Hydrological models that are used to analyse flood risk induced by tropical cyclones often input ERA5 reanalysis data. However, ERA5 precipitation has large systematic biases, especially over heavy precipitation events like Tropical Cyclones, compromising its usefulness in such scenarios. Few studies to date have performed bias correction of ERA5 precipitation and none of them for extreme rainfall induced by tropical cyclones. Additionally, most existing works on bias adjustment focus on adjusting pixel-wise metrics of bias, such as the Mean Squared Error (MSE). However, it is equally important to ensure that the rainfall peaks are correctly located within the rainfall maps, especially if these maps are then used as input to hydrological models. In this paper, we describe a novel machine learning model that addresses both gaps, RA-Ucmpd, based on the popular U-Net model. The key novelty of RA-Ucmpd is its loss function, the compound loss, which optimizes both a pixel-wise bias metric (the MSE) and a spatial verification metric (a modified version of the Fractions Skill Score). Our results show how RA-Ucmpd improves ERA5 in almost all metrics by 3-28%—more than the other models we used for comparison which actually worsen the total rainfall bias of ERA5—at the cost of a slightly increased (3%) error on the magnitude of the peak. We analyse the behaviour of RA-Ucmpd by visualizing accumulated maps of four particularly wet tropical cyclones and by dividing our data according to the Saffir-Simpson scale and to whether they made landfall, and we perform an error analysis to understand under what conditions our model performs best.

中文翻译：

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使用深度学习对 ERA5 中的极端热带气旋降雨进行降尺度、偏差校正和空间调整


用于分析热带气旋引起的洪水风险的水文模型通常输入 ERA5 再分析数据。然而，ERA5 降水具有较大的系统偏差，尤其是在热带气旋等强降水事件中，从而影响了其在这种情况下的实用性。迄今为止，很少有研究对 ERA5 降水进行偏倚校正，也没有一项研究对热带气旋引起的极端降雨进行偏倚校正。此外，大多数现有的偏差调整工作都侧重于调整偏差的像素级指标，例如均方误差 （MSE）。但是，确保降雨峰值在降雨图中正确定位同样重要，尤其是在将这些地图用作水文模型的输入时。在本文中，我们描述了一种基于流行的 U-Net 模型的新型机器学习模型 RA-UCMPD，它解决了这两个差距。RA-Ucmpd 的关键新颖之处在于其损失函数，即化合物损失，它优化了像素偏差度量 （MSE） 和空间验证度量（分数技能分数的修改版本）。我们的结果表明，RA-Ucmpd 如何在几乎所有指标上将 ERA5 提高 3-28%——比我们用于比较的其他模型更多，这些模型实际上加剧了 ERA5 的总降雨偏差——代价是峰值幅度的误差略有增加 （3%）。我们通过可视化四个特别潮湿的热带气旋的累积地图并根据 Saffir-Simpson 量表和它们是否登陆来划分我们的数据来分析 RA-Ucmpd 的行为，并进行误差分析以了解我们的模型在什么条件下表现最佳。