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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

机译:西南印度海洋盆地热带旋风预测评价艺术印度洋对流允许数值天气预报系统

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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. Trajectories and intensities (colors, see caption at top) of all storms tracked by RSMC La Reunion in the SWIO basin during (a) 2017–2018 and (b) 2018–2019 (bottom) TC seasons. The black rectangle in (b) show the footprint of the AROME‐IO model.
机译:为了促进热带气旋(TC)预测的持续努力,在4月份在西南印度洋盆地(SWIO)部署了一个新的,对流允许的,有限的地区耦合模型(AROME-IO) 2016年,对TC预测的这种数控天气预测系统的技能评估了其耦合模型(欧洲中范围天气预报 - 集成的预测系统[ECMWF-IFS]),其中使用了11个主要风暴的120-HR ReforeCast。 TC Seasons 2017-2018和2018-2019的地区。结果表明,Arome-IO通常提供比强度(最大风)和结构(风延长,最大风的半径)在所有转速时间预测的显着更好的性能,在轨迹方面具有类似的性能。在TC Fakir(2018年4月)的情况下,还评估了AROME-IO的原型,12-成员集合预测系统(EPS)的性能,这是全球决定性和集合模型的极低可预测性的风暴。显示AROME-IO EPS显着提高系统的可预测性,并产生了两种情况:最可能的一个(〜66%),这跟随AROME-IO的预测,并将第二个(〜33%)密切相关匹配现实。在SWIO盆地在(a)2017-2018和(b)2018-2019(bottom)TC Seasons期间,在SwioMC La Reunion追踪的所有风暴(A)2018-2019(底部)TC Seasons的所有风暴的轨迹和强度(顶部有顶部) (b)中的黑色矩形显示了Arome-IO模型的占地面积。

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