Work

Meteorologically‐Informed Adaptive Conformal Prediction for Tropical Cyclone Intensity Forecasting

Year: 2025

Type: article

Abstract: Rapid intensification of tropical cyclones poses a great challenge due to their highly nonlinear dynamics and inherent uncertainties. Conventional statistical dynamics and artificial intelligence prediction models typically rely on static parameterization schemes, which limits their ability to capture the non‐stationary error structure in the intensity evolution. To address this issue, we propose a physically‐inspired covariate adaptive conformal prediction framework that dynamically adjusts uncertainty quantification by incorporating process information such as intensity and evolutionary stage. Our approach not only surpasses state‐of‐the‐art models in point prediction accuracy but also delivers physically consistent and interpretable forecast intervals, establishing a more process‐aware framework for probabilistic prediction of extreme weather events.

Source: Journal of Geophysical Research Machine Learning and Computation

DOI: 10.1029/2025jh000830

Authors: Xuepeng Chen, Jing‐Jia Luo, Qingqing Li, Fan Meng

Institution: Nanjing University of Information Science and Technology

Language: English

Topic: Tropical and Extratropical Cyclones Research

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,500