Volume 28, Issue 15 p. 2031-2064
Research Article

Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States

Christopher Daly

Corresponding Author

Christopher Daly

Oregon State University, Corvallis, OR 97331, USA

PRISM Group, Department of Geosciences, 2000 Kelley Engineering Center, Oregon State University, Corvallis, OR 97331, USA.Search for more papers by this author
Michael Halbleib

Michael Halbleib

Oregon State University, Corvallis, OR 97331, USA

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Joseph I. Smith

Joseph I. Smith

Oregon State University, Corvallis, OR 97331, USA

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Wayne P. Gibson

Wayne P. Gibson

Oregon State University, Corvallis, OR 97331, USA

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Matthew K. Doggett

Matthew K. Doggett

Oregon State University, Corvallis, OR 97331, USA

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George H. Taylor

George H. Taylor

Oregon State University, Corvallis, OR 97331, USA

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Jan Curtis

Jan Curtis

USDA Natural Resources Conservation Service Water and Climate Center, Portland, OR 97232, USA

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Phillip P. Pasteris

Phillip P. Pasteris

USDA Natural Resources Conservation Service Water and Climate Center, Portland, OR 97232, USA

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First published: 12 March 2008
Citations: 2,123

Abstract

Spatial climate data sets of 1971–2000 mean monthly precipitation and minimum and maximum temperature were developed for the conterminous United States. These 30-arcsec (∼800-m) grids are the official spatial climate data sets of the U.S. Department of Agriculture. The PRISM (Parameter-elevation Relationships on Independent Slopes Model) interpolation method was used to develop data sets that reflected, as closely as possible, the current state of knowledge of spatial climate patterns in the United States. PRISM calculates a climate–elevation regression for each digital elevation model (DEM) grid cell, and stations entering the regression are assigned weights based primarily on the physiographic similarity of the station to the grid cell. Factors considered are location, elevation, coastal proximity, topographic facet orientation, vertical atmospheric layer, topographic position, and orographic effectiveness of the terrain. Surface stations used in the analysis numbered nearly 13 000 for precipitation and 10 000 for temperature. Station data were spatially quality controlled, and short-period-of-record averages adjusted to better reflect the 1971–2000 period.

PRISM interpolation uncertainties were estimated with cross-validation (C-V) mean absolute error (MAE) and the 70% prediction interval of the climate–elevation regression function. The two measures were not well correlated at the point level, but were similar when averaged over large regions. The PRISM data set was compared with the WorldClim and Daymet spatial climate data sets. The comparison demonstrated that using a relatively dense station data set and the physiographically sensitive PRISM interpolation process resulted in substantially improved climate grids over those of WorldClim and Daymet. The improvement varied, however, depending on the complexity of the region. Mountainous and coastal areas of the western United States, characterized by sparse data coverage, large elevation gradients, rain shadows, inversions, cold air drainage, and coastal effects, showed the greatest improvement. The PRISM data set benefited from a peer review procedure that incorporated local knowledge and data into the development process. Copyright © 2008 Royal Meteorological Society