Article ID Journal Published Year Pages File Type
82689 Agricultural and Forest Meteorology 2008 10 Pages PDF
Abstract

The time-domain stochastic models for daily weather data known as “weather generators” are very useful for producing time series of arbitrary length that statistically resemble real weather data. One limitation to their use is that synthetic weather series may be needed at locations for which no real data exist, on which to base parameter estimates. This paper describes an approach to defining weather generators at such locations through interpolation of their parameters using weighted local regressions. This flexible method can capture nonlinear parameter variations in space and allows objective and automatic selection of both the regression predictor and the size of each local neighborhood of influence. It is found to perform well for a moderately large region of the northeastern U.S.

Related Topics
Physical Sciences and Engineering Earth and Planetary Sciences Atmospheric Science
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