RELATIONSHIP BETWEEN MONTHLY ATMOSPHERIC CIRCULATION PATTERNS AND PRECIPITATION - FUZZY-LOGIC AND REGRESSION APPROACHES

In order to link the monthly areal precipitation to large-scale circul ation patterns, a fuzzy indexing technique is used in conjunction with a fuzzy rule-based technique and also a standard linear regression. A fter clustering the lag-correlation centers, fuzziness is introduced, and several representative indices of the monthly areal precipitation in Arizona are calculated and interpreted. The relation between the in dices and the precipitation is analyzed to develop the fuzzy model and then a multivariate linear regression model. To measure the forecasti ng capability of the models, the data are divided into a calibration p eriod (1947-79) and a validation period (1980-1988), A comparison of t he results shows that the fuzzy rule-based model performs better than the regression model and has potential for monthly precipitation forec asting. Moreover, an adaptive fuzzy rule-based framework is described so that the model can be used under climate change.