  • wrf_projection
    ()
    object
    +proj=lcc +x_0=0 +y_0=0 +a=63700...

    crs_wkt :

    PROJCRS["unknown",BASEGEOGCRS["unknown",DATUM["unknown",ELLIPSOID["unknown",6370000,0,LENGTHUNIT["metre",1,ID["EPSG",9001]]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8901]]],CONVERSION["unknown",METHOD["Lambert Conic Conformal (2SP)",ID["EPSG",9802]],PARAMETER["Latitude of false origin",38.900016784668,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8821]],PARAMETER["Longitude of false origin",-107,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8822]],PARAMETER["Latitude of 1st standard parallel",30,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8823]],PARAMETER["Latitude of 2nd standard parallel",50,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8824]],PARAMETER["Easting at false origin",0,LENGTHUNIT["metre",1],ID["EPSG",8826]],PARAMETER["Northing at false origin",0,LENGTHUNIT["metre",1],ID["EPSG",8827]]],CS[Cartesian,2],AXIS["(E)",east,ORDER[1],LENGTHUNIT["metre",1,ID["EPSG",9001]]],AXIS["(N)",north,ORDER[2],LENGTHUNIT["metre",1,ID["EPSG",9001]]]]

    semi_major_axis :

    6370000.0

    semi_minor_axis :

    6370000.0

    inverse_flattening :

    0.0

    reference_ellipsoid_name :

    unknown

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    unknown

    horizontal_datum_name :

    unknown

    projected_crs_name :

    unknown

    grid_mapping_name :

    lambert_conformal_conic

    standard_parallel :

    (30.0, 50.0)

    latitude_of_projection_origin :

    38.90001678466797

    longitude_of_central_meridian :

    -107.0

    false_easting :

    0.0

    false_northing :

    0.0

    array(<Projected CRS: +proj=lcc +x_0=0 +y_0=0 +a=6370000 +b=6370000 +lat ...>
    Name: unknown
    Axis Info [cartesian]:
    - E[east]: Easting (metre)
    - N[north]: Northing (metre)
    Area of Use:
    - undefined
    Coordinate Operation:
    - name: unknown
    - method: Lambert Conic Conformal (2SP)
    Datum: unknown
    - Ellipsoid: unknown
    - Prime Meridian: Greenwich
    , dtype=object)

• Indexes: (7)
  • z
    PandasIndex

    PandasIndex(Index([  0.9967355728149414,   0.9893206357955933,   0.9799258708953857,
             0.9681073427200317,   0.9533711671829224,   0.9351978302001953,
             0.9130828976631165,   0.8865982294082642,   0.8554682731628418,
             0.8196507692337036,   0.7794029712677002,   0.7353180646896362,
             0.6907134056091309,   0.6484591960906982,   0.6084318161010742,
             0.5705137252807617,   0.5345938205718994,   0.5005667209625244,
             0.4683328866958618,   0.4377976655960083,    0.408871591091156,
             0.3814697861671448,  0.35551202297210693,   0.3309221863746643,
            0.30762815475463867,  0.28556162118911743,   0.2646579444408417,
             0.2448558509349823,  0.22609728574752808,   0.2083272635936737,
            0.19149364531040192,  0.17554712295532227,  0.16044092178344727,
            0.14613081514835358,  0.13257481157779694,  0.11973315477371216,
            0.10756823420524597,   0.0960443988442421,   0.0851278156042099,
             0.0747864842414856,  0.06499014794826508,   0.0557100772857666,
            0.04691901057958603,  0.03859121352434158, 0.030702289193868637,
           0.023229092359542847, 0.016149718314409256, 0.009443413466215134,
           0.003090500133112073],
          dtype='float32', name='z'))

  • z_stag
    PandasIndex

    PandasIndex(Index([                 1.0,    0.993471086025238,   0.9851701259613037,
             0.9746816754341125,   0.9615330100059509,   0.9452093839645386,
             0.9251862168312073,   0.9009795784950256,   0.8722169399261475,
             0.8387195467948914,   0.8005820512771606,   0.7582239508628845,
             0.7124121785163879,    0.669014573097229,   0.6279038786888123,
             0.5889596939086914,   0.5520676970481873,   0.5171198844909668,
             0.4840136170387268,  0.45265212655067444,  0.42294323444366455,
             0.3947999179363251,  0.36813968420028687,    0.342884361743927,
             0.3189599812030792,   0.2962963581085205,  0.27482691407203674,
             0.2544889748096466,   0.2352227419614792,  0.21697182953357697,
            0.19968268275260925,  0.18330460786819458,  0.16778962314128876,
            0.15309223532676697,  0.13916939496994019,   0.1259802281856537,
            0.11348608881235123,  0.10165038704872131,  0.09043841063976288,
            0.07981722801923752,  0.06975574791431427,  0.06022455543279648,
           0.051195595413446426,  0.04264242574572563,  0.03454000502824783,
            0.02686457335948944, 0.019593609496951103, 0.012705827131867409,
           0.006181000266224146,                  0.0],
          dtype='float32', name='z_stag'))

