Enhancing Near Real Time AI-NWP Hurricane Forecasts: Improving Explainability and Performance Through Physics-Based Models and Land Surface Feedback Article Swipe
Naveen Sudharsan
,
Manmeet Singh
,
Shamitaksha Talukdar
,
Shyama Mohanty
,
Harsh Kamath
,
Krishna K. Osuri
,
Hassan Dashtian
,
Michael H. Young
,
Zong‐Liang Yang
,
Clint Dawson
,
L. Ruby Leung
,
Sundararaman Gopalakrishnan
,
Avichal Mehra
,
Vijay Tallapragada
,
Dev Niyogi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.01797
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.01797
Hurricane track forecasting remains a significant challenge due to the complex interactions between the atmosphere, land, and ocean. Although AI-based numerical weather prediction models, such as Google Graphcast operation, have significantly improved hurricane track forecasts, they currently function as atmosphere-only models, omitting critical land and ocean interactions. To investigate the impact of land feedback, we conducted independent simulations using the physics-based Hurricane WRF experimental model to assess how soil moisture variations influence storm trajectories. Our results show that land surface conditions significantly alter storm paths, demonstrating the importance of land-atmosphere coupling in hurricane prediction. Although recent advances have introduced AI-based atmosphere-ocean coupled models, a fully functional AI-driven atmosphere-land-ocean model does not yet exist. Our findings suggest that AI-NWP models could be further improved by incorporating land surface interactions, improving both forecast accuracy and explainability. Developing a fully coupled AI-based weather model would mark a critical step toward more reliable and physically consistent hurricane forecasting, with direct applications for disaster preparedness and risk mitigation.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2502.01797
- https://arxiv.org/pdf/2502.01797
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407184880
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407184880Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2502.01797Digital Object Identifier
- Title
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Enhancing Near Real Time AI-NWP Hurricane Forecasts: Improving Explainability and Performance Through Physics-Based Models and Land Surface FeedbackWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-02-03Full publication date if available
- Authors
-
Naveen Sudharsan, Manmeet Singh, Shamitaksha Talukdar, Shyama Mohanty, Harsh Kamath, Krishna K. Osuri, Hassan Dashtian, Michael H. Young, Zong‐Liang Yang, Clint Dawson, L. Ruby Leung, Sundararaman Gopalakrishnan, Avichal Mehra, Vijay Tallapragada, Dev NiyogiList of authors in order
- Landing page
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https://arxiv.org/abs/2502.01797Publisher landing page
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https://arxiv.org/pdf/2502.01797Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2502.01797Direct OA link when available
- Concepts
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Meteorology, Environmental science, Surface (topology), Numerical weather prediction, Climatology, Aerospace engineering, Computer science, Physics, Geology, Engineering, Mathematics, GeometryTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.complex | 10 |
| abstract_inverted_index.coupled | 101, 137 |
| abstract_inverted_index.further | 121 |
| abstract_inverted_index.models, | 23, 40, 102 |
| abstract_inverted_index.remains | 3 |
| abstract_inverted_index.results | 75 |
| abstract_inverted_index.suggest | 115 |
| abstract_inverted_index.surface | 79, 126 |
| abstract_inverted_index.weather | 21, 139 |
| abstract_inverted_index.AI-based | 19, 99, 138 |
| abstract_inverted_index.Although | 18, 94 |
| abstract_inverted_index.accuracy | 131 |
| abstract_inverted_index.advances | 96 |
| abstract_inverted_index.coupling | 90 |
| abstract_inverted_index.critical | 42, 144 |
| abstract_inverted_index.disaster | 158 |
| abstract_inverted_index.findings | 114 |
| abstract_inverted_index.forecast | 130 |
| abstract_inverted_index.function | 37 |
| abstract_inverted_index.improved | 31, 122 |
| abstract_inverted_index.moisture | 69 |
| abstract_inverted_index.omitting | 41 |
| abstract_inverted_index.reliable | 148 |
| abstract_inverted_index.AI-driven | 106 |
| abstract_inverted_index.Graphcast | 27 |
| abstract_inverted_index.Hurricane | 0, 61 |
| abstract_inverted_index.challenge | 6 |
| abstract_inverted_index.conducted | 55 |
| abstract_inverted_index.currently | 36 |
| abstract_inverted_index.feedback, | 53 |
| abstract_inverted_index.hurricane | 32, 92, 152 |
| abstract_inverted_index.improving | 128 |
| abstract_inverted_index.influence | 71 |
| abstract_inverted_index.numerical | 20 |
| abstract_inverted_index.Developing | 134 |
| abstract_inverted_index.conditions | 80 |
| abstract_inverted_index.consistent | 151 |
| abstract_inverted_index.forecasts, | 34 |
| abstract_inverted_index.functional | 105 |
| abstract_inverted_index.importance | 87 |
| abstract_inverted_index.introduced | 98 |
| abstract_inverted_index.operation, | 28 |
| abstract_inverted_index.physically | 150 |
| abstract_inverted_index.prediction | 22 |
| abstract_inverted_index.variations | 70 |
| abstract_inverted_index.atmosphere, | 14 |
| abstract_inverted_index.forecasting | 2 |
| abstract_inverted_index.independent | 56 |
| abstract_inverted_index.investigate | 48 |
| abstract_inverted_index.mitigation. | 162 |
| abstract_inverted_index.prediction. | 93 |
| abstract_inverted_index.significant | 5 |
| abstract_inverted_index.simulations | 57 |
| abstract_inverted_index.applications | 156 |
| abstract_inverted_index.experimental | 63 |
| abstract_inverted_index.forecasting, | 153 |
| abstract_inverted_index.interactions | 11 |
| abstract_inverted_index.preparedness | 159 |
| abstract_inverted_index.demonstrating | 85 |
| abstract_inverted_index.incorporating | 124 |
| abstract_inverted_index.interactions, | 127 |
| abstract_inverted_index.interactions. | 46 |
| abstract_inverted_index.physics-based | 60 |
| abstract_inverted_index.significantly | 30, 81 |
| abstract_inverted_index.trajectories. | 73 |
| abstract_inverted_index.atmosphere-only | 39 |
| abstract_inverted_index.explainability. | 133 |
| abstract_inverted_index.land-atmosphere | 89 |
| abstract_inverted_index.atmosphere-ocean | 100 |
| abstract_inverted_index.atmosphere-land-ocean | 107 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 15 |
| citation_normalized_percentile |