Work

Beyond isotropic uncertainty in tropical cyclone tracks with adaptive conformal prediction

Year: 2026

Type: article

Abstract: Tropical cyclones are among the most destructive natural hazards, necessitating reliable uncertainty quantification in track forecasting. However, traditional probability prediction has problems such as isotropic regions and difficulty in reflecting geometric distortion. This paper proposes an adaptive uncertainty quantification framework based on distribution-free conformal prediction. This framework utilizes deep learning models to construct anisotropic probability ellipses and integrates an online calibration mechanism to achieve joint dynamic adjustment of the ellipse scale and principal axis direction. Results demonstrate our proposed framework moves beyond conventional methods, offering a more expressive and operationally relevant characterization of forecast uncertainty.

Source: npj natural hazards.

DOI: 10.1038/s44304-026-00254-z

Authors: Xuepeng Chen, Yaqiang Wang, Jing-Jia Luo, Fan Meng

Institutions: China Meteorological Administration, Nanjing University of Information Science and Technology, Chinese Academy of Meteorological Sciences, State Key Laboratory of Severe Weather

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: $2,090