  • Time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-04-03 00:00:00', '2023-04-03 06:00:00',
                   '2023-04-03 12:00:00', '2023-04-03 18:00:00',
                   '2023-04-04 00:00:00', '2023-04-04 06:00:00',
                   '2023-04-04 12:00:00', '2023-04-04 18:00:00',
                   '2023-04-05 00:00:00', '2023-04-05 06:00:00',
                   '2023-04-05 12:00:00', '2023-04-05 18:00:00',
                   '2023-04-06 00:00:00', '2023-04-06 06:00:00',
                   '2023-04-06 12:00:00', '2023-04-06 18:00:00',
                   '2023-04-07 00:00:00', '2023-04-07 06:00:00',
                   '2023-04-07 12:00:00', '2023-04-07 18:00:00',
                   '2023-04-08 00:00:00', '2023-04-08 06:00:00',
                   '2023-04-08 12:00:00', '2023-04-08 18:00:00',
                   '2023-04-09 00:00:00', '2023-04-09 06:00:00',
                   '2023-04-09 12:00:00', '2023-04-09 18:00:00'],
                  dtype='datetime64[ns]', name='Time', freq=None))

  • x
    PandasIndex

    PandasIndex(Index([-53443.82594293087, -52943.82594293087, -52443.82594293087,
           -51943.82594293087, -51443.82594293087, -50943.82594293087,
           -50443.82594293087, -49943.82594293087, -49443.82594293087,
           -48943.82594293087,
           ...
            42056.17405706913,  42556.17405706913,  43056.17405706913,
            43556.17405706913,  44056.17405706913,  44556.17405706913,
            45056.17405706913,  45556.17405706913,  46056.17405706913,
            46556.17405706913],
          dtype='float64', name='x', length=201))

  • y
    PandasIndex

    PandasIndex(Index([-55999.21763437279, -55499.21763437279, -54999.21763437279,
           -54499.21763437279, -53999.21763437279, -53499.21763437279,
           -52999.21763437279, -52499.21763437279, -51999.21763437279,
           -51499.21763437279,
           ...
            39500.78236562721,  40000.78236562721,  40500.78236562721,
            41000.78236562721,  41500.78236562721,  42000.78236562721,
            42500.78236562721,  43000.78236562721,  43500.78236562721,
            44000.78236562721],
          dtype='float64', name='y', length=201))

  • y_stag
    PandasIndex

    PandasIndex(Index([-56249.21763437279, -55749.21763437279, -55249.21763437279,
           -54749.21763437279, -54249.21763437279, -53749.21763437279,
           -53249.21763437279, -52749.21763437279, -52249.21763437279,
           -51749.21763437279,
           ...
            39750.78236562721,  40250.78236562721,  40750.78236562721,
            41250.78236562721,  41750.78236562721,  42250.78236562721,
            42750.78236562721,  43250.78236562721,  43750.78236562721,
            44250.78236562721],
          dtype='float64', name='y_stag', length=202))

  • x_stag
    PandasIndex

    PandasIndex(Index([-53693.82594293087, -53193.82594293087, -52693.82594293087,
           -52193.82594293087, -51693.82594293087, -51193.82594293087,
           -50693.82594293087, -50193.82594293087, -49693.82594293087,
           -49193.82594293087,
           ...
            42306.17405706913,  42806.17405706913,  43306.17405706913,
            43806.17405706913,  44306.17405706913,  44806.17405706913,
            45306.17405706913,  45806.17405706913,  46306.17405706913,
            46806.17405706913],
          dtype='float64', name='x_stag', length=202))

• Attributes: (153)

  TITLE :

  OUTPUT FROM WRF V4.4 MODEL

  START_DATE :

  2023-03-25_00:00:00

  SIMULATION_START_DATE :

  2021-09-15_00:00:00

  WEST-EAST_GRID_DIMENSION :

  202

  SOUTH-NORTH_GRID_DIMENSION :

  202

  BOTTOM-TOP_GRID_DIMENSION :

  50

  DX :

  500.0

  DY :

  500.0

  AERCU_OPT :

  0

  AERCU_FCT :

  1.0

  IDEAL_CASE :

  0

  DIFF_6TH_SLOPEOPT :

  0

  AUTO_LEVELS_OPT :

  2

  DIFF_6TH_THRESH :

  0.1

  DZBOT :

  50.0

  DZSTRETCH_S :

  1.3

  DZSTRETCH_U :

  1.1

  SKEBS_ON :

  0

  USE_Q_DIABATIC :

  0

  GRIDTYPE :

  C

  DIFF_OPT :

  1

  KM_OPT :

  4

  DAMP_OPT :

  0

  DAMPCOEF :

  0.2

  KHDIF :

  0.0

  KVDIF :

  0.0

  MP_PHYSICS :

  8

  RA_LW_PHYSICS :

  3

  RA_SW_PHYSICS :

  3

  SF_SFCLAY_PHYSICS :

  2

  SF_SURFACE_PHYSICS :

  4

  BL_PBL_PHYSICS :

  2

  CU_PHYSICS :

  0

  SF_LAKE_PHYSICS :

