MPAS Atmosphere

This recipe demonstrates how to create visualizations using 30km MPAS Atmosphere model output. We’ll explore techniques for visualizing atmospheric variables on both the primal and dual MPAS grids, focusing on relative humidity and vorticity at the 200hPa pressure level.

Visualization Objectives

This recipe will guide you through:

• Creating polygon plots using the MPAS primal grid to visualize relative humidity at 200hPa

• Developing polygon plots using the MPAS dual grid to visualize vorticity at 200hPa

• Understanding the differences between primal and dual grid visualizations in MPAS

---

import uxarray as ux
import cartopy.crs as ccrs

Relative Humidity

For visualizing relative humidity, we use the Primal MPAS grid, which is composed of hexagons.

grid_path = "../../meshfiles/x1.655362.grid.nc"
data_path = "../../meshfiles/x1.655362.data.nc"

uxds_primal = ux.open_dataset(grid_path, data_path)
uxds_primal

<xarray.UxDataset> Size: 8MB
Dimensions:           (Time: 1, n_face: 655362, n_node: 1310720)
Dimensions without coordinates: Time, n_face, n_node
Data variables:
    relhum_200hPa     (Time, n_face) float32 3MB ...
    vorticity_200hPa  (Time, n_node) float32 5MB ...

<xarray.UxDataset> Size: 8MB
Dimensions:           (Time: 1, n_face: 655362, n_node: 1310720)
Dimensions without coordinates: Time, n_face, n_node
Data variables:
    relhum_200hPa     (Time, n_face) float32 3MB ...
    vorticity_200hPa  (Time, n_node) float32 5MB ...

uxarray.UxDataset

• Dimensions:
  • Time: 1
  • n_face: 655362
  • n_node: 1310720
• Coordinates: (0)
• Data variables: (2)
  • relhum_200hPa
    (Time, n_face)
    float32
    ...

    units :

    percent

    long_name :

    Relative humidity vertically interpolated to 200 hPa

    [655362 values with dtype=float32]

  • vorticity_200hPa
    (Time, n_node)
    float32
    ...

    units :

    s^{-1}

    long_name :

    Relative vorticity vertically interpolated to 200 hPa

    [1310720 values with dtype=float32]

• Indexes: (0)
• Attributes: (0)

Show Grid Information

<uxarray.Grid>
Original Grid Type: MPAS
Grid Dimensions:
  * n_node: 1310720
  * n_face: 655362
  * n_max_face_nodes: 10
  * n_max_node_faces: 3
  * n_nodes_per_face: (655362,)
Grid Coordinates (Spherical):
  * node_lon: (1310720,)
  * node_lat: (1310720,)
  * face_lon: (655362,)
  * face_lat: (655362,)
Grid Coordinates (Cartesian):
Grid Connectivity Variables:
  * face_node_connectivity: (655362, 10)
  * node_face_connectivity: (1310720, 3)
Grid Descriptor Variables:
  * n_nodes_per_face: (655362,)

uxarray.UxDataset.uxgrid

• Dimensions:
  • n_node: 1310720
  • n_face: 655362
  • n_max_face_nodes: 10
  • n_max_node_faces: 3
• Spherical Coordinates: (4)
  • node_lon
    (n_node)
    float32
    -175.0 -174.8 ... -103.0 -175.0

    standard_name :

    longitude

    long name :

    Longitude of the corner nodes of each face

    units :

    degrees_east

    array([-174.95294, -174.81796, -174.86942, ...,  113.04705, -102.95294,
           -174.95294], dtype=float32)

  • node_lat
    (n_node)
    float32
    26.44 26.53 26.67 ... -52.62 -52.62

    standard_name :

    latitude

    long name :

    Latitude of the corner nodes of each face

    units :

    degrees_north

    array([ 26.43807 ,  26.525747,  26.667755, ..., -10.812317, -52.62263 ,
           -52.62263 ], dtype=float32)

  • face_lon
    (n_face)
    float32
    -175.0 -103.0 ... -175.2 -174.7

    standard_name :

    longitude

    long name :

    Longitude of the center of each face

    units :

    degrees_east

    array([-174.95294, -102.95294,  115.01709, ..., -174.95294, -175.1781 ,
           -174.72784], dtype=float32)

  • face_lat
    (n_face)
    float32
    26.57 26.57 90.0 ... -52.54 -52.54

    standard_name :

    latitude

    long name :

    Latitude of the center of each face

    units :

    degrees_north

    array([ 26.56505 ,  26.56505 ,  90.      , ..., -52.780724, -52.54337 ,
           -52.54337 ], dtype=float32)

• Cartesian Coordinates: (0)
• Connectivity: (2)
  • face_node_connectivity
    (n_face, n_max_face_nodes)
    int64
    0 1 ... -9223372036854775808

    cf_role :

    face_node_connectivity

    long name :

