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nbren12/one_step_output_description.txt Secret

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one_step_output_description.txt
output structure:
vcm-ml-data/testing-noah/one-step/big.zarr/
vcm-ml-data/testing-noah/one-step/one_step_config/
vcm-ml-data/testing-noah/one-step/one_step_config/runfile.py
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.001500/
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.003000/
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.004500/
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.010000/
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.011500/
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.013000/
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.014500/
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.020000/
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.021500/
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.023000/
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.024500/
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.001500/diag_table_one_step
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.001500/fv3config.yml
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.001500/fv_core.res.nc
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.003000/diag_table_one_step
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.003000/fv3config.yml
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.003000/fv_core.res.nc
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.004500/diag_table_one_step
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.004500/fv3config.yml
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.004500/fv_core.res.nc
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.010000/diag_table_one_step
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.010000/fv3config.yml
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.010000/fv_core.res.nc
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.011500/diag_table_one_step
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.011500/fv3config.yml
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.011500/fv_core.res.nc
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.013000/diag_table_one_step
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.013000/fv3config.yml
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.013000/fv_core.res.nc
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.014500/diag_table_one_step
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.014500/fv3config.yml
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.014500/fv_core.res.nc
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.020000/diag_table_one_step
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.020000/fv3config.yml
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.020000/fv_core.res.nc
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.021500/diag_table_one_step
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.021500/fv3config.yml
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.021500/fv_core.res.nc
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.023000/diag_table_one_step
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.023000/fv3config.yml
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.023000/fv_core.res.nc
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.024500/diag_table_one_step
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.024500/fv3config.yml
vcm-ml-data/testing-noah/one-step/one_step_config/20160801.024500/fv_core.res.nc
big.zarr info:
<xarray.Dataset>
Dimensions: (forecast_time: 15, initial_time: 11, step: 3, tile: 6, x: 48, x_interface: 49, y: 48, y_interface: 49, z: 79, z_soil: 4)
Coordinates:
* forecast_time (forecast_time) timedelta64[ns] NaT ... 03:30:00
* initial_time (initial_time) object '20160801.001500' ... '20160801.024500'
* step (step) object 'begin' ... 'after_physics'
Dimensions without coordinates: tile, x, x_interface, y, y_interface, z, z_soil
Data variables:
DLWRFsfc (initial_time, forecast_time, tile, y, x) float32 dask.array<chunksize=(1, 15, 1, 48, 48), meta=np.ndarray>
DSWRFsfc (initial_time, forecast_time, tile, y, x) float32 dask.array<chunksize=(1, 15, 1, 48, 48), meta=np.ndarray>
DSWRFtoa (initial_time, forecast_time, tile, y, x) float32 dask.array<chunksize=(1, 15, 1, 48, 48), meta=np.ndarray>
ULWRFsfc (initial_time, forecast_time, tile, y, x) float32 dask.array<chunksize=(1, 15, 1, 48, 48), meta=np.ndarray>
ULWRFtoa (initial_time, forecast_time, tile, y, x) float32 dask.array<chunksize=(1, 15, 1, 48, 48), meta=np.ndarray>
USWRFsfc (initial_time, forecast_time, tile, y, x) float32 dask.array<chunksize=(1, 15, 1, 48, 48), meta=np.ndarray>
USWRFtoa (initial_time, forecast_time, tile, y, x) float32 dask.array<chunksize=(1, 15, 1, 48, 48), meta=np.ndarray>
