Notebook

Data Description of Rechunked National Water Model v2.1 Retrospective Simulation¶

This notebook gives a look at the metadata of the six Zarr stores which comprise the rechunked NWM v2.1 retrospective simulation. The perspective of the metadata is from the xarray.Dataset point of view (as xarray would work with the data in memory). For a more detailed data description please see this (html) (notebook) which presents information from the Zarr perspective as well (storage, compression), for each variable.

In [1]:

import xarray as xr

/glade/work/jamesmcc/python_envs/379zr/lib/python3.7/site-packages/xarray/backends/cfgrib_.py:28: UserWarning: Failed to load cfgrib - most likely there is a problem accessing the ecCodes library. Try `import cfgrib` to get the full error message
  "Failed to load cfgrib - most likely there is a problem accessing the ecCodes library. "

In [2]:

files = {
    'gwout': '/glade/p/datashare/ishitas/nwm_retro_v2.1/gwout.zarr',
    'lakeout': '/glade/p/datashare/jamesmcc/nwm_retro_v2.1/lakeout.zarr',
    'chrtout': '/glade/p/datashare/ishitas/nwm_retro_v2.1/chrtout.zarr',
    'precip': '/glade/p/datashare/jamesmcc/nwm_retro_v2.1/precip.zarr',
    'ldasout': '/glade/p/datashare/ishitas/nwm_retro_v2.1/ldasout.zarr',
    'rtout': '/glade/p/datashare/jamesmcc/nwm_retro_v2.1/rtout.zarr', 
}

message = ('Please click on the dropdown carets, metadata (file) symbols, and the data' 
           'information (silos) symbols below for additional information.')

def describe(tag):
    ds = xr.open_zarr(files[tag])
    print(message)
    display(ds)
    ds.close()
    del ds
    return None

LAKEOUT: lake model output¶

In [3]:

describe('lakeout')

Please click on the dropdown carets, metadata (file) symbols, and the datainformation (silos) symbols below for additional information.

/glade/work/jamesmcc/python_envs/379zr/lib/python3.7/site-packages/xarray/backends/plugins.py:61: RuntimeWarning: Engine 'cfgrib' loading failed:
Cannot find the ecCodes library
  warnings.warn(f"Engine {name!r} loading failed:\n{ex}", RuntimeWarning)

<xarray.Dataset>
Dimensions:         (feature_id: 5783, time: 367439)
Coordinates:
  * feature_id      (feature_id) int32 491 531 747 ... 947070204 1021092845
    latitude        (feature_id) float32 dask.array<chunksize=(5783,), meta=np.ndarray>
    longitude       (feature_id) float32 dask.array<chunksize=(5783,), meta=np.ndarray>
  * time            (time) datetime64[ns] 1979-02-01T01:00:00 ... 2020-12-31T...
Data variables:
    crs             |S1 ...
    inflow          (time, feature_id) float64 dask.array<chunksize=(8064, 500), meta=np.ndarray>
    outflow         (time, feature_id) float64 dask.array<chunksize=(8064, 500), meta=np.ndarray>
    water_sfc_elev  (time, feature_id) float32 dask.array<chunksize=(8064, 500), meta=np.ndarray>
Attributes:
    Conventions:                  CF-1.6
    TITLE:                        OUTPUT FROM WRF-Hydro v5.2.0-beta2
    code_version:                 v5.2.0-beta2
    featureType:                  timeSeries
    model_configuration:          retrospective
    model_output_type:            reservoir
    proj4:                        +proj=lcc +units=m +a=6370000.0 +b=6370000....
    reservoir_assimilated_value:  Assimilation not performed
    reservoir_type:               1 = level pool everywhere
    station_dimension:            lake_id

xarray.Dataset

Dimensions:
- feature_id: 5783
- time: 367439

Coordinates: (4)

feature_id
(feature_id)
int32
491 531 ... 947070204 1021092845
cf_role :
timeseries_id
comment :
ComID from NHDPlusV2 waterbody layer
long_name :
Lake ComID
```
array([       491,        531,        747, ...,  947070203,  947070204,
       1021092845], dtype=int32)
```

latitude

(feature_id)

float32

dask.array<chunksize=(5783,), meta=np.ndarray>

long_name :: Lake latitude
standard_name :: latitude
units :: degrees_north

	Array	Chunk
Bytes	22.59 kiB	22.59 kiB
Shape	(5783,)	(5783,)
Count	2 Tasks	1 Chunks
Type	float32	numpy.ndarray

longitude

(feature_id)

float32

dask.array<chunksize=(5783,), meta=np.ndarray>

long_name :: Lake longitude
standard_name :: longitude
units :: degrees_east

	Array	Chunk
Bytes	22.59 kiB	22.59 kiB
Shape	(5783,)	(5783,)
Count	2 Tasks	1 Chunks
Type	float32	numpy.ndarray

time

(time)

datetime64[ns]

1979-02-01T01:00:00 ... 2020-12-...

long_name :: valid output time
standard_name :: time
valid_max :: 4862880
valid_min :: 4777980

array(['1979-02-01T01:00:00.000000000', '1979-02-01T02:00:00.000000000',
       '1979-02-01T03:00:00.000000000', ..., '2020-12-31T21:00:00.000000000',
       '2020-12-31T22:00:00.000000000', '2020-12-31T23:00:00.000000000'],
      dtype='datetime64[ns]')

Data variables: (4)

crs
()
|S1
...
_CoordinateAxes :
latitude longitude
esri_pe_string :
GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]];-400 -400 1000000000;-100000 10000;-100000 10000;8.98315284119521E-09;0.001;0.001;IsHighPrecision
grid_mapping_name :
latitude longitude
inverse_flattening :
298.2572326660156
long_name :
CRS definition
longitude_of_prime_meridian :
0.0
semi_major_axis :
6378137.0
semi_minor_axis :
6356752.5
spatial_ref :
GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]];-400 -400 1000000000;-100000 10000;-100000 10000;8.98315284119521E-09;0.001;0.001;IsHighPrecision
transform_name :
latitude longitude
```
array(b'', dtype='|S1')
```

inflow

(time, feature_id)

float64

dask.array<chunksize=(8064, 500), meta=np.ndarray>

grid_mapping :: crs
long_name :: Lake Inflow
units :: m3 s-1

	Array	Chunk
Bytes	15.83 GiB	30.76 MiB
Shape	(367439, 5783)	(8064, 500)
Count	553 Tasks	552 Chunks
Type	float64	numpy.ndarray

outflow

(time, feature_id)

float64

dask.array<chunksize=(8064, 500), meta=np.ndarray>

grid_mapping :: crs
long_name :: Lake Outflow
units :: m3 s-1

	Array	Chunk
Bytes	15.83 GiB	30.76 MiB
Shape	(367439, 5783)	(8064, 500)
Count	553 Tasks	552 Chunks
Type	float64	numpy.ndarray

water_sfc_elev

(time, feature_id)

float32

dask.array<chunksize=(8064, 500), meta=np.ndarray>

comment :: If reservoir_type = 4, water_sfc_elev is invalid because this value corresponds only to level pool
long_name :: Water Surface Elevation
units :: m

	Array	Chunk
Bytes	7.92 GiB	15.38 MiB
Shape	(367439, 5783)	(8064, 500)
Count	553 Tasks	552 Chunks
Type	float32	numpy.ndarray

Attributes: (10)
Conventions :
CF-1.6
TITLE :
OUTPUT FROM WRF-Hydro v5.2.0-beta2
code_version :
v5.2.0-beta2
featureType :
timeSeries
model_configuration :
retrospective
model_output_type :
reservoir
proj4 :
+proj=lcc +units=m +a=6370000.0 +b=6370000.0 +lat_1=30.0 +lat_2=60.0 +lat_0=40.0 +lon_0=-97.0 +x_0=0 +y_0=0 +k_0=1.0 +nadgrids=@
reservoir_assimilated_value :
Assimilation not performed
reservoir_type :
1 = level pool everywhere
station_dimension :
lake_id

GWOUT: groundwater model ouput¶

In [4]:

describe('gwout')

Please click on the dropdown carets, metadata (file) symbols, and the datainformation (silos) symbols below for additional information.

<xarray.Dataset>
Dimensions:     (time: 367439, feature_id: 2776738)
Coordinates:
  * feature_id  (feature_id) int32 6635572 6635590 6635598 ... 25293410 15450136
  * time        (time) datetime64[ns] 1979-02-01T01:00:00 ... 2020-12-31T23:0...
Data variables:
    depth       (time, feature_id) float64 dask.array<chunksize=(672, 30000), meta=np.ndarray>
    inflow      (time, feature_id) float64 dask.array<chunksize=(672, 30000), meta=np.ndarray>
    outflow     (time, feature_id) float64 dask.array<chunksize=(672, 30000), meta=np.ndarray>
Attributes:
    Conventions:          CF-1.6
    TITLE:                OUTPUT FROM WRF-Hydro v5.2.0-beta2
    code_version:         v5.2.0-beta2
    featureType:          timeSeries
    model_configuration:  retrospective
    model_output_type:    groundwater_rt
    station_dimension:    gw_id

xarray.Dataset

Dimensions:
- time: 367439
- feature_id: 2776738

Coordinates: (2)

feature_id
(feature_id)
int32
6635572 6635590 ... 15450136
cf_role :
timeseries_id
comment :
Groundwater Bucket ID
long_name :
Groundwater Bucket ID
```
array([ 6635572,  6635590,  6635598, ..., 15448486, 25293410, 15450136],
      dtype=int32)
```

time

(time)

datetime64[ns]

1979-02-01T01:00:00 ... 2020-12-...

long_name :: valid output time
standard_name :: time
valid_max :: 4862880
valid_min :: 4777980

array(['1979-02-01T01:00:00.000000000', '1979-02-01T02:00:00.000000000',
       '1979-02-01T03:00:00.000000000', ..., '2020-12-31T21:00:00.000000000',
       '2020-12-31T22:00:00.000000000', '2020-12-31T23:00:00.000000000'],
      dtype='datetime64[ns]')

