Tropical cyclones are dangerous natural hazards, but their hazard is challenging to quantify directly from historical datasets due to limited dataset size and quality. Models of cyclone intensification fill this data gap by simulating huge ensembles of synthetic hurricanes based on estimates of the storm's large scale environment. Both physics-based and statistical/ML intensification models have been developed to tackle this problem, but an open question is: can a physically reasonable and simple physics-style differential equation model of intensification be learned from data? In this paper, we answer this question in the affirmative by presenting a 10-term cubic stochastic differential equation model of Tropical Cyclone intensification. The model depends on a well-vetted suite of engineered environmental features known to drive intensification and is trained using a high quality dataset of hurricane intensity (IBTrACS) with estimates of the cyclone's large scale environment from a data-assimilated simulation (ERA5 reanalysis), restricted to the Northern Hemisphere. The model generates synthetic intensity series which capture many aspects of historical intensification statistics and hazard estimates in the Northern Hemisphere. Our results show promise that interpretable, physics style models of complex earth system dynamics can be learned using automated system identification techniques.

翻译：热带气旋是危险的自然灾害，但由于数据集规模和质量有限，直接依据历史数据集量化其危害具有挑战性。气旋增强模型通过基于风暴大尺度环境估计模拟大量合成飓风集合，填补了这一数据空白。目前已有基于物理学的模型和统计/机器学习增强模型被开发用于解决此问题，但一个悬而未决的问题是：能否从数据中学习到一个物理上合理且简单的物理学风格增强微分方程模型？本文通过提出一个包含10项的三次随机微分方程模型来肯定地回答这个问题，该模型用于描述热带气旋增强过程。该模型依赖于一套经过充分验证的、已知能驱动增强的工程化环境特征，并使用高质量飓风强度数据集（IBTrACS）进行训练，其中气旋的大尺度环境估计来自数据同化模拟（ERA5再分析），且数据范围限定在北半球。该模型生成的合成强度序列能够捕捉北半球历史增强统计和危害估计的多个方面。我们的结果表明，利用自动化系统辨识技术学习复杂地球系统动力学的可解释、物理学风格模型具有广阔前景。