Exploring Bayesian deep learning for weather forecasting with the Lorenz 84 system

Yang Liu, Jisk Attema, Wilco Hazeleger

Research output: Contribution to conferencePosterAcademic

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 languageEnglish
DOIs
Publication statusPublished - 30 Sept 2020
EventECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction - Online
Duration: 5 Oct 20208 Oct 2020

Workshop

WorkshopECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction
Period5/10/208/10/20

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