Fuzzified effect of ENSO and macrocirculation patterns on precipitation: an Arizona case study

Abstract

A fuzzy rule-based model (FRBM) is developed to analyse local monthly precipitation events conditioned on macrocirculation patterns and El Nino-Southern Oscillation (ENSO). A case study in Arizona is presented to illustrate the methodology. The inputs of the FRBM are those Southern Oscillation Index (SOI) values which have high absolute lag correlation with monthly Arizona precipitation and the frequencies of all circulation patterns (CPs) in a given month; the output of the model is an estimate of local monthly precipitation. After analysing the basic properties of the precipitation events, fuzzy rules are constructed, and then the results are interpreted and compared with those of a multivariate linear regression model. Using two goodness-of-fit criteria, first, the root mean squared error (RMSE) and then the correlation between the model results and the observed values, the FRBM is found to perform better than the multiple linear regression model for the Arizona case investigated. The results show that the FRBM can provide a good basis for future work to downscale general circulation model results to study local precipitation under climate change. The results of using only SOI lags or CP frequencies as inputs, which are also presented here, clearly show how much the results are improved using both inputs jointly instead of only one. Copyright © 1999 Royal Meteorological Society.