OPENDAP NAM

# Install required libraries
# !pip install -q xarray_subset_grid@git+https://github.com/asascience-open/xarray-subset-grid.git
# !pip install -q s3fs cftime xarray cf-xarray fsspec dask h5netcdf

import cf_xarray  # noqa
import datetime
import xarray as xr



current_date = datetime.datetime.now().strftime("%Y%m%d")

ds = xr.open_dataset(
    f"https://nomads.ncep.noaa.gov/dods/nam/nam{current_date}/nam1hr_00z", chunks={}
)
ds

/Users/matthew.iannucci/Developer/xarray-subset-grid/venv/lib/python3.10/site-packages/xarray/coding/times.py:170: SerializationWarning: Ambiguous reference date string: 1-1-1 00:00:0.0. The first value is assumed to be the year hence will be padded with zeros to remove the ambiguity (the padded reference date string is: 0001-1-1 00:00:0.0). To remove this message, remove the ambiguity by padding your reference date strings with zeros.
  warnings.warn(warning_msg, SerializationWarning)

<xarray.Dataset> Size: 31GB
Dimensions:         (time: 37, lev: 42, lat: 443, lon: 913)
Coordinates:
  * time            (time) datetime64[ns] 296B 2024-07-24 ... 2024-07-25T12:0...
  * lev             (lev) float64 336B 1e+03 975.0 950.0 925.0 ... 20.0 10.0 5.0
  * lat             (lat) float64 4kB 12.22 12.33 12.44 ... 60.98 61.09 61.21
  * lon             (lon) float64 7kB -152.9 -152.8 -152.7 ... -49.59 -49.47
Data variables: (12/141)
    absvprs         (time, lev, lat, lon) float32 3GB dask.array<chunksize=(37, 42, 443, 913), meta=np.ndarray>
    no4lftx180_0mb  (time, lat, lon) float32 60MB dask.array<chunksize=(37, 443, 913), meta=np.ndarray>
    acpcpsfc        (time, lat, lon) float32 60MB dask.array<chunksize=(37, 443, 913), meta=np.ndarray>
    albdosfc        (time, lat, lon) float32 60MB dask.array<chunksize=(37, 443, 913), meta=np.ndarray>
    apcpsfc         (time, lat, lon) float32 60MB dask.array<chunksize=(37, 443, 913), meta=np.ndarray>
    bmixlhy1        (time, lat, lon) float32 60MB dask.array<chunksize=(37, 443, 913), meta=np.ndarray>
    ...              ...
    vucsh0_6000m    (time, lat, lon) float32 60MB dask.array<chunksize=(37, 443, 913), meta=np.ndarray>
    vvcsh0_6000m    (time, lat, lon) float32 60MB dask.array<chunksize=(37, 443, 913), meta=np.ndarray>
    vvelprs         (time, lev, lat, lon) float32 3GB dask.array<chunksize=(37, 42, 443, 913), meta=np.ndarray>
    weasdaccsfc     (time, lat, lon) float32 60MB dask.array<chunksize=(37, 443, 913), meta=np.ndarray>
    weasdsfc        (time, lat, lon) float32 60MB dask.array<chunksize=(37, 443, 913), meta=np.ndarray>
    wiltsfc         (time, lat, lon) float32 60MB dask.array<chunksize=(37, 443, 913), meta=np.ndarray>
Attributes:
    title:        NAM hourly fcst staring from 00Z24jul2024, download Jul 24 ...
    Conventions:  COARDS\nGrADS
    dataType:     Grid
    history:      Wed Jul 24 02:49:03 UTC 2024 : imported by GrADS Data Serve...

xarray.Dataset

• Dimensions:
  • time: 37
  • lev: 42
  • lat: 443
  • lon: 913
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2024-07-24 ... 2024-07-25T12:00:00

    grads_dim :

    t

    grads_mapping :

    linear

    grads_size :

    37

    grads_min :

    00z24jul2024

    grads_step :

    1hr

    long_name :

    time

    minimum :

    00z24jul2024

    maximum :

    12z25jul2024

    resolution :

    0.041666668

    array(['2024-07-24T00:00:00.000000000', '2024-07-24T01:00:00.000000000',
           '2024-07-24T02:00:00.000000000', '2024-07-24T03:00:00.000000000',
           '2024-07-24T04:00:00.000000000', '2024-07-24T05:00:00.000000000',
           '2024-07-24T06:00:00.000000000', '2024-07-24T07:00:00.000000000',
           '2024-07-24T08:00:00.000000000', '2024-07-24T09:00:00.000000000',
           '2024-07-24T10:00:00.000000000', '2024-07-24T11:00:00.000000000',
           '2024-07-24T12:00:00.000000000', '2024-07-24T13:00:00.000000000',
           '2024-07-24T14:00:00.000000000', '2024-07-24T15:00:00.000000000',
           '2024-07-24T16:00:00.000000000', '2024-07-24T17:00:00.000000000',
           '2024-07-24T18:00:00.000000000', '2024-07-24T19:00:00.000000000',
           '2024-07-24T20:00:00.000000000', '2024-07-24T21:00:00.000000000',
           '2024-07-24T22:00:00.000000000', '2024-07-24T23:00:00.000000000',
           '2024-07-25T00:00:00.000000000', '2024-07-25T01:00:00.000000000',
           '2024-07-25T02:00:00.000000000', '2024-07-25T03:00:00.000000000',
           '2024-07-25T04:00:00.000000000', '2024-07-25T05:00:00.000000000',
           '2024-07-25T06:00:00.000000000', '2024-07-25T07:00:00.000000000',
           '2024-07-25T08:00:00.000000000', '2024-07-25T09:00:00.000000000',
           '2024-07-25T10:00:00.000000000', '2024-07-25T11:00:00.000000000',
           '2024-07-25T12:00:00.000000000'], dtype='datetime64[ns]')

