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We investigated temporal and spatial precipitation data from the view point of scale invariance or scaling , and examined applicability of random cascades whose generators are defined by ƒÀ model , since random cascades generate data which exhibit fractal behavior . Although ƒÀ model is too simple to simulate daily precipitation time series, 151 of observation points are classified into two groups depending on p value of ƒÀ model; one is a set of observation points along the Japan Sea, and the other is along the Pacific . Regardless of the groups, daily precipitation at 151 observation points exhibit properties of multifractal . Regarding spatial data, we investigated scaling properties using relatively long term's hourly precipitation data . Although each scan exhibits well scaling, the relationship between p value and spatial mean precipitation is not one to one function, if all data are used. If relatively short term's data, namely, data of each precipitation event is used , the relationship is relatively clear and parameters in the relationship depend on events .

ANALYSES OF TEMPORAL AND SPATIAL PRECIPITATION DATA BY RANDOM CASCADES