Data variables: (3)

depth

(time, feature_id)

float64

dask.array<chunksize=(672, 30000), meta=np.ndarray>

long_name :: Bucket Depth
units :: mm

	Array	Chunk
Bytes	7.42 TiB	153.81 MiB
Shape	(367439, 2776738)	(672, 30000)
Count	50872 Tasks	50871 Chunks
Type	float64	numpy.ndarray

inflow

(time, feature_id)

float64

dask.array<chunksize=(672, 30000), meta=np.ndarray>

long_name :: Bucket Inflow
units :: m3 s-1

	Array	Chunk
Bytes	7.42 TiB	153.81 MiB
Shape	(367439, 2776738)	(672, 30000)
Count	50872 Tasks	50871 Chunks
Type	float64	numpy.ndarray

outflow

(time, feature_id)

float64

dask.array<chunksize=(672, 30000), meta=np.ndarray>

long_name :: Bucket Outflow
units :: m3 s-1

	Array	Chunk
Bytes	7.42 TiB	153.81 MiB
Shape	(367439, 2776738)	(672, 30000)
Count	50872 Tasks	50871 Chunks
Type	float64	numpy.ndarray

Attributes: (7)
Conventions :
CF-1.6
TITLE :
OUTPUT FROM WRF-Hydro v5.2.0-beta2
code_version :
v5.2.0-beta2
featureType :
timeSeries
model_configuration :
retrospective
model_output_type :
groundwater_rt
station_dimension :
gw_id

CHRTOUT: streamflow model output¶

In [5]:

describe('chrtout')

Please click on the dropdown carets, metadata (file) symbols, and the datainformation (silos) symbols below for additional information.

<xarray.Dataset>
Dimensions:     (feature_id: 2776738, time: 367439)
Coordinates:
    elevation   (feature_id) float32 dask.array<chunksize=(2776738,), meta=np.ndarray>
  * feature_id  (feature_id) int32 101 179 181 ... 1180001803 1180001804
    gage_id     (feature_id) |S15 dask.array<chunksize=(2776738,), meta=np.ndarray>
    latitude    (feature_id) float32 dask.array<chunksize=(2776738,), meta=np.ndarray>
    longitude   (feature_id) float32 dask.array<chunksize=(2776738,), meta=np.ndarray>
    order       (feature_id) int32 dask.array<chunksize=(2776738,), meta=np.ndarray>
  * time        (time) datetime64[ns] 1979-02-01T01:00:00 ... 2020-12-31T23:0...
Data variables:
    crs         |S1 ...
    streamflow  (time, feature_id) float64 dask.array<chunksize=(672, 30000), meta=np.ndarray>
    velocity    (time, feature_id) float64 dask.array<chunksize=(672, 30000), meta=np.ndarray>
Attributes:
    TITLE:                OUTPUT FROM WRF-Hydro v5.2.0-beta2
    code_version:         v5.2.0-beta2
    featureType:          timeSeries
    model_configuration:  retrospective
    proj4:                +proj=lcc +units=m +a=6370000.0 +b=6370000.0 +lat_1...

xarray.Dataset

Dimensions:
- feature_id: 2776738
- time: 367439

Coordinates: (7)

elevation

(feature_id)

float32

dask.array<chunksize=(2776738,), meta=np.ndarray>

long_name :: feature elevation
units :: meters

	Array	Chunk
Bytes	10.59 MiB	10.59 MiB
Shape	(2776738,)	(2776738,)
Count	2 Tasks	1 Chunks
Type	float32	numpy.ndarray

feature_id
(feature_id)
int32
101 179 ... 1180001803 1180001804
cf_role :
timeseries_id
comment :
NHDPlusv2 ComIDs within CONUS, arbitrary Reach IDs outside of CONUS
long_name :
Reach ID
```
array([       101,        179,        181, ..., 1180001802, 1180001803,
       1180001804], dtype=int32)
```

gage_id

(feature_id)

|S15

dask.array<chunksize=(2776738,), meta=np.ndarray>

coordinates :: lat lon
long_name :: NHD Gage Event ID from SOURCE_FEA field in Gages feature class

	Array	Chunk
Bytes	39.72 MiB	39.72 MiB
Shape	(2776738,)	(2776738,)
Count	2 Tasks	1 Chunks
Type	\|S15	numpy.ndarray

latitude

(feature_id)

float32

dask.array<chunksize=(2776738,), meta=np.ndarray>

long_name :: feature latitude
standard_name :: latitude
units :: degrees_north

	Array	Chunk
Bytes	10.59 MiB	10.59 MiB
Shape	(2776738,)	(2776738,)
Count	2 Tasks	1 Chunks
Type	float32	numpy.ndarray

longitude

(feature_id)

float32

dask.array<chunksize=(2776738,), meta=np.ndarray>

long_name :: feature longitude
standard_name :: longitude
units :: degrees_east

	Array	Chunk
Bytes	10.59 MiB	10.59 MiB
Shape	(2776738,)	(2776738,)
Count	2 Tasks	1 Chunks
Type	float32	numpy.ndarray

order

(feature_id)

int32

dask.array<chunksize=(2776738,), meta=np.ndarray>

long_name :: stream order

	Array	Chunk
Bytes	10.59 MiB	10.59 MiB
Shape	(2776738,)	(2776738,)
Count	2 Tasks	1 Chunks
Type	int32	numpy.ndarray

time

(time)

datetime64[ns]

