A Timescale Decomposed Threshold Regression Downscaling Approach to Forecasting South China Early Summer Rainfall

作     者：Linye SONG Wansuo DUAN Yun LI Jiangyu MAO 

作者机构：Institute of Urban MeteorologyChina Meteorological AdministrationBeijing 100089 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid DynamicsInstitute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029 Business Intelligence&Data AnalyticsWestern PowerPerth WA6000Australia 

出 版 物：《Advances in Atmospheric Sciences》 (大气科学进展（英文版）)

年 卷 期：2016年第33卷第9期

页      面：1071-1084页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 070601[理学-气象学] 0706[理学-大气科学] 0816[工学-测绘科学与技术] 0714[理学-统计学（可授理学、经济学学位）] 0825[工学-航空宇航科学与技术] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：sponsored by the National Basic Research Program of China (Grant No. 2012CB955202) the China Scholarship Council under the Joint-PhD program for conducting research at CSIRO supported by the Indian Ocean Climate Initiative 

主　　题：timescale decomposed threshold regression South China early summer rainfall forecasting skill 

摘      要：A timescale decomposed threshold regression （TSDTR） downscaling approach to forecasting South China early summer rainfall （SCESR） is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR. The two models are developed based on the partial least squares （PLS） regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915-84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation （PDO）. On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985-2006, compared to other simpler approaches. This study suggests that the TSDTR approach, considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.