An evaluation of tropical cyclone forecast in the Southwest Indian Ocean basin with AROME‐Indian Ocean convection‐permitting numerical weather predicting system,Atmospheric Science Letters

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An evaluation of tropical cyclone forecast in the Southwest Indian Ocean basin with AROME‐Indian Ocean convection‐permitting numerical weather predicting system
Atmospheric Science Letters ( IF 2.0 ) Pub Date : 2019-12-14 , DOI: 10.1002/asl2.950
Olivier Bousquet ₁ , David Barbary ₂ , Soline Bielli ₁ , Selim Kebir ₁ , Laure Raynaud ₃ , Sylvie Malardel ₁ , Ghislain Faure ₃

Affiliation

In order to contribute to ongoing efforts on tropical cyclone (TC) forecasting, a new, convection‐permitting, limited‐area coupled model called AROME‐Indian Ocean (AROME‐IO) was deployed in the Southwest Indian Ocean basin (SWIO) in April 2016. The skill of this numerical weather predicting system for TC prediction is evaluated against its coupling model (European Center for Medium Range Weather Forecasting‐Integrated Forecasting System [ECMWF‐IFS]) using 120‐hr reforecasts of 11 major storms that developed in this area over TC seasons 2017–2018 and 2018–2019. Results show that AROME‐IO generally provides significantly better performance than IFS for intensity (maximum wind) and structure (wind extensions, radius of maximum wind) forecasts at all lead times, with similar performance in terms of trajectories. The performance of a prototype, 12‐member ensemble prediction system (EPS), of AROME‐IO is also evaluated on the case of TC Fakir (April 2018), a storm characterized by an extremely low predictability in global deterministic and ensemble models. AROME‐IO EPS is shown to significantly improve the predictability of the system with two scenarios being produced: a most probable one (~66%), which follows the prediction of AROME‐IO, and a second one (~33%) that closely matches reality.

中文翻译：

利用AROME-印度洋对流允许数值天气预报系统评估西南印度洋盆地的热带气旋

为了促进正在进行的热带气旋（TC）预报工作，4月在西南印度洋海盆（SWIO）部署了一种新的对流允许的有限区域耦合模型，称为AROME-印度洋（AROME-IO）。 2016年。根据此耦合模型（欧洲中距离天气预报集成预报系统[ECMWF-IFS]中心）的耦合模型，使用该预报中产生的120小时重预报，评估了此数字天气预报系统的技能。 2017–2018和2018–2019 TC季节的面积。结果表明，对于所有提前期的强度（最大风）和结构（风向扩展，最大风半径）的预测，AROME‐IO通常提供比IFS更好的性能，在轨迹方面，性能相似。原型的性能，还以TC Fakir（2018年4月）为例评估了AROME‐IO的12人集合预测系统（EPS），这是一场风暴，其特征在于全局确定性和集合模型的可预测性极低。事实证明，AREMO-IO EPS可以显着提高系统的可预测性，其中产生了两种情况：一种最有可能（约66％）紧随AROME-IO的预测，另一种则很接近（约33％）符合现实。

更新日期：2019-12-14

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