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ARS Home » Pacific West Area » Boise, Idaho » Northwest Watershed Research Center » Research » Publications at this Location » Publication #104136
Title: SPATIAL VARIABILITY AND INTERPOLATION OF STOCHASTIC WEATHER SIMULATION MODEL PARAMETERS

Author
item JOHNSON, GREGORY - USDA-NRCS
item DALY, C - OREGON STATE UNIVERSITY
item TAYLOR, G - OREGON STATE UNIVERSITY
item Hanson, Clayton

Submitted to: Journal of Applied Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/20/1999
Publication Date: 1/20/2000
Citation: N/A

Interpretive Summary: Stochastic weather simulation models, or "weather generators," increasingly are used for a broad spectrum of applications. Originally developed to deliver serially- complete climate data sets for biological and hydrological models, weather generators have gained wider acceptance and have been utilized for a variety of purposes in recent years, including their use in climate change investigation (Katz 1966; Mearns et al. 1996). Due to their ability to mimic the true climate of a location, their ease of use, and their functionality for generating long time series of weather data quickly and without many of the problems associated with real climate data the popularity of weather generators continues to grow. The spatial variability of 58 precipitation and temperature parameters from the weather generator GEM has been investigated over a region of significant complexity in topography and climate. GEM parameters were derived for 80 climate stations in southern Idaho and southeastern Oregon. Important dependencies were noted between most of these parameters and elevation location, and other factors. The PRISM spatial modeling system was used to develop approximate 4 km gridded data fields of each of these parameters. A feature was developed in PRISM that models temperatures above and below mean inversions differently.

Technical Abstract: The spatial variability of 58 precipitation and temperature parameters from the weather generator GEM has been investigated over a region of significant complexity in topography and climate. GEM parameters were derived for 80 climate stations in southern Idaho and southeastern Oregon. A technique was developed and used to determine the GEM parameters from high elevation SNOTEL stations that report precipitation in non-standard 2.5 mm (versus 0.25 mm) increments. Important dependencies were noted between most of these parameters and elevation (both domainwide and local), location, and other factors. The PRISM spatial modeling system was used to develop approximate 4 km gridded data fields of each of these parameters. A feature was developed in PRISM that models temperatures above and below mean inversions differently. Examples of the spatial fields derived from this study, and a discussion of the application of these spatial parameter fields, are included.