National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) is a serially complete collection of meteorological and solar irradiance data sets for the United States and a growing list of international locations for 1998-2017. The NSRDB provides foundational information to support U.S. Department of Energy programs, research, and the general public.

The NSRDB provides time-series data at 30 minute resolution of resource averaged over surface cells of 0.038 degrees in both latitude and longitude, or nominally 4 km in size. The solar radiation values represent the resource available to solar energy systems. The data was created using cloud properties which are generated using the AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) algorithms developed by the University of Wisconsin. Fast all-sky radiation model for solar applications (FARMS) in conjunction with the cloud properties, and aerosol optical depth (AOD) and precipitable water vapor (PWV) from ancillary source are used to estimate solar irradiance (GHI, DNI, and DHI). The Global Horizontal Irradiance (GHI) is computed for clear skies using the REST2 model. For cloud scenes identified by the cloud mask, FARMS is used to compute GHI. The Direct Normal Irradiance (DNI) for cloud scenes is then computed using the DISC model. The PATMOS-X model uses half-hourly radiance images in visible and infrared channels from the GOES series of geostationary weather satellites. Ancillary variables needed to run REST2 and FARMS (e.g., aerosol optical depth, precipitable water vapor, and albedo) are derived from the the Modern Era-Retrospective Analysis (MERRA-2) dataset. Temperature and wind speed data are also derived from MERRA-2 and provided for use in SAM to compute PV generation.

The following variables are provided by the NSRDB:

  • Irradiance:

    • Global Horizontal (ghi)

    • Direct Normal (dni)

    • Diffuse (dhi)

  • Clear-sky Irradiance

  • Cloud Type

  • Dew Point

  • Temperature

  • Surface Albedo

  • Pressure

  • Relative Humidity

  • Solar Zenith Angle

  • Precipitable Water

  • Wind Direction

  • Wind Speed

  • Fill Flag

  • Angstrom wavelength exponent (alpha)

  • Aerosol optical depth (aod)

  • Aerosol asymmetry parameter (asymmetry)

  • Cloud optical depth (cld_opd_dcomp)

  • Cloud effective radius (cld_ref_dcomp)

  • cloud_press_acha

  • Reduced ozone vertical pathlength (ozone)

  • Aerosol single-scatter albedo (ssa)

Data Format

The data is provided in high density data file (.h5) separated by year. The variables mentioned above are provided in 2 dimensional time-series arrays with dimensions (time x location). The temporal axis is defined by the time_index dataset, while the positional axis is defined by the meta dataset. For storage efficiency each variable has been scaled and stored as an integer. The scale-factor is provided in the psm_scale-factor attribute. The units for the variable data is also provided as an attribute (psm_units).

Data Access Examples

Example scripts to extract wave resource data using the command line or python are provided below:

The easiest way to access and extract data is by using the Resource eXtraction tool rex.

To use rex with HSDS you will need to install h5pyd:

pip install h5pyd

NSRDB CLI

The NSRDBX command line utility provides the following options and commands:

NSRDBX --help

Usage: NSRDBX [OPTIONS] COMMAND [ARGS]...

  NSRDBX Command Line Interface

Options:
  -h5, --solar_h5 PATH  Path to Resource .h5 file  [required]
  -o, --out_dir PATH    Directory to dump output files  [required]
  -v, --verbose         Flag to turn on debug logging. Default is not verbose.
  --help                Show this message and exit.

Commands:
  dataset     Extract a single dataset
  multi-site  Extract multiple sites given in '--sites' .csv or .json as...
  sam-file    Extract all datasets needed for SAM for the nearest pixel to...

NSRDBX python class

from rex import NSRDBX

nsrdb_file = '/nrel/nsrdb/v3/nsrdb_2018.h5'
with NSRDBX(nsrdb_file, hsds=True) as f:
    meta = f.meta
    time_index = f.time_index
    dni = f['dni', :, ::1000]

NSRDBX also allows easy extraction of the nearest site to a desired (lat, lon) location:

from rex import NSRDBX

nsrdb_file = '/nrel/nsrdb/v3/nsrdb_2018.h5'
nrel = (39.741931, -105.169891)
with NSRDBX(nsrdb_file, hsds=True) as f:
    nrel_dni = f.get_lat_lon_df('dni', nrel)

or to extract all sites in a given region:

from rex import NSRDBX

nsrdb_file = '/nrel/nsrdb/v3/nsrdb_2018.h5'
state='Colorado'
with NSRDBX(nsrdb_file, hsds=True) as f:
    date = '2018-07-04 18:00:00'
    dni_map = f.get_timestep_map('dni', date, region=region,
                                 region_col='state')

Lastly, NSRDBX can be used to extract all variables needed to run SAM at a given location:

from rex import NSRDBX

nsrdb_file = '/nrel/nsrdb/v3/nsrdb_2018.h5'
nrel = (39.741931, -105.169891)
with NSRDBX(nsrdb_file, hsds=True) as f:
    nrel_sam_vars = f.get_SAM_lat_lon(nrel)