All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 233 49 3
PDF Downloads 81 31 2

Incorporating Spatial Dependence and Atmospheric Data in a Model of Precipitation

James P. HughesDepartment of Biostatistics, University of Washington, Seattle, Washington

Search for other papers by James P. Hughes in
Current site
Google Scholar
PubMed
Close
and
Peter GuttorpDepartment of Statistics, University of Washington, Seattle, Washington

Search for other papers by Peter Guttorp in
Current site
Google Scholar
PubMed
Close
Full access

Abstract

Nonhomogeneous hidden Markov models (NHMM) provide a method of relating synoptic atmospheric measurements to precipitation occurrence at a network of rain gauge stations. In previous work it was assumed that, conditional on the current atmospheric pattern (termed a “weather state”), rain gauge stations in a network could be considered spatially independent. For a spatially dense network, this assumption is not tenable. In the present work, the NHMM is extended to include the case of spatial dependence by postulating an autologistic model for the conditional probability of rainfall given the weather state. Methods for fitting the parameters, assessing the goodness of fit of the model, and generating rainfall simulations are presented. The model is applied to a network of 24 stations in the Puget Sound region of western Washington State.

Abstract

Nonhomogeneous hidden Markov models (NHMM) provide a method of relating synoptic atmospheric measurements to precipitation occurrence at a network of rain gauge stations. In previous work it was assumed that, conditional on the current atmospheric pattern (termed a “weather state”), rain gauge stations in a network could be considered spatially independent. For a spatially dense network, this assumption is not tenable. In the present work, the NHMM is extended to include the case of spatial dependence by postulating an autologistic model for the conditional probability of rainfall given the weather state. Methods for fitting the parameters, assessing the goodness of fit of the model, and generating rainfall simulations are presented. The model is applied to a network of 24 stations in the Puget Sound region of western Washington State.

Save