INCORPORATING SPATIAL DEPENDENCE AND ATMOSPHERIC DATA IN A MODEL OF PRECIPITATION

Nonhomogeneous hidden Markov models (NHMM) provide a method of relatin g synoptic atmospheric measurements to precipitation occurrence at a n etwork of rain gauge stations. In previous work it was assumed that, c onditional on the current atmospheric pattern (termed a ''weather stat e''), rain gauge stations in a network could be considered spatially i ndependent. For a spatially dense network, this assumption is not tena ble. In the present work, the NHMM is extended to include the case of spatial dependence by postulating an autologistic model for the condit ional probability of rainfall given the weather state. Methods for fit ting the parameters, assessing the goodness of fit of the model, and g enerating rainfall simulations are presented. The model is applied to a network of 24 stations in the Puget Sound region of western Washingt on State.