Tropical cyclone tracks are one of the most critical factors in tropical cyclone forecasting, but there is an inherent uncertainty in their forecasts, but there are no relevant machine learning methods to carry out uncertainty studies. This study proposes an uncertainty forecasting system using machine learning models within a conformal forecasting framework, aiming to provide reliable forecast regions to improve the ability of decision makers to prepare for and respond to potential hazards. This study jointly examines the path forecasting performance of 10 major machine learning models and 10 major conformal forecasting methods through a comparative study. The research work models forecast timescales of 6, 12 and 24 h, and the study covers hurricanes from 1975 to 2021. The experimental results show that the deterministic forecast performance of the model is comparable to the skill of the operated benchmark model, demonstrating that the machine learning model possesses forecasting skill, while also providing tight uncertainty intervals about the path forecast. The method has high prediction accuracy and reliability and is expected to be widely used in the field of tropical cyclone track forecasting and risk communication in the future.

中文翻译：

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基于共形预测的热带气旋路径不确定性预报系统

热带气旋路径是热带气旋预报中最关键的因素之一，但其预报存在固有的不确定性，但目前还没有相关的机器学习方法来开展不确定性研究。本研究提出了一种在共形预测框架内使用机器学习模型的不确定性预测系统，旨在提供可靠的预测区域，以提高决策者准备和应对潜在危险的能力。本研究通过比较研究，共同检验了10种主要机器学习模型和10种主要共形预测方法的路径预测性能。研究工作模型预测的时间尺度为6小时、12小时和24小时，研究范围涵盖1975年至2021年的飓风。实验结果表明，该模型的确定性预测性能与所运行的基准模型的技能相当，表明机器学习模型具有预测能力，同时还提供路径预测的严格不确定性区间。该方法预测精度和可靠性较高，未来有望在热带气旋路径预报和风险沟通领域得到广泛应用。