Advancing seasonal prediction of tropical cyclone activity with a hybrid AI-physics climate model
Machine learning (ML) models are successful with weather forecasting and have shown progress in climate simulations, yet leveraging them for useful climate predictions needs exploration. Here we show this feasibility using neural general circulation model (NeuralGCM), a hybrid ML-physics atmospheric...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Environmental Research Letters |
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| Online Access: | https://doi.org/10.1088/1748-9326/adf864 |
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| _version_ | 1849390315012620288 |
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| author | Gan Zhang Megha Rao Janni Yuval Ming Zhao |
| author_facet | Gan Zhang Megha Rao Janni Yuval Ming Zhao |
| author_sort | Gan Zhang |
| collection | DOAJ |
| description | Machine learning (ML) models are successful with weather forecasting and have shown progress in climate simulations, yet leveraging them for useful climate predictions needs exploration. Here we show this feasibility using neural general circulation model (NeuralGCM), a hybrid ML-physics atmospheric model developed by Google, for seasonal predictions of large-scale atmospheric variability and Northern Hemisphere tropical cyclone (TC) activity. Inspired by physical model studies, we simplify boundary conditions, assuming sea surface temperature and sea ice follow their climatological cycle but persist anomalies present at the initialization time. With such forcings, NeuralGCM can generate 100 simulation days in ∼8 min with a single graphics processing unit while simulating realistic atmospheric circulation and TC climatology patterns. This configuration yields useful seasonal predictions (July–November) for the tropical atmosphere and various TC activity metrics. Notably, the predicted and observed TC frequency in the North Atlantic and East Pacific basins are significantly correlated during 1990–2023 ( r = ∼0.7), suggesting prediction skill comparable to existing physical GCMs. Despite challenges associated with model resolution and simplified boundary forcings, the model-predicted interannual variations demonstrate significant correlations with the observed sub-basin TC tracks ( p < 0.1) and basin-wide accumulated cyclone energy (ACE) ( p < 0.01) of the North Atlantic and North Pacific basins. These findings highlight the promise of leveraging ML models with physical insights to model TC risks and deliver seamless weather-climate predictions. |
| format | Article |
| id | doaj-art-26a3297fc474419f93278ce4b0b0f5a6 |
| institution | Kabale University |
| issn | 1748-9326 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj-art-26a3297fc474419f93278ce4b0b0f5a62025-08-20T03:41:43ZengIOP PublishingEnvironmental Research Letters1748-93262025-01-0120909403110.1088/1748-9326/adf864Advancing seasonal prediction of tropical cyclone activity with a hybrid AI-physics climate modelGan Zhang0https://orcid.org/0000-0002-7323-3409Megha Rao1Janni Yuval2Ming Zhao3Department of Climate, Meteorology, and Atmospheric Sciences, University of Illinois at Urbana-Champaign , Urbana, IL 61801, United States of AmericaDepartment of Climate, Meteorology, and Atmospheric Sciences, University of Illinois at Urbana-Champaign , Urbana, IL 61801, United States of AmericaGoogle Research , Mountain View, CA 94043, United States of AmericaGeophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration , Princeton, NJ 08540, United States of AmericaMachine learning (ML) models are successful with weather forecasting and have shown progress in climate simulations, yet leveraging them for useful climate predictions needs exploration. Here we show this feasibility using neural general circulation model (NeuralGCM), a hybrid ML-physics atmospheric model developed by Google, for seasonal predictions of large-scale atmospheric variability and Northern Hemisphere tropical cyclone (TC) activity. Inspired by physical model studies, we simplify boundary conditions, assuming sea surface temperature and sea ice follow their climatological cycle but persist anomalies present at the initialization time. With such forcings, NeuralGCM can generate 100 simulation days in ∼8 min with a single graphics processing unit while simulating realistic atmospheric circulation and TC climatology patterns. This configuration yields useful seasonal predictions (July–November) for the tropical atmosphere and various TC activity metrics. Notably, the predicted and observed TC frequency in the North Atlantic and East Pacific basins are significantly correlated during 1990–2023 ( r = ∼0.7), suggesting prediction skill comparable to existing physical GCMs. Despite challenges associated with model resolution and simplified boundary forcings, the model-predicted interannual variations demonstrate significant correlations with the observed sub-basin TC tracks ( p < 0.1) and basin-wide accumulated cyclone energy (ACE) ( p < 0.01) of the North Atlantic and North Pacific basins. These findings highlight the promise of leveraging ML models with physical insights to model TC risks and deliver seamless weather-climate predictions.https://doi.org/10.1088/1748-9326/adf864climate predictionmachine learningtropical cycloneclimate modelmodel evaluation |
| spellingShingle | Gan Zhang Megha Rao Janni Yuval Ming Zhao Advancing seasonal prediction of tropical cyclone activity with a hybrid AI-physics climate model Environmental Research Letters climate prediction machine learning tropical cyclone climate model model evaluation |
| title | Advancing seasonal prediction of tropical cyclone activity with a hybrid AI-physics climate model |
| title_full | Advancing seasonal prediction of tropical cyclone activity with a hybrid AI-physics climate model |
| title_fullStr | Advancing seasonal prediction of tropical cyclone activity with a hybrid AI-physics climate model |
| title_full_unstemmed | Advancing seasonal prediction of tropical cyclone activity with a hybrid AI-physics climate model |
| title_short | Advancing seasonal prediction of tropical cyclone activity with a hybrid AI-physics climate model |
| title_sort | advancing seasonal prediction of tropical cyclone activity with a hybrid ai physics climate model |
| topic | climate prediction machine learning tropical cyclone climate model model evaluation |
| url | https://doi.org/10.1088/1748-9326/adf864 |
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