  • lev
    (lev)
    float64
    1e+03 975.0 950.0 ... 20.0 10.0 5.0

    grads_dim :

    z

    grads_mapping :

    levels

    units :

    millibar

    long_name :

    altitude

    minimum :

    1000.0

    maximum :

    5.0

    resolution :

    24.268293

    array([1000.,  975.,  950.,  925.,  900.,  875.,  850.,  825.,  800.,  775.,
            750.,  725.,  700.,  675.,  650.,  625.,  600.,  575.,  550.,  525.,
            500.,  475.,  450.,  425.,  400.,  375.,  350.,  325.,  300.,  275.,
            250.,  225.,  200.,  175.,  150.,  125.,  100.,   75.,   50.,   20.,
             10.,    5.])

  • lat
    (lat)
    float64
    12.22 12.33 12.44 ... 61.09 61.21

    grads_dim :

    y

    grads_mapping :

    linear

    grads_size :

    443

    units :

    degrees_north

    long_name :

    latitude

    minimum :

    12.219908

    maximum :

    61.20556254545

    resolution :

    0.110827275

    array([12.219908, 12.330735, 12.441563, ..., 60.983908, 61.094735, 61.205563])

  • lon
    (lon)
    float64
    -152.9 -152.8 ... -49.59 -49.47

    grads_dim :

    x

    grads_mapping :

    linear

    grads_size :

    913

    units :

    degrees_east

    long_name :

    longitude

    minimum :

    -152.878623

    maximum :

    -49.47263081081

    resolution :

    0.11338376

    array([-152.878623, -152.765239, -152.651855, ...,  -49.699398,  -49.586015,
            -49.472631])

• Data variables: (141)
  • absvprs
    (time, lev, lat, lon)
    float32
    dask.array<chunksize=(37, 42, 443, 913), meta=np.ndarray>

    long_name :

    ** (1000 850 700 500 250) absolute vorticity [1/s]

    Array Chunk Bytes 2.34 GiB 2.34 GiB Shape (37, 42, 443, 913) (37, 42, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • no4lftx180_0mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 180-0 mb above ground best (4 layer) lifted index [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • acpcpsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface convective precipitation [kg/m^2]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • albdosfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface albedo [%]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • apcpsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface total precipitation [kg/m^2]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • bmixlhy1
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 1 hybrid level blackadars mixing length scale [m]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • capesfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface convective available potential energy [j/kg]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cape180_0mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 180-0 mb above ground convective available potential energy [j/kg]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cdsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface drag coefficient [non-dim]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cfrzrsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface categorical freezing rain [-]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cicepsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface categorical ice pellets [-]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cinsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface convective inhibition [j/kg]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cin180_0mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 180-0 mb above ground convective inhibition [j/kg]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cnwatsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface plant canopy surface water [kg/m^2]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • crainsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface categorical rain [-]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • csnowsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface categorical snow [-]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dlwrfsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface downward long-wave rad. flux [w/m^2]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dpt2m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 2 m above ground dew point temperature [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dswrfsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface downward short-wave radiation flux [w/m^2]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dzdtprs
    (time, lev, lat, lon)
    float32
    dask.array<chunksize=(37, 42, 443, 913), meta=np.ndarray>

    long_name :

    ** (1000 975 950 925 900.. 75 50 20 10 5) vertical velocity (geometric) [m/s]

    Array Chunk Bytes 2.34 GiB 2.34 GiB Shape (37, 42, 443, 913) (37, 42, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • fricvsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface frictional velocity [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • gustsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface wind speed (gust) [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • hgtsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface geopotential height [gpm]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • hgtprs
    (time, lev, lat, lon)
    float32
    dask.array<chunksize=(37, 42, 443, 913), meta=np.ndarray>

    long_name :

