Coupled stochastic weather generation using spatial and generalized linear models
We introduce a stochastic weather generator for the variables of minimum temperature, maximum temperature and precipitation occurrence. Temperature variables are modeled in vector autoregressive framework, conditional on precipitation occurrence. Precipitation occurrence arises via a probit model, and both temperature and occurrence are spatially correlated using spatial Gaussian processes. Additionally, local climate is included by spatially varying model coefficients, allowing spatially evolving relationships between variables. The method is illustrated on a network of stations in the Pampas region of Argentina where nonstationary relationships and historical spatial correlation challenge existing approaches.
document
http://n2t.net/ark:/85065/d7g44r7m
eng
geoscientificInformation
Text
publication
2016-01-01T00:00:00Z
publication
2014-07-05T00:00:00Z
Copyright 2014 Spring-Verlag Berlin Heidelberg
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