A Note Concerning the Proper Choice for Markov Model Order for Daily Precipitation in the Humid Tropics: A Case Study in Costa Rica

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Geography and Geology


The use of chain-dependent hydroclimatological models (sometimes referred to as 'combined models' or 'two-part models') in analysing daily precipitation requires that rainfall be modelled using both occurrence and intensity statistics. Markov processes in the context of precipitation climatology have been studied in such regions as monsoonal Asia, sub-Saharan Africa and South America. Many studies have indicated that the use of a first-order Markov model is often adequate when describing daily precipitation occurrences, particularly when working in temperate regions, but relatively little work has been done in the humid tropics regarding proper Markov model order, particularly in the western hemisphere. This research examines the occurrence characteristics of Costa Rican daily precipitation by comparing the Akaike and Bayesian information criteria (AIC and BIG) for three long-term meteorological stations. It is found that the most parsimonious models generally are those of first order (winter) or zero order (summer). Overall, the BIC yields less ambiguous results than the AIC, and thus, a higher level of model confidence is achieved when using the BIC as the model-order selection criteria. Copyright (C) 2000 Royal Meteorological Society.

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International Journal of Climatology





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