Improving Ensemble Extreme Precipitation Forecasts Using Generative Artificial Intelligence Article Swipe
Yingkai Sha
,
Ryan A. Sobash
,
David John Gagne
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1175/aies-d-24-0063.1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1175/aies-d-24-0063.1
An ensemble postprocessing method is developed to improve the probabilistic forecasts of extreme precipitation events across the conterminous United States (CONUS). The method combines a 3D vision transformer (ViT) for bias correction with a latent diffusion model (LDM), a generative artificial intelligence (AI) method, to postprocess 6-hourly precipitation ensemble forecasts and produce an enlarged generative ensemble that contains spatiotemporally consistent precipitation trajectories. These trajectories are expected to improve the characterization of extreme precipitation events and offer skillful multiday accumulated and 6-hourly precipitation guidance. The method is tested using the Global Ensemble Forecast System (GEFS) precipitation forecasts out to day 6 and is verified against the Climatology-Calibrated Precipitation Analysis (CCPA) data. Verification results indicate that the method generated skillful ensemble members with improved continuous ranked probabilistic skill scores (CRPSSs) and Brier skill scores (BSSs) over the raw operational GEFS and a multivariate statistical postprocessing baseline. It showed skillful and reliable probabilities for events at extreme precipitation thresholds. Explainability studies were further conducted, which revealed the decision-making process of the method and confirmed its effectiveness on ensemble member generation. This work introduces a novel, generative AI–based approach to address the limitation of small numerical ensembles and the need for larger ensembles to identify extreme precipitation events. Significance Statement We use a new artificial intelligence (AI) technique to improve extreme precipitation forecasts from a numerical weather prediction ensemble, generating more scenarios that better characterize extreme precipitation events. This AI-generated ensemble improved the accuracy of precipitation forecasts and probabilistic warnings for extreme precipitation events. The study explores AI methods to generate precipitation forecasts and explains the decision-making mechanisms of such AI techniques to prove their effectiveness.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1175/aies-d-24-0063.1
- https://journals.ametsoc.org/downloadpdf/view/journals/aies/aop/AIES-D-24-0063.1/AIES-D-24-0063.1.pdf
- OA Status
- bronze
- Cited By
- 2
- References
- 86
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410115988
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410115988Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1175/aies-d-24-0063.1Digital Object Identifier
- Title
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Improving Ensemble Extreme Precipitation Forecasts Using Generative Artificial IntelligenceWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-04-01Full publication date if available
- Authors
-
Yingkai Sha, Ryan A. Sobash, David John GagneList of authors in order
- Landing page
-
https://doi.org/10.1175/aies-d-24-0063.1Publisher landing page
- PDF URL
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https://journals.ametsoc.org/downloadpdf/view/journals/aies/aop/AIES-D-24-0063.1/AIES-D-24-0063.1.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://journals.ametsoc.org/downloadpdf/view/journals/aies/aop/AIES-D-24-0063.1/AIES-D-24-0063.1.pdfDirect OA link when available
- Concepts
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Precipitation, Generative grammar, Climatology, Artificial intelligence, Environmental science, Ensemble learning, Computer science, Meteorology, Geology, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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86Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.This | 178, 235 |
| abstract_inverted_index.bias | 31 |
| abstract_inverted_index.from | 220 |
| abstract_inverted_index.more | 227 |
| abstract_inverted_index.need | 196 |
| abstract_inverted_index.over | 134 |
| abstract_inverted_index.such | 266 |
| abstract_inverted_index.that | 57, 114, 229 |
| abstract_inverted_index.were | 159 |
| abstract_inverted_index.with | 33, 121 |
| abstract_inverted_index.work | 179 |
| abstract_inverted_index.(ViT) | 29 |
| abstract_inverted_index.Brier | 130 |
| abstract_inverted_index.These | 63 |
| abstract_inverted_index.data. | 110 |
| abstract_inverted_index.model | 37 |
| abstract_inverted_index.offer | 76 |
| abstract_inverted_index.prove | 270 |
| abstract_inverted_index.skill | 126, 131 |
| abstract_inverted_index.small | 191 |
| abstract_inverted_index.study | 252 |
| abstract_inverted_index.their | 271 |
| abstract_inverted_index.using | 88 |
| abstract_inverted_index.which | 162 |
| abstract_inverted_index.(BSSs) | 133 |
| abstract_inverted_index.(CCPA) | 109 |
| abstract_inverted_index.(GEFS) | 94 |
| abstract_inverted_index.(LDM), | 38 |
| abstract_inverted_index.Global | 90 |