air_temperature (initial_time, step, forecast_time, tile, z, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 79, 48, 48), meta=np.ndarray>
air_temperature_at_2m (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
canopy_water (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
cloud_amount (initial_time, step, forecast_time, tile, z, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 79, 48, 48), meta=np.ndarray>
cloud_ice_mixing_ratio (initial_time, step, forecast_time, tile, z, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 79, 48, 48), meta=np.ndarray>
cloud_water_mixing_ratio (initial_time, step, forecast_time, tile, z, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 79, 48, 48), meta=np.ndarray>
convective_cloud_bottom_pressure (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
convective_cloud_fraction (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
convective_cloud_top_pressure (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
deep_soil_temperature (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
eastward_wind_at_surface (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
fh_parameter (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
fm_at_10m (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
fm_parameter (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
fractional_coverage_with_strong_cosz_dependency (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
fractional_coverage_with_weak_cosz_dependency (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
friction_velocity (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
graupel_mixing_ratio (initial_time, step, forecast_time, tile, z, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 79, 48, 48), meta=np.ndarray>
ice_fraction_over_open_water (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
land_sea_mask (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
latent_heat_flux (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
liquid_soil_moisture (initial_time, step, forecast_time, tile, z_soil, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 4, 48, 48), meta=np.ndarray>
maximum_fractional_coverage_of_green_vegetation (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
maximum_snow_albedo_in_fraction (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
mean_cos_zenith_angle (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
mean_near_infrared_albedo_with_strong_cosz_dependency (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
mean_near_infrared_albedo_with_weak_cosz_dependency (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
mean_visible_albedo_with_strong_cosz_dependency (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
mean_visible_albedo_with_weak_cosz_dependency (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
minimum_fractional_coverage_of_green_vegetation (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
ozone_mixing_ratio (initial_time, step, forecast_time, tile, z, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 79, 48, 48), meta=np.ndarray>
pressure_thickness_of_atmospheric_layer (initial_time, step, forecast_time, tile, z, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 79, 48, 48), meta=np.ndarray>
rain_mixing_ratio (initial_time, step, forecast_time, tile, z, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 79, 48, 48), meta=np.ndarray>
sea_ice_thickness (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
sensible_heat_flux (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
snow_cover_in_fraction (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
snow_depth_water_equivalent (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
snow_mixing_ratio (initial_time, step, forecast_time, tile, z, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 79, 48, 48), meta=np.ndarray>
snow_rain_flag (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
soil_temperature (initial_time, step, forecast_time, tile, z_soil, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 4, 48, 48), meta=np.ndarray>
soil_type (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
specific_humidity (initial_time, step, forecast_time, tile, z, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 79, 48, 48), meta=np.ndarray>
specific_humidity_at_2m (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
surface_geopotential (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
surface_roughness (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
surface_slope_type (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
surface_temperature (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
surface_temperature_over_ice_fraction (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
total_precipitation (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
