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.

The demand for spatial climate data in digital form has risen dramatically in recent years. In response to this need, a variety of statistical techniques have been used to facilitate the production of GIS-compatible climate maps. However, observational data are often too sparse and unrepresentative to directly support the creation of high-quality climate maps and data sets that truly represent the current state of knowledge. An effective approach is to use the wealth of expert knowledge on the spatial patterns of climate and their relationships with geographic features, termed 'geospatial climatology', to help enhance, control, and parameterize a statistical technique. Described here is a dynamic knowledge-based framework that allows for the effective accumulation, application, and refinement of climatic knowledge, as expressed in a statistical regression model known as PRISM (parameter-elevation regressions on independent slopes model). The ultimate goal is to develop an expert system capable of reproducing the process a knowledgeable climatologist would use to create high-quality climate maps, with the added benefits of consistency and repeatability. However, knowledge must first be accumulated and evaluated through an ongoing process of model application; development of knowledge prototypes, parameters and parameter settings; testing; evaluation; and modification. This paper describes the current state of a knowledge-based framework for climate mapping and presents specific algorithms from PRISM to demonstrate how this framework is applied and refined to accommodate difficult climate mapping situations. A weighted climate-elevation regression function acknowledges the dominant influence of elevation on climate. Climate stations are assigned weights that account for other climatically important factors besides elevation. Aspect and topographic exposure, which affect climate at a variety of scales, from hill slope to windward and leeward sides of mountain ranges, are simulated by dividing the terrain into topographic facets. A coastal proximity measure is used to account for sharp climatic gradients near coastlines. A 2-layer model structure divides the atmosphere into a lower boundary layer and an upper free atmosphere layer, allowing the simulation of temperature inversions, as well as mid-slope precipitation maxima. The effectiveness of various terrain configurations at producing orographic precipitation enhancement is also estimated. Climate mapping examples are presented.KEY WORDS: Climate map · Knowledge-based system · Climate interpolation · Spatial climate · Climate data sets · GIS · PRISM · Geospatial climatology Resale or republication not permitted without written consent of the publisherClim Res 22: [99][100][101][102][103][104][105][106][107][108][109][110][111][112][113] 2002 expert and statistical. Human-expert methods use human experience, expertise, and knowledge acquisition capabilities to infer climate patterns from meteorological regimes, physiographic features, biotic character...

T he demand for spatial data sets of climate elements in digital form has risen dramatically over the past several years. This demand has been fueled by the maturation of computer technology enabling a variety of agricultural, hydrological, ecological, and natural resource models and expert systems to be linked to geographic information systems (GIS) (e.g., Bishop et al., 1998a; 1998b; Vogel et al., 1999). In turn, the use of such model/GIS linkages has stemmed partially from the increasingly complex nature of today's environmental issues, requiring multiple layers of spatial information to be analyzed in a relational manner (Johnson et al., 1998; 1999). Over the past several years, innovative methods for mapping climatic elements have been developed at Oregon State University's Spatial Climate Analysis Service (SCAS). The ultimate goal of the SCAS is to describe the climatic environment of the world in a spatially detailed, physically realistic manner. A major focus has been the ongoing development and enhancement of PRISM (Parameter-elevation Regressions on Independent Slopes Model), a knowledge-based approach to mapping climate that seeks to combine the strengths of human-expert and statistical methods (

In many regions of the world, the extremes of winter cold are a major determinant of the geographic distribution of perennial plant species and of their successful cultivation. In the United States, the U.S. Department of Agriculture (USDA) Plant Hardiness Zone Map (PHZM) is the primary reference for defining geospatial patterns of extreme winter cold for the horticulture and nursery industries, home gardeners, agrometeorologists, and plant scientists. This paper describes the approaches followed for updating the USDA PHZM, the last version of which was published in 1990. The new PHZM depicts 1976-2005 mean annual extreme minimum temperature, in 2.88C (58F) half zones, for the conterminous United States, Alaska, Hawaii, and Puerto Rico. Station data were interpolated to a grid with the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) climate-mapping system. PRISM accounts for the effects of elevation, terrain-induced airmass blockage, coastal effects, temperature inversions, and cold-air pooling on extreme minimum temperature patterns. Climatologically aided interpolation was applied, based on the 1971-2000 mean minimum temperature of the coldest month as the predictor grid. Evaluation of a standard-deviation map and two 15-yr maps (1976-90 and 1991-2005 averaging periods) revealed substantial vertical and horizontal gradients in trend and variability, especially in complex terrain. The new PHZM is generally warmer by one 2.88C (58F) half zone than the previous PHZM throughout much of the United States, as a result of a more recent averaging period. Nonetheless, a more sophisticated interpolation technique, greater physiographic detail, and more comprehensive station data were the main causes of zonal changes in complex terrain, especially in the western United States. The updated PHZM can be accessed online (http://www.planthardiness.ars.usda.gov).

The Cooperative Observer Program (COOP), established over 100 years ago, has become the backbone of temperature and precipitation data that characterize means, trends, and extremes in U.S. climate. However, significant and widespread biases in the way COOP observers measure daily precipitation have been discovered. These include 1) underreporting of light precipitation events (daily totals of less than 0.05 in., or 1.27 mm), and 2) overreporting of daily precipitation amounts evenly divisible by five- and/or ten-hundredths of an inch, that is, 0.10, 0.25, 0.30 in., etc. (2.54, 6.35, 7.62 mm, etc.). Observer biases were found to be highly variable in space and time, which has serious implications for the spatial and temporal trends and variations of commonly used precipitation statistics. In addition, it was found that few COOP stations had sufficiently complete data to allow the calculation of stable precipitation statistics for a stochastic weather simulation model. Out of more than 12,000 COOP stations nationally, only 784 (6%) passed data completeness and observer bias screening tests for the climatological period 1971–2000. Of the 1221 COOP stations selected for the U.S. Historical Climate Network (USHCN), which provides much of the country's official data on climate trends and variability over the past century, only 221 stations (18%) passed these tests. More effective training materials and regular communication with COOP observers could reduce observer bias in the future. However, it is unlikely that observer bias can be eliminated. One solution is to automate the COOP precipitation measurement system, but this is an expensive option, and may increase other biases associated with automated precipitation measurement. Further analyses are needed to better quantify and characterize observer bias, and to develop methods for dealing with its effects.