1979-02-01T01:00:00 ... 2020-12-...

long_name :: valid output time
standard_name :: time

array(['1979-02-01T01:00:00.000000000', '1979-02-01T02:00:00.000000000',
       '1979-02-01T03:00:00.000000000', ..., '2020-12-31T21:00:00.000000000',
       '2020-12-31T22:00:00.000000000', '2020-12-31T23:00:00.000000000'],
      dtype='datetime64[ns]')

Data variables: (3)

crs
()
|S1
...
_CoordinateAxes :
latitude longitude
esri_pe_string :
GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]];-400 -400 1000000000;-100000 10000;-100000 10000;8.98315284119521E-09;0.001;0.001;IsHighPrecision
grid_mapping_name :
latitude longitude
inverse_flattening :
298.2572326660156
long_name :
CRS definition
longitude_of_prime_meridian :
0.0
semi_major_axis :
6378137.0
semi_minor_axis :
6356752.5
spatial_ref :
GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]];-400 -400 1000000000;-100000 10000;-100000 10000;8.98315284119521E-09;0.001;0.001;IsHighPrecision
transform_name :
latitude longitude
```
array(b'', dtype='|S1')
```

streamflow

(time, feature_id)

float64

dask.array<chunksize=(672, 30000), meta=np.ndarray>

grid_mapping :: crs
long_name :: River Flow
units :: m3 s-1

	Array	Chunk
Bytes	7.42 TiB	153.81 MiB
Shape	(367439, 2776738)	(672, 30000)
Count	50872 Tasks	50871 Chunks
Type	float64	numpy.ndarray

velocity

(time, feature_id)

float64

dask.array<chunksize=(672, 30000), meta=np.ndarray>

grid_mapping :: crs
long_name :: River Velocity
units :: m s-1

	Array	Chunk
Bytes	7.42 TiB	153.81 MiB
Shape	(367439, 2776738)	(672, 30000)
Count	50872 Tasks	50871 Chunks
Type	float64	numpy.ndarray

Attributes: (5)
TITLE :
OUTPUT FROM WRF-Hydro v5.2.0-beta2
code_version :
v5.2.0-beta2
featureType :
timeSeries
model_configuration :
retrospective
proj4 :
+proj=lcc +units=m +a=6370000.0 +b=6370000.0 +lat_1=30.0 +lat_2=60.0 +lat_0=40.0 +lon_0=-97.0 +x_0=0 +y_0=0 +k_0=1.0 +nadgrids=@

Precipitation forcing¶

In [6]:

describe('precip')

Please click on the dropdown carets, metadata (file) symbols, and the datainformation (silos) symbols below for additional information.

<xarray.Dataset>
Dimensions:   (time: 367440, y: 3840, x: 4608)
Coordinates:
  * time      (time) datetime64[ns] 1979-02-01 ... 2020-12-31T23:00:00
  * x         (x) float64 -2.303e+06 -2.302e+06 ... 2.303e+06 2.304e+06
  * y         (y) float64 -1.92e+06 -1.919e+06 ... 1.918e+06 1.919e+06
Data variables:
    RAINRATE  (time, y, x) float32 dask.array<chunksize=(672, 350, 350), meta=np.ndarray>
    crs       |S1 ...
Attributes:
    NWM_version_number:   v2.1
    model_configuration:  AORC
    model_output_type:    forcing

xarray.Dataset

Dimensions:
- time: 367440
- y: 3840
- x: 4608
Coordinates: (3)
- time
  (time)
  datetime64[ns]
  1979-02-01 ... 2020-12-31T23:00:00
  long_name :
  valid output time
  standard_name :
  time
```
array(['1979-02-01T00:00:00.000000000', '1979-02-01T01:00:00.000000000',
       '1979-02-01T02:00:00.000000000', ..., '2020-12-31T21:00:00.000000000',
       '2020-12-31T22:00:00.000000000', '2020-12-31T23:00:00.000000000'],
      dtype='datetime64[ns]')
```
- x
  (x)
  float64
  -2.303e+06 -2.302e+06 ... 2.304e+06
  _CoordinateAxisType :
  GeoX
  long_name :
  x coordinate of projection
  resolution :
  1000.0
  standard_name :
  projection_x_coordinate
  units :
  m
```
array([-2303499.25, -2302499.25, -2301499.25, ...,  2301500.75,  2302500.75,
        2303500.75])
```
- y
  (y)
  float64
  -1.92e+06 -1.919e+06 ... 1.919e+06
  _CoordinateAxisType :
  GeoY
  long_name :
  y coordinate of projection
  resolution :
  1000.0
  standard_name :
  projection_y_coordinate
  units :
  m
```
array([-1919500.375, -1918500.375, -1917500.375, ...,  1917499.625,
        1918499.625,  1919499.625])
```

