Extended Abstract

Development of QPF approaches in NMC/CMAJian LIN 1 , Xing JIANG 1 , and Kan DAI 1Fanghua ZHANG 1 , Xuekuan MA 1 and Lin DONG 1National Meteorological Center 1 , CMAlinjian@cma.gov.cn1. IntroductionTo meet the needs of econom y growth and public community, especially needs of theweather services on som e important societal activities, such as Olym pic Games and NationalDay’s Celeb ration, fine quantitative precipitatio n forecast (QPF) is of great im portance. InMay 2008, QPF section is set up in NMC to be m ainly responsible for providing QPFproducts with an interval of 6 hours in the coming 24 hours. The operational QPF systeminvolves in some new data, including real-time data (radar, AWS et al.) and new NWP data.At present, the QPF forecast just pro vide the distribution of the dif ferent rainfall gradeshown by different isolines, such as light, moderate, heavy rain, rain storm and so on. There isno quantitative precipitation forecast in deed. In order to obtain the real QPF products, som eQPF approaches should be developed ,for instance, m ulti-model rainfall ensemble forecastbased on dif ferent rainfall m odels, which can be regarded as an esse ntial rainfall forecast.Meanwhile, meso-scale ensemble probability forecast and in gredient-based methodology arealso used as supplem ents and corrections for QP F. In this paper , our attempts to develop theabove mentioned approaches.2. Multi-model rainfall ensemble forecastFor the use of numerous NWP data in QPF, the major challenge is how to use them in thelimited time and how to improve the forecast efficiency and accuracy. Short range ensem bleforecasting (SREF) of quantitative precipitation (Du J, 1997, Stensrud D J, 2000, Du J, 2002)can in crease the forecast accu racy. Due to the inab ility of ensemble m ean results to theextreme rainfall event, multi-m odel rainfall ensemble forecast, with th e consideration of theshape of rainfall belt and intensity of rainfall, is applied in the operation of QPF, of which theweight of dif ferent m odel is decided by the similarity degree (L i Kaile, 1986) of dif ferentrainfall forecast of models in terms of stepwise regression (Chen Liqiang, 2005). This methodtakes into account the diversity and probability distribution of the rainfall forecast of differentmodels.Fig 1 presents the flow chart of multi-model rainfall ensemble forecast. According to theverification of routine operation, the rainfall forecasts of T639, EC, Japan and NCEP modelsare of bette r perf ormance and chosen to the m embers of m ulti-model rainf all e nsembleforecast.-312-