Abstract
Recent developments in deep learning have led to many new neural networks potentially applicable to weather forecasting. However, these techniques are always based on deterministic deep neural networks (DNN) and therefore prone to over-confident forecasts. This brings Bayesian deep learning (BDL) into our scope. In this study, we use Bayesian Long-Short Term Memory neural networks (BayesLSTM) to forecast output from the Lorenz 84 system with seasonal forcing, so as to examine if BDL is useful for weather forecast.
Original language | English |
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DOIs | |
Publication status | Published - 30 Sept 2020 |
Event | ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction - Online Duration: 5 Oct 2020 → 8 Oct 2020 |
Workshop
Workshop | ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction |
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Period | 5/10/20 → 8/10/20 |