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Session IV: Geospatial tools for estimating salmon distribution
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The PRISM Approach to Climate Mapping, by Christopher Daly
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Alaska Salmon Habitat Prediction Workshop
The PRISM Approach to Climate Mapping, by Christopher Daly
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Web Admin
on 6/5/2007 | Keyword(s):
Session iv: geospatial tools for estimating salmon distribution
The PRISM Approach to Climate Mapping: Model Overview and Data Sets Available for Alaska The PRISM Group (formerly known as the Spatial Climate Analysis Service) at Oregon State University is the de facto climate mapping center for the United States, and a leader in the emerging discipline of geospatial climatology. Under funding from the USDA-NRCS, NOAA, NPS, USFS, and other agencies, The PRISM Group has mapped the long-term mean climate on a monthly basis for all US states and possessions. These maps are the official climate data sets of the USDA, and have been used in thousands of applications worldwide. Other projects include near-real time monthly maps of the lower 48 states on an ongoing basis; detailed climate maps of China, Taiwan, Mongolia, the European Alps, and much of Canada; and mapping of daily and event-based meteorological fields. The PRISM Group mapping capability is based on the PRISM (Parameter-elevation Regressions on Independent Slopes Model) climate mapping system. PRISM has been thoroughly tested and documented (see, for example, Daly et al., 1994, 2001, 2002, 2003, 2006; Gibson et al., 2002; Johnson et al., 2000). It has been continuously developed and refined at Oregon State University since 1991. PRISM is a moving-window regression of climate vs. elevation (or another independent variable) that is calculated for each grid cell in a digital elevation grid. Stations surrounding the grid cell provide data points for the regression. The heart of the model, and what makes it unique, is the extensive spatial climate knowledge base that calculates station weights upon entering the regression function. These weights are based on each station’s physiographic similarity to the grid cell being estimated. The knowledge base and resulting station weighting functions currently account for spatial variations in climate caused by elevation, terrain orientation, effectiveness of terrain as a barrier to flow, coastal proximity, moisture availability, a two-layer atmosphere (to handle inversions), and topographic position (valley, mid-slope, ridge).