  0

  SURFACE_INPUT_SOURCE :

  1

  SST_UPDATE :

  1

  GHG_INPUT :

  1

  GRID_FDDA :

  0

  GFDDA_INTERVAL_M :

  0

  GFDDA_END_H :

  0

  GRID_SFDDA :

  0

  SGFDDA_INTERVAL_M :

  0

  SGFDDA_END_H :

  0

  HYPSOMETRIC_OPT :

  2

  USE_THETA_M :

  1

  GWD_OPT :

  0

  SF_URBAN_PHYSICS :

  0

  SF_SURFACE_MOSAIC :

  0

  SF_OCEAN_PHYSICS :

  0

  SHCU_PHYSICS :

  0

  MFSHCONV :

  0

  FEEDBACK :

  0

  SMOOTH_OPTION :

  0

  SWRAD_SCAT :

  1.0

  W_DAMPING :

  0

  DT :

  2.6666667

  RADT :

  3.0

  BLDT :

  0.0

  CUDT :

  0.0

  AER_OPT :

  0

  SWINT_OPT :

  0

  AER_TYPE :

  1

  AER_AOD550_OPT :

  1

  AER_ANGEXP_OPT :

  1

  AER_SSA_OPT :

  1

  AER_ASY_OPT :

  1

  AER_AOD550_VAL :

  0.12

  AER_ANGEXP_VAL :

  1.3

  AER_SSA_VAL :

  0.85

  AER_ASY_VAL :

  0.9

  MOIST_ADV_OPT :

  1

  SCALAR_ADV_OPT :

  1

  TKE_ADV_OPT :

  1

  DIFF_6TH_OPT :

  2

  DIFF_6TH_FACTOR :

  0.12

  OBS_NUDGE_OPT :

  0

  BUCKET_MM :

  -1.0

  BUCKET_J :

  -1.0

  PREC_ACC_DT :

  0.0

  ISFTCFLX :

  0

  ISHALLOW :

  0

  ISFFLX :

  1

  ICLOUD :

  1

  ICLOUD_CU :

  0

  TRACER_PBLMIX :

  1

  SCALAR_PBLMIX :

  0

  YSU_TOPDOWN_PBLMIX :

  1

  GRAV_SETTLING :

  0

  CLDOVRLP :

  2

  IDCOR :

  0

  OPT_SFC :

  1

  DVEG :

  4

  OPT_CRS :

  1

  OPT_BTR :

  1

  OPT_RUN :

  1

  OPT_FRZ :

  1

  OPT_INF :

  1

  OPT_RAD :

  3

  OPT_ALB :

  2

  OPT_SNF :

  4

  OPT_TBOT :

  2

  OPT_STC :

  1

  OPT_GLA :

  1

  OPT_RSF :

  1

  OPT_SOIL :

  1

  OPT_PEDO :

  1

  OPT_CROP :

  0

  OPT_IRR :

  0

  OPT_IRRM :

  0

  DFI_OPT :

  0

  NTASKS_X :

  8

  NTASKS_Y :

  10

  NTASKS_TOTAL :

  80

  SIMULATION_INITIALIZATION_TYPE :

  REAL-DATA CASE

  WEST-EAST_PATCH_START_UNSTAG :

  1

  WEST-EAST_PATCH_END_UNSTAG :

  201

  WEST-EAST_PATCH_START_STAG :

  1

  WEST-EAST_PATCH_END_STAG :

  202

  SOUTH-NORTH_PATCH_START_UNSTAG :

  1

  SOUTH-NORTH_PATCH_END_UNSTAG :

  201

  SOUTH-NORTH_PATCH_START_STAG :

  1

  SOUTH-NORTH_PATCH_END_STAG :

  202

  BOTTOM-TOP_PATCH_START_UNSTAG :

  1

  BOTTOM-TOP_PATCH_END_UNSTAG :

  49

  BOTTOM-TOP_PATCH_START_STAG :

  1

  BOTTOM-TOP_PATCH_END_STAG :

  50

  GRID_ID :

  3

  PARENT_ID :

  2

  I_PARENT_START :

  67

  J_PARENT_START :

  67

  PARENT_GRID_RATIO :

  3

  CEN_LAT :

  38.84523

  CEN_LON :

  -107.040375

  TRUELAT1 :

  30.0

  TRUELAT2 :

  50.0

  MOAD_CEN_LAT :

  38.900017

  STAND_LON :

  -107.0

  POLE_LAT :

  90.0

  POLE_LON :

  0.0

  GMT :

  0.0

  JULYR :

  2023

  JULDAY :

  84

  MAP_PROJ :

  1

  MAP_PROJ_CHAR :

  Lambert Conformal

  MMINLU :

  MODIFIED_IGBP_MODIS_NOAH

  NUM_LAND_CAT :

  21

  ISWATER :

  17

  ISLAKE :

  21

  ISICE :

  15

  ISURBAN :

  13

  ISOILWATER :

  14

  HYBRID_OPT :

  2

  ETAC :

  0.2

ds_dust["T2"].load()
ds_dust["T2"].mean(dim=['x', 'y']).hvplot(x="Time")