    ** (1000 975 950 925 900.. 150 125 100 75 50) geopotential height [gpm]

    Array Chunk Bytes 2.34 GiB 2.34 GiB Shape (37, 42, 443, 913) (37, 42, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • hgtclb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** cloud base geopotential height [gpm]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • hgtpbl
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** planetary boundary layer geopotential height [gpm]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • hgtlwb0
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** lowest level of the wet bulb zero geopotential height [gpm]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • hgt0c
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 0c isotherm geopotential height [gpm]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • hindexsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface haines index [numeric]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • hlcy3000_0m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 3000-0 m above ground storm relative helicity [m^2/s^2]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • hpblsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface planetary boundary layer height [m]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • icecsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface ice cover [proportion]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • landsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface land cover (0=sea, 1=land) [proportion]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lftxl100_100
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 500-1000 mb surface lifted index [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lhtflsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface latent heat net flux [w/m^2]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ltngsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface lightning [non-dim]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • maxrh2m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 2 m above ground maximum relative humidity [%]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • maxuw10m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 10 m above ground u component of hourly maximum 10m wind speed [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • maxvw10m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 10 m above ground v component of hourly maximum 10m wind speed [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • minrh2m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 2 m above ground minimum relative humidity [%]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • msletmsl
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** mean sea level mslp (eta model reduction) [pa]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mstav0_100cm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 0-1 m below ground moisture availability [%]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • pli30_0mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 30-0 mb above ground parcel lifted index (to 500 hpa) [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • porossfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface soil porosity [proportion]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • pressfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface pressure [pa]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • pres80m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 80 m above ground pressure [pa]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • presmwl
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** max wind pressure [pa]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • prestrop
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** tropopause pressure [pa]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • prmslmsl
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** mean sea level pressure reduced to msl [pa]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • pwatclm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** entire atmosphere (considered as a single layer) precipitable water [kg/m^2]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • refcclm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** entire atmosphere (considered as a single layer) composite reflectivity [db]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • refd4000m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 4000 m above ground reflectivity [db]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • refd1000m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 1000 m above ground reflectivity [db]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • refdhy1
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 1 hybrid level reflectivity [db]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • retopclm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** entire atmosphere (considered as a single layer) echo top [m]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rhprs
    (time, lev, lat, lon)
    float32
    dask.array<chunksize=(37, 42, 443, 913), meta=np.ndarray>

    long_name :

    ** (1000 975 950 925 900.. 150 125 100 75 50) relative humidity [%]

    Array Chunk Bytes 2.34 GiB 2.34 GiB Shape (37, 42, 443, 913) (37, 42, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rh2m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 2 m above ground relative humidity [%]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rhsg330_1000
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 0.33-1 sigma layer relative humidity [%]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rh30_0mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 30-0 mb above ground relative humidity [%]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rh60_30mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 60-30 mb above ground relative humidity [%]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rh90_60mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 90-60 mb above ground relative humidity [%]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rh120_90mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 120-90 mb above ground relative humidity [%]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rh150_120mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 150-120 mb above ground relative humidity [%]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rh180_150mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 180-150 mb above ground relative humidity [%]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rh0c
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 0c isotherm relative humidity [%]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rlyrssfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface number of soil layers in root zone [non-dim]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sfcrsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface surface roughness [m]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • shtflsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface sensible heat net flux [w/m^2]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • smdrysfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface direct evaporation cease (soil moisture) [proportion]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • smrefsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface transpiration stress-onset (soil moisture) [proportion]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • snodsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface snow depth [m]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • snowcsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface snow cover [%]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • soill0_10cm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 0-0.1 m below ground liquid volumetric soil moisture (non frozen) [proportion]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • soill10_40cm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 0.1-0.4 m below ground liquid volumetric soil moisture (non frozen) [proportion]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • soill40_100cm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 0.4-1 m below ground liquid volumetric soil moisture (non frozen) [proportion]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • soill100_200cm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 1-2 m below ground liquid volumetric soil moisture (non frozen) [proportion]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • soilm0_200cm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 0-2 m below ground soil moisture content [kg/m^2]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • soilw0_10cm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 0-0.1 m below ground volumetric soil moisture content [fraction]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • soilw10_40cm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 0.1-0.4 m below ground volumetric soil moisture content [fraction]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • soilw40_100cm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 0.4-1 m below ground volumetric soil moisture content [fraction]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • soilw100_200cm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 1-2 m below ground volumetric soil moisture content [fraction]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sotypsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface soil type [-]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • spfh2m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 2 m above ground specific humidity [kg/kg]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • spfh80m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 80 m above ground specific humidity [kg/kg]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tcdcclm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** entire atmosphere (considered as a single layer) total cloud cover [%]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tkeprs
    (time, lev, lat, lon)
    float32
    dask.array<chunksize=(37, 42, 443, 913), meta=np.ndarray>

    long_name :

    ** (1000 975 950 925 900.. 150 125 100 75 50) turbulent kinetic energy [j/kg]

    Array Chunk Bytes 2.34 GiB 2.34 GiB Shape (37, 42, 443, 913) (37, 42, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tmax2m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 2 m above ground maximum temperature [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tmin2m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 2 m above ground minimum temperature [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tmpsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface temperature [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tmpprs
    (time, lev, lat, lon)
    float32
    dask.array<chunksize=(37, 42, 443, 913), meta=np.ndarray>

    long_name :