Data variables: (2)

RAINRATE

(time, y, x)

float32

dask.array<chunksize=(672, 350, 350), meta=np.ndarray>

cell_methods :: time: mean
esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: Surface Precipitation Rate
proj4 :: +proj=lcc +units=m +a=6370000.0 +b=6370000.0 +lat_1=30.0 +lat_2=60.0 +lat_0=40.0 +lon_0=-97.0 +x_0=0 +y_0=0 +k_0=1.0 +nadgrids=@null +wktext +no_defs
remap :: remapped via ESMF regrid_with_weights: Bilinear
standard_name :: precipitation_flux
units :: mm s^-1

	Array	Chunk
Bytes	23.65 TiB	314.03 MiB
Shape	(367440, 3840, 4608)	(672, 350, 350)
Count	84239 Tasks	84238 Chunks
Type	float32	numpy.ndarray

crs
()
|S1
...
GeoTransform :
-2303999.17655 1000.0 0 1919999.66329 0 -1000.0
_CoordinateAxes :
y x
_CoordinateTransformType :
Projection
earth_radius :
6370000.0
esri_pe_string :
PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
false_easting :
0.0
false_northing :
0.0
grid_mapping_name :
lambert_conformal_conic
inverse_flattening :
0.0
latitude_of_projection_origin :
40.0
long_name :
CRS definition
longitude_of_central_meridian :
-97.0
longitude_of_prime_meridian :
0.0
semi_major_axis :
6370000.0
spatial_ref :
PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
standard_parallel :
[30.0, 60.0]
transform_name :
lambert_conformal_conic
```
array(b'', dtype='|S1')
```

Attributes: (3)
NWM_version_number :
v2.1
model_configuration :
AORC
model_output_type :
forcing

LDASOUT: Land model (NoahMP) output¶

In [7]:

describe('ldasout')

Please click on the dropdown carets, metadata (file) symbols, and the datainformation (silos) symbols below for additional information.

<xarray.Dataset>
Dimensions:   (time: 122479, y: 3840, x: 4608, vis_nir: 2, soil_layers_stag: 4)
Coordinates:
  * time      (time) datetime64[ns] 1979-02-01T03:00:00 ... 2020-12-31T21:00:00
  * x         (x) float64 -2.303e+06 -2.302e+06 ... 2.303e+06 2.304e+06
  * y         (y) float64 -1.92e+06 -1.919e+06 ... 1.918e+06 1.919e+06
Dimensions without coordinates: vis_nir, soil_layers_stag
Data variables: (12/20)
    ACCET     (time, y, x) float64 dask.array<chunksize=(224, 350, 350), meta=np.ndarray>
    ACSNOM    (time, y, x) float64 dask.array<chunksize=(224, 350, 350), meta=np.ndarray>
    ALBEDO    (time, y, x) float64 dask.array<chunksize=(224, 350, 350), meta=np.ndarray>
    ALBSND    (time, y, vis_nir, x) float64 dask.array<chunksize=(224, 350, 1, 350), meta=np.ndarray>
    ALBSNI    (time, y, vis_nir, x) float64 dask.array<chunksize=(224, 350, 1, 350), meta=np.ndarray>
    COSZ      (time, y, x) float64 dask.array<chunksize=(224, 350, 350), meta=np.ndarray>
    ...        ...
    SNEQV     (time, y, x) float64 dask.array<chunksize=(224, 350, 350), meta=np.ndarray>
    SNOWH     (time, y, x) float64 dask.array<chunksize=(224, 350, 350), meta=np.ndarray>
    SOIL_M    (time, y, soil_layers_stag, x) float64 dask.array<chunksize=(224, 350, 1, 350), meta=np.ndarray>
    SOIL_W    (time, y, soil_layers_stag, x) float64 dask.array<chunksize=(224, 350, 1, 350), meta=np.ndarray>
    TRAD      (time, y, x) float64 dask.array<chunksize=(224, 350, 350), meta=np.ndarray>
    UGDRNOFF  (time, y, x) float64 dask.array<chunksize=(224, 350, 350), meta=np.ndarray>
Attributes:
    Conventions:          CF-1.6
    GDAL_DataType:        Generic
    TITLE:                OUTPUT FROM WRF-Hydro v5.2.0-beta2
    code_version:         v5.2.0-beta2
    model_configuration:  retrospective
    model_output_type:    land
    proj4:                +proj=lcc +units=m +a=6370000.0 +b=6370000.0 +lat_1...

