Tropical cyclone rainfall (TCR) extensively affects coastal communities, primarily through inland flooding. The impact of global climate changes on TCR is complex and debatable. This study uses an XGBoost machine learning model with 19-year meteorological data and hourly satellite precipitation observations to predict TCR for individual storms. The model identifies dust optical depth (DOD) as a key predictor that enhances performance evidently. The model also uncovers a nonlinear and boomerang-shape relationship between Saharan dust and TCR, with a TCR peak at 0.06 DOD and a sharp decrease thereafter. This indicates a shift from microphysical enhancement to radiative suppression at high dust concentrations. The model also highlights meaningful correlations between TCR and meteorological factors like sea surface temperature and equivalent potential temperature near storm cores. These findings illustrate the effectiveness of machine learning in predicting TCR and understanding its driving factors and physical mechanisms.

中文翻译：

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撒哈拉沙尘对大西洋盆地热带气旋降雨的主导作用


热带气旋降雨 (TCR) 主要通过内陆洪水对沿海社区产生广泛影响。全球气候变化对 TCR 的影响是复杂且有争议的。本研究使用 XGBoost 机器学习模型以及 19 年气象数据和每小时卫星降水观测来预测单个风暴的 TCR。该模型将尘埃光学深度 (DOD) 确定为可显着提高性能的关键预测因子。该模型还揭示了撒哈拉尘埃和 TCR 之间的非线性和回旋镖形状关系，TCR 峰值位于 0.06 DOD，此后急剧下降。这表明在高粉尘浓度下从微物理增强到辐射抑制的转变。该模型还强调了 TCR 与海面温度和风暴核心附近的等效潜在温度等气象因素之间的有意义的相关性。这些发现说明了机器学习在预测 TCR 和理解其驱动因素和物理机制方面的有效性。