On the Ability of Global Ensemble Prediction Systems to Predict Tropical Cyclone Track Probabilities

被引:61
|
作者
Majumdar, Sharanya J. [1 ]
Finocchio, Peter M. [1 ]
机构
[1] Univ Miami, Miami, FL USA
关键词
SINGULAR VECTORS; BAROTROPIC MODEL; PACIFIC BASIN; FORECASTS;
D O I
10.1175/2009WAF2222327.1
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
The ability of ensemble prediction systems to predict the probability that a tropical cyclone will fall within a certain area is evaluated Ensemble forecasts of up to 5 clays issued by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Met Office (WAFT) were evaluated for the 2008 Atlantic and western North Pacific seasons In the Atlantic. the ECMWF ensemble mean was comparable in skill to a consensus of deterministic models Dynamic "probability circles" that contained 67% of the ECMWF ensemble captured the best track in similar to 67% of all cases for 24-84-h forecasts, and were slightly underdispersive beyond 96 h In contrast. the Goerss predicted consensus error (GPCE) was overdispersive The addition of the UKMET ensemble yielded improvements in the short range and degradations for longer-range forecasts The ECMWF ensemble performed similarly when the size was reduced from 50 to 20 On average. It produced a lower measure of independence between its members than an ensemble comprising different deterministic models The 67% circles normally captured the best track during straight-line motion. but less so for sharply turning tracks In contrast to the Atlantic. the ECMWF ensemble (and GPCE) was unable to capture sufficient verifications within the 67% probability circles in the western North Pacific. in part because of a less skillful ensemble mean (and consensus) Though further evaluations are necessary, the results demonstrate the potential for ensemble prediction systems to enhance probabilistic forecasts. and for The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) to be embraced by the operational and research communities
引用
收藏
页码:659 / 680
页数:22
相关论文
共 50 条
  • [1] Selective ensemble-mean technique for tropical cyclone track forecast by using ensemble prediction systems
    Qi, Liangbo
    Yu, Hui
    Chen, Peiyan
    [J]. QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2014, 140 (680) : 805 - 813
  • [2] Comparison of different global ensemble prediction systems for tropical cyclone intensity forecasting
    Lu, Deyu
    Ding, Ruiqiang
    Mao, Jiangyu
    Zhong, Quanjia
    Zou, Qian
    [J]. ATMOSPHERIC SCIENCE LETTERS, 2024, 25 (04):
  • [3] A Probabilistic Tropical Cyclone Track Forecast Scheme Based on the Selective Consensus of Ensemble Prediction Systems
    Zhang, Xiping
    Yu, Hui
    [J]. WEATHER AND FORECASTING, 2017, 32 (06) : 2143 - 2157
  • [4] Forecasting of tropical cyclone using global and regional ensemble prediction systems of NCMRWF : A review
    Sarkar, Abhijit
    Kumar, Sushant
    Dube, Anumeha
    Prasad, S. Kiran
    Mamgain, Ashu
    Chakraborty, Paromita
    Ashrit, Raghavendra
    Mitra, A. K.
    [J]. MAUSAM, 2021, 72 (01): : 77 - 86
  • [5] A Multiresolution Ensemble Hybrid 4DEnVar with Variable Ensemble Sizes to Improve Global and Tropical Cyclone Track Numerical Prediction
    Jones, Erin A.
    Wang, Xuguang
    [J]. MONTHLY WEATHER REVIEW, 2023, 151 (05) : 1145 - 1166
  • [6] Applying clustering and ensemble prediction concepts to consensus tropical cyclone track forecasting
    Elsberry, RL
    Carr, LE
    [J]. 24TH CONFERENCE ON HURRICANES AND TROPICAL METEOROLOGY/10TH CONFERENCE ON INTERACTION OF THE SEA AND ATMOSPHERE, 2000, : 431 - 432
  • [7] Detection of tropical cyclone track changes from the ECMWF ensemble prediction system
    Tsai, Hsiao-Chung
    Elsberry, Russell L.
    [J]. GEOPHYSICAL RESEARCH LETTERS, 2013, 40 (04) : 797 - 801
  • [8] Numerical tests for tropical cyclone track prediction by the global WRF model
    Jingmei Yu
    [J]. Tropical Cyclone Research and Review, 2022, (04) : 252 - 264
  • [9] Numerical tests for tropical cyclone track prediction by the global WRF model
    Yu, Jingmei
    [J]. TROPICAL CYCLONE RESEARCH AND REVIEW, 2022, 11 (04) : 252 - 264
  • [10] Assessment of NCMRWF Global Ensemble System with differing ensemble populations for Tropical cyclone prediction
    Chakraborty, Paromita
    Sarkar, Abhijit
    Kumar, Sushant
    George, John P.
    Rajagopal, E. N.
    Bhatla, R.
    [J]. ATMOSPHERIC RESEARCH, 2020, 244