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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.24525 |
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| _version_ | 1866917177850855424 |
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| author | Cooper, Fenwick C. Nath, Shruti McRae, Andrew T. T. Antonio, Bobby Weisheimer, Antje Palmer, Tim Gudoshava, Masilin Kalladath, Nishadh Amidhun, Ahmed Kinyua, Jason Kimani, Hannah Koros, David Mwai, Zacharia Maswi, Christine Chanzu, Benard Abebe, Samrawit Tamene, Bekalu Kebebe, Bekele Teshome, Asaminew Pappenberger, Florian Chantry, Matthew Obai, Isaac Mason, Jesse |
| author_facet | Cooper, Fenwick C. Nath, Shruti McRae, Andrew T. T. Antonio, Bobby Weisheimer, Antje Palmer, Tim Gudoshava, Masilin Kalladath, Nishadh Amidhun, Ahmed Kinyua, Jason Kimani, Hannah Koros, David Mwai, Zacharia Maswi, Christine Chanzu, Benard Abebe, Samrawit Tamene, Bekalu Kebebe, Bekele Teshome, Asaminew Pappenberger, Florian Chantry, Matthew Obai, Isaac Mason, Jesse |
| contents | Ensemble forecasting has proven over the years to be a vital tool for predicting extreme or only partially predictable weather events. In particular life-threatening weather events. Many National Meteorological Services in East Africa do not have the computing resources to enable them to run their local area models in full ensemble mode over the full period of the 2 week medium range. As a result, weather users in these countries are not being given sufficient information about weather risk that is needed to make reliable decisions about taking preventative action. Consequently, society in many parts of the world is not as resilient to weather events as they could be. In this paper we test the performance of our forecast system, cGAN, which is the only high-resolution (10 km) ensemble rainfall product that does real-time, probabilistic correction of global forecasts for East Africa. Compared to existing state-of-the-art AI models, our system offers higher spatial resolution. It is cheap to train/run and requires no additional post-processing. It is run on laptops and can generate many thousands of ensemble members at little computational cost (compared with physical local area models). It is ideally suited to Meteorological Services with limited computational facilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_24525 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rainfall forecasts in daily use over East Africa improved by machine learning Cooper, Fenwick C. Nath, Shruti McRae, Andrew T. T. Antonio, Bobby Weisheimer, Antje Palmer, Tim Gudoshava, Masilin Kalladath, Nishadh Amidhun, Ahmed Kinyua, Jason Kimani, Hannah Koros, David Mwai, Zacharia Maswi, Christine Chanzu, Benard Abebe, Samrawit Tamene, Bekalu Kebebe, Bekele Teshome, Asaminew Pappenberger, Florian Chantry, Matthew Obai, Isaac Mason, Jesse Atmospheric and Oceanic Physics Ensemble forecasting has proven over the years to be a vital tool for predicting extreme or only partially predictable weather events. In particular life-threatening weather events. Many National Meteorological Services in East Africa do not have the computing resources to enable them to run their local area models in full ensemble mode over the full period of the 2 week medium range. As a result, weather users in these countries are not being given sufficient information about weather risk that is needed to make reliable decisions about taking preventative action. Consequently, society in many parts of the world is not as resilient to weather events as they could be. In this paper we test the performance of our forecast system, cGAN, which is the only high-resolution (10 km) ensemble rainfall product that does real-time, probabilistic correction of global forecasts for East Africa. Compared to existing state-of-the-art AI models, our system offers higher spatial resolution. It is cheap to train/run and requires no additional post-processing. It is run on laptops and can generate many thousands of ensemble members at little computational cost (compared with physical local area models). It is ideally suited to Meteorological Services with limited computational facilities. |
| title | Rainfall forecasts in daily use over East Africa improved by machine learning |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2512.24525 |