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.
引用
收藏
页数:7
相关论文
共 37 条
  • [21] Sub-Seasonal Predictability of the Northeast China Cold Vortex in BCC and ECMWF S2S Model Forecasts for 2006-2021
    Yu, Yiqiu
    Wu, Jie
    Fang, Yihe
    Zhao, Chunyu
    Ke, Zongjian
    Lin, Yitong
    JOURNAL OF METEOROLOGICAL RESEARCH, 2024, 38 (03) : 453 - 468
  • [22] Global precipitation hindcast quality assessment of the Subseasonal to Seasonal (S2S) prediction project models
    de Andrade, Felipe M.
    Coelho, Caio A. S.
    Cavalcanti, Iracema F. A.
    CLIMATE DYNAMICS, 2019, 52 (9-10) : 5451 - 5475
  • [23] Subseasonal Prediction of Wintertime Northern Hemisphere Extratropical Cyclone Activity by SubX and S2S Models
    Zheng, Cheng
    Chang, Edmund Kar-Man
    Kim, Hyemi
    Zhang, Minghua
    Wang, Wanqiu
    WEATHER AND FORECASTING, 2021, 36 (01) : 75 - 89
  • [24] Introduction to Special Collection: "Bridging Weather and Climate: Subseasonal-to-Seasonal (S2S) Prediction"
    Lang, Andrea L.
    Pegion, Kathleen
    Barnes, Elizabeth A.
    JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2020, 125 (04)
  • [25] Simulations of the Asian summer monsoon in the sub-seasonal to seasonal prediction project (S2S) database
    Jie, Weihua
    Vitart, Frederic
    Wu, Tongwen
    Liu, Xiangwen
    QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2017, 143 (706) : 2282 - 2295
  • [26] Sub-seasonal to seasonal (S2S) prediction of dry and wet extremes for climate adaptation in India
    Malik, Iqura
    Mishra, Vimal
    CLIMATE SERVICES, 2024, 34
  • [27] The Impact of Tropical Pacific SST Biases on the S2S Forecast Skill over North America in the UFS Global Coupled Model
    Stan, Cristiana
    Krishnamurthy, V.
    Bai, Hedanqiu
    Li, Bin
    Mehra, Avichal
    Meixner, Jessica
    Moorthi, Shrinivas
    Stefanova, Lydia
    Wang, Jiande
    Wang, Jun
    Worthen, Denise
    Yang, Fanglin
    JOURNAL OF CLIMATE, 2023, 36 (08) : 2439 - 2456
  • [28] The Impact of Tropical SST Biases on the S2S Precipitation Forecast Skill over the Contiguous United States in the UFS Global Coupled Model
    Bai, Hedanqiu
    Li, Bin
    Mehra, Avichal
    Meixner, Jessica
    Moorthi, Shrinivas
    Ray, Sulagna
    Stefanova, Lydia
    Wang, Jiande
    Wang, Jun
    Worthen, Denise
    Yang, Fanglin
    Stan, Cristiana
    WEATHER AND FORECASTING, 2023, 38 (06) : 937 - 952
  • [29] Prediction of near-surface conditions following the 2023/24 sudden stratospheric warming by the S2S project models
    Rao, Jian
    Zhang, Xiaoqi
    Lu, Qian
    Liu, Siming
    ATMOSPHERIC RESEARCH, 2025, 315
  • [30] Effects of the Madden–Julian Oscillation on 2-m air temperature prediction over China during boreal winter in the S2S database
    Yang Zhou
    Ben Yang
    Haishan Chen
    Yaocun Zhang
    Anning Huang
    Mengke La
    Climate Dynamics, 2019, 52 : 6671 - 6689