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 (

This study uses the new satellite-based Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission to retrieve tropospheric profiles of temperature and moisture over the data-sparse eastern Pacific Ocean. The COSMIC retrievals, which employ a global positioning system radio occultation technique combined with “first-guess” information from numerical weather prediction model analyses, are evaluated through the diagnosis of an intense atmospheric river (AR; i.e., a narrow plume of strong water vapor flux) that devastated the Pacific Northwest with flooding rains in early November 2006. A detailed analysis of this AR is presented first using conventional datasets and highlights the fact that ARs are critical contributors to West Coast extreme precipitation and flooding events. Then, the COSMIC evaluation is provided. Offshore composite COSMIC soundings north of, within, and south of this AR exhibited vertical structures that are meteorologically consistent with satellite imagery and global reanalysis fields of this case and with earlier composite dropsonde results from other landfalling ARs. Also, a curtain of 12 offshore COSMIC soundings through the AR yielded cross-sectional thermodynamic and moisture structures that were similarly consistent, including details comparable to earlier aircraft-based dropsonde analyses. The results show that the new COSMIC retrievals, which are global (currently yielding ∼2000 soundings per day), provide high-resolution vertical-profile information beyond that found in the numerical model first-guess fields and can help monitor key lower-tropospheric mesoscale phenomena in data-sparse regions. Hence, COSMIC will likely support a wide array of applications, from physical process studies to data assimilation, numerical weather prediction, and climate research.

This preamble explains why a paper on precipitation probabilities at the 2-hour and 24-hour time periods for the State of Washington, USA, was solicited for a special symposium honouring Dr. McCullochs contributions to hydrology. Indeed, the specific subject of the paper has no particular connection with Dr. McCullochs technical specialties or the thrust of IH work, but it does have much to do with Dr Mc Cullochs prowess and vision for running a research institute.In 1984, I was invited to spend a Sabbatical year at IH to work on the then new technique of Regional Probability Weighted Moments (PWM) in connection with Generalised Extreme Value (GEV) distribution. The IH Flood Studies Report (FSR) had used the GEV distribution with a graphical method involving rather arbitrary and subjective steps in its fitting procedure for determining the regional distributions to use in different parts of the UK. Because physically impossibly-large flood probabilities had been produced by the FSR in connection with some Scottish dams, there was a controversy with large economic and social implications. I, working together with Jon Hosking, a young, very bright mathematical statistician employed by IH, addressed the problem and devised a PWM solution for the GEV so that the FSR flood probability estimation difficulty was resolved which was invaluable to IH at the time.After much more research in the UK and the USA, a book Regional Frequency Analysis; An Approach Based on L-Moments by J.R.M. Hosking and J.R. Wallis was published by Cambridge University Press. It is worth noting that the Regional L-moment algorithm is numerically equivalent to the Regional PWM algorithm, but the Regional L-moment approach is much more complete and powerful; and has appeared in a myriad of other investigations published in hydrological and meteorological journals worldwide, as well as in other studies reported at this General Assembly. Presumably Regional L-moments would have been discovered eventually, but their prompt appearance can be largely attributed to Dr. McCullochs stewardship of IH, and in particular on his insistence on the value of inviting outside scientists to IH. AbstractThis study is an update of the information contained in the precipitation-frequency atlas published by the US National Weather Service in 1973. Data collection for the NWS study ended in 1966 while this study uses the current data base which more than doubles the record length used in the NWS study. Washington State has highly variable topography and climate; in particular Mean Annual Precipitation (MAP) varies from over 260 inches a year to less than 7 inches. Steep high mountain ranges provide very wet slopes as well as pronounced rain shadows with large climate changes occurring in relatively short distances. In addition there are four distinct sources for the atmospheric moisture needed for precipitation which gives rise to complex seasonal and spatial interactions. The PRISM mapping system used in this study has greatly improved the spatial mapping of precipita...