xarray.Dataset

Dimensions:
- time: 122479
- y: 3840
- x: 4608
- vis_nir: 2
- soil_layers_stag: 4
Coordinates: (3)
- time
  (time)
  datetime64[ns]
  1979-02-01T03:00:00 ... 2020-12-...
  long_name :
  valid output time
  standard_name :
  time
  valid_max :
  4862880
  valid_min :
  4778100
```
array(['1979-02-01T03:00:00.000000000', '1979-02-01T06:00:00.000000000',
       '1979-02-01T09:00:00.000000000', ..., '2020-12-31T15:00:00.000000000',
       '2020-12-31T18:00:00.000000000', '2020-12-31T21:00:00.000000000'],
      dtype='datetime64[ns]')
```
- x
  (x)
  float64
  -2.303e+06 -2.302e+06 ... 2.304e+06
  _CoordinateAxisType :
  GeoX
  long_name :
  x coordinate of projection
  resolution :
  1000.0
  standard_name :
  projection_x_coordinate
  units :
  m
```
array([-2303499.25, -2302499.25, -2301499.25, ...,  2301500.75,  2302500.75,
        2303500.75])
```
- y
  (y)
  float64
  -1.92e+06 -1.919e+06 ... 1.919e+06
  _CoordinateAxisType :
  GeoY
  long_name :
  y coordinate of projection
  resolution :
  1000.0
  standard_name :
  projection_y_coordinate
  units :
  m
```
array([-1919500.375, -1918500.375, -1917500.375, ...,  1917499.625,
        1918499.625,  1919499.625])
```

Data variables: (20)

ACCET

(time, y, x)

float64

dask.array<chunksize=(224, 350, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: Accumulated total ET
units :: mm
valid_range :: [-100000, 100000000]

	Array	Chunk
Bytes	15.77 TiB	209.35 MiB
Shape	(122479, 3840, 4608)	(224, 350, 350)
Count	84239 Tasks	84238 Chunks
Type	float64	numpy.ndarray

ACSNOM

(time, y, x)

float64

dask.array<chunksize=(224, 350, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: accumulated melting water out of snow bottom
units :: mm
valid_range :: [0, 10000000]

	Array	Chunk
Bytes	15.77 TiB	209.35 MiB
Shape	(122479, 3840, 4608)	(224, 350, 350)
Count	84239 Tasks	84238 Chunks
Type	float64	numpy.ndarray

ALBEDO

(time, y, x)

float64

dask.array<chunksize=(224, 350, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: Surface albedo
units :: -
valid_range :: [0, 100]

	Array	Chunk
Bytes	15.77 TiB	209.35 MiB
Shape	(122479, 3840, 4608)	(224, 350, 350)
Count	84239 Tasks	84238 Chunks
Type	float64	numpy.ndarray

ALBSND

(time, y, vis_nir, x)

float64

dask.array<chunksize=(224, 350, 1, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: snowpack albedo, direct
units :: -
valid_range :: [0, 100]

	Array	Chunk
Bytes	31.54 TiB	209.35 MiB
Shape	(122479, 3840, 2, 4608)	(224, 350, 1, 350)
Count	168477 Tasks	168476 Chunks
Type	float64	numpy.ndarray

ALBSNI

(time, y, vis_nir, x)

float64

dask.array<chunksize=(224, 350, 1, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: snowpack albedo, diffuse
units :: -
valid_range :: [0, 100]

	Array	Chunk
Bytes	31.54 TiB	209.35 MiB
Shape	(122479, 3840, 2, 4608)	(224, 350, 1, 350)
Count	168477 Tasks	168476 Chunks
Type	float64	numpy.ndarray

COSZ

(time, y, x)

float64

dask.array<chunksize=(224, 350, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: Cosine of zenith angle
units :: -
valid_range :: [-100, 100]

	Array	Chunk
Bytes	15.77 TiB	209.35 MiB
Shape	(122479, 3840, 4608)	(224, 350, 350)
Count	84239 Tasks	84238 Chunks
Type	float64	numpy.ndarray

EDIR

(time, y, x)

float64

dask.array<chunksize=(224, 350, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: Direct from soil evaporation rate
units :: kg m-2 s-1
valid_range :: [-10000000, 10000000]

	Array	Chunk
Bytes	15.77 TiB	209.35 MiB
Shape	(122479, 3840, 4608)	(224, 350, 350)
Count	84239 Tasks	84238 Chunks
Type	float64	numpy.ndarray

FIRA

(time, y, x)

float64

dask.array<chunksize=(224, 350, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: Total net LW radiation to atmosphere
units :: W m-2
valid_range :: [-15000, 15000]

	Array	Chunk
Bytes	15.77 TiB	209.35 MiB
Shape	(122479, 3840, 4608)	(224, 350, 350)
Count	84239 Tasks	84238 Chunks
Type	float64	numpy.ndarray

FSA

(time, y, x)

float64

dask.array<chunksize=(224, 350, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: Total absorbed SW radiation
units :: W m-2
valid_range :: [-15000, 15000]

	Array	Chunk
Bytes	15.77 TiB	209.35 MiB
Shape	(122479, 3840, 4608)	(224, 350, 350)
Count	84239 Tasks	84238 Chunks
Type	float64	numpy.ndarray

FSNO

(time, y, x)

float64

dask.array<chunksize=(224, 350, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: Snow-cover fraction on the ground
units :: 1
valid_range :: [0, 1000]