    ** (1000 975 950 925 900.. 150 125 100 75 50) temperature [k]

    Array Chunk Bytes 2.34 GiB 2.34 GiB Shape (37, 42, 443, 913) (37, 42, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tmp2m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 2 m above ground temperature [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tmp80m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 80 m above ground temperature [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tmp30_0mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 30-0 mb above ground temperature [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tmp60_30mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 60-30 mb above ground temperature [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tmp90_60mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 90-60 mb above ground temperature [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tmp120_90mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 120-90 mb above ground temperature [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tmp150_120mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 150-120 mb above ground temperature [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tmp180_150mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 180-150 mb above ground temperature [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tmptrop
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** tropopause temperature [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tsoil0_10cm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 0-0.1 m below ground soil temperature validation to deprecate [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tsoil10_40cm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 0.1-0.4 m below ground soil temperature validation to deprecate [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tsoil40_100cm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 0.4-1 m below ground soil temperature validation to deprecate [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tsoil100_200cm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 1-2 m below ground soil temperature validation to deprecate [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tsoil300cm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 3 m underground soil temperature validation to deprecate [k]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ugrdprs
    (time, lev, lat, lon)
    float32
    dask.array<chunksize=(37, 42, 443, 913), meta=np.ndarray>

    long_name :

    ** (1000 975 950 925 900.. 150 125 100 75 50) u-component of wind [m/s]

    Array Chunk Bytes 2.34 GiB 2.34 GiB Shape (37, 42, 443, 913) (37, 42, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ugrd10m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 10 m above ground u-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ugrd80m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 80 m above ground u-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ugrd30_0mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 30-0 mb above ground u-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ugrd60_30mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 60-30 mb above ground u-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ugrd90_60mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 90-60 mb above ground u-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ugrd120_90mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 120-90 mb above ground u-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ugrd150_120mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 150-120 mb above ground u-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ugrd180_150mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 180-150 mb above ground u-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ugrdpbl
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** planetary boundary layer u-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ugrdmwl
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** max wind u-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ugrdtrop
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** tropopause u-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ulwrfsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface upward long-wave rad. flux [w/m^2]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • uswrfsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface upward short-wave radiation flux [w/m^2]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vegsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface vegetation [%]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vgrdprs
    (time, lev, lat, lon)
    float32
    dask.array<chunksize=(37, 42, 443, 913), meta=np.ndarray>

    long_name :

    ** (1000 975 950 925 900.. 150 125 100 75 50) v-component of wind [m/s]

    Array Chunk Bytes 2.34 GiB 2.34 GiB Shape (37, 42, 443, 913) (37, 42, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vgrd10m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 10 m above ground v-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vgrd80m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 80 m above ground v-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vgrd30_0mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 30-0 mb above ground v-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vgrd60_30mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 60-30 mb above ground v-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vgrd90_60mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 90-60 mb above ground v-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vgrd120_90mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 120-90 mb above ground v-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vgrd150_120mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 150-120 mb above ground v-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vgrd180_150mb
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 180-150 mb above ground v-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vgrdpbl
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** planetary boundary layer v-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vgrdmwl
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** max wind v-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vgrdtrop
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** tropopause v-component of wind [m/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vgtypsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface vegetation type [integer(0-13)]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vissfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface visibility [m]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • visclt
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** cloud top visibility [m]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vratepbl
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** planetary boundary layer ventilation rate [m^2/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vucsh0_6000m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 0-6000 m above ground vertical u-component shear [1/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vvcsh0_6000m
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** 0-6000 m above ground vertical v-component shear [1/s]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vvelprs
    (time, lev, lat, lon)
    float32
    dask.array<chunksize=(37, 42, 443, 913), meta=np.ndarray>

    long_name :

    ** (1000 975 950 925 900.. 150 125 100 75 50) vertical velocity (pressure) [pa/s]

    Array Chunk Bytes 2.34 GiB 2.34 GiB Shape (37, 42, 443, 913) (37, 42, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • weasdaccsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface water equivalent of accumulated snow depth [kg/m^2]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • weasdsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface water equivalent of accumulated snow depth [kg/m^2]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wiltsfc
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** surface wilting point [fraction]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (4)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2024-07-24 00:00:00', '2024-07-24 01:00:00',
                   '2024-07-24 02:00:00', '2024-07-24 03:00:00',
                   '2024-07-24 04:00:00', '2024-07-24 05:00:00',
                   '2024-07-24 06:00:00', '2024-07-24 07:00:00',
                   '2024-07-24 08:00:00', '2024-07-24 09:00:00',
                   '2024-07-24 10:00:00', '2024-07-24 11:00:00',
                   '2024-07-24 12:00:00', '2024-07-24 13:00:00',
                   '2024-07-24 14:00:00', '2024-07-24 15:00:00',
                   '2024-07-24 16:00:00', '2024-07-24 17:00:00',
                   '2024-07-24 18:00:00', '2024-07-24 19:00:00',
                   '2024-07-24 20:00:00', '2024-07-24 21:00:00',
                   '2024-07-24 22:00:00', '2024-07-24 23:00:00',
                   '2024-07-25 00:00:00', '2024-07-25 01:00:00',
                   '2024-07-25 02:00:00', '2024-07-25 03:00:00',
                   '2024-07-25 04:00:00', '2024-07-25 05:00:00',
                   '2024-07-25 06:00:00', '2024-07-25 07:00:00',
                   '2024-07-25 08:00:00', '2024-07-25 09:00:00',
                   '2024-07-25 10:00:00', '2024-07-25 11:00:00',
                   '2024-07-25 12:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • lev
    PandasIndex

