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Ensemble data assimilation and prediction of typhoon and associated hazards using TEDAPS: evaluation for 2015–2018 seasons
Frontiers of Earth Science ( IF 1.8 ) Pub Date : 2019-11-21 , DOI: 10.1007/s11707-019-0794-4
Hong Li , Jingyao Luo , Mengting Xu

The initial condition accuracy is a major concern for tropical cyclone (TC) numerical forecast. The ensemble-based data assimilation techniques have shown great promise to initialize TC forecast. In addition to initial condition uncertainty, representing model errors (e.g. physics deficiencies) is another important issue in an ensemble forecasting system. To improve TC prediction from both deterministic and probabilistic standpoints, a Typhoon Ensemble Data Assimilation and Prediction System (TEDAPS) using an ensemble-based data assimilation scheme and a multi-physics approach based on Weather Research and Forecasting (WRF) model, has been developed in Shanghai Typhoon Institute and running realtime since 2015. Performance of TEDAPS in the prediction of track, intensity and associated disaster has been evaluated for the Western North Pacific TCs in the years of 2015–2018, and compared against the NCEP GEFS.TEDAPS produces markedly better intensity forecast by effectively reducing the weak biases and therefore the degree of underdispersion compared to GEFS. The errors of TEDAPS track forecasts are comparative with (slightly worse than) those of GEFS at longer (shorter) forecast leads. TEDAPS ensemble-mean exhibits advantage over deterministic forecast in track forecasts at long lead times, whereas this superiority is limited to typhoon or weaker TCs in intensity forecasts due to systematical underestimation. Four case-studies for three landfalling cyclones and one recurving cyclone demonstrate the capacities of TEDAPS in predicting some challenging TCs, as well as in capturing the forecast uncertainty and the potential threat from TC-associated hazards.

中文翻译:

使用TEDAPS进行数据同化和台风及相关危害的综合预测:2015–2018季节评估

初始条件精度是热带气旋(TC)数值预报的主要关注点。基于集合的数据同化技术显示出初始化TC预报的巨大希望。除了初始条件不确定性外,表示模型错误(例如物理缺陷)也是集成预测系统中的另一个重要问题。为了从确定性和概率的角度改善TC预报,已经开发了使用基于集合的数据同化方案的台风集合数据同化和预测系统(TEDAPS)和基于天气研究和预报(WRF)模型的多物理场方法。自2015年以来一直在上海台风学院进行实况转播。TEDAPS在预测轨道,已对2015-2018年间西北太平洋TC的强度和相关灾害进行了评估,并与NCEP GEFS进行了比较。与GEFS相比,TEDAPS通过有效减少弱偏差并因此降低了分散程度,从而产生了更好的强度预报。在较长(较短)的预测线索上,TEDAPS跟踪预报的误差与GEFS的误差相比(稍差)。在较长的交货时间中,TEDAPS集合平均法在确定性预报中表现出优势,而在强度预报中,此优势仅限于台风或较弱的TC,这是由于系统性低估所致。对三个降落旋风和一个反曲旋风的四个案例研究证明了TEDAPS预测某些挑战性TC的能力,
更新日期:2019-11-21
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