Skill improvement of the yearly updated reforecasts in ECMWF S2S prediction from 2016 to 2022

被引:5
|
作者
Peng, Yihao [1 ]
Liu, Xiaolei [1 ]
Su, Jingzhi [1 ,2 ]
Liu, Xinli [1 ]
Zhang, Yixu [3 ]
机构
[1] Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing, Peoples R China
[2] Ctr Earth Syst Modeling & Predict CMA CEMC, Beijing, Peoples R China
[3] Chengdu Univ Informat Technol, Chengdu, Peoples R China
关键词
Reforecast; S2S; Prediction skill; ECMWF; MADDEN-JULIAN OSCILLATION; PREDICTABILITY;
D O I
10.1016/j.aosl.2023.100357
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Hazardous weather events are often accompanied by subseasonal processes, but the forecast skills of subseasonal prediction are still limited. To assess the skill improvement of the constantly updated model version in ECMWF subseasonal-seasonal (S2S) prediction from 2016 to 2022, the performance of yearly updated reforecasts was evaluated against ERA5 reanalysis data using the temporal anomaly correlation coefficient (TCC) as a metric. The newly updated reforecasts exhibit stable superiority at the weather scale of the first two weeks, regardless of whether the 2-m temperature or precipitation forecast is being considered. At the subseasonal time scale starting from the third week, some slight improvements in prediction skills are only found in several tropical regions. Generally, the week-3 TCC values averaged over global land grids still reflect an advancement in prediction skills for updated reforecasts. For the Madden-Julian Oscillation (MJO), reforecasts can reproduce the characteristics of eastward propagation, but there are deviations in the intensity and propagation range of convection anomalies for reforecasts of all seven years. Based on an evaluation of MJO prediction skill using the bivariate anomaly correlation coefficient and bivariate root-mean-square error, some differences are apparent in the MJO prediction skills among the updated reforecasts, but the improvements do not increase monotonically year by year. Despite the inherent limitation of S2S prediction, positive progress has already been achieved via the constantly updated S2S prediction in ECMWF, which reinforces the confidence in further collaboratively improving S2S prediction in the future.
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收藏
页数:7
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