Heavy rainfall events, typically associated with tropical cyclones (TCs), provoke intense flooding, consequently causing severe losses to life and property. Therefore, the amount and distribution of rain associated with TCs must be forecasted precisely within a reasonable time to guarantee the protection of lives and goods. In this study, the skill of the Numerical Tool for Hurricane Forecast (NTHF) for determining rainfall pattern, average rainfall, rainfall volume, and extreme amounts of rain observed during TCs is evaluated against Tropical Rainfall Measuring Mission (TRMM) data. A sample comprising nine systems formed in the North Atlantic basin from 2016 to 2018 is used, where the analysis begins 24 h before landfall. Several statistical indices characterising the abilities of the NTHF and climatology and persistence model for rainfalls (R-CLIPER) for forecasting rain as measured by the TRMM are calculated at 24, 48, and 72 h forecasts for each TC and averaged. The model under consideration presents better forecasting skills than the R-CLIPER for all the attributes evaluated and demonstrates similar performances compared with models reported in the literature. The proposed model predicts the average rainfall well and presents a good description of the rain pattern. However, its forecast of extreme rain is only applicable for 24 h.

中文翻译：

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系统降水预报评估：飓风预报的数值工具

通常与热带气旋（TCs）相关的强降雨事件引发了严重的洪灾，因此造成严重的生命财产损失。因此，必须在合理的时间内准确预测与TC相关的雨水的数量和分布，以确保对生命和财产的保护。在这项研究中，根据热带降雨测量任务（TRMM）数据评估了飓风预报数字工具（NTHF）确定TC期间观测到的降雨模式，平均降雨量，降雨量和极端降雨的能力。使用了一个样本，该样本由2016年至2018年在北大西洋盆地形成的九个系统组成，在该分析开始于登陆前24小时开始。由TRMM测得的表征NTHF能力，气候和降雨持久性模型（R-CLIPER）的几个统计指标（通过TRMM测量）在每个TC的24、48和72 h预测中计算并取平均值。对于所评估的所有属性，与R-CLIPER相比，所考虑的模型具有更好的预测技能，并且与文献报道的模型相比，其表现出相似的性能。所提出的模型可以很好地预测平均降雨量，并很好地描述了降雨模式。但是，其对极端降雨的预测仅适用于24小时。对于所评估的所有属性，所考虑的模型比R-CLIPER表现出更好的预测技能，并且与文献报道的模型相比，其表现出相似的性能。所提出的模型可以很好地预测平均降雨量，并很好地描述了降雨模式。但是，其对极端降雨的预测仅适用于24小时。对于所评估的所有属性，所考虑的模型比R-CLIPER表现出更好的预测技能，并且与文献报道的模型相比，其表现出相似的性能。所提出的模型可以很好地预测平均降雨量，并很好地描述了降雨模式。但是，其对极端降雨的预测仅适用于24小时。