total_soil_moisture (initial_time, step, forecast_time, tile, z_soil, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 4, 48, 48), meta=np.ndarray>
vegetation_fraction (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
vegetation_type (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
vertical_thickness_of_atmospheric_layer (initial_time, step, forecast_time, tile, z, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 79, 48, 48), meta=np.ndarray>
vertical_wind (initial_time, step, forecast_time, tile, z, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 79, 48, 48), meta=np.ndarray>
water_equivalent_of_accumulated_snow_depth (initial_time, step, forecast_time, tile, y, x) float64 dask.array<chunksize=(1, 3, 1, 6, 48, 48), meta=np.ndarray>
x_wind (initial_time, step, forecast_time, tile, z, y_interface, x) float64 dask.array<chunksize=(1, 3, 1, 6, 79, 48, 48), meta=np.ndarray>
y_wind (initial_time, step, forecast_time, tile, z, y, x_interface) float64 dask.array<chunksize=(1, 3, 1, 6, 79, 48, 48), meta=np.ndarray>
xarray.Dataset {
dimensions:
forecast_time = 15 ;
initial_time = 11 ;
step = 3 ;
tile = 6 ;
x = 48 ;
x_interface = 49 ;
y = 48 ;
y_interface = 49 ;
z = 79 ;
z_soil = 4 ;
variables:
float32 DLWRFsfc(initial_time, forecast_time, tile, y, x) ;
DLWRFsfc:cell_methods = time: point ;
DLWRFsfc:long_name = surface downward longwave flux ;
DLWRFsfc:units = W/m**2 ;
float32 DSWRFsfc(initial_time, forecast_time, tile, y, x) ;
DSWRFsfc:cell_methods = time: point ;
DSWRFsfc:long_name = averaged surface downward shortwave flux ;
DSWRFsfc:units = W/m**2 ;
float32 DSWRFtoa(initial_time, forecast_time, tile, y, x) ;
DSWRFtoa:cell_methods = time: point ;
DSWRFtoa:long_name = top of atmos downward shortwave flux ;
DSWRFtoa:units = W/m**2 ;
float32 ULWRFsfc(initial_time, forecast_time, tile, y, x) ;
ULWRFsfc:cell_methods = time: point ;
ULWRFsfc:long_name = surface upward longwave flux ;
ULWRFsfc:units = W/m**2 ;
float32 ULWRFtoa(initial_time, forecast_time, tile, y, x) ;
ULWRFtoa:cell_methods = time: point ;
ULWRFtoa:long_name = top of atmos upward longwave flux ;
ULWRFtoa:units = W/m**2 ;
float32 USWRFsfc(initial_time, forecast_time, tile, y, x) ;
USWRFsfc:cell_methods = time: point ;
USWRFsfc:long_name = averaged surface upward shortwave flux ;
USWRFsfc:units = W/m**2 ;
float32 USWRFtoa(initial_time, forecast_time, tile, y, x) ;
USWRFtoa:cell_methods = time: point ;
USWRFtoa:long_name = top of atmos upward shortwave flux ;
USWRFtoa:units = W/m**2 ;
float64 air_temperature(initial_time, step, forecast_time, tile, z, y, x) ;
air_temperature:units = degK ;
float64 air_temperature_at_2m(initial_time, step, forecast_time, tile, y, x) ;
air_temperature_at_2m:units = degK ;
float64 canopy_water(initial_time, step, forecast_time, tile, y, x) ;
canopy_water:units = unknown ;
float64 cloud_amount(initial_time, step, forecast_time, tile, z, y, x) ;
cloud_amount:units = 1 ;
float64 cloud_ice_mixing_ratio(initial_time, step, forecast_time, tile, z, y, x) ;
cloud_ice_mixing_ratio:units = kg/kg ;
float64 cloud_water_mixing_ratio(initial_time, step, forecast_time, tile, z, y, x) ;
cloud_water_mixing_ratio:units = kg/kg ;
float64 convective_cloud_bottom_pressure(initial_time, step, forecast_time, tile, y, x) ;
convective_cloud_bottom_pressure:units = Pa ;
float64 convective_cloud_fraction(initial_time, step, forecast_time, tile, y, x) ;
convective_cloud_fraction:units = ;
float64 convective_cloud_top_pressure(initial_time, step, forecast_time, tile, y, x) ;
convective_cloud_top_pressure:units = Pa ;
float64 deep_soil_temperature(initial_time, step, forecast_time, tile, y, x) ;
deep_soil_temperature:units = degK ;
float64 eastward_wind_at_surface(initial_time, step, forecast_time, tile, y, x) ;
eastward_wind_at_surface:units = m/s ;
float64 fh_parameter(initial_time, step, forecast_time, tile, y, x) ;
fh_parameter:units = unknown ;
float64 fm_at_10m(initial_time, step, forecast_time, tile, y, x) ;
fm_at_10m:units = unknown ;
float64 fm_parameter(initial_time, step, forecast_time, tile, y, x) ;
fm_parameter:units = unknown ;
timedelta64[ns] forecast_time(forecast_time) ;
float64 fractional_coverage_with_strong_cosz_dependency(initial_time, step, forecast_time, tile, y, x) ;
fractional_coverage_with_strong_cosz_dependency:units = ;
float64 fractional_coverage_with_weak_cosz_dependency(initial_time, step, forecast_time, tile, y, x) ;
fractional_coverage_with_weak_cosz_dependency:units = ;
float64 friction_velocity(initial_time, step, forecast_time, tile, y, x) ;
friction_velocity:units = m/s ;
float64 graupel_mixing_ratio(initial_time, step, forecast_time, tile, z, y, x) ;
graupel_mixing_ratio:units = kg/kg ;
float64 ice_fraction_over_open_water(initial_time, step, forecast_time, tile, y, x) ;
ice_fraction_over_open_water:units = ;
