Generative Ensemble Deep Learning Severe Weather Prediction from a Deterministic Convection-Allowing Model Article Swipe
Yingkai Sha
,
Ryan A. Sobash
,
David John Gagne
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1175/aies-d-23-0094.1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1175/aies-d-23-0094.1
An ensemble postprocessing method is developed for the probabilistic prediction of severe weather (tornadoes, hail, and wind gusts) over the conterminous United States (CONUS). The method combines conditional generative adversarial networks (CGANs), a type of deep generative model, with a convolutional neural network (CNN) to postprocess convection-allowing model (CAM) forecasts. The CGANs are designed to create synthetic ensemble members from deterministic CAM forecasts, and their outputs are processed by the CNN to estimate the probability of severe weather. The method is tested using High-Resolution Rapid Refresh (HRRR) 1–24-h forecasts as inputs and Storm Prediction Center (SPC) severe weather reports as targets. The method produced skillful predictions with up to 20% Brier skill score (BSS) increases compared to other neural-network-based reference methods using a testing dataset of HRRR forecasts in 2021. For the evaluation of uncertainty quantification, the method is overconfident but produces meaningful ensemble spreads that can distinguish good and bad forecasts. The quality of CGAN outputs is also evaluated. Results show that the CGAN outputs behave similarly to a numerical ensemble; they preserved the intervariable correlations and the contribution of influential predictors as in the original HRRR forecasts. This work provides a novel approach to postprocess CAM output using neural networks that can be applied to severe weather prediction. Significance Statement We use a new machine learning (ML) technique to generate probabilistic forecasts of convective weather hazards, such as tornadoes and hailstorms, with the output from high-resolution numerical weather model forecasts. The new ML system generates an ensemble of synthetic forecast fields from a single forecast, which are then used to train ML models for convective hazard prediction. Using this ML-generated ensemble for training leads to improvements of 10%–20% in severe weather forecast skills compared to using other ML algorithms that use only output from the single forecast. This work is unique in that it explores the use of ML methods for producing synthetic forecasts of convective storm events and using these to train ML systems for high-impact convective weather prediction.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1175/aies-d-23-0094.1
- https://journals.ametsoc.org/downloadpdf/view/journals/aies/aop/AIES-D-23-0094.1/AIES-D-23-0094.1.pdf
- OA Status
- bronze
- Cited By
- 5
- References
- 65
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392699162
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392699162Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1175/aies-d-23-0094.1Digital Object Identifier
- Title
-
Generative Ensemble Deep Learning Severe Weather Prediction from a Deterministic Convection-Allowing ModelWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-12Full 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-23-0094.1Publisher landing page
- PDF URL
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https://journals.ametsoc.org/downloadpdf/view/journals/aies/aop/AIES-D-23-0094.1/AIES-D-23-0094.1.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://journals.ametsoc.org/downloadpdf/view/journals/aies/aop/AIES-D-23-0094.1/AIES-D-23-0094.1.pdfDirect OA link when available
- Concepts
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Artificial intelligence, Computer science, Ensemble forecasting, Probabilistic forecasting, Convolutional neural network, Probabilistic logic, Artificial neural network, Machine learning, Deep learning, Numerical weather prediction, Generative grammar, Meteorology, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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65Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2102636708, https://openalex.org/W4298324479, https://openalex.org/W2259421489, https://openalex.org/W2611991295, https://openalex.org/W2078451609, https://openalex.org/W2765811365, https://openalex.org/W2170860720, https://openalex.org/W2177509644, https://openalex.org/W4281788506, https://openalex.org/W2743153755, https://openalex.org/W2229759802, https://openalex.org/W2787546361, https://openalex.org/W3096889213, https://openalex.org/W4382369444, https://openalex.org/W391985582, https://openalex.org/W1689711448, https://openalex.org/W2962949934, https://openalex.org/W2170766202, https://openalex.org/W4311814635, https://openalex.org/W3187052348, https://openalex.org/W3014703899, https://openalex.org/W4220946747, https://openalex.org/W1999697113, https://openalex.org/W4281478270, https://openalex.org/W2560767742, https://openalex.org/W2141290483, https://openalex.org/W2995197345, https://openalex.org/W2964121744, https://openalex.org/W2990985257, https://openalex.org/W3025786591, https://openalex.org/W3026449040, https://openalex.org/W4223533121, https://openalex.org/W4200309788, https://openalex.org/W3033714468, https://openalex.org/W4226470217, https://openalex.org/W4285106044, https://openalex.org/W4302424651, https://openalex.org/W2125389028, https://openalex.org/W6662553190, https://openalex.org/W1996819337, https://openalex.org/W4390306794, https://openalex.org/W3202525453, https://openalex.org/W2906333971, https://openalex.org/W3172949370, https://openalex.org/W4225553680, https://openalex.org/W2123987184, https://openalex.org/W1974680387, https://openalex.org/W2189951138, https://openalex.org/W3077522170, https://openalex.org/W2095705004, https://openalex.org/W2037327690, https://openalex.org/W2965679379, https://openalex.org/W3059519521, https://openalex.org/W3011655242, https://openalex.org/W4388184243, https://openalex.org/W4320013936, https://openalex.org/W1901129140, https://openalex.org/W2047634553, https://openalex.org/W4313893684, https://openalex.org/W2915783575, https://openalex.org/W2786055572, https://openalex.org/W4300912893, https://openalex.org/W4238663167, https://openalex.org/W2964059111, https://openalex.org/W2981122634 |
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| abstract_inverted_index.An | 1 |
| abstract_inverted_index.ML | 245, 264, 290, 311, 326 |
| abstract_inverted_index.We | 213 |
| abstract_inverted_index.an | 248 |
| abstract_inverted_index.as | 90, 100, 184, 230 |
| abstract_inverted_index.be | 205 |
| abstract_inverted_index.by | 69 |
| abstract_inverted_index.in | 129, 185, 281, 304 |
| abstract_inverted_index.is | 5, 81, 139, 158, 302 |
| abstract_inverted_index.it | 306 |
| abstract_inverted_index.of | 11, 35, 76, 126, 134, 155, 181, 225, 250, 279, 310, 317 |
| abstract_inverted_index.to | 45, 55, 72, 109, 117, 169, 196, 207, 221, 262, 277, 287, 324 |
| abstract_inverted_index.up | 108 |
| abstract_inverted_index.20% | 110 |
| abstract_inverted_index.CAM | 62, 198 |
| abstract_inverted_index.CNN | 71 |
| abstract_inverted_index.For | 131 |
| abstract_inverted_index.The | 25, 51, 79, 102, 153, 243 |
| abstract_inverted_index.and | 16, 64, 92, 150, 178, 232, 321 |
| abstract_inverted_index.are | 53, 67, 259 |
| abstract_inverted_index.bad | 151 |
| abstract_inverted_index.but | 141 |
| abstract_inverted_index.can | 147, 204 |
| abstract_inverted_index.for | 7, 266, 274, 313, 328 |
| abstract_inverted_index.new | 216, 244 |
| abstract_inverted_index.the | 8, 20, 70, 74, 132, 137, 164, 175, 179, 186, 235, 297, 308 |
| abstract_inverted_index.use | 214, 293, 309 |
| abstract_inverted_index.(ML) | 219 |
| abstract_inverted_index.CGAN | 156, 165 |
| abstract_inverted_index.HRRR | 127, 188 |
| abstract_inverted_index.This | 190, 300 |
| abstract_inverted_index.also | 159 |
| abstract_inverted_index.deep | 36 |
| abstract_inverted_index.from | 60, 237, 254, 296 |
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| abstract_inverted_index.only | 294 |
| abstract_inverted_index.over | 19 |
| abstract_inverted_index.show | 162 |
| abstract_inverted_index.such | 229 |
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| abstract_inverted_index.they | 173 |
| abstract_inverted_index.this | 271 |
| abstract_inverted_index.type | 34 |
| abstract_inverted_index.used | 261 |
| abstract_inverted_index.wind | 17 |
| abstract_inverted_index.with | 39, 107, 234 |
| abstract_inverted_index.work | 191, 301 |
| abstract_inverted_index.(BSS) | 114 |
| abstract_inverted_index.(CAM) | 49 |
| abstract_inverted_index.(CNN) | 44 |
| abstract_inverted_index.(SPC) | 96 |
| abstract_inverted_index.2021. | 130 |
| abstract_inverted_index.Brier | 111 |
| abstract_inverted_index.CGANs | 52 |
| abstract_inverted_index.Rapid | 85 |
| abstract_inverted_index.Storm | 93 |
| abstract_inverted_index.Using | 270 |
| abstract_inverted_index.hail, | 15 |
| abstract_inverted_index.leads | 276 |
| abstract_inverted_index.model | 48, 241 |
| abstract_inverted_index.novel | 194 |
| abstract_inverted_index.other | 118, 289 |
| abstract_inverted_index.score | 113 |
| abstract_inverted_index.skill | 112 |
| abstract_inverted_index.storm | 319 |
| abstract_inverted_index.their | 65 |
| abstract_inverted_index.these | 323 |
| abstract_inverted_index.train | 263, 325 |
| abstract_inverted_index.using | 83, 122, 200, 288, 322 |
| abstract_inverted_index.which | 258 |
| abstract_inverted_index.(HRRR) | 87 |
| abstract_inverted_index.Center | 95 |
| abstract_inverted_index.States | 23 |
| abstract_inverted_index.United | 22 |
| abstract_inverted_index.behave | 167 |
| abstract_inverted_index.create | 56 |
| abstract_inverted_index.events | 320 |
| abstract_inverted_index.fields | 253 |
| abstract_inverted_index.gusts) | 18 |
| abstract_inverted_index.hazard | 268 |
| abstract_inverted_index.inputs | 91 |
| abstract_inverted_index.method | 4, 26, 80, 103, 138 |
| abstract_inverted_index.model, | 38 |
| abstract_inverted_index.models | 265 |
| abstract_inverted_index.neural | 42, 201 |
| abstract_inverted_index.output | 199, 236, 295 |
| abstract_inverted_index.severe | 12, 77, 97, 208, 282 |
| abstract_inverted_index.single | 256, 298 |
| abstract_inverted_index.skills | 285 |
| abstract_inverted_index.system | 246 |
| abstract_inverted_index.tested | 82 |
| abstract_inverted_index.unique | 303 |
| abstract_inverted_index.Refresh | 86 |
| abstract_inverted_index.Results | 161 |
| abstract_inverted_index.applied | 206 |
| abstract_inverted_index.dataset | 125 |
| abstract_inverted_index.machine | 217 |
| abstract_inverted_index.members | 59 |
| abstract_inverted_index.methods | 121, 312 |
| abstract_inverted_index.network | 43 |
| abstract_inverted_index.outputs | 66, 157, 166 |
| abstract_inverted_index.quality | 154 |
| abstract_inverted_index.reports | 99 |
| abstract_inverted_index.spreads | 145 |
| abstract_inverted_index.systems | 327 |
| abstract_inverted_index.testing | 124 |
| abstract_inverted_index.weather | 13, 98, 209, 227, 240, 283, 331 |
| abstract_inverted_index.(CGANs), | 32 |
| abstract_inverted_index.(CONUS). | 24 |
| abstract_inverted_index.1–24-h | 88 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.approach | 195 |
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| abstract_inverted_index.ensemble | 2, 58, 144, 249, 273 |
| abstract_inverted_index.estimate | 73 |
| abstract_inverted_index.explores | 307 |
| abstract_inverted_index.forecast | 252, 284 |
| abstract_inverted_index.generate | 222 |
| abstract_inverted_index.hazards, | 228 |
| abstract_inverted_index.learning | 218 |
| abstract_inverted_index.networks | 31, 202 |
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| abstract_inverted_index.produces | 142 |
| abstract_inverted_index.provides | 192 |
| abstract_inverted_index.skillful | 105 |
| abstract_inverted_index.targets. | 101 |
| abstract_inverted_index.training | 275 |
| abstract_inverted_index.weather. | 78 |
| abstract_inverted_index.10%–20% | 280 |
| abstract_inverted_index.Statement | 212 |
| abstract_inverted_index.developed | 6 |
| abstract_inverted_index.ensemble; | 172 |
| abstract_inverted_index.forecast, | 257 |
| abstract_inverted_index.forecast. | 299 |
| abstract_inverted_index.forecasts | 89, 128, 224, 316 |
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| abstract_inverted_index.increases | 115 |
| abstract_inverted_index.numerical | 171, 239 |
| abstract_inverted_index.preserved | 174 |
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| abstract_inverted_index.reference | 120 |
| abstract_inverted_index.similarly | 168 |
| abstract_inverted_index.synthetic | 57, 251, 315 |
| abstract_inverted_index.technique | 220 |
| abstract_inverted_index.tornadoes | 231 |
| abstract_inverted_index.Prediction | 94 |
| abstract_inverted_index.algorithms | 291 |
| abstract_inverted_index.convective | 226, 267, 318, 330 |
| abstract_inverted_index.evaluated. | 160 |
| abstract_inverted_index.evaluation | 133 |
| abstract_inverted_index.forecasts, | 63 |
| abstract_inverted_index.forecasts. | 50, 152, 189, 242 |
| abstract_inverted_index.generative | 29, 37 |
| abstract_inverted_index.meaningful | 143 |
| abstract_inverted_index.prediction | 10 |
| abstract_inverted_index.predictors | 183 |
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| abstract_inverted_index.adversarial | 30 |
| abstract_inverted_index.conditional | 28 |
| abstract_inverted_index.distinguish | 148 |
| abstract_inverted_index.hailstorms, | 233 |
| abstract_inverted_index.high-impact | 329 |
| abstract_inverted_index.influential | 182 |
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| abstract_inverted_index.probability | 75 |
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| abstract_inverted_index.overconfident | 140 |
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| abstract_inverted_index.high-resolution | 238 |
| abstract_inverted_index.quantification, | 136 |
| abstract_inverted_index.convection-allowing | 47 |
| abstract_inverted_index.neural-network-based | 119 |
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| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5109543680 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I107766831 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.6100000143051147 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.87509346 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |