Climatologically Aided Mapping of Daily Precipitation and Temperature

Accurately mapped meteorological data are an essential component for hydrologic and ecological research conducted at broad scales. A simple yet effective method for mapping daily weather conditions across heterogeneous landscapes is described and assessed. Daily weather data recorded at point locati...

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Bibliographic Details
Published in:Journal of applied meteorology (1988) 2005-10, Vol.44 (10), p.1501-1510
Main Authors: Hunter, Richard D., Meentemeyer, Ross K.
Format: Article
Language:English
Subjects:
Accuracy
Assessments
Climate
Climate models
Climatology
Climatology. Bioclimatology. Climate change
Earth, ocean, space
Exact sciences and technology
External geophysics
Geographic regions
Interpolation
Kriging
Mathematical models
Meteorology
Precipitation
Stations
Temperature
Weather
Weather conditions
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Summary:Accurately mapped meteorological data are an essential component for hydrologic and ecological research conducted at broad scales. A simple yet effective method for mapping daily weather conditions across heterogeneous landscapes is described and assessed. Daily weather data recorded at point locations are integrated with long-term-average climate maps to reconstruct spatially explicit estimates of daily precipitation and temperature extrema. The method uses ordinary kriging to interpolate base station data spatially into fields of approximately 2-km grain size. The fields are subsequently adjusted by 30-yr-average climate maps [Parameter-Elevation Regression on Independent Slopes Model (PRISM)], which incorporate adiabatic lapse rates, orographic effects, coastal proximity, and other environmental factors. The accuracy assessment evaluated an interpolation-only approach and the new method by comparing predicted and observed values from an independent validation dataset. The results of the accuracy assessment are compared for a 24-yr period for California. For all three weather variables, mean absolute errors (MAE) of the climate-imprint method were considerably smaller than those of the interpolation-only approach. MAE for predicted daily precipitation was ±2.5 mm, with a bias of +0.01. MAE for predicted daily minimum and maximum temperatures were ±1.7° and ±2.0°C, respectively, with corresponding biases of −0.41° and −0.38°C. MAE differed seasonally for all three weather variables, but the method was stable despite variation in the number of base stations available for each day.
ISSN:0894-8763
1520-0450
DOI:10.1175/JAM2295.1