Extended Abstract

Multimodel consensus forecasting of the precipitationusing TIGGE dataZHI Xiefei, ZHANG LingKey Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University ofInformation Science and Technology, Nanjing 2100441. IntroductionThe extreme events from 11 January until 2 February 2008 brought severe low temperatureand icy weather to broad reg ions in cen tral and southern Ch ina, which caused billions ofdollars w orth of dire ct economic loss and hun dreds casualty. The se e xtreme ic y w eatherevents were c losely linked to the c hange in the Midd le East jet stre am (MEJS), w hichintensified and shifted southeastward (Wen et al. 2009). Wang et al. (2008) indicated that thevery active AO is a very important factor which leads to the anomalous atmospheric circulationand th us these severe w eather ev ents. Du ring the e vents more fre quent blockings ov erEurasia were ob served (Zh ang et al. 2009; Zho u et al. 2009). Mean while, the subtropicalwestern Pacific high (SWPH) was stronger and its ridgeline was farther north than normal. Theanomalous high slowed down the eastward movement of weather systems to the Pacific andwas fav orable for th e c onvergence of w ater v apor ov er c entral–southern China (Wen et a l.2009; Zhang et al. 2009). Further study indicated that extraordinarily strong convections overthe maritime continent and South China Sea may play an important role in the formation of theanomalies of the SWPH during early 2008 (Zhang et al. 2009).Nowadays, the numerical weather prediction (NWP) h as become the major guidance in ourdaily weather forecast. However, the skill of extended range forecast for surface temperatureand rainfall using available NWP models is still not satisfactory to address the detailed aspectsof extreme weather ev ents. Mu ltimodel superensemble forecast me thod is a pra cticalpost-processing technique capable of reducing model output errors (Krishnamurti et al. 1999;2000a; 2000b; Yun et al. 2003; Mutemi et al. 2007; Zhi et al. 2009a; Lin et al. 2009). Zhi et al.(2009b) a nd Lin et a l. (2 009) ap plied th e multimo del s uperensemble tec hnique in theforecasting of the su rface temp erature in No rthern He misphere. Th e fo recast skill o f themultimodel superensemble with fixed training period is higher than that of the ensemble meanand the best in dividual mod el for t he 2 4h-144h s urface temperature fore cast. Thesuperensemble with running training period has superior forecast skill to that with fixed trainingperiod for the 24h-168h forecast. Further study indicates that bias-removed ensemble meancan considerably reduce the RMSEs of the 24h-144h surface temperature forecast as well. Inthis study, the bias-removed e nsemble mean forecasting experiment was performed for theprecipitation in central and southern China during the extreme weather events in early 2008 toimprove the extended range forecast skill of the high-impact weather events.2. Data and methods-370-