Postprocessing East African Rainfall Forecasts Using a Generative Machine Learning Model
Abstract Existing weather models are known to have poor skill at forecasting rainfall over East Africa. Improved forecasts could reduce the effects of extreme weather events and provide significant socioeconomic benefits to the region. We present a novel machine learning (ML)‐based method to improve...
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| Format: | Article |
| Language: | English |
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American Geophysical Union (AGU)
2025-03-01
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| Series: | Journal of Advances in Modeling Earth Systems |
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| Online Access: | https://doi.org/10.1029/2024MS004796 |
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| author | Bobby Antonio Andrew T. T. McRae David MacLeod Fenwick C. Cooper John Marsham Laurence Aitchison Tim N. Palmer Peter A. G. Watson |
| author_facet | Bobby Antonio Andrew T. T. McRae David MacLeod Fenwick C. Cooper John Marsham Laurence Aitchison Tim N. Palmer Peter A. G. Watson |
| author_sort | Bobby Antonio |
| collection | DOAJ |
| description | Abstract Existing weather models are known to have poor skill at forecasting rainfall over East Africa. Improved forecasts could reduce the effects of extreme weather events and provide significant socioeconomic benefits to the region. We present a novel machine learning (ML)‐based method to improve precipitation forecasts in East Africa, using postprocessing based on a conditional generative adversarial network (cGAN). This addresses the challenge of realistically representing tropical rainfall, where convection dominates and is poorly simulated in conventional global forecast models. We postprocess hourly forecasts made by the European Centre for Medium‐Range Weather Forecasts Integrated Forecast System at 6–18 hr lead times, at 0.1° resolution. We combine the cGAN predictions with a novel neighborhood version of quantile mapping, to integrate the strengths of ML and conventional postprocessing. Our results indicate that the cGAN substantially improves the diurnal cycle of rainfall, and improves predictions up to the 99.9th percentile (∼10mm/hr). This improvement extends to the March–May 2018 season, which had extremely high rainfall, indicating that the approach has some ability to generalize to more extreme conditions. We explore the potential for the cGAN to produce probabilistic forecasts and find that the spread of this ensemble broadly reflects the predictability of the observations, but is also characterized by a mixture of under‐ and over‐dispersion. Overall our results demonstrate how the strengths of ML and conventional postprocessing methods can be combined, and illuminate what benefits ML approaches can bring to this region. |
| format | Article |
| id | doaj-art-9acb1e64d748435c8ec11b9308416fad |
| institution | DOAJ |
| issn | 1942-2466 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj-art-9acb1e64d748435c8ec11b9308416fad2025-08-20T02:54:12ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662025-03-01173n/an/a10.1029/2024MS004796Postprocessing East African Rainfall Forecasts Using a Generative Machine Learning ModelBobby Antonio0Andrew T. T. McRae1David MacLeod2Fenwick C. Cooper3John Marsham4Laurence Aitchison5Tim N. Palmer6Peter A. G. Watson7Department of Physics University of Oxford Oxford UKDepartment of Physics University of Oxford Oxford UKSchool of Earth and Environmental Sciences University of Cardiff Cardiff UKDepartment of Physics University of Oxford Oxford UKSchool of Earth and Environment University of Leeds Leeds UKMachine Learning and Computational Neuroscience Unit University of Bristol Bristol UKDepartment of Physics University of Oxford Oxford UKSchool of Geographical Sciences University of Bristol Bristol UKAbstract Existing weather models are known to have poor skill at forecasting rainfall over East Africa. Improved forecasts could reduce the effects of extreme weather events and provide significant socioeconomic benefits to the region. We present a novel machine learning (ML)‐based method to improve precipitation forecasts in East Africa, using postprocessing based on a conditional generative adversarial network (cGAN). This addresses the challenge of realistically representing tropical rainfall, where convection dominates and is poorly simulated in conventional global forecast models. We postprocess hourly forecasts made by the European Centre for Medium‐Range Weather Forecasts Integrated Forecast System at 6–18 hr lead times, at 0.1° resolution. We combine the cGAN predictions with a novel neighborhood version of quantile mapping, to integrate the strengths of ML and conventional postprocessing. Our results indicate that the cGAN substantially improves the diurnal cycle of rainfall, and improves predictions up to the 99.9th percentile (∼10mm/hr). This improvement extends to the March–May 2018 season, which had extremely high rainfall, indicating that the approach has some ability to generalize to more extreme conditions. We explore the potential for the cGAN to produce probabilistic forecasts and find that the spread of this ensemble broadly reflects the predictability of the observations, but is also characterized by a mixture of under‐ and over‐dispersion. Overall our results demonstrate how the strengths of ML and conventional postprocessing methods can be combined, and illuminate what benefits ML approaches can bring to this region.https://doi.org/10.1029/2024MS004796machine learningpostprocessingprecipitationEast Africaforecastingtropical |
| spellingShingle | Bobby Antonio Andrew T. T. McRae David MacLeod Fenwick C. Cooper John Marsham Laurence Aitchison Tim N. Palmer Peter A. G. Watson Postprocessing East African Rainfall Forecasts Using a Generative Machine Learning Model Journal of Advances in Modeling Earth Systems machine learning postprocessing precipitation East Africa forecasting tropical |
| title | Postprocessing East African Rainfall Forecasts Using a Generative Machine Learning Model |
| title_full | Postprocessing East African Rainfall Forecasts Using a Generative Machine Learning Model |
| title_fullStr | Postprocessing East African Rainfall Forecasts Using a Generative Machine Learning Model |
| title_full_unstemmed | Postprocessing East African Rainfall Forecasts Using a Generative Machine Learning Model |
| title_short | Postprocessing East African Rainfall Forecasts Using a Generative Machine Learning Model |
| title_sort | postprocessing east african rainfall forecasts using a generative machine learning model |
| topic | machine learning postprocessing precipitation East Africa forecasting tropical |
| url | https://doi.org/10.1029/2024MS004796 |
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