Evaluating Global Machine Learning Models for Tropical Cyclone Dynamics and Thermodynamics
Abstract Machine Learning Weather Prediction (MLWP) models have recently demonstrated remarkable potential to rival physics‐based Numerical Weather Prediction (NWP) models, offering global weather forecasts at a fraction of the computational cost. However, thorough evaluations are essential before c...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
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| Online Access: | https://doi.org/10.1029/2025JH000594 |
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| author | Pankaj Lal Sahu Sukumaran Sandeep Hariprasad Kodamana |
| author_facet | Pankaj Lal Sahu Sukumaran Sandeep Hariprasad Kodamana |
| author_sort | Pankaj Lal Sahu |
| collection | DOAJ |
| description | Abstract Machine Learning Weather Prediction (MLWP) models have recently demonstrated remarkable potential to rival physics‐based Numerical Weather Prediction (NWP) models, offering global weather forecasts at a fraction of the computational cost. However, thorough evaluations are essential before considering MLWP models as replacements for NWP models. This study presents a comprehensive evaluation of four leading MLWP models—GraphCast, PanguWeather, Aurora, and FourCastNet—against observations and three state‐of‐the‐art NWP models in predicting tropical cyclones (TCs) across all tropical ocean basins. All MLWP models exhibited strong skill in forecasting TC tracks, achieving an average track error of less than 200 km at a 96‐hr forecast lead time. However, they consistently underestimated maximum sustained wind speeds compared to NWP models and observations. The low bias in TC intensity forecasts by MLWP models is linked to similar bias in their training data, along with the double penalization effect. MLWP models realistically captured the absolute vorticity patterns and their advection, demonstrating their ability to represent the dynamics underlying TC translation. They also captured the low‐level convergence and vertical warm core structure of TCs, although the magnitudes were weaker than observed, highlighting the linkage between dynamical and thermodynamical processes. The consistency in magnitude between various physical fields in the MLWP models suggests that they intuitively learn the interrelationships among different physical fields during the evolution of weather systems, demonstrating their ability to capture complex physical interactions. Among the MLWP models, Aurora showed superior performance, surpassing GraphCast, PanguWeather, and FourCastNet. |
| format | Article |
| id | doaj-art-4cbc09fbf2cf4b78a47c1ca548ade027 |
| institution | Kabale University |
| issn | 2993-5210 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-4cbc09fbf2cf4b78a47c1ca548ade0272025-08-20T03:27:37ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102025-06-0122n/an/a10.1029/2025JH000594Evaluating Global Machine Learning Models for Tropical Cyclone Dynamics and ThermodynamicsPankaj Lal Sahu0Sukumaran Sandeep1Hariprasad Kodamana2Centre for Atmospheric Sciences Indian Institute of Technology Delhi New Delhi IndiaCentre for Atmospheric Sciences Indian Institute of Technology Delhi New Delhi IndiaYardi School of Artificial Intelligence Indian Institute of Technology Delhi New Delhi IndiaAbstract Machine Learning Weather Prediction (MLWP) models have recently demonstrated remarkable potential to rival physics‐based Numerical Weather Prediction (NWP) models, offering global weather forecasts at a fraction of the computational cost. However, thorough evaluations are essential before considering MLWP models as replacements for NWP models. This study presents a comprehensive evaluation of four leading MLWP models—GraphCast, PanguWeather, Aurora, and FourCastNet—against observations and three state‐of‐the‐art NWP models in predicting tropical cyclones (TCs) across all tropical ocean basins. All MLWP models exhibited strong skill in forecasting TC tracks, achieving an average track error of less than 200 km at a 96‐hr forecast lead time. However, they consistently underestimated maximum sustained wind speeds compared to NWP models and observations. The low bias in TC intensity forecasts by MLWP models is linked to similar bias in their training data, along with the double penalization effect. MLWP models realistically captured the absolute vorticity patterns and their advection, demonstrating their ability to represent the dynamics underlying TC translation. They also captured the low‐level convergence and vertical warm core structure of TCs, although the magnitudes were weaker than observed, highlighting the linkage between dynamical and thermodynamical processes. The consistency in magnitude between various physical fields in the MLWP models suggests that they intuitively learn the interrelationships among different physical fields during the evolution of weather systems, demonstrating their ability to capture complex physical interactions. Among the MLWP models, Aurora showed superior performance, surpassing GraphCast, PanguWeather, and FourCastNet.https://doi.org/10.1029/2025JH000594machine learning based weather predictiontropical cycloneslearning of physics |
| spellingShingle | Pankaj Lal Sahu Sukumaran Sandeep Hariprasad Kodamana Evaluating Global Machine Learning Models for Tropical Cyclone Dynamics and Thermodynamics Journal of Geophysical Research: Machine Learning and Computation machine learning based weather prediction tropical cyclones learning of physics |
| title | Evaluating Global Machine Learning Models for Tropical Cyclone Dynamics and Thermodynamics |
| title_full | Evaluating Global Machine Learning Models for Tropical Cyclone Dynamics and Thermodynamics |
| title_fullStr | Evaluating Global Machine Learning Models for Tropical Cyclone Dynamics and Thermodynamics |
| title_full_unstemmed | Evaluating Global Machine Learning Models for Tropical Cyclone Dynamics and Thermodynamics |
| title_short | Evaluating Global Machine Learning Models for Tropical Cyclone Dynamics and Thermodynamics |
| title_sort | evaluating global machine learning models for tropical cyclone dynamics and thermodynamics |
| topic | machine learning based weather prediction tropical cyclones learning of physics |
| url | https://doi.org/10.1029/2025JH000594 |
| work_keys_str_mv | AT pankajlalsahu evaluatingglobalmachinelearningmodelsfortropicalcyclonedynamicsandthermodynamics AT sukumaransandeep evaluatingglobalmachinelearningmodelsfortropicalcyclonedynamicsandthermodynamics AT hariprasadkodamana evaluatingglobalmachinelearningmodelsfortropicalcyclonedynamicsandthermodynamics |