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  "documentTitle": "2021 Air Street Capital The State of AI Report 2021",
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      "text": "This work evaluated the DGM’s performance against fifty meteorologists from the UK’s Met Office and preferred it to other deep learning methods (PySTEPS and Axial Attention) based on accuracy and the usefulness across 88% of evaluation cases.",
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      "text": "A DGM is trained on historically observed radar-based estimates of precipitation. The DGM learns a probability distribution of this data from which it can generate future radar predictions.\nThe model represents uncertainty across multiple spatial and temporal scales, which makes it amenable to predicting smaller-scale weather phenomena that are particularly stochastic.\nThis work evaluated the DGM’s performance against fifty meteorologists from the UK’s Met Office and preferred it to other deep learning methods (PySTEPS and Axial Attention) based on accuracy and the usefulness across 88% of evaluation cases.",
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      "text": "Proportion selected: 88%",
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      "text": "Predicting rainfall at high-resolution with a short lead time (<2h, i.e. “nowcasting”) is important for businesses and people when making weather-dependent decisions. New deep generative model (DGM)-based methods bring added resolution and prediction accuracy beyond that of physics-based simulations and current ML methods.",
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