DustNet: skillful neural network predictions of Saharan dust
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909225516531712 |
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| author | Nowak, Trish E. Augousti, Andy T. Simmons, Benno I. Siegert, Stefan |
| author_facet | Nowak, Trish E. Augousti, Andy T. Simmons, Benno I. Siegert, Stefan |
| contents | Suspended in the atmosphere are millions of tonnes of mineral dust which interacts with weather and climate. Accurate representation of mineral dust in weather models is vital, yet remains challenging. Large scale weather models use high power supercomputers and take hours to complete the forecast. Such computational burden allows them to only include monthly climatological means of mineral dust as input states inhibiting their forecasting accuracy. Here, we introduce DustNet a simple, accurate and super fast forecasting model for 24-hours ahead predictions of aerosol optical depth AOD. DustNet trains in less than 8 minutes and creates predictions in 2 seconds on a desktop computer. Created by DustNet predictions outperform the state-of-the-art physics-based model on coarse 1 x 1 degree resolution at 95% of grid locations when compared to ground truth satellite data. Our results show DustNet has a potential for fast and accurate AOD forecasting which could transform our understanding of dust impacts on weather patterns. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_11754 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DustNet: skillful neural network predictions of Saharan dust Nowak, Trish E. Augousti, Andy T. Simmons, Benno I. Siegert, Stefan Geophysics Artificial Intelligence Atmospheric and Oceanic Physics Data Analysis, Statistics and Probability 86-06(Primary), 86A10(Secondary) J.2; I.2.1; I.2.7 Suspended in the atmosphere are millions of tonnes of mineral dust which interacts with weather and climate. Accurate representation of mineral dust in weather models is vital, yet remains challenging. Large scale weather models use high power supercomputers and take hours to complete the forecast. Such computational burden allows them to only include monthly climatological means of mineral dust as input states inhibiting their forecasting accuracy. Here, we introduce DustNet a simple, accurate and super fast forecasting model for 24-hours ahead predictions of aerosol optical depth AOD. DustNet trains in less than 8 minutes and creates predictions in 2 seconds on a desktop computer. Created by DustNet predictions outperform the state-of-the-art physics-based model on coarse 1 x 1 degree resolution at 95% of grid locations when compared to ground truth satellite data. Our results show DustNet has a potential for fast and accurate AOD forecasting which could transform our understanding of dust impacts on weather patterns. |
| title | DustNet: skillful neural network predictions of Saharan dust |
| topic | Geophysics Artificial Intelligence Atmospheric and Oceanic Physics Data Analysis, Statistics and Probability 86-06(Primary), 86A10(Secondary) J.2; I.2.1; I.2.7 |
| url | https://arxiv.org/abs/2406.11754 |