A weather classification scheme was coupled with a semi-Markov model to represent the coincident occurrence of rain/no rain states at a single rain gauge and classes representing regional atmospheric circulation patterns, as identified from National Meteorological Center gridded observations for a large area of the North Pacific. Weather classes were identified from daily observations of surface pressure and 850 mb pressure height at five selected ten degree latitude by ten degree longitude cells using a K-means clustering algorithm, which was applied on a month-by-month basis. The number of climate classes, K, for each month was chosen based on a preliminary analysis of the model's ability to describe statistics of observed precipitation occurrences at the Stampede Pass, Washington weather station. The length of stay distributions within each precipitation occurrence/weather class were assumed to be geometric, and the precipitation amounts for each class and season were fitted with a mixed exponential distribution. Parameters of the length of stay distributions, transition probabilities, and precipitation amounts were estimated from the period of record 1975-84. The fitted model was used to simulate a ten year sequence of daily precipitation. It was found that the semi-Markov model of climate class/wet-dry states preserved the length of wet and dry day runs reasonably well, with the exception of months with long average run lengths. Likewise, the occurrence frequencies of the climate classes were reasonably well preserved with a few exceptions. An exploratory analysis of the properties of wet and dry period runs for those classes and months whose run frequencies were poorly preserved showed that the log survivor functions and variance time curves were also poorly preserved, which suggests that more complex distributions may be required for some of the run length distributions.
All Science Journal Classification (ASJC) codes
- Geochemistry and Petrology