| abstract_inverted_index.States | 20 |
| abstract_inverted_index.System | 93 |
| abstract_inverted_index.United | 19 |
| abstract_inverted_index.across | 16 |
| abstract_inverted_index.better | 230 |
| abstract_inverted_index.events | 15, 74, 152 |
| abstract_inverted_index.larger | 198 |
| abstract_inverted_index.latent | 35 |
| abstract_inverted_index.member | 176 |
| abstract_inverted_index.method | 4, 23, 85, 116, 169 |
| abstract_inverted_index.novel, | 182 |
| abstract_inverted_index.ranked | 124 |
| abstract_inverted_index.scores | 127, 132 |
| abstract_inverted_index.showed | 146 |
| abstract_inverted_index.tested | 87 |
| abstract_inverted_index.vision | 27 |
| abstract_inverted_index.address | 187 |
| abstract_inverted_index.against | 104 |
| abstract_inverted_index.events. | 204, 234, 250 |
| abstract_inverted_index.extreme | 13, 72, 154, 202, 217, 232, 248 |
| abstract_inverted_index.further | 160 |
| abstract_inverted_index.improve | 8, 68, 216 |
| abstract_inverted_index.members | 120 |
| abstract_inverted_index.method, | 44 |
| abstract_inverted_index.methods | 255 |
| abstract_inverted_index.process | 166 |
| abstract_inverted_index.produce | 52 |
| abstract_inverted_index.results | 112 |
| abstract_inverted_index.studies | 158 |
| abstract_inverted_index.weather | 223 |
| abstract_inverted_index.(CONUS). | 21 |
| abstract_inverted_index.(CRPSSs) | 128 |
| abstract_inverted_index.6-hourly | 47, 81 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Analysis | 108 |
| abstract_inverted_index.Ensemble | 91 |
| abstract_inverted_index.Forecast | 92 |
| abstract_inverted_index.accuracy | 240 |
| abstract_inverted_index.approach | 185 |
| abstract_inverted_index.combines | 24 |
| abstract_inverted_index.contains | 58 |
| abstract_inverted_index.enlarged | 54 |
| abstract_inverted_index.ensemble | 2, 49, 56, 119, 175, 237 |
| abstract_inverted_index.expected | 66 |
| abstract_inverted_index.explains | 261 |
| abstract_inverted_index.explores | 253 |
| abstract_inverted_index.generate | 257 |
| abstract_inverted_index.identify | 201 |
| abstract_inverted_index.improved | 122, 238 |
| abstract_inverted_index.indicate | 113 |
| abstract_inverted_index.multiday | 78 |
| abstract_inverted_index.reliable | 149 |
| abstract_inverted_index.revealed | 163 |
| abstract_inverted_index.skillful | 77, 118, 147 |
| abstract_inverted_index.verified | 103 |
| abstract_inverted_index.warnings | 246 |
| abstract_inverted_index.Statement | 206 |
| abstract_inverted_index.baseline. | 144 |
| abstract_inverted_index.confirmed | 171 |
| abstract_inverted_index.developed | 6 |
| abstract_inverted_index.diffusion | 36 |
| abstract_inverted_index.ensemble, | 225 |
| abstract_inverted_index.ensembles | 193, 199 |
| abstract_inverted_index.forecasts | 11, 50, 96, 219, 243, 259 |
| abstract_inverted_index.generated | 117 |
| abstract_inverted_index.guidance. | 83 |
| abstract_inverted_index.numerical | 192, 222 |
| abstract_inverted_index.scenarios | 228 |
| abstract_inverted_index.technique | 214 |
| abstract_inverted_index.AI–based | 184 |
| abstract_inverted_index.artificial | 41, 211 |
| abstract_inverted_index.conducted, | 161 |
| abstract_inverted_index.consistent | 60 |
| abstract_inverted_index.continuous | 123 |
| abstract_inverted_index.correction | 32 |
| abstract_inverted_index.generating | 226 |
| abstract_inverted_index.generative | 40, 55, 183 |
| abstract_inverted_index.introduces | 180 |
| abstract_inverted_index.limitation | 189 |
| abstract_inverted_index.mechanisms | 264 |
| abstract_inverted_index.prediction | 224 |
| abstract_inverted_index.techniques | 268 |
| abstract_inverted_index.accumulated | 79 |
| abstract_inverted_index.generation. | 177 |
| abstract_inverted_index.operational | 137 |
| abstract_inverted_index.postprocess | 46 |
| abstract_inverted_index.statistical | 142 |
| abstract_inverted_index.thresholds. | 156 |
| abstract_inverted_index.transformer | 28 |
| abstract_inverted_index.AI-generated | 236 |
| abstract_inverted_index.Significance | 205 |
| abstract_inverted_index.Verification | 111 |
| abstract_inverted_index.characterize | 231 |
| abstract_inverted_index.conterminous | 18 |
| abstract_inverted_index.intelligence | 42, 212 |
| abstract_inverted_index.multivariate | 141 |
| abstract_inverted_index.trajectories | 64 |
| abstract_inverted_index.Precipitation | 107 |
| abstract_inverted_index.effectiveness | 173 |
| abstract_inverted_index.precipitation | 14, 48, 61, 73, 82, 95, 155, 203, 218, 233, 242, 249, 258 |
| abstract_inverted_index.probabilistic | 10, 125, 245 |
| abstract_inverted_index.probabilities | 150 |
| abstract_inverted_index.trajectories. | 62 |
| abstract_inverted_index.Explainability | 157 |
| abstract_inverted_index.effectiveness. | 272 |
| abstract_inverted_index.postprocessing | 3, 143 |
| abstract_inverted_index.decision-making | 165, 263 |
| abstract_inverted_index.characterization | 70 |
| abstract_inverted_index.spatiotemporally | 59 |
| abstract_inverted_index.Climatology-Calibrated | 106 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.93754339 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |