University of Idaho
Gridded Surface Meteorological Data

UofI Gridded Surface Meteorological Dataset
Landscape-scale modeling has been hindered by suitable high-resolution surface meteorological datasets that include temperature, precipitation, downward shortwave radiation, humidity and winds. To overcome these limitations, desirable spatial attributes of gridded climate data from PRISM are combined with desirable temporal attributes of regional-scale reanalysis and daily gauge-based precipitation from NLDAS-2 to derive a spatially and temporally complete, high-resolution (1/24th degree ~4-km) gridded dataset of surface meteorological variables required in modeling for the coterminous United States from 1979-present.

Validation of the resulting gridded surface meteorological data was conducted against an extensive network of weather stations including RAWS, AgriMet, AgWeatherNet and USHCN-2. For more information on validation measures see Abatzoglou (2011).

This Dataset has the following features:

  • Spatial Resolution: 4-km (1/24-degree) grid
  • Spatial Extent: Coterminous United States
  • Temporal Resolution: Daily (some sub-daily)
  • Temporal Extent: 1979-present (1-2 day lag)
  • Variables: (all variables are daily extrema/sums/means over a given calendar day)
    • Precipitation
    • Temperature (maximum and minimum)
    • Humidity (maximum and minimum relative humidity and specific humidity)
    • Surface downward shortwave radiation (daily mean)
    • 10-meter Wind velocity (daily mean)
    • Reference evapotranspiration
    • NFDRS fire danger indices
  • Definition of day: ie. Jan 21 is 6Z Jan 21 to 6Z Jan 22
  • Format: netCDF adhering to Climate and Forecasting Metadata standards

uilogo NIFA
This work was made possible through the use of NLDAS-2 forcing output from NASA and PRISM data from Oregon State University.
This research was supported by the NSF Idaho EPSCoR Program and by the National Science Foundation under award number EPS-0814387 and the National Institute for Food and Agriculture competitive grant, award number: 2011-68002-30191.

Public Domain Mark
This work (METDATA, by John Abatzoglou) is free of known copyright restrictions.