Revolutionizing Typhoon Forecasting: The Hybrid Shanghai Typhoon Model’s Breakthrough in Predicting Typhoon Danas (2025)
In the relentless quest to enhance the accuracy and timeliness of tropical cyclone forecasts, a pioneering research team from the Shanghai Typhoon Institute and Fudan University has unveiled a transformative evolution of the Shanghai Typhoon Model. This new hybrid forecasting system adeptly combines the strengths of traditional physics-based models with the emerging power of machine learning. Their latest work, focusing on Typhoon Danas in 2025, marks a significant leap forward in typhoon track prediction, promising to reshape how meteorologists prepare for and mitigate the impacts of these devastating storms.
Over decades, numerical weather prediction has been dominated by physics-driven regional models. These models rely on solving complex equations that describe atmospheric dynamics and thermodynamics. While sophisticated parameterization schemes and ensemble forecasting have enhanced their performance, the computational demands have ballooned, challenging real-time operational feasibility. Increasing model resolution and extending ensemble sizes have inevitably led to higher costs, limiting the frequency and scope of accurate forecasts. This backdrop sets the stage for integrating advanced artificial intelligence techniques into meteorology.
The infusion of machine learning, particularly deep learning architectures like convolutional neural networks (CNNs) and transformers, into weather prediction illustrates a paradigm shift. Early machine-learning weather prediction models proved the concept that data-driven methods could analyze vast meteorological datasets and generate rapidly executed forecasts. However, these models excel primarily at coarse, large-scale atmospheric features and often fall short in resolving mesoscale or convective-scale phenomena essential for accurate typhoon intensity and structure predictions. The challenge remains to balance computational speed with physical fidelity in forecasting models.
The Shanghai team’s bold question was whether a hybrid approach, blending the complementary strengths of physics-based and AI-driven models, could overcome these limitations. Physics models effectively simulate mesoscale typhoon structures, while artificial intelligence models better capture voluminous large-scale atmospheric flows. Combining these two fronts could achieve a synergistic enhancement in forecast accuracy, particularly for potentially catastrophic events like typhoons.
Machine learning models developed worldwide, including PanGu, GraphCast, FengWu, FuXi, and the Artificial Intelligence Forecasting System, largely employ the transformer architecture, known for handling sequential data and long-range dependencies. While outperforming traditional physics models on large-scale metrics, they struggle with mesoscale details and often underestimate tropical cyclone intensities. Despite their lightning-fast forecast generation, the high training costs and ongoing accuracy challenges underscore the need for innovative hybrid methodologies.
The Shanghai Typhoon Model embodies this innovation by embedding machine-learning components within a physics-driven framework, creating a hybrid system that leverages the physics model’s mesoscale resolving power and the AI’s ability to capture large-scale atmospheric dynamics. This fusion empowers the model to maintain mean track forecast errors below 200 kilometers for extended lead times up to 108 hours, a feat that surpasses even ECMWF’s Integrated Forecasting System and state-of-the-art ML models like PanGu and FuXi during Typhoon Danas.
Typhoon Danas, which occurred in mid-2025, offered an exemplary testbed for this hybrid forecasting system. Using sequential forecasts initialized from July 5 to July 7, captured vividly in FY-4B satellite visible light imagery, the hybrid model consistently delivered superior track predictions compared to the purely physics-based or fully machine-learning approaches. This robustness in sustained accuracy indicates the hybrid model’s operational viability for real-time typhoon forecasting.
Wei Huang, a leading researcher at the Shanghai Typhoon Institute, emphasized that the hybrid model “used artificial intelligence to anchor the large-scale flow and physics to resolve mesoscale structure,” tackling the core challenge of mesoscale prediction skill that pure ML models face. The success with Danas points toward a new operational forecasting paradigm where hybrid models provide both accuracy and computational efficiency, essential for disaster preparedness and response.
Despite remarkable progress, the hybrid system is not without limitations. Early lead-time forecasts still encounter some challenges, and computational efficiency, while improved, remains constrained by the underlying physics-based elements. Addressing these issues inspires the team’s forward-looking ambition—to develop a purely data-driven, regional machine-learning typhoon forecasting model that maintains uncompromised physics credibility and dramatically enhances forecast speed without sacrificing accuracy.
To this end, ongoing research focuses on refining initial conditions, improving data assimilation techniques, and exploring scenario-specific machine-learning models tailored for tropical cyclone dynamics. This trajectory aligns with the broader meteorological community’s shift toward AI-augmented weather prediction frameworks, anticipating an era where machine learning fully complements or even replaces traditional physics-based models in operational forecasting.
The implications extend beyond regional forecasting: advances cultivated in the Shanghai Typhoon Model set a precedent for hybrid and pure data-driven approaches in other severe weather phenomena worldwide. By marrying the deep physical understanding of the atmosphere with the pattern recognition and computational efficiency of AI, the next generation of weather models could revolutionize early warning systems, risk management, and climate resilience strategies on a global scale.
The team’s full findings were published in the September 18, 2025, issue of Advances in Atmospheric Sciences, marking a milestone in tropical cyclone research. Their work underscores not only the scientific advancement but also the necessity of interdisciplinary collaboration between meteorologists, computer scientists, and data engineers to conceptualize and implement these complex, hybrid forecasting systems.
Looking ahead, the Shanghai team remains committed to pushing the boundary from hybrid towards fully data-driven typhoon forecasting models, while ensuring physical realism remains integral. Success in this endeavor will hinge on advances in both physical modeling—enabling more credible training datasets—and machine learning methodology, ultimately enhancing society’s preparedness against the increasing threats posed by tropical cyclones amid climate change.
As weather prediction enters this new frontier, the Shanghai Typhoon Model’s evolution epitomizes the transformative potential of artificial intelligence harmonized with established physical science. Their groundbreaking case study of Typhoon Danas stands as a beacon illuminating the path toward the future of high-accuracy, efficient, and reliable typhoon forecasting.
Subject of Research: Development and evaluation of hybrid and machine-learning typhoon forecasting models, with a case study on Typhoon Danas (2025).
Article Title: Evaluating the Shanghai Typhoon Model against State-of-the-Art Machine-Learning Weather Prediction Models: A Case Study for Typhoon Danas (2025)
News Publication Date: September 18, 2025
Image Credits: Northwest Pacific Tropical Cyclone Retrieval System, Shanghai Typhoon Institute
