AI Foundation Models for Weather and Climate: Applications, Design, and Implementation Article Swipe
S. Karthik Mukkavilli
,
Daniel Civitarese
,
Johannes Schmude
,
Johannes Jakubik
,
Anne Jones
,
Nam V. Nguyen
,
Christopher Phillips
,
Sujit Roy
,
Shraddha Singh
,
Campbell Watson
,
Raghu Ganti
,
Hendrik F. Hamann
,
U. S. Nair
,
Rahul Ramachandran
,
Kommy Weldemariam
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.10808
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.10808
Machine learning and deep learning methods have been widely explored in understanding the chaotic behavior of the atmosphere and furthering weather forecasting. There has been increasing interest from technology companies, government institutions, and meteorological agencies in building digital twins of the Earth. Recent approaches using transformers, physics-informed machine learning, and graph neural networks have demonstrated state-of-the-art performance on relatively narrow spatiotemporal scales and specific tasks. With the recent success of generative artificial intelligence (AI) using pre-trained transformers for language modeling and vision with prompt engineering and fine-tuning, we are now moving towards generalizable AI. In particular, we are witnessing the rise of AI foundation models that can perform competitively on multiple domain-specific downstream tasks. Despite this progress, we are still in the nascent stages of a generalizable AI model for global Earth system models, regional climate models, and mesoscale weather models. Here, we review current state-of-the-art AI approaches, primarily from transformer and operator learning literature in the context of meteorology. We provide our perspective on criteria for success towards a family of foundation models for nowcasting and forecasting weather and climate predictions. We also discuss how such models can perform competitively on downstream tasks such as downscaling (super-resolution), identifying conditions conducive to the occurrence of wildfires, and predicting consequential meteorological phenomena across various spatiotemporal scales such as hurricanes and atmospheric rivers. In particular, we examine current AI methodologies and contend they have matured enough to design and implement a weather foundation model.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2309.10808
- https://arxiv.org/pdf/2309.10808
- OA Status
- green
- Cited By
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386942610
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386942610Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2309.10808Digital Object Identifier
- Title
-
AI Foundation Models for Weather and Climate: Applications, Design, and ImplementationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-09-19Full publication date if available
- Authors
-
S. Karthik Mukkavilli, Daniel Civitarese, Johannes Schmude, Johannes Jakubik, Anne Jones, Nam V. Nguyen, Christopher Phillips, Sujit Roy, Shraddha Singh, Campbell Watson, Raghu Ganti, Hendrik F. Hamann, U. S. Nair, Rahul Ramachandran, Kommy WeldemariamList of authors in order
- Landing page
-
https://arxiv.org/abs/2309.10808Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2309.10808Direct 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
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https://arxiv.org/pdf/2309.10808Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Downscaling, Machine learning, Transformer, Deep learning, Artificial neural network, Weather modification, Data science, Meteorology, Engineering, Geography, Voltage, Electrical engineering, PrecipitationTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
14Total citation count in OpenAlex
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2025: 7, 2024: 7Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.on | 57, 109, 164, 191 |
| abstract_inverted_index.to | 201, 234 |
| abstract_inverted_index.we | 87, 96, 117, 142, 223 |
| abstract_inverted_index.AI. | 93 |
| abstract_inverted_index.and | 2, 18, 32, 49, 62, 80, 85, 137, 151, 176, 179, 206, 218, 228, 236 |
| abstract_inverted_index.are | 88, 97, 118 |
| abstract_inverted_index.can | 106, 188 |
| abstract_inverted_index.for | 77, 129, 166, 174 |
| abstract_inverted_index.has | 23 |
| abstract_inverted_index.how | 185 |
| abstract_inverted_index.now | 89 |
| abstract_inverted_index.our | 162 |
| abstract_inverted_index.the | 12, 16, 40, 66, 99, 121, 156, 202 |
| abstract_inverted_index.(AI) | 73 |
| abstract_inverted_index.With | 65 |
| abstract_inverted_index.also | 183 |
| abstract_inverted_index.been | 7, 24 |
| abstract_inverted_index.deep | 3 |
| abstract_inverted_index.from | 27, 149 |
| abstract_inverted_index.have | 6, 53, 231 |
| abstract_inverted_index.rise | 100 |
| abstract_inverted_index.such | 186, 194, 215 |
| abstract_inverted_index.that | 105 |
| abstract_inverted_index.they | 230 |
| abstract_inverted_index.this | 115 |
| abstract_inverted_index.with | 82 |
| abstract_inverted_index.Earth | 131 |
| abstract_inverted_index.Here, | 141 |
| abstract_inverted_index.There | 22 |
| abstract_inverted_index.graph | 50 |
| abstract_inverted_index.model | 128 |
| abstract_inverted_index.still | 119 |
| abstract_inverted_index.tasks | 193 |
| abstract_inverted_index.twins | 38 |
| abstract_inverted_index.using | 44, 74 |
