Theoretical and Applied Climatology

, Volume 112, Issue 3–4, pp 495–519 | Cite as

Evaluating a modified point-based method to downscale cell-based climate variable data to high-resolution grids

Original Paper

Abstract

To address the demand for high spatial resolution gridded climate data, we have advanced the Daymet point-based interpolation algorithm for downscaling global, coarsely gridded data with additional output variables. The updated algorithm, High-Resolution Climate Downscaler (HRCD), performs very good downscaling of daily, global, historical reanalysis data from 1° input resolution to 2.5 arcmin output resolution for day length, downward longwave radiation, pressure, maximum and minimum temperature, and vapor pressure deficit. It gives good results for monthly and yearly cumulative precipitation and fair results for wind speed distributions and modeled downward shortwave radiation. Over complex terrain, 2.5 arcmin resolution is likely too low and aggregating it up to 15 arcmin preserves accuracy. HRCD performs comparably to existing daily and monthly US datasets but with a global extent for nine daily climate variables spanning 1948–2006. Furthermore, HRCD can readily be applied to other gridded climate datasets.

Abbreviations

DAY

Day length

LWRAD

Downward long wave radiation

PRCP

Precipitation

PRES

Surface pressure

RH

Relative humidity

SH

Specific humidity

SWRAD

Downward shortwave radiation

TAVG

Average air temperature

TDAY

Average daytime temperature

TDEW

Dew point temperature

TMAX

Maximum temperature

TMIN

Minimum temperature

VPD

Vapor pressure deficit

WND

Wind speed

HRCD

High-Resolution Climate Downscaler

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Energy Biosciences InstituteUniversity of CaliforniaBerkeleyUSA
  2. 2.Earth Sciences DivisionLawrence Berkeley National LaboratoryBerkeleyUSA
  3. 3.Department of GeographyUniversity of CaliforniaBerkeleyUSA

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