大气科学学报2019，Vol.42Issue(2)：197-206,10.DOI:10.13878/j.cnki.dqkxxb.20181108001
基于卡尔曼滤波的中国区域气温和降水的多模式集成预报
Multimodel ensemble forecasts of surface air temperature and precipitation over China by using Kalman filter
摘要
Abstract
Based on the data from the TIGGE datasets of European Centre for Medium-Range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), National Centers for Environmental Prediction (NCEP), China Meteorological Administration (CMA) and United Kingdom Met Office (UKMO), the Kalman filter method was applied to conduct multimodel ensemble forecasts of the surface air temperature and precipitation.The results showthat the multimodel ensemble forecasts by using Kalman filter are superior to those of the bias-removed ensemble mean (BREM) and other individual models.However, the forecast results of Kalman filter method vary for different meteorological elements and different forecast lead times. For the surface air temperature forecast in China, Kalman filter method shows the best forecast capability while for the precipitation forecast, it has a higher TS score than the BREM.However, with longer forecast lead time, the TS scores for heavy rains are approximately equivalent to those of the best individual model UKMO.So the Kalman filter method does not improve the forecast capability of heavy rains significantly.To sum up, the root mean square error (RMSE) of surface air temperature and precipitation forecasts based on Kalman filter is the smallest among those of the multimodel ensemble forecasts and each individual model forecasts.关键词
卡尔曼滤波/消除偏差集合平均/多模式集成预报/TIGGEKey words
Kalman filter/bias-removed ensemble mean/multimodel ensemble forecast/TIGGE引用本文复制引用
智协飞,黄闻..基于卡尔曼滤波的中国区域气温和降水的多模式集成预报[J].大气科学学报,2019,42(2):197-206,10.基金项目
国家自然科学基金资助项目(41575104) （41575104）
北极阁开放研究基金南京大气科学联合研究中心(NJCAR)重点项目 （NJCAR）
江苏高校优势学科建设工程资助项目(PAPD) （PAPD）
