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QUANTIFICATION OF FORECAST UNCERTAINTY USING NEURAL NETWORKS QUANTIFICATION OF FORECAST UNCERTAINTY USING NEURAL NETWORKS

Abstract : Uncertainty quantification in numerical weather and climate prediction is usually achieved using a Monte Carlo estimation (i.e., ensemble forecasting) of the forecast probability distribution function of the state of the system. In this work, we present a method for uncertainty quantification based on neural networks and using a likelihood-based loss function to train the network. This provides state dependent uncertainty estimation, without the need of integrating an ensemble of forecasts. The method is evaluated with a chaotic low-dimensional model in two scenarios: with stochastic errors only (SE) and systematic and stochastic errors (SSE).
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Submitted on : Friday, September 18, 2020 - 12:00:53 PM
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Maximiliano Sacco, Yicun Zhen, Pierre Tandeo, Juan Ruiz, Manuel Pulido. QUANTIFICATION OF FORECAST UNCERTAINTY USING NEURAL NETWORKS QUANTIFICATION OF FORECAST UNCERTAINTY USING NEURAL NETWORKS. Climate Informatics 2020, Sep 2020, Oxford, United Kingdom. ⟨hal-02942802⟩

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