object initial_time(initial_time) ;
float64 land_sea_mask(initial_time, step, forecast_time, tile, y, x) ;
land_sea_mask:units = ;
float64 latent_heat_flux(initial_time, step, forecast_time, tile, y, x) ;
latent_heat_flux:units = W/m^2 ;
float64 liquid_soil_moisture(initial_time, step, forecast_time, tile, z_soil, y, x) ;
liquid_soil_moisture:units = unknown ;
float64 maximum_fractional_coverage_of_green_vegetation(initial_time, step, forecast_time, tile, y, x) ;
maximum_fractional_coverage_of_green_vegetation:units = ;
float64 maximum_snow_albedo_in_fraction(initial_time, step, forecast_time, tile, y, x) ;
maximum_snow_albedo_in_fraction:units = ;
float64 mean_cos_zenith_angle(initial_time, step, forecast_time, tile, y, x) ;
mean_cos_zenith_angle:units = ;
float64 mean_near_infrared_albedo_with_strong_cosz_dependency(initial_time, step, forecast_time, tile, y, x) ;
mean_near_infrared_albedo_with_strong_cosz_dependency:units = ;
float64 mean_near_infrared_albedo_with_weak_cosz_dependency(initial_time, step, forecast_time, tile, y, x) ;
mean_near_infrared_albedo_with_weak_cosz_dependency:units = ;
float64 mean_visible_albedo_with_strong_cosz_dependency(initial_time, step, forecast_time, tile, y, x) ;
mean_visible_albedo_with_strong_cosz_dependency:units = ;
float64 mean_visible_albedo_with_weak_cosz_dependency(initial_time, step, forecast_time, tile, y, x) ;
mean_visible_albedo_with_weak_cosz_dependency:units = ;
float64 minimum_fractional_coverage_of_green_vegetation(initial_time, step, forecast_time, tile, y, x) ;
minimum_fractional_coverage_of_green_vegetation:units = ;
float64 ozone_mixing_ratio(initial_time, step, forecast_time, tile, z, y, x) ;
ozone_mixing_ratio:units = kg/kg ;
float64 pressure_thickness_of_atmospheric_layer(initial_time, step, forecast_time, tile, z, y, x) ;
pressure_thickness_of_atmospheric_layer:units = Pa ;
float64 rain_mixing_ratio(initial_time, step, forecast_time, tile, z, y, x) ;
rain_mixing_ratio:units = kg/kg ;
float64 sea_ice_thickness(initial_time, step, forecast_time, tile, y, x) ;
sea_ice_thickness:units = unknown ;
float64 sensible_heat_flux(initial_time, step, forecast_time, tile, y, x) ;
sensible_heat_flux:units = W/m^2 ;
float64 snow_cover_in_fraction(initial_time, step, forecast_time, tile, y, x) ;
snow_cover_in_fraction:units = ;
float64 snow_depth_water_equivalent(initial_time, step, forecast_time, tile, y, x) ;
snow_depth_water_equivalent:units = mm ;
float64 snow_mixing_ratio(initial_time, step, forecast_time, tile, z, y, x) ;
snow_mixing_ratio:units = kg/kg ;
float64 snow_rain_flag(initial_time, step, forecast_time, tile, y, x) ;
snow_rain_flag:units = ;
float64 soil_temperature(initial_time, step, forecast_time, tile, z_soil, y, x) ;
soil_temperature:units = degK ;
float64 soil_type(initial_time, step, forecast_time, tile, y, x) ;
soil_type:units = ;
float64 specific_humidity(initial_time, step, forecast_time, tile, z, y, x) ;
specific_humidity:units = kg/kg ;
float64 specific_humidity_at_2m(initial_time, step, forecast_time, tile, y, x) ;
specific_humidity_at_2m:units = kg/kg ;
object step(step) ;
float64 surface_geopotential(initial_time, step, forecast_time, tile, y, x) ;
surface_geopotential:units = m^2 s^-2 ;
float64 surface_roughness(initial_time, step, forecast_time, tile, y, x) ;
surface_roughness:units = cm ;
float64 surface_slope_type(initial_time, step, forecast_time, tile, y, x) ;
surface_slope_type:units = ;
float64 surface_temperature(initial_time, step, forecast_time, tile, y, x) ;
surface_temperature:units = degK ;
float64 surface_temperature_over_ice_fraction(initial_time, step, forecast_time, tile, y, x) ;
surface_temperature_over_ice_fraction:units = degK ;
float64 total_precipitation(initial_time, step, forecast_time, tile, y, x) ;
total_precipitation:units = unknown ;
float64 total_soil_moisture(initial_time, step, forecast_time, tile, z_soil, y, x) ;
total_soil_moisture:units = unknown ;
float64 vegetation_fraction(initial_time, step, forecast_time, tile, y, x) ;
vegetation_fraction:units = ;
float64 vegetation_type(initial_time, step, forecast_time, tile, y, x) ;
vegetation_type:units = ;
float64 vertical_thickness_of_atmospheric_layer(initial_time, step, forecast_time, tile, z, y, x) ;
vertical_thickness_of_atmospheric_layer:units = m ;
float64 vertical_wind(initial_time, step, forecast_time, tile, z, y, x) ;
vertical_wind:units = m/s ;
float64 water_equivalent_of_accumulated_snow_depth(initial_time, step, forecast_time, tile, y, x) ;
water_equivalent_of_accumulated_snow_depth:units = kg/m^2 ;
float64 x_wind(initial_time, step, forecast_time, tile, z, y_interface, x) ;
x_wind:units = m/s ;
float64 y_wind(initial_time, step, forecast_time, tile, z, y, x_interface) ;
y_wind:units = m/s ;
// global attributes:
}None
data size: 5.785136844 GB/initial time
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