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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()
Dimensions:      (lon: 464, lat: 224, time: 1, bnds: 2)
  * 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
    ...                     ...
    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. PyNLDAS2 provides access to NLDAS2 data from two sources: grib and netcdf. They are mostly similar so if for some reason one of the sources is not temporarly available, you can use the other one. The default source is grib.

clm = nldas.get_bygeom(geometry, "2010-01-08", "2010-01-08", 4326, variables="prcp", 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")