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A STATISTICAL PREDICTION MODEL FOR SPRING RAINFALL OVER NORTHERN CHINA BASED ON THE interannual increment approach

Journal of Tropical Meteorology 2012年第4期18卷 543-550页

作者：范可 Nansen-Zhu International Research Centre/Key Laboratory of Regional Climate-Environment for East Asia Institute of Atmospheric PhysicsChinese Academy of Sciences

The spring (March-April-May) rainfall over northern China (SPRNC) is predicted by using the interannual increment approach. DY denotes the difference between the current year and previous years. The seasonal forecast model for the DY of SPRNC is constructed based on the data that are taken from the 1965-2002 period (38 years), in which six predictors are available no later than the current month of February. This is favorable so that the seasonal forecasts can be made one month ahead. Then, SPRNC and the percentage anomaly of SPRNC are obtained by the predicted DY of SPRNC. The model performs well in the prediction of the inter-annual variation of the DY of SPRNC during 1965-2002, with a correlation coefficient between the predicted and observed DY of SPRNC of 0.87. This accounts for 76% of the total variance, with a low value for the average root mean square error (RMSE) of 20%. Both the results of the hindcast for the period of 2003-2010 (eight years) and the cross-validation test for the period of 1965-2009 (45 years) illustrate the good prediction capability of the model, with a small mean relative error of 10%, an RMSE of 17% and a high rate of coherence of 87.5% for the hindcasts of the percentage anomaly of SPRNC.

关键词： spring rainfall over northern China statistical prediction model interannual increment approach

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A skillful method for precipitation prediction over eastern China

引用

Atmospheric and Oceanic Science Letters 2022年第1期15卷 27-34页

作者： Yanyan Huang Huijun Wang Peiyi Zhang Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of EducationNanjing University of Information Science and TechnologyNanjingChina Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) ZhuhaiChina Nansen−Zhu International Research Centre Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina Climate change research center Chinese academy of SciencesBeijingChina

Precipitation prediction is essential for disaster prevention,yet it still remains a challenging issue in weather and climate *** paper proposes an effective prediction method for summer precipitation over eastern China(PEC) by combining empirical orthogonal function(EOF) analysis with the interannual increment *** statistical prediction models are individually developed for respective predictions of the three principal components(PCs) corresponding to the three leading EOF modes for the interannual increment of PEC(hereafter DY;EC).Each model is run for the month of March with two previous predictors derived from sea-ice concentration/soil moisture/sea surface temperature/snow depth/sea level pressure over specific *** predicted PCs are projected to the EOF modes derived from observations of DY;EC to produce a new DY;*** new DY;EC is then added to the observed PEC of the previous year to obtain the final predicted *** spatial features of the predicted PEC are highly consistent with observations,with the anomaly correlation coefficient skill ranging from 0.32 to 0.64 during *** method is applied for real-time prediction of PEC in *** the results indicate two rain belts located over northeastern China and the Yangtze-Huaihe River valley,respectively,although the chance for the occurrence of a "super" mei-yu with a similar intensity to that in 2020 would be rare in 2021.

关键词： Precipitation prediction interannual increment approach EOF

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