Visualizing the cloud-capped boundary layer

Data is from a large eddy simulation of trade cumulus clouds.

• Single timestep at 12.5 m x, y, z grid spacing

• Variable is QN – cloud liquid water (g/kg)

link to data file

The netcdf file: bomex_qv_qn.nc – 527 Mbytes

import xarray as xr
from pathlib import Path
from vapor import session, renderer, dataset, camera

Vapor 3.8.3
Python 3.9.16 (/home/phil/mini39/envs/vapor)
OpenGL 4.6.0 NVIDIA 470.182.03

adjust path to point to file

the_file = Path().resolve() / 'bomex_qv_qn.nc'
print(the_file)
the_file.exists()

/home/phil/repos/vapor-python-cookbook/notebooks/bomex_qv_qn.nc

True

Inspect the xarray dataset

bomex_data = xr.open_dataset(the_file)
bomex_data

<xarray.Dataset>
Dimensions:  (x: 512, y: 512, z: 256, time: 1)
Coordinates:
  * x        (x) float32 0.0 12.5 25.0 37.5 ... 6.362e+03 6.375e+03 6.388e+03
  * y        (y) float32 0.0 12.5 25.0 37.5 ... 6.362e+03 6.375e+03 6.388e+03
  * z        (z) float32 12.5 25.0 37.5 50.0 ... 3.175e+03 3.188e+03 3.2e+03
  * time     (time) float32 0.1264
Data variables:
    p        (z) float32 ...
    QV       (time, z, y, x) float32 ...
    QN       (time, z, y, x) float32 ...

xarray.Dataset

• Dimensions:
  • x: 512
  • y: 512
  • z: 256
  • time: 1
• Coordinates: (4)
  • x
    (x)
    float32
    0.0 12.5 ... 6.375e+03 6.388e+03

    units :

    m

    axis :

    X

    array([   0. ,   12.5,   25. , ..., 6362.5, 6375. , 6387.5], dtype=float32)

  • y
    (y)
    float32
    0.0 12.5 ... 6.375e+03 6.388e+03

    units :

    m

    axis :

    Y

    array([   0. ,   12.5,   25. , ..., 6362.5, 6375. , 6387.5], dtype=float32)

  • z
    (z)
    float32
    12.5 25.0 ... 3.188e+03 3.2e+03

    units :

    m

    long_name :

    height

    axis :

    Z

    array([  12.5,   25. ,   37.5, ..., 3175. , 3187.5, 3200. ], dtype=float32)

  • time
    (time)
    float32
    0.1264

    units :

    d

    long_name :

    time

    axis :

    T

    array([0.126389], dtype=float32)

• Data variables: (3)
  • p
    (z)
    float32
    ...

    units :

    mb

    long_name :

    pressure

    [256 values with dtype=float32]

  • QV
    (time, z, y, x)
    float32
    ...

    long_name :

    Water Vapor

    units :

    g/kg

    [67108864 values with dtype=float32]

  • QN
    (time, z, y, x)
    float32
    ...

    long_name :

    Non-precipitating Condensate (Water+Ice)

    units :

    g/kg

    [67108864 values with dtype=float32]

• Indexes: (4)
  • x
    PandasIndex

    PandasIndex(Float64Index([   0.0,   12.5,   25.0,   37.5,   50.0,   62.5,   75.0,   87.5,
                   100.0,  112.5,
                  ...
                  6275.0, 6287.5, 6300.0, 6312.5, 6325.0, 6337.5, 6350.0, 6362.5,
                  6375.0, 6387.5],
                 dtype='float64', name='x', length=512))

  • y
    PandasIndex

    PandasIndex(Float64Index([   0.0,   12.5,   25.0,   37.5,   50.0,   62.5,   75.0,   87.5,
                   100.0,  112.5,
                  ...
                  6275.0, 6287.5, 6300.0, 6312.5, 6325.0, 6337.5, 6350.0, 6362.5,
                  6375.0, 6387.5],
                 dtype='float64', name='y', length=512))

  • z
    PandasIndex

    PandasIndex(Float64Index([  12.5,   25.0,   37.5,   50.0,   62.5,   75.0,   87.5,  100.0,
                   112.5,  125.0,
                  ...
                  3087.5, 3100.0, 3112.5, 3125.0, 3137.5, 3150.0, 3162.5, 3175.0,
                  3187.5, 3200.0],
                 dtype='float64', name='z', length=256))

  • time
    PandasIndex

    PandasIndex(Float64Index([0.12638889253139496], dtype='float64', name='time'))

• Attributes: (0)

open as a Vapor dataset

ses = session.Session()
data = ses.OpenDataset(dataset.CF, [str(the_file)])
ses.Load('session.vs3')

0

print("Time Coordinate Variable Name:", data.GetTimeCoordVarName())
print("Coordinate Variable Names:", data.GetCoordVarNames())

print("Dimensions:")
for dim in data.GetDimensionNames():
    print(f"  {dim}:", data.GetDimensionLength(dim, 0))

print("Data Variables:")
for var in data.GetDataVarNames():
    print(f"  {var}")
    print(f"    Time Varying:", bool(data.IsTimeVarying(var)))
    print(f"    Dimensionality:", data.GetVarGeometryDim(var))
    print(f"    Coordinates:", data.GetVarCoordVars(var, True))
    print("     Data Range:", data.GetDataRange(var))

Time Coordinate Variable Name: time
Coordinate Variable Names: ['time', 'x', 'y', 'z']
Dimensions:
  time: 1
  x: 512
  y: 512
  z: 256
Data Variables:
  QN
    Time Varying: True
    Dimensionality: 3
    Coordinates: ['x', 'y', 'z']
     Data Range: [0.0, 2.0163745880126953]
  QV
    Time Varying: True
    Dimensionality: 3
    Coordinates: ['x', 'y', 'z']
     Data Range: [2.760067939758301, 18.385417938232422]

Show gridcells

• Red: watervapor

• Grey: cloud liquid water

Top view of cloud field, with condensation threshold set to 0.01 g/kg

ren = data.NewRenderer(renderer.VolumeIsoRenderer)
ren.SetVariableName(data.GetDataVarNames(3)[0]) # Set to first 3D data variable
ren.SetIsoValues([0.01])

ses.GetCamera().ViewAll()
ses.Show()

Dataset: 'bomex_qv_qn.nc'

Create visualizer widget

from jupyter_vapor_widget import *

viz = VaporVisualizerWidget(ses)
viz

Add a slider bar

tf = ren.GetPrimaryTransferFunction()
dataRange = tf.GetMinMaxMapValue()

def sliderChanged(change):
    ren.SetIsoValues([change.new])
    viz.Render(fast=True)

slider = widgets.FloatSlider(value=ren.GetIsoValues()[0], min=dataRange[0], max=dataRange[1], step=(dataRange[1]-dataRange[0])/100)
slider.observe(sliderChanged, names='value')

widgets.VBox([
    viz,
    widgets.HBox([widgets.Label("Iso value:"), slider])
])