    Maps every face to its corner nodes.

    start_index :

    0

    _FillValue :

    -9223372036854775808

    array([[                   0,                    1,                    2,
            ..., -9223372036854775808, -9223372036854775808,
            -9223372036854775808],
           [                   5,                    6,                    7,
            ..., -9223372036854775808, -9223372036854775808,
            -9223372036854775808],
           [                  10,                   11,                   12,
            ..., -9223372036854775808, -9223372036854775808,
            -9223372036854775808],
           ...,
           [              983022,              1228798,               983034,
            ..., -9223372036854775808, -9223372036854775808,
            -9223372036854775808],
           [             1310719,               983028,               983033,
            ..., -9223372036854775808, -9223372036854775808,
            -9223372036854775808],
           [             1310719,               983039,               983038,
            ..., -9223372036854775808, -9223372036854775808,
            -9223372036854775808]])

  • node_face_connectivity
    (n_node, n_max_node_faces)
    int64
    0 163843 163844 ... 655360 655361

    cf_role :

    node_face_connectivity

    long name :

    Faces that neighbor each node

    start_index :

    0

    _FillValue :

    -9223372036854775808

    array([[     0, 163843, 163844],
           [     0, 163844, 163845],
           [     0, 163845, 163846],
           ...,
           [655353, 655354, 655355],
           [655356, 655357, 655358],
           [655359, 655360, 655361]])

• Descriptors: (0)
• Attributes: (54)

  model_name :

  mpas

  core_name :

  init_atmosphere

  source :

  MPAS

  Conventions :

  MPAS

  git_version :

  v6.0-dirty

  on_a_sphere :

  YES

  sphere_radius :

  6371229.0

  is_periodic :

  NO

  x_period :

  0.0

  y_period :

  0.0

  history :

  Mon Aug 16 12:09:52 2021: ncks -v latVertex,lonVertex,latCell,lonCell,latEdge,lonEdge,cellsOnVertex,verticesOnCell,verticesOnEdge,edgesOnVertex,edgesOnCell,nEdgesOnCell,angleEdge x1.655362.init.nc foo.nc mpirun -n 18 ./init_atmosphere_model

  parent_id :

  0hhmjs26t0

  mesh_spec :

  0.0

  config_init_case :

  7

  config_calendar_type :

  gregorian

  config_start_time :

  2016-08-01_00:00:00

  config_stop_time :

  2016-09-10_00:00:00

  config_theta_adv_order :

  3

  config_coef_3rd_order :

  0.25

  config_num_halos :

  2

  config_nvertlevels :

  75

  config_nsoillevels :

  4

  config_nfglevels :

  38

  config_nfgsoillevels :

  4

  config_months :

  12

  config_geog_data_path :

  /glade/p/work/wrfhelp/WPS_GEOG/

  config_met_prefix :

  FILE

  config_sfc_prefix :

  SST

  config_fg_interval :

  86400

  config_landuse_data :

  USGS

  config_topo_data :

  GMTED2010

  config_use_spechumd :

  NO

  config_ztop :

  40000.0

  config_nsmterrain :

  1

  config_smooth_surfaces :

  YES

  config_dzmin :

  0.3

  config_nsm :

  30

  config_tc_vertical_grid :

  YES

  config_extrap_airtemp :

  linear

  config_static_interp :

  NO

  config_native_gwd_static :

  NO

  config_gwd_cell_scaling :

  1.0

  config_vertical_grid :

  YES

  config_met_interp :

  YES

  config_input_sst :

  NO

  config_frac_seaice :

  YES

  config_pio_num_iotasks :

  0

  config_pio_stride :

  1

  config_block_decomp_file_prefix :

  x1.655362.graph.info.part.

  config_number_of_blocks :

  0

  config_explicit_proc_decomp :

  NO

  config_proc_decomp_file_prefix :

  graph.info.part.

  file_id :

  ytj0fh0qmd

  NCO :

  netCDF Operators version 4.9.5 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)

uxds_primal['relhum_200hPa'][0].plot(projection=ccrs.Robinson(), backend='matplotlib', pixel_ratio=4.0, features=['coastline'], width=1000, height=500, cmap='viridis', title="30km Relative Humidity (MPAS Primal Grid)")

Vorticity

For visualizing relative humidity, we use the Dual MPAS grid, which is composed of triangles.

grid_path = "../../meshfiles/x1.655362.grid.nc"
data_path = "../../meshfiles/x1.655362.data.nc"

uxds_dual = ux.open_dataset(grid_path, data_path, use_dual=True)
uxds_dual['vorticity_200hPa']

<xarray.UxDataArray 'vorticity_200hPa' (Time: 1, n_face: 1310720)> Size: 5MB
[1310720 values with dtype=float32]
Dimensions without coordinates: Time, n_face
Attributes:
    units:      s^{-1}
    long_name:  Relative vorticity vertically interpolated to 200 hPa

<xarray.UxDataArray 'vorticity_200hPa' (Time: 1, n_face: 1310720)> Size: 5MB
[1310720 values with dtype=float32]
Dimensions without coordinates: Time, n_face
Attributes:
    units:      s^{-1}
    long_name:  Relative vorticity vertically interpolated to 200 hPa

uxarray.UxDataArray

'vorticity_200hPa'

• Time: 1
• n_face: 1310720

• ...

  [1310720 values with dtype=float32]

• Coordinates: (0)
• Indexes: (0)
• Attributes: (2)

  units :

  s^{-1}

  long_name :

  Relative vorticity vertically interpolated to 200 hPa

Show Grid Information

<uxarray.Grid>
Original Grid Type: MPAS
Grid Dimensions:
  * n_node: 655362
  * n_face: 1310720
  * n_max_face_nodes: 3
  * n_max_node_faces: 10
  * n_nodes_per_face: (1310720,)
Grid Coordinates (Spherical):
  * node_lon: (655362,)
  * node_lat: (655362,)
  * face_lon: (1310720,)
  * face_lat: (1310720,)
Grid Coordinates (Cartesian):
Grid Connectivity Variables:
  * face_node_connectivity: (1310720, 3)
  * node_face_connectivity: (655362, 10)
Grid Descriptor Variables:
  * n_nodes_per_face: (1310720,)

uxarray.UxDataArray.uxgrid

• Dimensions:
  • n_node: 655362
  • n_face: 1310720
  • n_max_face_nodes: 3
  • n_max_node_faces: 10
• Spherical Coordinates: (4)
  • node_lon
    (n_node)
    float32
    -175.0 -103.0 ... -175.2 -174.7

    standard_name :

    longitude

    long name :

    Longitude of the corner nodes of each face

    units :

    degrees_east

    array([-174.95294, -102.95294,  115.01709, ..., -174.95294, -175.1781 ,
           -174.72784], dtype=float32)

  • node_lat
    (n_node)
    float32
    26.57 26.57 90.0 ... -52.54 -52.54

    standard_name :

    latitude

    long name :

    Latitude of the corner nodes of each face

    units :

    degrees_north

    array([ 26.56505 ,  26.56505 ,  90.      , ..., -52.780724, -52.54337 ,
           -52.54337 ], dtype=float32)

  • face_lon
    (n_face)
    float32
    -175.0 -174.8 ... -103.0 -175.0

    standard_name :

    longitude

    long name :

    Longitude of the center of each face

    units :

    degrees_east

    array([-174.95294, -174.81796, -174.86942, ...,  113.04705, -102.95294,
           -174.95294], dtype=float32)

  • face_lat
    (n_face)
    float32
    26.44 26.53 26.67 ... -52.62 -52.62

    standard_name :

    latitude

    long name :

    Latitude of the center of each face

    units :

    degrees_north

    array([ 26.43807 ,  26.525747,  26.667755, ..., -10.812317, -52.62263 ,
           -52.62263 ], dtype=float32)

• Cartesian Coordinates: (0)
• Connectivity: (2)
  • face_node_connectivity
    (n_face, n_max_face_nodes)
    int64
    0 163843 163844 ... 655360 655361

    cf_role :

    face_node_connectivity

    long name :

    Maps every face to its corner nodes.

    start_index :

    0

    _FillValue :

    -9223372036854775808

    array([[     0, 163843, 163844],
           [     0, 163844, 163845],
           [     0, 163845, 163846],
           ...,
           [655353, 655354, 655355],
           [655356, 655357, 655358],
           [655359, 655360, 655361]])

  • node_face_connectivity
    (n_node, n_max_node_faces)
    int64
    0 1 ... -9223372036854775808

    cf_role :

    node_face_connectivity

    long name :

    Faces that neighbor each node

    start_index :

    0

    _FillValue :

    -9223372036854775808

    array([[                   0,                    1,                    2,
            ..., -9223372036854775808, -9223372036854775808,
            -9223372036854775808],
           [                   5,                    6,                    7,
            ..., -9223372036854775808, -9223372036854775808,
            -9223372036854775808],
           [                  10,                   11,                   12,
            ..., -9223372036854775808, -9223372036854775808,
            -9223372036854775808],
           ...,
           [              983022,              1228798,               983034,
            ..., -9223372036854775808, -9223372036854775808,
            -9223372036854775808],
           [             1310719,               983028,               983033,
            ..., -9223372036854775808, -9223372036854775808,
            -9223372036854775808],
           [             1310719,               983039,               983038,
            ..., -9223372036854775808, -9223372036854775808,
            -9223372036854775808]])

