Uncertainty forecasting system for tropical cyclone tracks based on conformal prediction

Abstract

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.