	Array	Chunk
Bytes	15.77 TiB	209.35 MiB
Shape	(122479, 3840, 4608)	(224, 350, 350)
Count	84239 Tasks	84238 Chunks
Type	float64	numpy.ndarray

HFX

(time, y, x)

float64

dask.array<chunksize=(224, 350, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: Total sensible heat to the atmosphere
units :: W m-2
valid_range :: [-15000, 15000]

	Array	Chunk
Bytes	15.77 TiB	209.35 MiB
Shape	(122479, 3840, 4608)	(224, 350, 350)
Count	84239 Tasks	84238 Chunks
Type	float64	numpy.ndarray

(time, y, x)

float64

dask.array<chunksize=(224, 350, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: Total latent heat to the atmosphere
units :: W m-2
valid_range :: [-15000, 15000]

	Array	Chunk
Bytes	15.77 TiB	209.35 MiB
Shape	(122479, 3840, 4608)	(224, 350, 350)
Count	84239 Tasks	84238 Chunks
Type	float64	numpy.ndarray

QRAIN

(time, y, x)

float64

dask.array<chunksize=(224, 350, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: Rainfall rate on the ground
units :: mm s-1
valid_range :: [0, 10000000]

	Array	Chunk
Bytes	15.77 TiB	209.35 MiB
Shape	(122479, 3840, 4608)	(224, 350, 350)
Count	84239 Tasks	84238 Chunks
Type	float64	numpy.ndarray

QSNOW

(time, y, x)

float64

dask.array<chunksize=(224, 350, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: Snowfall rate on the ground
units :: mm s-1
valid_range :: [0, 10000000]

	Array	Chunk
Bytes	15.77 TiB	209.35 MiB
Shape	(122479, 3840, 4608)	(224, 350, 350)
Count	84239 Tasks	84238 Chunks
Type	float64	numpy.ndarray

SNEQV

(time, y, x)

float64

dask.array<chunksize=(224, 350, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: Snow water equivalent
units :: kg m-2
valid_range :: [0, 1000000]

	Array	Chunk
Bytes	15.77 TiB	209.35 MiB
Shape	(122479, 3840, 4608)	(224, 350, 350)
Count	84239 Tasks	84238 Chunks
Type	float64	numpy.ndarray

SNOWH

(time, y, x)

float64

dask.array<chunksize=(224, 350, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: Snow depth
units :: m
valid_range :: [0, 1000000]

	Array	Chunk
Bytes	15.77 TiB	209.35 MiB
Shape	(122479, 3840, 4608)	(224, 350, 350)
Count	84239 Tasks	84238 Chunks
Type	float64	numpy.ndarray

SOIL_M

(time, y, soil_layers_stag, x)

float64

dask.array<chunksize=(224, 350, 1, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: volumetric soil moisture, the dimensionless ratio of water volume (m3) to soil volume (m3)
units :: m3 m-3
valid_range :: [0, 100]

	Array	Chunk
Bytes	63.07 TiB	209.35 MiB
Shape	(122479, 3840, 4, 4608)	(224, 350, 1, 350)
Count	336953 Tasks	336952 Chunks
Type	float64	numpy.ndarray

SOIL_W

(time, y, soil_layers_stag, x)

float64

dask.array<chunksize=(224, 350, 1, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: liquid volumetric soil moisture
units :: m3 m-3
valid_range :: [0, 100]

	Array	Chunk
Bytes	63.07 TiB	209.35 MiB
Shape	(122479, 3840, 4, 4608)	(224, 350, 1, 350)
Count	336953 Tasks	336952 Chunks
Type	float64	numpy.ndarray

TRAD

(time, y, x)

float64

dask.array<chunksize=(224, 350, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: Surface radiative temperature
units :: K
valid_range :: [0, 4000]

	Array	Chunk
Bytes	15.77 TiB	209.35 MiB
Shape	(122479, 3840, 4608)	(224, 350, 350)
Count	84239 Tasks	84238 Chunks
Type	float64	numpy.ndarray

UGDRNOFF

(time, y, x)

float64

dask.array<chunksize=(224, 350, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: Accumulated underground runoff
units :: mm
valid_range :: [-10000, 10000000]

	Array	Chunk
Bytes	15.77 TiB	209.35 MiB
Shape	(122479, 3840, 4608)	(224, 350, 350)
Count	84239 Tasks	84238 Chunks
Type	float64	numpy.ndarray

Attributes: (7)
Conventions :
CF-1.6
GDAL_DataType :
Generic
TITLE :
OUTPUT FROM WRF-Hydro v5.2.0-beta2
code_version :
v5.2.0-beta2
model_configuration :
retrospective
model_output_type :
land
proj4 :
+proj=lcc +units=m +a=6370000.0 +b=6370000.0 +lat_1=30.0 +lat_2=60.0 +lat_0=40.0 +lon_0=-97.0 +x_0=0 +y_0=0 +k_0=1.0 +nadgrids=@null +wktext +no_defs

RTOUT: Overland and subsurface routing model output¶

In [8]:

describe('rtout')

Please click on the dropdown carets, metadata (file) symbols, and the datainformation (silos) symbols below for additional information.