| abstract_inverted_index.Earth. | 41 |
| abstract_inverted_index.Recent | 42 |
| abstract_inverted_index.across | 211 |
| abstract_inverted_index.design | 235 |
| abstract_inverted_index.enough | 233 |
| abstract_inverted_index.family | 170 |
| abstract_inverted_index.global | 130 |
| abstract_inverted_index.model. | 241 |
| abstract_inverted_index.models | 104, 173, 187 |
| abstract_inverted_index.moving | 90 |
| abstract_inverted_index.narrow | 59 |
| abstract_inverted_index.neural | 51 |
| abstract_inverted_index.prompt | 83 |
| abstract_inverted_index.recent | 67 |
| abstract_inverted_index.review | 143 |
| abstract_inverted_index.scales | 61, 214 |
| abstract_inverted_index.stages | 123 |
| abstract_inverted_index.system | 132 |
| abstract_inverted_index.tasks. | 64, 113 |
| abstract_inverted_index.vision | 81 |
| abstract_inverted_index.widely | 8 |
| abstract_inverted_index.Despite | 114 |
| abstract_inverted_index.Machine | 0 |
| abstract_inverted_index.chaotic | 13 |
| abstract_inverted_index.climate | 135, 180 |
| abstract_inverted_index.contend | 229 |
| abstract_inverted_index.context | 157 |
| abstract_inverted_index.current | 144, 225 |
| abstract_inverted_index.digital | 37 |
| abstract_inverted_index.discuss | 184 |
| abstract_inverted_index.examine | 224 |
| abstract_inverted_index.machine | 47 |
| abstract_inverted_index.matured | 232 |
| abstract_inverted_index.methods | 5 |
| abstract_inverted_index.models, | 133, 136 |
| abstract_inverted_index.models. | 140 |
| abstract_inverted_index.nascent | 122 |
| abstract_inverted_index.perform | 107, 189 |
| abstract_inverted_index.provide | 161 |
| abstract_inverted_index.rivers. | 220 |
| abstract_inverted_index.success | 68, 167 |
| abstract_inverted_index.towards | 91, 168 |
| abstract_inverted_index.various | 212 |
| abstract_inverted_index.weather | 20, 139, 178, 239 |
| abstract_inverted_index.agencies | 34 |
| abstract_inverted_index.behavior | 14 |
| abstract_inverted_index.building | 36 |
| abstract_inverted_index.criteria | 165 |
| abstract_inverted_index.explored | 9 |
| abstract_inverted_index.interest | 26 |
| abstract_inverted_index.language | 78 |
| abstract_inverted_index.learning | 1, 4, 153 |
| abstract_inverted_index.modeling | 79 |
| abstract_inverted_index.multiple | 110 |
| abstract_inverted_index.networks | 52 |
| abstract_inverted_index.operator | 152 |
| abstract_inverted_index.regional | 134 |
| abstract_inverted_index.specific | 63 |
| abstract_inverted_index.conducive | 200 |
| abstract_inverted_index.implement | 237 |
| abstract_inverted_index.learning, | 48 |
| abstract_inverted_index.mesoscale | 138 |
| abstract_inverted_index.phenomena | 210 |
| abstract_inverted_index.primarily | 148 |
| abstract_inverted_index.progress, | 116 |
| abstract_inverted_index.approaches | 43 |
| abstract_inverted_index.artificial | 71 |
| abstract_inverted_index.atmosphere | 17 |
| abstract_inverted_index.companies, | 29 |
| abstract_inverted_index.conditions | 199 |
| abstract_inverted_index.downstream | 112, 192 |
| abstract_inverted_index.foundation | 103, 172, 240 |
| abstract_inverted_index.furthering | 19 |
| abstract_inverted_index.generative | 70 |
| abstract_inverted_index.government | 30 |
| abstract_inverted_index.hurricanes | 217 |
| abstract_inverted_index.increasing | 25 |
| abstract_inverted_index.literature | 154 |
| abstract_inverted_index.nowcasting | 175 |
| abstract_inverted_index.occurrence | 203 |
| abstract_inverted_index.predicting | 207 |
| abstract_inverted_index.relatively | 58 |
| abstract_inverted_index.technology | 28 |
| abstract_inverted_index.wildfires, | 205 |
| abstract_inverted_index.witnessing | 98 |
| abstract_inverted_index.approaches, | 147 |
| abstract_inverted_index.atmospheric | 219 |
| abstract_inverted_index.downscaling | 196 |
| abstract_inverted_index.engineering | 84 |
| abstract_inverted_index.forecasting | 177 |
| abstract_inverted_index.identifying | 198 |
| abstract_inverted_index.particular, | 95, 222 |
| abstract_inverted_index.performance | 56 |
| abstract_inverted_index.perspective | 163 |
| abstract_inverted_index.pre-trained | 75 |
| abstract_inverted_index.transformer | 150 |
| abstract_inverted_index.demonstrated | 54 |
| abstract_inverted_index.fine-tuning, | 86 |
| abstract_inverted_index.forecasting. | 21 |
| abstract_inverted_index.intelligence | 72 |
| abstract_inverted_index.meteorology. | 159 |
| abstract_inverted_index.predictions. | 181 |
| abstract_inverted_index.transformers | 76 |
| abstract_inverted_index.competitively | 108, 190 |
| abstract_inverted_index.consequential | 208 |
| abstract_inverted_index.generalizable | 92, 126 |
| abstract_inverted_index.institutions, | 31 |
| abstract_inverted_index.methodologies | 227 |
| abstract_inverted_index.transformers, | 45 |
| abstract_inverted_index.understanding | 11 |
| abstract_inverted_index.meteorological | 33, 209 |
| abstract_inverted_index.spatiotemporal | 60, 213 |
| abstract_inverted_index.domain-specific | 111 |
| abstract_inverted_index.physics-informed | 46 |
| abstract_inverted_index.state-of-the-art | 55, 145 |
| abstract_inverted_index.(super-resolution), | 197 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 15 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.8299999833106995 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile |