基于次季节—季节(S2S)模式的中国东部夏季日降水预报评估

为理解次季节—季节(subseasonal to seasonal，S2S)模式的预报技巧，利用台站降水观测资料对中国气象局(China Meteorological Administration，CMA)、欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts，ECMWF)和美国国家环境预报中心(National Centers for Environmental Prediction，NCEP)模式公共回报期1999—2010 年108°E 以东的中国大陆东部夏季日降水及极端降水预报展开评估。结果表明，ECMWF 预报整体表现最佳、NCEP 次之、CMA 相对较弱，各模式随预报时间的增长均呈现当观测偏湿(干)时预报倾向偏干(湿)的特点，在 S2S 时间尺度基本丧失预报技巧，具有很大的改进空间。极端降水临界阈值的界定方法会直接影响单个台站的评估结果，但对区域整体预报技巧影响不大。S2S 模式预报的均方根误差在观测降水量越多时往往越大；预报值与观测值的相关系数在所有(极端)降水事件中呈连续(振荡)衰减，甚至出现负相关；均方技巧评分在所研究降水事件较多的情况下表现更好。各模式在所有降水事件中的空报率要远高于漏报率，但在极端降水事件中恰好相反。降水预报检验指标在绝对极端降水分级检验中的表现逐级变差，各模式预报中基本不出现特大暴雨，CMA 对极端降水事件发生的预报准确率较低。

Evaluation of summer daily precipitation forecast over eastern China based on subseasonal to seasonal (S2S) models

In order to understand the forecast skills of subseasonal to seasonal (S2S) models, the summer daily precipitation and extreme precipitation forecasts from China Meteorological Administration (CMA), European Centre for Medium-Range Weather Forecasts (ECMWF), and National Centers for Environmental Prediction (NCEP) models during their common hindcast period from 1999 to 2010 are evaluated by using the precipitation observation data from stations in the eastern part of Chinese mainland east of 108°E. The results show that ECMWF has the best overall forecast performance, followed by NCEP, and CMA is relatively worse. With the increase of forecast time, each model shows a characteristic that the forecast tends to be dry (wet) when the observation is wet (dry). The forecast skills of the models are almost lost on the S2S time scale, so there is a lot of room for improvement. The evaluation results of a single station are directly influenced by the definition method of the critical threshold of extreme precipitation, which has little effect on the overall forecast skills of the region. With the increase of observed precipitation, the root mean square error of S2S model forecast tends to increase. The correlation coefficient between forecast and observation shows a continuous (oscillatory) decrease or even negative correlation in all (extreme) precipitation events. The mean square skill score is higher in the case of more precipitation events. In all precipitation events, the false alarm rate of each model is much higher than the missing alarm rate, but it is opposite in extreme precipitation events. The performance of precipitation forecast verification indices in absolute extreme precipitation classification verification becomes worse with the increase of precipitation grade. There is almost no severe rainstorm forecast in all models, and the forecast accuracy of CMA model on the occurrence of extreme precipitation events is poor.