Enhancing the Rainfall Forecasting Accuracy of Ensemble Numerical Prediction Systems via Convolutional Neural Networks Article Swipe
Kuan-Cheng Lin
,
Wanting Chen
,
Pao‐Liang Chang
,
Zhiyuan Ye
,
Chin-Cheng Tsai
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1175/aies-d-23-0105.1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1175/aies-d-23-0105.1
Ensemble prediction systems are commonly used to demonstrate the potential uncertainty of weather forecasts. These systems also help provide weather predictions to prepare for disasters. Specifically, a type of prediction called a quantitative precipitation forecast (QPF) is calculated from the average of multiple forecasts. The probability-matched mean (PMM) method is used to improve these QPF predictions when they underestimate rainfall. However, the PMM method can exhibit limitations when dealing with certain types of rain patterns. To address this issue, this study focuses on improving short-term heavy rainfall predictions using an artificial intelligence (AI) algorithm that combines deep neural networks (DNNs), including convolutional neural networks (CNNs), with a space-based attention mechanism [convolutional block attention module (CBAM)]. Four experiments were conducted to evaluate the new method, including those performed to optimize the timing of a 24-h QPF model, adjust the training dataset, and refine the training algorithm. Two specific weather events were assessed: rainfall influenced by southwest winds after typhoons and afternoon thunderstorm rainfall. A comparison of the results obtained via the root-mean-square error (RMSE) and structural similarity index measure (SSIM) reveals significant improvements in both accuracy and distribution of the QPF predictions over those of the PMM method. Compared with the PMM method, our approach can reduce the RMSE from 77.51 to 19.52 in the mei-yu case, an improvement of approximately 74.82%. The SSIM also increased from 0.33 to 0.56, indicating a 70.9% enhancement. Overall, the new approach successfully enhances rainfall prediction accuracy via AI techniques and has the potential to be applied in disaster preparedness operations. Significance Statement An ensemble prediction system is a tool that is often used to showcase the uncertainty inherent in numerical weather forecasts and to provide critical insights for disaster preparedness. This study attempts to enhance the short-term predictions produced for intense rain events by integrating an AI algorithm, which exploits the advantages of deep neural networks (DNNs), such as convolutional neural networks (CNNs) with a space-based attention mechanism. The methodology underwent rigorous testing, including lead time optimization, training data sensitivity assessment, and algorithm refinement. When applied to weather events, such as rainfall patterns influenced by posttyphoon southwestern winds and afternoon thunderstorms, this approach outperforms traditional methods, as evidenced by verification metrics, such as the RMSE and SSIM. This research introduces an innovative framework that leverages artificial intelligence (AI) and DNNs to achieve enhanced rainfall prediction accuracy, which is a critical advancement for effective disaster readiness.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1175/aies-d-23-0105.1
- https://journals.ametsoc.org/downloadpdf/view/journals/aies/aop/AIES-D-23-0105.1/AIES-D-23-0105.1.pdf
- OA Status
- bronze
- Cited By
- 2
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404060842
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404060842Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1175/aies-d-23-0105.1Digital Object Identifier
- Title
-
Enhancing the Rainfall Forecasting Accuracy of Ensemble Numerical Prediction Systems via Convolutional Neural NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-01Full publication date if available
- Authors
-
Kuan-Cheng Lin, Wanting Chen, Pao‐Liang Chang, Zhiyuan Ye, Chin-Cheng TsaiList of authors in order
- Landing page
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https://doi.org/10.1175/aies-d-23-0105.1Publisher landing page
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https://journals.ametsoc.org/downloadpdf/view/journals/aies/aop/AIES-D-23-0105.1/AIES-D-23-0105.1.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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-
bronzeOpen access status per OpenAlex
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https://journals.ametsoc.org/downloadpdf/view/journals/aies/aop/AIES-D-23-0105.1/AIES-D-23-0105.1.pdfDirect OA link when available
- Concepts
