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
The monthly forecast of Indian monsoon rainfall during June to September is investigated by using the hindcast data sets of the National Centre for Environmental Prediction (NCEP)’s operational coupled model (known a...The monthly forecast of Indian monsoon rainfall during June to September is investigated by using the hindcast data sets of the National Centre for Environmental Prediction (NCEP)’s operational coupled model (known as the Climate Forecast System) for 25 years from 1981 to 2005 with 15 ensemble members each. The ensemble mean monthly rainfall over land region of India from CFS with one month lead forecast is underestimated during June to September. With respect to the inter-annual variability of monthly rainfall it is seen that the only significant correlation coefficients (CCs) are found to be for June forecast with May initial condition and September rainfall with August initial conditions. The CFS has got lowest skill for the month of August followed by that of July. Considering the lower skill of monthly forecast based on the ensemble mean, all 15 ensemble members are used separately for the preparation of probability forecast and different probability scores like Brier Score (BS), Brier Skill Score (BSS), Accuracy, Probability of Detection (POD), False Alarm Ratio (FAR), Threat Score (TS) and Heidke Skill Score (HSS) for all the three categories of forecasts (above normal, below normal and normal) have been calculated. In terms of the BS and BSS the skill of the monthly probability forecast in all the three categories are better than the climatology forecasts with positive BSS values except in case of normal forecast of June and July. The “TS”, “HSS” and other scores also provide useful probability forecast in case of CFS except the normal category of July forecast. Thus, it is seen that the monthly probability forecast based on NCEP CFS coupled model during the southwest monsoon season is very encouraging and is found to be very useful.展开更多
In this paper,a revised method for typhoon precipitation probability forecast,based on the frequencymatching method,is developed by combining the screening and the neighborhood methods.The frequency of the high-resolu...In this paper,a revised method for typhoon precipitation probability forecast,based on the frequencymatching method,is developed by combining the screening and the neighborhood methods.The frequency of the high-resolution precipitation forecasts is used as the reference frequency,and the frequency of the lowresolution ensemble forecasts is used as the forecast frequency.Based on frequency–matching method,the frequency of rainfall above the rainstorm magnitude increases.The forecast members are then selected by using the typhoon tracks of the short-term predictions,and the precipitation probability is calculated for each member using a combination of the neighbor and the traditional probability statistical methods.Moreover,four landfalling typhoons(i.e.,STY Lekima and STS Bailu in 2019,and TY Hagupit and Higos in 2020)were chose to test the rainfall probability forecast.The results show that the method performs well with respect to the forecast rainfall area and magnitude for the four typhoons.The Brier and Brier skill scores are almost entirely positive for the probability forecast of 0.1–250 mm rainfall during Bailu,Hagupit and Higos(except for 0.1mm of Hagupit),and for<100 mm rainfall(except for 25 mm)during Lekima.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.
文摘The monthly forecast of Indian monsoon rainfall during June to September is investigated by using the hindcast data sets of the National Centre for Environmental Prediction (NCEP)’s operational coupled model (known as the Climate Forecast System) for 25 years from 1981 to 2005 with 15 ensemble members each. The ensemble mean monthly rainfall over land region of India from CFS with one month lead forecast is underestimated during June to September. With respect to the inter-annual variability of monthly rainfall it is seen that the only significant correlation coefficients (CCs) are found to be for June forecast with May initial condition and September rainfall with August initial conditions. The CFS has got lowest skill for the month of August followed by that of July. Considering the lower skill of monthly forecast based on the ensemble mean, all 15 ensemble members are used separately for the preparation of probability forecast and different probability scores like Brier Score (BS), Brier Skill Score (BSS), Accuracy, Probability of Detection (POD), False Alarm Ratio (FAR), Threat Score (TS) and Heidke Skill Score (HSS) for all the three categories of forecasts (above normal, below normal and normal) have been calculated. In terms of the BS and BSS the skill of the monthly probability forecast in all the three categories are better than the climatology forecasts with positive BSS values except in case of normal forecast of June and July. The “TS”, “HSS” and other scores also provide useful probability forecast in case of CFS except the normal category of July forecast. Thus, it is seen that the monthly probability forecast based on NCEP CFS coupled model during the southwest monsoon season is very encouraging and is found to be very useful.
基金funded by the Key Program for International S&T Cooperation Projects of China(No.2017YFE0107700)the National Natural Science Foundation of China(Grant Nos.41875080,41775065)+2 种基金the Research Program from Science and Technology Committee of Shanghai(Nos.19dz1200101,20ZR1469700)the National Key R&D Program of China(2020YFE0201900)in part by Shanghai Typhoon Innovation Team grants to Shanghai Typhoon Institute.
文摘In this paper,a revised method for typhoon precipitation probability forecast,based on the frequencymatching method,is developed by combining the screening and the neighborhood methods.The frequency of the high-resolution precipitation forecasts is used as the reference frequency,and the frequency of the lowresolution ensemble forecasts is used as the forecast frequency.Based on frequency–matching method,the frequency of rainfall above the rainstorm magnitude increases.The forecast members are then selected by using the typhoon tracks of the short-term predictions,and the precipitation probability is calculated for each member using a combination of the neighbor and the traditional probability statistical methods.Moreover,four landfalling typhoons(i.e.,STY Lekima and STS Bailu in 2019,and TY Hagupit and Higos in 2020)were chose to test the rainfall probability forecast.The results show that the method performs well with respect to the forecast rainfall area and magnitude for the four typhoons.The Brier and Brier skill scores are almost entirely positive for the probability forecast of 0.1–250 mm rainfall during Bailu,Hagupit and Higos(except for 0.1mm of Hagupit),and for<100 mm rainfall(except for 25 mm)during Lekima.
