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Potential Paradigm Shift in Hazard Risk Management: AI-Based Weather Forecast for Tropical Cyclone Hazards

Kairui Feng, Dazhi Xi, Wei Ma, Cao Wang, Yuanlong Li, Xuanhong Chen

TL;DR

This paper investigates AI-based weather forecasting for tropical cyclone risk management and proposes a perturbation-based ensemble for the Pangu forecast to generate thousands of scenarios. It demonstrates that a perturbation level $n=3$ yields uncertainty comparable to ECMWF ensembles. Retrospective tests on Hurricane Irma (2017), Typhoon Mangkhut (2018), and TC Debbie (2017) show the AI-generated ensembles reproduce key trajectory and wind-field patterns up to seven days before landfall, while maintaining computational efficiency for real-time analysis. The work provides open-source code and suggests that AI-driven forecasts could dramatically improve speed, scalability, and global applicability of pre-hazard decision-making, pending broader validation.

Abstract

The advents of Artificial Intelligence (AI)-driven models marks a paradigm shift in risk management strategies for meteorological hazards. This study specifically employs tropical cyclones (TCs) as a focal example. We engineer a perturbation-based method to produce ensemble forecasts using the advanced Pangu AI weather model. Unlike traditional approaches that often generate fewer than 20 scenarios from Weather Research and Forecasting (WRF) simulations for one event, our method facilitates the rapid nature of AI-driven model to create thousands of scenarios. We offer open-source access to our model and evaluate its effectiveness through retrospective case studies of significant TC events: Hurricane Irma (2017), Typhoon Mangkhut (2018), and TC Debbie (2017), affecting regions across North America, East Asia, and Australia. Our findings indicate that the AI-generated ensemble forecasts align closely with the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble predictions up to seven days prior to landfall. This approach could substantially enhance the effectiveness of weather forecast-driven risk analysis and management, providing unprecedented operational speed, user-friendliness, and global applicability.

Potential Paradigm Shift in Hazard Risk Management: AI-Based Weather Forecast for Tropical Cyclone Hazards

TL;DR

This paper investigates AI-based weather forecasting for tropical cyclone risk management and proposes a perturbation-based ensemble for the Pangu forecast to generate thousands of scenarios. It demonstrates that a perturbation level yields uncertainty comparable to ECMWF ensembles. Retrospective tests on Hurricane Irma (2017), Typhoon Mangkhut (2018), and TC Debbie (2017) show the AI-generated ensembles reproduce key trajectory and wind-field patterns up to seven days before landfall, while maintaining computational efficiency for real-time analysis. The work provides open-source code and suggests that AI-driven forecasts could dramatically improve speed, scalability, and global applicability of pre-hazard decision-making, pending broader validation.

Abstract

The advents of Artificial Intelligence (AI)-driven models marks a paradigm shift in risk management strategies for meteorological hazards. This study specifically employs tropical cyclones (TCs) as a focal example. We engineer a perturbation-based method to produce ensemble forecasts using the advanced Pangu AI weather model. Unlike traditional approaches that often generate fewer than 20 scenarios from Weather Research and Forecasting (WRF) simulations for one event, our method facilitates the rapid nature of AI-driven model to create thousands of scenarios. We offer open-source access to our model and evaluate its effectiveness through retrospective case studies of significant TC events: Hurricane Irma (2017), Typhoon Mangkhut (2018), and TC Debbie (2017), affecting regions across North America, East Asia, and Australia. Our findings indicate that the AI-generated ensemble forecasts align closely with the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble predictions up to seven days prior to landfall. This approach could substantially enhance the effectiveness of weather forecast-driven risk analysis and management, providing unprecedented operational speed, user-friendliness, and global applicability.
Paper Structure (6 sections, 3 figures, 1 table)

This paper contains 6 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Summarizing Recent AI-driven weather forecast models
  3. Weather Forecast
  4. Perturbation based Ensemble
  5. Potential Applications and Further Adjustment
  6. Discussion and Conclusion

Figures (3)

  • Figure 1: Comparative Weather Forecasts by Pangu 7 Days Before Landfall: Truth surface Wind speed (ERA5 reanalysis hersbach2020era5; upper panel) vs. Pangu's predictions (lower panel) for a) Hurricane Irma (2017), b) Typhoon Mangkhut (2018), and c) TC Debbie (2017).
  • Figure 2: Comparison of Ensemble Weather Forecasts: TC trajectories under Pangu Perturbations (left panel) vs. ECMWF Ensemble Haiden2018 (right panel) for a) Hurricane Irma (2017), b) Typhoon Mangkhut (2018), and c) TC Debbie (2017).
  • Figure 3: Comparative Analysis of Meteorological Hazard Simulation Scenarios in Decision-Making: WRF vs. AI Models. (a) Limited scenario simulation with WRF: Ensembles triggered by expert judgement. (b) Extensive decision tree simulations using AI: Probabilistic and high-branching. (c) Illustrative comparison between WRF model and AI-driven models.