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Specification and prediction of monthly and seasonal precipitation amounts in California and Arizona river drainage basins
Author(s) -
Klein William H.,
Bloom Hal J.
Publication year - 1992
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.3370120705
Subject(s) - percentile , precipitation , environmental science , regression analysis , regression , statistics , drainage basin , contrast (vision) , altitude (triangle) , stepwise regression , variance (accounting) , climatology , mathematics , hydrology (agriculture) , meteorology , geology , geography , cartography , geometry , accounting , geotechnical engineering , business , artificial intelligence , computer science
A forward selection procedure of stepwise regression is applied to 30 years of data to develop a set of multiple regression equations for percentiles of monthly and seasonal precipitation amounts ( P ) in high altitude river basins of California and Arizona during periods of 1–4 months in duration. Two main methods are used. The first is specification of P from simultaneous values of the field of 700 hPa mean height anomalies ( H ). The second is pure prediction, which uses only previous (observed) data as potential predictors. On average, 61 per cent of the monthly P variance is explained by specification equations containing about three concurrent values of H; seasonal specification equations explain about 2 per cent more of the variance by means of slightly fewer heights. In contrast, only 16 per cent of the prediction equations are significant at the 95 per cent level determined by a Monte Carlo test. All these equations select the local value of P observed during the month prior to the target period as the most important predictor.