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Convolutional neural network, Computer science, Artificial neural network, Ensemble forecasting, Artificial intelligence, Machine learning, Meteorology, GeographyTop concepts (fields/topics) attached by OpenAlex
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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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30Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.data | 335 |
| abstract_inverted_index.deep | 97, 310 |
| abstract_inverted_index.from | 39, 209, 226 |
| abstract_inverted_index.help | 18 |
| abstract_inverted_index.lead | 331 |
| abstract_inverted_index.mean | 47 |
| abstract_inverted_index.over | 192 |
| abstract_inverted_index.rain | 74, 298 |
| abstract_inverted_index.such | 314, 346, 368 |
| abstract_inverted_index.that | 95, 266, 380 |
| abstract_inverted_index.they | 58 |
| abstract_inverted_index.this | 78, 80, 358 |
| abstract_inverted_index.time | 332 |
| abstract_inverted_index.tool | 265 |
| abstract_inverted_index.type | 28 |
| abstract_inverted_index.used | 6, 51, 269 |
| abstract_inverted_index.were | 118, 150 |
| abstract_inverted_index.when | 57, 68 |
| abstract_inverted_index.with | 70, 106, 199, 320 |
| abstract_inverted_index.(PMM) | 48 |
| abstract_inverted_index.(QPF) | 36 |
| abstract_inverted_index.0.56, | 229 |
| abstract_inverted_index.19.52 | 212 |
| abstract_inverted_index.70.9% | 232 |
| abstract_inverted_index.77.51 | 210 |
| abstract_inverted_index.SSIM. | 373 |
| abstract_inverted_index.These | 15 |
| abstract_inverted_index.after | 157 |
| abstract_inverted_index.block | 112 |
| abstract_inverted_index.case, | 216 |
| abstract_inverted_index.error | 172 |
| abstract_inverted_index.heavy | 86 |
| abstract_inverted_index.index | 177 |
| abstract_inverted_index.often | 268 |
| abstract_inverted_index.study | 81, 288 |
| abstract_inverted_index.these | 54 |
| abstract_inverted_index.those | 126, 193 |
| abstract_inverted_index.types | 72 |
| abstract_inverted_index.using | 89 |
| abstract_inverted_index.which | 305, 393 |
| abstract_inverted_index.winds | 156, 354 |
| abstract_inverted_index.(CNNs) | 319 |
| abstract_inverted_index.(RMSE) | 173 |
| abstract_inverted_index.(SSIM) | 179 |
| abstract_inverted_index.adjust | 137 |
| abstract_inverted_index.called | 31 |
| abstract_inverted_index.events | 149, 299 |
| abstract_inverted_index.issue, | 79 |
| abstract_inverted_index.mei-yu | 215 |
| abstract_inverted_index.method | 49, 64 |
| abstract_inverted_index.model, | 136 |
| abstract_inverted_index.module | 114 |
| abstract_inverted_index.neural | 98, 103, 311, 317 |
| abstract_inverted_index.reduce | 206 |
| abstract_inverted_index.refine | 142 |
| abstract_inverted_index.system | 262 |
| abstract_inverted_index.timing | 131 |
| abstract_inverted_index.(CNNs), | 105 |
| abstract_inverted_index.(DNNs), | 100, 313 |
| abstract_inverted_index.74.82%. | 221 |
| abstract_inverted_index.achieve | 388 |
| abstract_inverted_index.address | 77 |
| abstract_inverted_index.applied | 252, 342 |
| abstract_inverted_index.average | 41 |
| abstract_inverted_index.certain | 71 |
| abstract_inverted_index.dealing | 69 |
| abstract_inverted_index.enhance | 291 |
| abstract_inverted_index.events, | 345 |
| abstract_inverted_index.exhibit | 66 |
| abstract_inverted_index.focuses | 82 |
| abstract_inverted_index.improve | 53 |
| abstract_inverted_index.intense | 297 |
| abstract_inverted_index.measure | 178 |
| abstract_inverted_index.method, | 124, 202 |
| abstract_inverted_index.method. | 197 |
| abstract_inverted_index.prepare | 23 |
| abstract_inverted_index.provide | 19, 281 |
| abstract_inverted_index.results | 167 |
| abstract_inverted_index.reveals | 180 |
| abstract_inverted_index.systems | 3, 16 |
| abstract_inverted_index.weather | 13, 20, 148, 277, 344 |
| abstract_inverted_index.(CBAM)]. | 115 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Compared | 198 |
| abstract_inverted_index.Ensemble | 1 |
| abstract_inverted_index.However, | 61 |
| abstract_inverted_index.Overall, | 234 |
| abstract_inverted_index.accuracy | 185, 242 |
| abstract_inverted_index.approach | 204, 237, 359 |
| abstract_inverted_index.attempts | 289 |
| abstract_inverted_index.combines | 96 |
| abstract_inverted_index.commonly | 5 |
| abstract_inverted_index.critical | 282, 396 |
| abstract_inverted_index.dataset, | 140 |
| abstract_inverted_index.disaster | 254, 285, 400 |
| abstract_inverted_index.enhanced | 389 |
| abstract_inverted_index.enhances | 239 |
| abstract_inverted_index.ensemble | 260 |
| abstract_inverted_index.evaluate | 121 |
| abstract_inverted_index.exploits | 306 |
| abstract_inverted_index.forecast | 35 |
| abstract_inverted_index.inherent | 274 |
| abstract_inverted_index.insights | 283 |