    PandasIndex(Index([1000.0,  975.0,  950.0,  925.0,  900.0,  875.0,  850.0,  825.0,  800.0,
            775.0,  750.0,  725.0,  700.0,  675.0,  650.0,  625.0,  600.0,  575.0,
            550.0,  525.0,  500.0,  475.0,  450.0,  425.0,  400.0,  375.0,  350.0,
            325.0,  300.0,  275.0,  250.0,  225.0,  200.0,  175.0,  150.0,  125.0,
            100.0,   75.0,   50.0,   20.0,   10.0,    5.0],
          dtype='float64', name='lev'))

  • lat
    PandasIndex

    PandasIndex(Index([     12.219908, 12.33073527273, 12.44156254545, 12.55238981818,
           12.66321709091, 12.77404436364, 12.88487163636, 12.99569890909,
           13.10652618182, 13.21735345455,
           ...
           60.20811709091, 60.31894436364, 60.42977163636, 60.54059890909,
           60.65142618182, 60.76225345455, 60.87308072727,      60.983908,
           61.09473527273, 61.20556254545],
          dtype='float64', name='lat', length=443))

  • lon
    PandasIndex

    PandasIndex(Index([     -152.878623, -152.76523923663, -152.65185547327,  -152.5384717099,
           -152.42508794654, -152.31170418317, -152.19832041981, -152.08493665644,
           -151.97155289308, -151.85816912971,
           ...
             -50.4930846811,  -50.37970091774,  -50.26631715437,  -50.15293339101,
            -50.03954962764,  -49.92616586428,  -49.81278210091,  -49.69939833755,
            -49.58601457418,  -49.47263081081],
          dtype='float64', name='lon', length=913))

• Attributes: (4)

  title :

  NAM hourly fcst staring from 00Z24jul2024, download Jul 24 02:41 UTC

  Conventions :

  COARDS GrADS

  dataType :

  Grid

  history :

  Wed Jul 24 02:49:03 UTC 2024 : imported by GrADS Data Server 2.0

See the size of the dataset

f"Dataset size: {ds.nbytes * 1.0e-9} Gb"

'Dataset size: 30.528576800000003 Gb'

Make sure the grid is recognized

ds.xsg.grid.name

'regular_grid'

Grab out only the composite reflectivity

ds_refd = ds.xsg.subset_vars(['refcclm'])
ds_refd

<xarray.Dataset> Size: 60MB
Dimensions:  (lat: 443, lon: 913, time: 37)
Coordinates:
  * lat      (lat) float64 4kB 12.22 12.33 12.44 12.55 ... 60.98 61.09 61.21
  * lon      (lon) float64 7kB -152.9 -152.8 -152.7 ... -49.7 -49.59 -49.47
  * time     (time) datetime64[ns] 296B 2024-07-24 ... 2024-07-25T12:00:00
Data variables:
    refcclm  (time, lat, lon) float32 60MB dask.array<chunksize=(37, 443, 913), meta=np.ndarray>
Attributes:
    title:        NAM hourly fcst staring from 00Z24jul2024, download Jul 24 ...
    Conventions:  COARDS\nGrADS
    dataType:     Grid
    history:      Wed Jul 24 02:49:03 UTC 2024 : imported by GrADS Data Serve...

xarray.Dataset

• Dimensions:
  • lat: 443
  • lon: 913
  • time: 37
• Coordinates: (3)
  • lat
    (lat)
    float64
    12.22 12.33 12.44 ... 61.09 61.21

    grads_dim :

    y

    grads_mapping :

    linear

    grads_size :

    443

    units :

    degrees_north

    long_name :

    latitude

    minimum :

    12.219908

    maximum :

    61.20556254545

    resolution :

    0.110827275

    array([12.219908, 12.330735, 12.441563, ..., 60.983908, 61.094735, 61.205563])

  • lon
    (lon)
    float64
    -152.9 -152.8 ... -49.59 -49.47

    grads_dim :

    x

    grads_mapping :

    linear

    grads_size :

    913

    units :

    degrees_east

    long_name :

    longitude

    minimum :

    -152.878623

    maximum :

    -49.47263081081

    resolution :

    0.11338376

    array([-152.878623, -152.765239, -152.651855, ...,  -49.699398,  -49.586015,
            -49.472631])

  • time
    (time)
    datetime64[ns]
    2024-07-24 ... 2024-07-25T12:00:00

    grads_dim :

    t

    grads_mapping :

    linear

    grads_size :

    37

    grads_min :

    00z24jul2024

    grads_step :

    1hr

    long_name :

    time

    minimum :

    00z24jul2024

    maximum :

    12z25jul2024

    resolution :

    0.041666668

    array(['2024-07-24T00:00:00.000000000', '2024-07-24T01:00:00.000000000',
           '2024-07-24T02:00:00.000000000', '2024-07-24T03:00:00.000000000',
           '2024-07-24T04:00:00.000000000', '2024-07-24T05:00:00.000000000',
           '2024-07-24T06:00:00.000000000', '2024-07-24T07:00:00.000000000',
           '2024-07-24T08:00:00.000000000', '2024-07-24T09:00:00.000000000',
           '2024-07-24T10:00:00.000000000', '2024-07-24T11:00:00.000000000',
           '2024-07-24T12:00:00.000000000', '2024-07-24T13:00:00.000000000',
           '2024-07-24T14:00:00.000000000', '2024-07-24T15:00:00.000000000',
           '2024-07-24T16:00:00.000000000', '2024-07-24T17:00:00.000000000',
           '2024-07-24T18:00:00.000000000', '2024-07-24T19:00:00.000000000',
           '2024-07-24T20:00:00.000000000', '2024-07-24T21:00:00.000000000',
           '2024-07-24T22:00:00.000000000', '2024-07-24T23:00:00.000000000',
           '2024-07-25T00:00:00.000000000', '2024-07-25T01:00:00.000000000',
           '2024-07-25T02:00:00.000000000', '2024-07-25T03:00:00.000000000',
           '2024-07-25T04:00:00.000000000', '2024-07-25T05:00:00.000000000',
           '2024-07-25T06:00:00.000000000', '2024-07-25T07:00:00.000000000',
           '2024-07-25T08:00:00.000000000', '2024-07-25T09:00:00.000000000',
           '2024-07-25T10:00:00.000000000', '2024-07-25T11:00:00.000000000',
           '2024-07-25T12:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (1)
  • refcclm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 443, 913), meta=np.ndarray>

    long_name :

    ** entire atmosphere (considered as a single layer) composite reflectivity [db]

    Array Chunk Bytes 57.09 MiB 57.09 MiB Shape (37, 443, 913) (37, 443, 913) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2024-07-24 00:00:00', '2024-07-24 01:00:00',
                   '2024-07-24 02:00:00', '2024-07-24 03:00:00',
                   '2024-07-24 04:00:00', '2024-07-24 05:00:00',
                   '2024-07-24 06:00:00', '2024-07-24 07:00:00',
                   '2024-07-24 08:00:00', '2024-07-24 09:00:00',
                   '2024-07-24 10:00:00', '2024-07-24 11:00:00',
                   '2024-07-24 12:00:00', '2024-07-24 13:00:00',
                   '2024-07-24 14:00:00', '2024-07-24 15:00:00',
                   '2024-07-24 16:00:00', '2024-07-24 17:00:00',
                   '2024-07-24 18:00:00', '2024-07-24 19:00:00',
                   '2024-07-24 20:00:00', '2024-07-24 21:00:00',
                   '2024-07-24 22:00:00', '2024-07-24 23:00:00',
                   '2024-07-25 00:00:00', '2024-07-25 01:00:00',
                   '2024-07-25 02:00:00', '2024-07-25 03:00:00',
                   '2024-07-25 04:00:00', '2024-07-25 05:00:00',
                   '2024-07-25 06:00:00', '2024-07-25 07:00:00',
                   '2024-07-25 08:00:00', '2024-07-25 09:00:00',
                   '2024-07-25 10:00:00', '2024-07-25 11:00:00',
                   '2024-07-25 12:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • lat
    PandasIndex

    PandasIndex(Index([     12.219908, 12.33073527273, 12.44156254545, 12.55238981818,
           12.66321709091, 12.77404436364, 12.88487163636, 12.99569890909,
           13.10652618182, 13.21735345455,
           ...
           60.20811709091, 60.31894436364, 60.42977163636, 60.54059890909,
           60.65142618182, 60.76225345455, 60.87308072727,      60.983908,
           61.09473527273, 61.20556254545],
          dtype='float64', name='lat', length=443))

  • lon
    PandasIndex

    PandasIndex(Index([     -152.878623, -152.76523923663, -152.65185547327,  -152.5384717099,
           -152.42508794654, -152.31170418317, -152.19832041981, -152.08493665644,
           -151.97155289308, -151.85816912971,
           ...
             -50.4930846811,  -50.37970091774,  -50.26631715437,  -50.15293339101,
            -50.03954962764,  -49.92616586428,  -49.81278210091,  -49.69939833755,
            -49.58601457418,  -49.47263081081],
          dtype='float64', name='lon', length=913))

• Attributes: (4)

  title :

  NAM hourly fcst staring from 00Z24jul2024, download Jul 24 02:41 UTC

  Conventions :

  COARDS GrADS

  dataType :

  Grid

  history :

  Wed Jul 24 02:49:03 UTC 2024 : imported by GrADS Data Server 2.0

Create our region of interest and subset

bbox = [-93.63283364104035, 16.18222316056857, -67.60864242620244, 34.02542167172069]

ds_refd_subset = ds_refd.xsg.subset_bbox(bbox)
ds_refd_subset

<xarray.Dataset> Size: 5MB
Dimensions:  (lat: 161, lon: 230, time: 37)
Coordinates:
  * lat      (lat) float64 1kB 16.21 16.32 16.43 16.54 ... 33.72 33.83 33.94
  * lon      (lon) float64 2kB -93.58 -93.47 -93.35 ... -67.84 -67.73 -67.61
  * time     (time) datetime64[ns] 296B 2024-07-24 ... 2024-07-25T12:00:00
Data variables:
    refcclm  (time, lat, lon) float32 5MB dask.array<chunksize=(37, 161, 230), meta=np.ndarray>
Attributes:
    title:        NAM hourly fcst staring from 00Z24jul2024, download Jul 24 ...
    Conventions:  COARDS\nGrADS
    dataType:     Grid
    history:      Wed Jul 24 02:49:03 UTC 2024 : imported by GrADS Data Serve...

xarray.Dataset

• Dimensions:
  • lat: 161
  • lon: 230
  • time: 37
• Coordinates: (3)
  • lat
    (lat)
    float64
    16.21 16.32 16.43 ... 33.83 33.94

    grads_dim :

    y

    grads_mapping :

    linear

    grads_size :

    443

    units :

    degrees_north

    long_name :

    latitude

    minimum :

    12.219908

    maximum :

    61.20556254545

    resolution :

    0.110827275

    array([16.20969 , 16.320517, 16.431344, 16.542172, 16.652999, 16.763826,
           16.874653, 16.985481, 17.096308, 17.207135, 17.317963, 17.42879 ,
           17.539617, 17.650444, 17.761272, 17.872099, 17.982926, 18.093753,
           18.204581, 18.315408, 18.426235, 18.537063, 18.64789 , 18.758717,
           18.869544, 18.980372, 19.091199, 19.202026, 19.312853, 19.423681,
           19.534508, 19.645335, 19.756163, 19.86699 , 19.977817, 20.088644,
           20.199472, 20.310299, 20.421126, 20.531953, 20.642781, 20.753608,
           20.864435, 20.975263, 21.08609 , 21.196917, 21.307744, 21.418572,
           21.529399, 21.640226, 21.751053, 21.861881, 21.972708, 22.083535,
           22.194363, 22.30519 , 22.416017, 22.526844, 22.637672, 22.748499,
           22.859326, 22.970153, 23.080981, 23.191808, 23.302635, 23.413463,
           23.52429 , 23.635117, 23.745944, 23.856772, 23.967599, 24.078426,
           24.189253, 24.300081, 24.410908, 24.521735, 24.632563, 24.74339 ,
           24.854217, 24.965044, 25.075872, 25.186699, 25.297526, 25.408353,
           25.519181, 25.630008, 25.740835, 25.851663, 25.96249 , 26.073317,
           26.184144, 26.294972, 26.405799, 26.516626, 26.627453, 26.738281,
           26.849108, 26.959935, 27.070763, 27.18159 , 27.292417, 27.403244,
           27.514072, 27.624899, 27.735726, 27.846553, 27.957381, 28.068208,
           28.179035, 28.289863, 28.40069 , 28.511517, 28.622344, 28.733172,
           28.843999, 28.954826, 29.065653, 29.176481, 29.287308, 29.398135,
           29.508963, 29.61979 , 29.730617, 29.841444, 29.952272, 30.063099,
           30.173926, 30.284753, 30.395581, 30.506408, 30.617235, 30.728063,
           30.83889 , 30.949717, 31.060544, 31.171372, 31.282199, 31.393026,
           31.503853, 31.614681, 31.725508, 31.836335, 31.947163, 32.05799 ,
           32.168817, 32.279644, 32.390472, 32.501299, 32.612126, 32.722953,
           32.833781, 32.944608, 33.055435, 33.166263, 33.27709 , 33.387917,
           33.498744, 33.609572, 33.720399, 33.831226, 33.942053])

  • lon
    (lon)
    float64
    -93.58 -93.47 ... -67.73 -67.61

    grads_dim :

    x

    grads_mapping :

    linear

    grads_size :

    913

    units :

    degrees_east

    long_name :

    longitude

    minimum :

    -152.878623

    maximum :

    -49.47263081081

    resolution :

    0.11338376

    array([-93.578915, -93.465531, -93.352147, ..., -67.8408  , -67.727417,
           -67.614033])

  • time
    (time)
    datetime64[ns]
    2024-07-24 ... 2024-07-25T12:00:00

    grads_dim :

    t

    grads_mapping :

    linear

    grads_size :

    37

    grads_min :

    00z24jul2024

    grads_step :

    1hr

    long_name :

    time

    minimum :

    00z24jul2024

    maximum :

    12z25jul2024

    resolution :