• Descriptors: (0)
• Attributes: (54)

  model_name :

  mpas

  core_name :

  init_atmosphere

  source :

  MPAS

  Conventions :

  MPAS

  git_version :

  v6.0-dirty

  on_a_sphere :

  YES

  sphere_radius :

  6371229.0

  is_periodic :

  NO

  x_period :

  0.0

  y_period :

  0.0

  history :

  Mon Aug 16 12:09:52 2021: ncks -v latVertex,lonVertex,latCell,lonCell,latEdge,lonEdge,cellsOnVertex,verticesOnCell,verticesOnEdge,edgesOnVertex,edgesOnCell,nEdgesOnCell,angleEdge x1.655362.init.nc foo.nc mpirun -n 18 ./init_atmosphere_model

  parent_id :

  0hhmjs26t0

  mesh_spec :

  0.0

  config_init_case :

  7

  config_calendar_type :

  gregorian

  config_start_time :

  2016-08-01_00:00:00

  config_stop_time :

  2016-09-10_00:00:00

  config_theta_adv_order :

  3

  config_coef_3rd_order :

  0.25

  config_num_halos :

  2

  config_nvertlevels :

  75

  config_nsoillevels :

  4

  config_nfglevels :

  38

  config_nfgsoillevels :

  4

  config_months :

  12

  config_geog_data_path :

  /glade/p/work/wrfhelp/WPS_GEOG/

  config_met_prefix :

  FILE

  config_sfc_prefix :

  SST

  config_fg_interval :

  86400

  config_landuse_data :

  USGS

  config_topo_data :

  GMTED2010

  config_use_spechumd :

  NO

  config_ztop :

  40000.0

  config_nsmterrain :

  1

  config_smooth_surfaces :

  YES

  config_dzmin :

  0.3

  config_nsm :

  30

  config_tc_vertical_grid :

  YES

  config_extrap_airtemp :

  linear

  config_static_interp :

  NO

  config_native_gwd_static :

  NO

  config_gwd_cell_scaling :

  1.0

  config_vertical_grid :

  YES

  config_met_interp :

  YES

  config_input_sst :

  NO

  config_frac_seaice :

  YES

  config_pio_num_iotasks :

  0

  config_pio_stride :

  1

  config_block_decomp_file_prefix :

  x1.655362.graph.info.part.

  config_number_of_blocks :

  0

  config_explicit_proc_decomp :

  NO

  config_proc_decomp_file_prefix :

  graph.info.part.

  file_id :

  ytj0fh0qmd

  NCO :

  netCDF Operators version 4.9.5 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)

uxds_dual['vorticity_200hPa'][0].plot(projection=ccrs.Robinson(), rasterize=True, backend='matplotlib', pixel_ratio=4.0, features=['coastline'], width=1000, height=500, cmap='coolwarm', title="30km Vorticity (MPAS Dual Grid)", clim=(-0.0001,0.0001))

MPAS Dual & Primal Grids

The Model for Prediction Across Scales (MPAS) utilizes two complementary grid structures for atmospheric modeling: the primal grid and the dual grid. The primal grid consists of hexagonal cells that form the primary computational mesh, while the dual grid is composed of triangular cells that connect the centers of the primary hexagons.

In the primal grid structure, scalar quantities like relative humidity are naturally represented at the centers of the hexagonal cells. The dual grid, with its triangular elements, is particularly well-suited for vector quantities and derived fields such as vorticity.

Below, we visualize both grid structures to illustrate their complementary nature.

(uxds_primal.uxgrid.subset.bounding_box(lon_bounds = (-1, 1), lat_bounds=(-0.5, 0.5)).plot(title="MPAS Primal Grid Structure", ) + 
 uxds_dual.uxgrid.subset.bounding_box(lon_bounds = (-1, 1), lat_bounds=(-0.5, 0.5)).plot(title="MPAS Dual Grid Structure")).cols(1).opts(fig_size=200)

The visualization below demonstrates the intricate geometric relationship between MPAS primal and dual grids. By overlaying both grid structures, we can observe how the vertices of each hexagonal cell in the primal grid serve as the cell centers for the triangular elements of the dual grid. Conversely, the vertices of the triangular cells in the dual grid correspond to the centers of the hexagonal cells in the primal grid.

(uxds_primal.uxgrid.subset.bounding_box(lon_bounds = (-1, 1), lat_bounds=(-0.5, 0.5)).plot() * 
 uxds_dual.uxgrid.subset.bounding_box(lon_bounds = (-1, 1), lat_bounds=(-0.5, 0.5)).plot()).opts(fig_size=200, title="Primal & Dual Grid Together")

3.75km Visualization

For another example of MPAS grid visualization, readers can refer to the UXarray documentation showcasing a 3.75km resolution grid

This example demonstrates MPAS visualization at a higher resolution compared to the 30km grid shown in this recipe.