| abstract_inverted_index.methods, | 362 |
| abstract_inverted_index.metrics, | 367 |
| abstract_inverted_index.multiple | 43 |
| abstract_inverted_index.networks | 99, 104, 312, 318 |
| abstract_inverted_index.obtained | 168 |
| abstract_inverted_index.optimize | 129 |
| abstract_inverted_index.patterns | 349 |
| abstract_inverted_index.produced | 295 |
| abstract_inverted_index.rainfall | 87, 152, 240, 348, 390 |
| abstract_inverted_index.research | 375 |
| abstract_inverted_index.rigorous | 328 |
| abstract_inverted_index.showcase | 271 |
| abstract_inverted_index.specific | 147 |
| abstract_inverted_index.testing, | 329 |
| abstract_inverted_index.training | 139, 144, 334 |
| abstract_inverted_index.typhoons | 158 |
| abstract_inverted_index.Statement | 258 |
| abstract_inverted_index.accuracy, | 392 |
| abstract_inverted_index.afternoon | 160, 356 |
| abstract_inverted_index.algorithm | 94, 339 |
| abstract_inverted_index.assessed: | 151 |
| abstract_inverted_index.attention | 109, 113, 323 |
| abstract_inverted_index.conducted | 119 |
| abstract_inverted_index.effective | 399 |
| abstract_inverted_index.evidenced | 364 |
| abstract_inverted_index.forecasts | 278 |
| abstract_inverted_index.framework | 379 |
| abstract_inverted_index.improving | 84 |
| abstract_inverted_index.including | 101, 125, 330 |
| abstract_inverted_index.increased | 225 |
| abstract_inverted_index.leverages | 381 |
| abstract_inverted_index.mechanism | 110 |
| abstract_inverted_index.numerical | 276 |
| abstract_inverted_index.patterns. | 75 |
| abstract_inverted_index.performed | 127 |
| abstract_inverted_index.potential | 10, 249 |
| abstract_inverted_index.rainfall. | 60, 162 |
| abstract_inverted_index.southwest | 155 |
| abstract_inverted_index.underwent | 327 |
| abstract_inverted_index.advantages | 308 |
| abstract_inverted_index.algorithm, | 304 |
| abstract_inverted_index.algorithm. | 145 |
| abstract_inverted_index.artificial | 91, 382 |
| abstract_inverted_index.calculated | 38 |
| abstract_inverted_index.comparison | 164 |
| abstract_inverted_index.disasters. | 25 |
| abstract_inverted_index.forecasts. | 14, 44 |
| abstract_inverted_index.indicating | 230 |
| abstract_inverted_index.influenced | 153, 350 |
| abstract_inverted_index.innovative | 378 |
| abstract_inverted_index.introduces | 376 |
| abstract_inverted_index.mechanism. | 324 |
| abstract_inverted_index.prediction | 2, 30, 241, 261, 391 |
| abstract_inverted_index.readiness. | 401 |
| abstract_inverted_index.short-term | 85, 293 |
| abstract_inverted_index.similarity | 176 |
| abstract_inverted_index.structural | 175 |
| abstract_inverted_index.techniques | 245 |
| abstract_inverted_index.advancement | 397 |
| abstract_inverted_index.assessment, | 337 |
| abstract_inverted_index.demonstrate | 8 |
| abstract_inverted_index.experiments | 117 |
| abstract_inverted_index.improvement | 218 |
| abstract_inverted_index.integrating | 301 |
| abstract_inverted_index.limitations | 67 |
| abstract_inverted_index.methodology | 326 |
| abstract_inverted_index.operations. | 256 |
| abstract_inverted_index.outperforms | 360 |
| abstract_inverted_index.posttyphoon | 352 |
| abstract_inverted_index.predictions | 21, 56, 88, 191, 294 |
| abstract_inverted_index.refinement. | 340 |
| abstract_inverted_index.sensitivity | 336 |
| abstract_inverted_index.significant | 181 |
| abstract_inverted_index.space-based | 108, 322 |
| abstract_inverted_index.traditional | 361 |
| abstract_inverted_index.uncertainty | 11, 273 |
| abstract_inverted_index.Significance | 257 |
| abstract_inverted_index.distribution | 187 |
| abstract_inverted_index.enhancement. | 233 |
| abstract_inverted_index.improvements | 182 |
| abstract_inverted_index.intelligence | 92, 383 |
| abstract_inverted_index.preparedness | 255 |
| abstract_inverted_index.quantitative | 33 |
| abstract_inverted_index.southwestern | 353 |
| abstract_inverted_index.successfully | 238 |
| abstract_inverted_index.thunderstorm | 161 |
| abstract_inverted_index.verification | 366 |
| abstract_inverted_index.Specifically, | 26 |
| abstract_inverted_index.approximately | 220 |
| abstract_inverted_index.convolutional | 102, 316 |
| abstract_inverted_index.optimization, | 333 |
| abstract_inverted_index.precipitation | 34 |
| abstract_inverted_index.preparedness. | 286 |
| abstract_inverted_index.underestimate | 59 |
| abstract_inverted_index.[convolutional | 111 |
| abstract_inverted_index.thunderstorms, | 357 |
| abstract_inverted_index.root-mean-square | 171 |
| abstract_inverted_index.probability-matched | 46 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.8100000023841858 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.65835182 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |