Archive > Volume 49 Issue 2 > 2021,49(2):200-210 Prev Next

Influencing Factors and Physical Statistical Prediction Methods of Summer Rainfall Anomaly in Yunnan

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    Abstract:
    Because of the obvious interannual variation of summer precipitation in Yunnan and various influencing factors, it is difficult to predict summer precipitation. The daily precipitation observation data from 122 meteorological stations in Yunnan Province from 1965 to 2017 and NCEP atmospheric circulation data and the yeartoyear increment method are used to predict summer precipitation in Yunnan. In order to provide a theoretical basis for the prediction of summer precipitation in Yunnan, it is indispensable to analyze the varying regularities and physical processes affecting the yeartoyear increments of summer rainfall and atmospheric circulation. The prediction model is established based on the method of multiple linear regression analysis. Six predictors that have explicitly physical meaning are selected: the anomaly of the SST (Sea Surface Temperature) in the South Pacific in February, SLP (Sea Level Pressure) in Northeast Asia in February, 500 hPa geopotential height in May over the North America in April, SLP in the northern Pacific in May, 500 hPa geopotential height in the northern India in January, and 200 hPa geopotential height in South Australia in February. Using the above six predictors, the prediction model of summer rainfall is established. In addition, not only the crossingtest verification is conducted on the prediction model is with the independent samples from 1965 to 2017, but also the prediction test verification is conducted from 1998 to 2017. In the crossingtest verification, the correlation coefficients between predicted and observed interannual increments of summer rainfall is 0.85, and the root mean square relative error is 8.0%. In the prediction test verification, the root mean square relative error of is 9.1%. The prediction model makes good predictions, about 63.0% of the summer rainfall anomaly. The prediction model shows satisfactory forecasting ability.

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External LinksChina Meteorological AdministrationCMA Meteorological Observation CentreChinese Academy of Meteorological SciencesBeijing Meteorological ServiceNational Satellite Meteorological Center
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