DOE OSTI · 1769692
Deep Learning for Ensemble Forecasting
Abstract
Focal Area: (2) Predictive modeling through the use of AI techniques and AI-derived model components and the use of AI and other tools to design a prediction system comprising a hierarchy of models. Science Challenge: While both climate and weather forecast systems have continued to improve due to substantial efforts to improve computational capabilities, observations, and numerical models, the atmosphere is a chaotic system, and this puts a fundamental limit on our ability to make predictions. Forecasts made by high-resolution models initialized with only slightly different atmospheric states can quickly diverge. Quantifying uncertainty in forecasts is essential to adequately understand them and to make the best-informed policy decisions particularly when it comes to hydrology, extreme weather (including extreme precipitation events), and climate.
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Geiss, Andrew, Hardin, Joseph, Silva, Sam, Gustafson, Jr., William I., Varble, Adam, Fan, Jiwen. 2021-04-15. Deep Learning for Ensemble Forecasting. https://doi.org/10.2172/1769692
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