<xarray.Dataset>
Dimensions:       (time: 122479, y: 15360, x: 18432)
Coordinates:
  * time          (time) datetime64[ns] 1979-02-01T03:00:00 ... 2020-12-31T21...
  * x             (x) float64 -2.304e+06 -2.304e+06 ... 2.304e+06 2.304e+06
  * y             (y) float64 -1.92e+06 -1.92e+06 ... 1.92e+06 1.92e+06
Data variables:
    crs           |S1 ...
    sfcheadsubrt  (time, y, x) float64 dask.array<chunksize=(224, 350, 350), meta=np.ndarray>
    zwattablrt    (time, y, x) float64 dask.array<chunksize=(224, 350, 350), meta=np.ndarray>
Attributes:
    Conventions:          CF-1.6
    GDAL_DataType:        Generic
    TITLE:                OUTPUT FROM WRF-Hydro v5.2.0-beta2
    code_version:         v5.2.0-beta2
    model_configuration:  retrospective
    model_output_type:    terrain_rt
    proj4:                +proj=lcc +units=m +a=6370000.0 +b=6370000.0 +lat_1...

xarray.Dataset

Dimensions:
- time: 122479
- y: 15360
- x: 18432
Coordinates: (3)
- time
  (time)
  datetime64[ns]
  1979-02-01T03:00:00 ... 2020-12-...
  long_name :
  valid output time
  standard_name :
  time
  valid_max :
  4862880
  valid_min :
  4777980
```
array(['1979-02-01T03:00:00.000000000', '1979-02-01T06:00:00.000000000',
       '1979-02-01T09:00:00.000000000', ..., '2020-12-31T15:00:00.000000000',
       '2020-12-31T18:00:00.000000000', '2020-12-31T21:00:00.000000000'],
      dtype='datetime64[ns]')
```
- x
  (x)
  float64
  -2.304e+06 -2.304e+06 ... 2.304e+06
  _CoordinateAxisType :
  GeoX
  long_name :
  x coordinate of projection
  resolution :
  250.0
  standard_name :
  projection_x_coordinate
  units :
  m
```
array([-2303875., -2303625., -2303375., ...,  2303375.,  2303625.,  2303875.])
```
- y
  (y)
  float64
  -1.92e+06 -1.92e+06 ... 1.92e+06
  _CoordinateAxisType :
  GeoY
  long_name :
  y coordinate of projection
  resolution :
  250.0
  standard_name :
  projection_y_coordinate
  units :
  m
```
array([-1919875., -1919625., -1919375., ...,  1919375.,  1919625.,  1919875.])
```

Data variables: (3)

crs
()
|S1
...
GeoTransform :
-2303999.17655 250.0 0 1919999.66329 0 -250.0
_CoordinateAxes :
y x
_CoordinateTransformType :
Projection
earth_radius :
6370000.0
esri_pe_string :
PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
false_easting :
0.0
false_northing :
0.0
grid_mapping_name :
lambert_conformal_conic
inverse_flattening :
0.0
latitude_of_projection_origin :
40.0
long_name :
CRS definition
longitude_of_central_meridian :
-97.0
longitude_of_prime_meridian :
0.0
semi_major_axis :
6370000.0
spatial_ref :
PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
standard_parallel :
[30.0, 60.0]
transform_name :
lambert_conformal_conic
```
array(b'', dtype='|S1')
```

sfcheadsubrt

(time, y, x)

float64

dask.array<chunksize=(224, 350, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: surface head
units :: mm

	Array	Chunk
Bytes	252.30 TiB	209.35 MiB
Shape	(122479, 15360, 18432)	(224, 350, 350)
Count	1275605 Tasks	1275604 Chunks
Type	float64	numpy.ndarray

zwattablrt

(time, y, x)

float64

dask.array<chunksize=(224, 350, 350), meta=np.ndarray>

esri_pe_string :: PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_Sphere",DATUM["D_Sphere",SPHEROID["Sphere",6370000.0,0.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-97.0],PARAMETER["standard_parallel_1",30.0],PARAMETER["standard_parallel_2",60.0],PARAMETER["latitude_of_origin",40.0],UNIT["Meter",1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision
grid_mapping :: crs
long_name :: depth to saturation, rounded to highest saturated layer
units :: m

	Array	Chunk
Bytes	252.30 TiB	209.35 MiB
Shape	(122479, 15360, 18432)	(224, 350, 350)
Count	1275605 Tasks	1275604 Chunks
Type	float64	numpy.ndarray

Attributes: (7)
Conventions :
CF-1.6
GDAL_DataType :
Generic
TITLE :
OUTPUT FROM WRF-Hydro v5.2.0-beta2
code_version :
v5.2.0-beta2
model_configuration :
retrospective
model_output_type :
terrain_rt
proj4 :
+proj=lcc +units=m +a=6370000.0 +b=6370000.0 +lat_1=30.0 +lat_2=60.0 +lat_0=40.0 +lon_0=-97.0 +x_0=0 +y_0=0 +k_0=1.0 +nadgrids=@null +wktext +no_defs