AI-GAMFS, a deep learning model trained on 42 years of MERRA-2 reanalysis, produces 5-day global forecasts of aerosol components and meteorology faster and more accurately than CAMS, GEOS-FP, and several regional dust models in the reported evaluations.
DustNet: skillful neural network predictions of Saharan dust
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abstract
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.
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physics.ao-ph 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
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Advancing global aerosol forecasting with artificial intelligence
AI-GAMFS, a deep learning model trained on 42 years of MERRA-2 reanalysis, produces 5-day global forecasts of aerosol components and meteorology faster and more accurately than CAMS, GEOS-FP, and several regional dust models in the reported evaluations.