Exploring Bayesian deep learning for weather forecasting with the Lorenz 84 system
| Main Authors: | Yang Liu, Jisk Attema, Wilco Hazeleger |
|---|---|
| Format: | Proceeding poster Journal |
| Terbitan: |
, 2020
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/4146850 |
Daftar Isi:
- 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.
- ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction (October 5-8th, 2020)