An Operational Deep Learning System for Satellite-Based High-Resolution Global Nowcasting
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| Autores principales: | , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| author | Agrawal, Shreya Hassen, Mohammed Alewi Brempong, Emmanuel Asiedu Babenko, Boris Zyda, Fred Graham, Olivia Li, Di Merchant, Samier Potes, Santiago Hincapie Russell, Tyler Cheresnick, Danny Kakkirala, Aditya Prakash Rasp, Stephan Hassidim, Avinatan Matias, Yossi Kalchbrenner, Nal Gupta, Pramod Hickey, Jason Bell, Aaron |
| author_facet | Agrawal, Shreya Hassen, Mohammed Alewi Brempong, Emmanuel Asiedu Babenko, Boris Zyda, Fred Graham, Olivia Li, Di Merchant, Samier Potes, Santiago Hincapie Russell, Tyler Cheresnick, Danny Kakkirala, Aditya Prakash Rasp, Stephan Hassidim, Avinatan Matias, Yossi Kalchbrenner, Nal Gupta, Pramod Hickey, Jason Bell, Aaron |
| contents | Precipitation nowcasting, which predicts rainfall up to a few hours ahead, is a critical tool for vulnerable communities in the Global South frequently exposed to intense, rapidly developing storms. Timely forecasts provide a crucial window to protect lives and livelihoods. Traditional numerical weather prediction (NWP) methods suffer from high latency, low spatial and temporal resolution, and significant gaps in accuracy across the world. Recent machine learning-based nowcasting methods, common in the Global North, cannot be extended to the Global South due to extremely sparse radar coverage. We present Global MetNet, an operational global machine learning nowcasting model. It leverages the Global Precipitation Mission's CORRA dataset, geostationary satellite data, and global NWP data to predict precipitation for the next 12 hours. The model operates at a high resolution of approximately 0.05° (~5km) spatially and 15 minutes temporally. Global MetNet significantly outperforms industry-standard hourly forecasts and achieves significantly higher skill, making forecasts useful over a much larger area of the world than previously available. Our model demonstrates better skill in data-sparse regions than even the best high-resolution NWP models achieve in the US. Validated using ground radar and satellite data, it shows significant improvements across key metrics like the critical success index and fractions skill score for all precipitation rates and lead times. Crucially, our model generates forecasts in under a minute, making it readily deployable for real-time applications. It is already deployed for millions of users on Google Search. This work represents a key step in reducing global disparities in forecast quality and integrating sparse, high-resolution satellite observations into weather forecasting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13050 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Operational Deep Learning System for Satellite-Based High-Resolution Global Nowcasting Agrawal, Shreya Hassen, Mohammed Alewi Brempong, Emmanuel Asiedu Babenko, Boris Zyda, Fred Graham, Olivia Li, Di Merchant, Samier Potes, Santiago Hincapie Russell, Tyler Cheresnick, Danny Kakkirala, Aditya Prakash Rasp, Stephan Hassidim, Avinatan Matias, Yossi Kalchbrenner, Nal Gupta, Pramod Hickey, Jason Bell, Aaron Machine Learning Atmospheric and Oceanic Physics Precipitation nowcasting, which predicts rainfall up to a few hours ahead, is a critical tool for vulnerable communities in the Global South frequently exposed to intense, rapidly developing storms. Timely forecasts provide a crucial window to protect lives and livelihoods. Traditional numerical weather prediction (NWP) methods suffer from high latency, low spatial and temporal resolution, and significant gaps in accuracy across the world. Recent machine learning-based nowcasting methods, common in the Global North, cannot be extended to the Global South due to extremely sparse radar coverage. We present Global MetNet, an operational global machine learning nowcasting model. It leverages the Global Precipitation Mission's CORRA dataset, geostationary satellite data, and global NWP data to predict precipitation for the next 12 hours. The model operates at a high resolution of approximately 0.05° (~5km) spatially and 15 minutes temporally. Global MetNet significantly outperforms industry-standard hourly forecasts and achieves significantly higher skill, making forecasts useful over a much larger area of the world than previously available. Our model demonstrates better skill in data-sparse regions than even the best high-resolution NWP models achieve in the US. Validated using ground radar and satellite data, it shows significant improvements across key metrics like the critical success index and fractions skill score for all precipitation rates and lead times. Crucially, our model generates forecasts in under a minute, making it readily deployable for real-time applications. It is already deployed for millions of users on Google Search. This work represents a key step in reducing global disparities in forecast quality and integrating sparse, high-resolution satellite observations into weather forecasting. |
| title | An Operational Deep Learning System for Satellite-Based High-Resolution Global Nowcasting |
| topic | Machine Learning Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2510.13050 |