Cover Image

ML-Physical Fusion Models Are Accelerating the Paradigm Shift in Operational Typhoon Forecasting

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Niu, Zeyi
Format:	Preprint
Published:	2025
Subjects:	Atmospheric and Oceanic Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Machine Learning (ML)-Physics Fusion Model Outperforms Both Physics-Only and ML-Only Models in Typhoon Predictions
by: Niu, Zeyi, et al.
Published: (2025)

Improving Typhoon Predictions by Integrating Data-Driven Machine Learning Models with Physics Models Based on the Spectral Nudging and Data Assimilation
by: Niu, Zeyi, et al.
Published: (2024)

Intelligent Shanghai Typhoon Model (ISTM): A generative probabilistic emulator for typhoon hybrid modeling
by: Niu, Zeyi, et al.
Published: (2025)

A Data-Driven Regional Model for Skillful Medium-Range Typhoon Prediction
by: Niu, Zeyi, et al.
Published: (2026)

Seasonal Changes -- Time for Paradigm Shift
by: Lalic, Branislava, et al.
Published: (2025)

Typhoon Tracks Regulated by Feedbacks of Fine-Scale Clouds to Environment
by: Zhao, Haoran, et al.
Published: (2025)

Assessing the Risks of Typhoon-Induced Multi-Hazards in South Korea
by: Liu, Ziyue, et al.
Published: (2025)

Insights from Ex-Typhoon Halong (2025) -- An Arctic Cyclone of Tropical Origin
by: Yang, Mingshi, et al.
Published: (2026)

A Simulation Methodology Testbed for Typhoon Sensitivity Analysis: Framework Development and Perturbation-Response Experiments with the Pangu Weather Model
by: Peng, Yuehua, et al.
Published: (2026)

Deep-Learned Observation Operators for Artificial Intelligence Weather Forecasting Models
by: Lieberman, Kelsey, et al.
Published: (2026)

The Jive Verification System and its Transformative Impact on Weather Forecasting Operations
by: Loveday, Nicholas, et al.
Published: (2024)

AI Models Still Lag Behind Traditional Numerical Models in Predicting Sudden-Turning Typhoons
by: Xu, Daosheng, et al.
Published: (2025)

FuXi-TC: A generative framework integrating deep learning and physics-based models for improved tropical cyclone forecasts
by: Guo, Shan, et al.
Published: (2025)

StormDiT: A generative AI model bridges the 2-6 hour 'gray zone' in precipitation nowcasting
by: Sun, Haofei, et al.
Published: (2026)

Assimilating Observed Surface Pressure into ML Weather Prediction Models
by: Slivinski, Laura C., et al.
Published: (2024)

Intraseasonal Equatorial Kelvin and Rossby Waves in Modern AI-ML Models
by: Jalan, Shrutee, et al.
Published: (2025)

Amplified Urban Climate Extremes from Global Warming-Urbanization Synergy: A Physics-Informed Intelligence Paradigm
by: Wu, Qiuxia, et al.
Published: (2026)

Assessment of Abrupt Shifts in CMIP6 Models using Edge Detection
by: Terpstra, Sjoerd, et al.
Published: (2024)

Comparing Ocean Forecasts Driven with Machine Learning-based and Physics-based Atmospheric Forcings
by: Zhou, Xiaobing, et al.
Published: (2026)

How does an AI Weather Model Learn to Forecast Extreme Weather?
by: Baiman, Rebecca, et al.
Published: (2025)

Evaluating the Predictability of Selected Weather Extremes with Aurora, an AI Weather Forecast Model
by: Huang, Qin, et al.
Published: (2026)

MAG-Net: Physics-Aware Multi-Modal Fusion of Geostationary Satellite and Radar for Severe Convective Precipitation Nowcasting
by: Chen, Dandan, et al.
Published: (2026)

BiXiao: An AI-Based Atmospheric Environment Forecasting Model Using Discontinuous Grids
by: Ji, Shengxuan, et al.
Published: (2025)

XiHe: A Data-Driven Model for Global Ocean Eddy-Resolving Forecasting
by: Wang, Xiang, et al.
Published: (2024)

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction
by: Subramaniam, Akshay, et al.
Published: (2025)

Improving Post-Processing for Quantitative Precipitation Forecasting Using Deep Learning: Learning Precipitation Physics from High-Resolution Observations
by: Lee, ChangJae, et al.
Published: (2025)

ACE2-NEMO: Coupling an ML atmospheric emulator to a full-depth dynamical ocean model
by: Antonio, Bobby, et al.
Published: (2026)

Impact of Forecast Stability on Navigational Contrail Avoidance
by: Dean, Thomas R, et al.
Published: (2025)

Forecast error diagnostics in neural weather models
by: Perkan, Uros, et al.
Published: (2025)

YingLong-weather: AI-Based Limited Area Models for Forecasting of Non-precipitation Surface Meteorological Variables
by: Xu, Pengbo, et al.
Published: (2024)

Investigating How Neighbourhood Scores Reflect Forecast Error
by: Antonio, Bobby
Published: (2026)

Forecast error growth: A dynamic-stochastic model
by: Bach, Eviatar, et al.
Published: (2024)

A Cloud Condensation Model on Insoluble Nuclei Validated with Radiosonde and Ceilometer Data: Implications for Dust based Rainfall Forecasting
by: Arielly, Rani, et al.
Published: (2026)

ACE2-SOM: Coupling an ML atmospheric emulator to a slab ocean and learning the sensitivity of climate to changed CO$_2$
by: Clark, Spencer K., et al.
Published: (2024)

Diffusion-based Probabilistic Air Quality Forecasting with Mechanistic Insight
by: Ding, Ao, et al.
Published: (2026)

Understanding Left-Moving Supercells: Environmental Factors and Forecasting Challenges
by: Zeeb, Aaron W., et al.
Published: (2026)

Evaluating Extreme Precipitation Forecasts: A Threshold-Weighted, Spatial Verification Approach for Comparing an AI Weather Prediction Model Against a High-Resolution NWP Model
by: Loveday, Nicholas, et al.
Published: (2025)

Meteorologically-Informed Adaptive Conformal Prediction for Tropical Cyclone Intensity Forecasting
by: Chen, Xuepeng, et al.
Published: (2025)

Modulated Adaptive Fourier Neural Operators for Temporal Interpolation of Weather Forecasts
by: Leinonen, Jussi, et al.
Published: (2024)

Physics of an AMOC Overshoot in a Box Model
by: Soons, Jelle, et al.
Published: (2025)