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A physically-based statistical forecast model for the middle-lower reaches of the Yangtze River Valley summer rainfall 被引量:61

A physically-based statistical forecast model for the middle-lower reaches of the Yangtze River Valley summer rainfall
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摘要 A new approach to forecast the middle-lower reaches of the Yangtze River Valley summer rainfall in June―August(JJA) is proposed in this paper.The year-to-year increment of the middle-lower reaches of the Yangtze River Valley is forecasted and hence the summer precipitation could be predicted.In this paper,DY is defined as the difference of a variable between the current year and the preceding year(year-to-year increment).YR denotes the seasonal mean precipitation rate of the middle-lower reaches of the Yangtze River Valley summer rainfall.After analyzing the atmospheric circulation anomalies in winter and spring that were associated with the DY of YR,six key predictors for the DY of YR have been identified.Then the forecast model for the DY of YR is established by using the multi-linear regression method.The predictors for the DY of YR are Antarctic Oscillation,the meridional wind shear between 850hPa and 200hPa over the Indo-Australian region,and so on.The prediction model shows a high skill for the hindcast during 1997-2006,with the average relative root mean square error is at 18%.The model can even reproduce the upward and downward trends of YR during 1984―1998 and 1998―2006.Considering that the current operational forecast models of the summer precipitation over the China region have the average forecast scores at 60%―70% and that the prediction skill for the middle-lower reaches of Yangtze River Valley summer precipitation remains quite limited up to now,thus this new approach to predict the year-to-year increment of the summer precipitation over the Yangtze River Valley(and hence the summer precipitation itself) has the potential to significantly increase the operational forecast skill of the summer precipitation. A new approach to forecast the middle-lower reaches of the Yangtze River Valley summer rainfall in June-August (JJA) is proposed in this paper. The year-to-year increment of the middle-lower reaches of the Yangtze River Valley is forecasted and hence the summer precipitation could be predicted. In this paper, DY is defined as the difference of a variable between the current year and the preceding year (year-to-year increment). YR denotes the seasonal mean precipitation rate of the middle-lower reaches of the Yangtze River Valley summer rainfall. After analyzing the atmospheric circulation anomalies in winter and spring that were associated with the DY of YR, six key predictors for the DY of YR have been identified. Then the forecast model for the DY of YR is established by using the multi-linear regression method. The predictors for the DY of YR are Antarctic Oscillation, the meridional wind shear between 850hPa and 200hPa over the Indo-Australian region, and so on. The prediction model shows a high skill for the hindcast during 1997-2006, with the average relative root mean square error is at 18%. The model can even reproduce the upward and downward trends of YR during 1984--1998 and 1998--2006. Considering that the current operational forecast models of the summer precipitation over the China region have the average forecast scores at 60%--70% and that the prediction skill for the middle-lower reaches of Yangtze River Valley summer precipitation remains quite limited up to now, thus this new approach to predict the year-to-year increment of the summer precipitation over the Yangtze River Valley (and hence the summer precipitation itself) has the potential to significantly increase the operational forecast skill of the summer precipitation.
出处 《Chinese Science Bulletin》 SCIE EI CAS 2008年第4期602-609,共8页
基金 Supported the National Natural Science Foundation of China (Grant Nos. 40631005,40620130113 and 40523001) the "Korea Enhanced Observing Program ofMeiyu Project"
关键词 长江流域 夏季 降雨量 天气预报 统计预报模型 year-to-year increment, Yangtze River Valley summer precipitation, prediction model, prediction skill
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