This page was generated from nldas.ipynb. Interactive online version:

NLDAS2 Forcing

from pathlib import Path

import pynldas2 as nldas
from pygeohydro import WBD

The NLDAS2 database provides forcing data at 1/8th-degree grid spacing and range from 01 Jan 1979 to present. Let’s take a look at NLDAS2 grid mask that includes land, water, soil, and vegetation masks:

grid = nldas.get_grid_mask()
grid

<xarray.Dataset>
Dimensions:      (lon: 464, lat: 224, time: 1, bnds: 2)
Coordinates:
  * lon          (lon) float32 -124.9 -124.8 -124.7 ... -67.31 -67.19 -67.06
  * lat          (lat) float32 25.06 25.19 25.31 25.44 ... 52.69 52.81 52.94
  * time         (time) datetime64[ns] 2000-01-01
    spatial_ref  int64 0
Dimensions without coordinates: bnds
Data variables:
    time_bnds    (time, bnds) datetime64[ns] ...
    NLDAS_mask   (time, lat, lon) float32 ...
    CONUS_mask   (time, lat, lon) float32 ...
    NLDAS_veg    (time, lat, lon) float32 ...
    NLDAS_soil   (time, lat, lon) float32 ...
Attributes: (12/13)
    missing_value:          -9999.0
    time_definition:        constant
    title:                  NLDAS masks and predominant vegetation/soil
    institution:            NASA GSFC
    history:                created on date: Fri Mar  8 15:58:50 2019
    references:             Mitchell_etal_JGR_2004; Xia_etal_JGR_2012
    ...                     ...
    website:                https://ldas.gsfc.nasa.gov/nldas/
    MAP_PROJECTION:         EQUIDISTANT CYLINDRICAL
    SOUTH_WEST_CORNER_LAT:  25.0625
    SOUTH_WEST_CORNER_LON:  -124.9375
    DX:                     0.125
    DY:                     0.125

xarray.Dataset

• Dimensions:
  • lon: 464
  • lat: 224
  • time: 1
  • bnds: 2
• Coordinates: (4)
  • lon
    (lon)
    float32
    -124.9 -124.8 ... -67.19 -67.06

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    vmin :

    -124.9375

    vmax :

    -67.0625

    axis :

    X

    array([-124.9375, -124.8125, -124.6875, ...,  -67.3125,  -67.1875,  -67.0625],
          dtype=float32)

  • lat
    (lat)
    float32
    25.06 25.19 25.31 ... 52.81 52.94

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    vmin :

    25.0625

    vmax :

    52.9375

    axis :

    Y

    array([25.0625, 25.1875, 25.3125, ..., 52.6875, 52.8125, 52.9375],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    2000-01-01

    long_name :

    time

    time_increment :

    1

    bounds :

    time_bnds

    array(['2000-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

  • spatial_ref
    ()
    int64
    0

    crs_wkt :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    grid_mapping_name :

    latitude_longitude

    spatial_ref :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    GeoTransform :

    -125.0 0.125 0.0 25.0 0.0 0.125

    array(0)

• Data variables: (5)
  • time_bnds
    (time, bnds)
    datetime64[ns]
    ...

    [2 values with dtype=datetime64[ns]]

  • NLDAS_mask
    (time, lat, lon)
    float32
    ...

    units :

    0 = water; 1 = land

    standard_name :

    land mask

    long_name :

    NLDAS UMD unified land mask

    cell_methods :

    time: mean

    vmin :

    0.0

    vmax :

    1.0

    [103936 values with dtype=float32]

  • CONUS_mask
    (time, lat, lon)
    float32
    ...

    units :

    0 = water; 1 = land

    standard_name :

    CONUS mask

    long_name :

    NLDAS CONUS mask

    cell_methods :

    time: mean

    vmin :

    0.0

    vmax :

    1.0

    [103936 values with dtype=float32]

  • NLDAS_veg
    (time, lat, lon)
    float32
    ...

    units :

    0 - 13

    standard_name :

    vegetation class

    long_name :

    NLDAS UMD predominant vegetation class

    cell_methods :

    time: mean

    vmin :

    0.0

    vmax :

    13.0

    [103936 values with dtype=float32]

  • NLDAS_soil
    (time, lat, lon)
    float32
    ...

    units :

    0 - 14

    standard_name :

    soil texture type

    long_name :

    NLDAS STATSGO predominant soil texture type

    cell_methods :

    time: mean

    vmin :

    0.0

    vmax :

    14.0

    [103936 values with dtype=float32]

• Indexes: (3)
  • lon
    PandasIndex

    PandasIndex(Float64Index([-124.9375, -124.8125, -124.6875, -124.5625, -124.4375, -124.3125,
                  -124.1875, -124.0625, -123.9375, -123.8125,
                  ...
                   -68.1875,  -68.0625,  -67.9375,  -67.8125,  -67.6875,  -67.5625,
                   -67.4375,  -67.3125,  -67.1875,  -67.0625],
                 dtype='float64', name='lon', length=464))

  • lat
    PandasIndex

    PandasIndex(Float64Index([25.0625, 25.1875, 25.3125, 25.4375, 25.5625, 25.6875, 25.8125,
                  25.9375, 26.0625, 26.1875,
                  ...
                  51.8125, 51.9375, 52.0625, 52.1875, 52.3125, 52.4375, 52.5625,
                  52.6875, 52.8125, 52.9375],
                 dtype='float64', name='lat', length=224))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2000-01-01'], dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (13)

  missing_value :

  -9999.0

  time_definition :

  constant

  title :

  NLDAS masks and predominant vegetation/soil

  institution :

  NASA GSFC

  history :

  created on date: Fri Mar 8 15:58:50 2019

  references :

  Mitchell_etal_JGR_2004; Xia_etal_JGR_2012

  conventions :

  CF-1.6

  website :

  https://ldas.gsfc.nasa.gov/nldas/

  MAP_PROJECTION :

  EQUIDISTANT CYLINDRICAL

  SOUTH_WEST_CORNER_LAT :

  25.0625

  SOUTH_WEST_CORNER_LON :

  -124.9375

  DX :

  0.125

  DY :

  0.125

For example, let’s plot the vegetation mask.

ax = grid.NLDAS_veg.plot()
ax.figure.savefig(Path("_static", "nldas_grid.png"), facecolor="w", bbox_inches="tight")

Next, we use PyGeoHydro to get the geometry of a HUC8 with ID of 1306003:

huc8 = WBD("huc8")
geometry = huc8.byids("huc8", "13060003").geometry[0]

PyNLDAS2 allows us to get the data for a list of coordinates using pynldas2.get_bycoords or for a region as gridded data using pynldas2.get_bygeom. Here, we use the latter. Note that if we don’t pass any variables, all variables will be downloaded. Additionally, we can pass snow=True to separate snow portion of precipitation using temperature.

clm = nldas.get_bygeom(geometry, "2010-01-01", "2010-01-31", 4326, snow=True)

ax = clm.snow.sel(time=slice("2010-01-08T05:00:00", "2010-01-08T010:00:00")).plot(
    col="time", col_wrap=3
)
ax.fig.savefig(Path("_static", "nldas_snow.png"), facecolor="w", bbox_inches="tight")