Table 3. PRISM weighting algorithms and associated physiographic climate forcing factors.

Inputs to the algorithms and references on their formulation and use are included. Methods used to prepare the gridded model inputs are summarized in [8], Tables 1 and 2.

PRISM Algorithm	Description	Physiographic Forcing Factors	Inputs to Algorithm	Reference
Climate Regression Function (CAI)	Develops local relationships between a climate predictor grid and the interpolated variable	Incorporates physiographic features implicit in the climate predictor grid	Station data; climate predictor grid	This paper, Mapping Methods Section; [37] Section 4b
Cluster Weighting	Downweights stations clustered with others	--	Station locations and elevations	[8], Appendix B
Distance Weighting	Upweights stations that are horizontally close	Horizontal coherence of climate regimes	Station locations	[8], Section 4.2.1
Elevation Weighting	Upweights stations that are vertically close	Vertical coherence of climate regimes	DEM; station locations and elevations	[7], Section 4.1
Coastal Proximity Weighting	Upweights stations having similar exposure to coastal influences	Effects of water bodies on temperature and moisture	DEM; coastal proximity grid; station locations	[7], Section 6; [30] Section 2.3.2; [8] Section 4.2.2
Two-Layer Atmosphere Weighting	If an inversion is present, upweights stations in the same vertical layer (boundary layer or free atmosphere)	Temperature inversions; vertical limit to moist boundary layer (humidity)	DEM; inversion height grid; station locations	[7], Section 7; [30], Section 2.3.4
Topographic Position Weighting	Upweights stations having similar heights above the local terrain	Cold air drainage and pooling in topographic depressions	DEM; topographic index grid; station locations	[38], Section 4; [8], Section 4.2.5