使用世界气象组织季节内至季节尺度(Subseasonal to Seasonal,S2S)预测项目数据库评估了多个集合预报系统在S2S时间尺度对台风的预报能力。评估的时间段为1999—2010年期间每年5月1日—10月31日。为评估S2S时间尺度台风的预报技巧,使用...使用世界气象组织季节内至季节尺度(Subseasonal to Seasonal,S2S)预测项目数据库评估了多个集合预报系统在S2S时间尺度对台风的预报能力。评估的时间段为1999—2010年期间每年5月1日—10月31日。为评估S2S时间尺度台风的预报技巧,使用了台风密集度来描述台风的生成及移动状况。台风密集度定义为一段时间内500 km范围内台风出现的概率。台风密集度由6个S2S集合预报系统后报结果计算得出,它们分别由BoM、CMA、ECMWF、JMA、CNRM和NCEP开发使用。这6个预报系统台风密集度的预报技巧评分表明,当预报时效为11~30天时,ECMWF预报系统的评分为正值,比基于气候状态的参考预报能略好地预报台风。展开更多
Accurately predicting drought a few months in advance is important for drought mitigation and agricultural and water resources management,especially for a river basin like that of the Yellow River in North China.Howev...Accurately predicting drought a few months in advance is important for drought mitigation and agricultural and water resources management,especially for a river basin like that of the Yellow River in North China.However,summer drought predictability over the Yellow River basin is limited because of the low influence from ENSO and the large interannual variations of the East Asian summer monsoon.To explore the drought predictability from an ensemble prediction perspective,29-year seasonal hindcasts of soil moisture drought,taken directly from several North American multimodel ensemble(NMME)models with different ensemble sizes,were compared with those produced by combining bias-corrected NMME model predictions and variable infiltration capacity(VIC)land surface hydrological model simulations.It was found that the NMME/VIC approach reduced the root-mean-square error from the best NMME raw products by 48%for summer soil moisture drought prediction at the lead-1 season,and increased the correlation significantly.Within the NMME/VIC framework,the multimodel ensemble mean further reduced the error from the best single model by 6%.Compared with the NMME raw forecasts,NMME/VIC had a higher probabilistic drought forecasting skill in terms of a higher Brier skill score and better reliability and resolution of the ensemble.However,the performance of the multimodel grand ensemble was not necessarily better than any single model ensemble,suggesting the need to optimize the ensemble for a more skillful probabilistic drought forecast.展开更多
文摘使用世界气象组织季节内至季节尺度(Subseasonal to Seasonal,S2S)预测项目数据库评估了多个集合预报系统在S2S时间尺度对台风的预报能力。评估的时间段为1999—2010年期间每年5月1日—10月31日。为评估S2S时间尺度台风的预报技巧,使用了台风密集度来描述台风的生成及移动状况。台风密集度定义为一段时间内500 km范围内台风出现的概率。台风密集度由6个S2S集合预报系统后报结果计算得出,它们分别由BoM、CMA、ECMWF、JMA、CNRM和NCEP开发使用。这6个预报系统台风密集度的预报技巧评分表明,当预报时效为11~30天时,ECMWF预报系统的评分为正值,比基于气候状态的参考预报能略好地预报台风。
基金supported by the China Special Fund for Meteorological Research in the Public Interest(Major projects)(Grant No.GYHY201506001)the National Natural Science Foundation of China(Grant No.91547103)
文摘Accurately predicting drought a few months in advance is important for drought mitigation and agricultural and water resources management,especially for a river basin like that of the Yellow River in North China.However,summer drought predictability over the Yellow River basin is limited because of the low influence from ENSO and the large interannual variations of the East Asian summer monsoon.To explore the drought predictability from an ensemble prediction perspective,29-year seasonal hindcasts of soil moisture drought,taken directly from several North American multimodel ensemble(NMME)models with different ensemble sizes,were compared with those produced by combining bias-corrected NMME model predictions and variable infiltration capacity(VIC)land surface hydrological model simulations.It was found that the NMME/VIC approach reduced the root-mean-square error from the best NMME raw products by 48%for summer soil moisture drought prediction at the lead-1 season,and increased the correlation significantly.Within the NMME/VIC framework,the multimodel ensemble mean further reduced the error from the best single model by 6%.Compared with the NMME raw forecasts,NMME/VIC had a higher probabilistic drought forecasting skill in terms of a higher Brier skill score and better reliability and resolution of the ensemble.However,the performance of the multimodel grand ensemble was not necessarily better than any single model ensemble,suggesting the need to optimize the ensemble for a more skillful probabilistic drought forecast.
