TROPHY: A Topologically Robust Physics-Informed Tracking Framework for Tropical Cyclones.

Area of Science:

• Atmospheric Science
• Geophysics
• Climate Science

Background:

• Tropical cyclones (TCs) are destructive weather systems, necessitating accurate tracking for risk assessment and forecasting.
• Existing TC tracking methods often require complex, multi-variable data and can be computationally intensive.
• A recent multilevel robustness framework shows promise for TC tracking using 2D wind fields but faces scalability challenges.

Purpose of the Study:

• To introduce a novel, computationally efficient framework for tracking tropical cyclones (TCs).
• To enhance the scalability of existing robustness-based tracking methods for large climate datasets.
• To improve the accuracy and reliability of TC detection and characteristic analysis.

Main Methods:

• Developed a topologically robust physics-informed tracking framework (TROPHY) integrating physical knowledge of TCs.
• Implemented a physics-informed feature selection strategy to filter out unstable critical points during preprocessing.
• Applied constraints during multilevel robustness computation to focus on physics-informed neighborhoods, improving efficiency.

Main Results:

• TROPHY was applied to 30 years of ERA5 reanalysis data, generating numerous TC tracks.
• The framework successfully captured key TC characteristics, including frequency, intensity, duration, and genesis.
• Performance was comparable or superior to a validated multi-variable tracking algorithm, demonstrating TROPHY's effectiveness.

Conclusions:

• TROPHY offers a computationally efficient and accurate method for tracking tropical cyclones using 2D wind fields.
• The physics-informed approach significantly improves the scalability of robustness-based tracking for large-scale climate data.
• This framework advances the operational forecasting and climate risk assessment of tropical cyclones.