2010年8月环流形势的多模式超级集合预报

Multi-model Super-ensemble Forecasts for the Circulation in August 2010

利用多模式超级集合预报法,以欧洲中期天气预报中心、日本气象厅、德国气象局、中国气象局和中国空军气象中心共5个决定性7 d预报产品为集合成员,对2010年8月500 hPa高度场和850 hPa温度场分别进行固定训练期和滑动训练期超级集合预报。采用均方根误差和相关系数对超级集合预报、单一模式预报和简单集合平均预报进行对比检验,同时对各预报结果的均方根误差空间分布进行对比分析。结果表明:超级集合预报在所有预报结果中最佳,且滑动集合预报对8月后期时段预报要略好于固定集合预报,两者预报效果均好于参与集合预报的各模式,也好于集合平均预报。但随着预报时效的延长,集合平均预报的优势也随之提升。从预报结果均方根误差的空间分布可知,多模式超级集合预报相比于单一模式预报效果提高的区域,500 hPa位势高度场主要位于印度半岛、印度洋、青藏高原及以西地区,而850 hPa温度场则主要位于蒙古、青藏高原、中国新疆及以西地区。

Based on 24--168-hour model forecast of ECMWF, JMA, German Bureau of Meteorology, CMA and China Air Force, the multi-model super-ensemble and the multi-model ensemble mean forecasts of 500 hPa geo-potential height and 850 hPa temperature from 8 August to 31 August in 2010 are conducted using fixed training period and running training period. The root mean square error （RMSE） and correlation co- efficient are utilized to evaluate forecasts of the super-ensemble, the multi-model ensemble mean, and any- one of five models. Meanwhile, the distribution of RMSE about different forecasts is analyzed, respective-- ly. The results show that ECMWF model performs the best in 500 hPa geo-potential height forecast and JMA model performs best in 850 hPa temperature forecast among five models from 24 hours to 168 hours, and the skill of super-ensemble forecast is the best of all. The super-ensemble forecast skill is improved not only in fixed training period but also in running training period, and the super-ensemble result of running training is slightly better than the result of fixed training at the end of the forecast period, and both of them are better than the result of any single model and the simple ensemble mean. But the result of simple ensemble mean has its advantage along with the forecast time. The forecast result of the whole August shows that the value of RMSE in super-ensemble forecast is the least not only in 500 hPa geo-potential height but also in 850 hPa temperature, which is the best of all forecast results, and the result of running training is better than the fixed training. But the value of the correlation coefficient is similar between the forecasts, and the skill of the simple ensemble mean is the highest of all, which means the simple ensemble mean has its advantage. The RMSE distribution of super-ensemble, multi-model ensemble mean, and any single models is quite different over various regions, the skill of super-ensemble is the best of all. The are- as of skill improvement by the super-ensemble forecast in 500 hPa geo-potential height are mainly located in Indian Peninsula, Indian Ocean, the Tibet Plateau and its west. The areas of skill improvement by the super-ensemble forecast in 850 hPa temperature are mainly in Mongolia, the Tibet Plateau and Xinjiang of China and its west.