    0.041666668

    array(['2024-07-24T00:00:00.000000000', '2024-07-24T01:00:00.000000000',
           '2024-07-24T02:00:00.000000000', '2024-07-24T03:00:00.000000000',
           '2024-07-24T04:00:00.000000000', '2024-07-24T05:00:00.000000000',
           '2024-07-24T06:00:00.000000000', '2024-07-24T07:00:00.000000000',
           '2024-07-24T08:00:00.000000000', '2024-07-24T09:00:00.000000000',
           '2024-07-24T10:00:00.000000000', '2024-07-24T11:00:00.000000000',
           '2024-07-24T12:00:00.000000000', '2024-07-24T13:00:00.000000000',
           '2024-07-24T14:00:00.000000000', '2024-07-24T15:00:00.000000000',
           '2024-07-24T16:00:00.000000000', '2024-07-24T17:00:00.000000000',
           '2024-07-24T18:00:00.000000000', '2024-07-24T19:00:00.000000000',
           '2024-07-24T20:00:00.000000000', '2024-07-24T21:00:00.000000000',
           '2024-07-24T22:00:00.000000000', '2024-07-24T23:00:00.000000000',
           '2024-07-25T00:00:00.000000000', '2024-07-25T01:00:00.000000000',
           '2024-07-25T02:00:00.000000000', '2024-07-25T03:00:00.000000000',
           '2024-07-25T04:00:00.000000000', '2024-07-25T05:00:00.000000000',
           '2024-07-25T06:00:00.000000000', '2024-07-25T07:00:00.000000000',
           '2024-07-25T08:00:00.000000000', '2024-07-25T09:00:00.000000000',
           '2024-07-25T10:00:00.000000000', '2024-07-25T11:00:00.000000000',
           '2024-07-25T12:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (1)
  • refcclm
    (time, lat, lon)
    float32
    dask.array<chunksize=(37, 161, 230), meta=np.ndarray>

    long_name :

    ** entire atmosphere (considered as a single layer) composite reflectivity [db]

    Array Chunk Bytes 5.23 MiB 5.23 MiB Shape (37, 161, 230) (37, 161, 230) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2024-07-24 00:00:00', '2024-07-24 01:00:00',
                   '2024-07-24 02:00:00', '2024-07-24 03:00:00',
                   '2024-07-24 04:00:00', '2024-07-24 05:00:00',
                   '2024-07-24 06:00:00', '2024-07-24 07:00:00',
                   '2024-07-24 08:00:00', '2024-07-24 09:00:00',
                   '2024-07-24 10:00:00', '2024-07-24 11:00:00',
                   '2024-07-24 12:00:00', '2024-07-24 13:00:00',
                   '2024-07-24 14:00:00', '2024-07-24 15:00:00',
                   '2024-07-24 16:00:00', '2024-07-24 17:00:00',
                   '2024-07-24 18:00:00', '2024-07-24 19:00:00',
                   '2024-07-24 20:00:00', '2024-07-24 21:00:00',
                   '2024-07-24 22:00:00', '2024-07-24 23:00:00',
                   '2024-07-25 00:00:00', '2024-07-25 01:00:00',
                   '2024-07-25 02:00:00', '2024-07-25 03:00:00',
                   '2024-07-25 04:00:00', '2024-07-25 05:00:00',
                   '2024-07-25 06:00:00', '2024-07-25 07:00:00',
                   '2024-07-25 08:00:00', '2024-07-25 09:00:00',
                   '2024-07-25 10:00:00', '2024-07-25 11:00:00',
                   '2024-07-25 12:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • lat
    PandasIndex

    PandasIndex(Index([16.20968981818, 16.32051709091, 16.43134436364, 16.54217163636,
           16.65299890909, 16.76382618182, 16.87465345455, 16.98548072727,
                17.096308, 17.20713527273,
           ...
                32.944608, 33.05543527273, 33.16626254545, 33.27708981818,
           33.38791709091, 33.49874436364, 33.60957163636, 33.72039890909,
           33.83122618182, 33.94205345455],
          dtype='float64', name='lat', length=161))

  • lon
    PandasIndex

    PandasIndex(Index([-93.57891475993, -93.46553099656,  -93.3521472332, -93.23876346983,
           -93.12537970647,  -93.0119959431, -92.89861217974, -92.78522841637,
           -92.67184465301, -92.55846088964,
           ...
           -68.63448681956, -68.52110305619, -68.40771929283, -68.29433552946,
           -68.18095176609, -68.06756800273, -67.95418423936,   -67.840800476,
           -67.72741671263, -67.61403294927],
          dtype='float64', name='lon', length=230))

• Attributes: (4)

  title :

  NAM hourly fcst staring from 00Z24jul2024, download Jul 24 02:41 UTC

  Conventions :

  COARDS GrADS

  dataType :

  Grid

  history :

  Wed Jul 24 02:49:03 UTC 2024 : imported by GrADS Data Server 2.0

Plot the first timestep

ds_refd_subset.cf.isel(time=0).refcclm.plot(vmin=0, vmax=80, cmap="Greens")

<matplotlib.collections.QuadMesh at 0x311d7a350>

f"Subset dataset size: {ds_refd_subset.nbytes * 1.0e-6} Mb"

'Subset dataset size: 5.483864 Mb'