Calibration of solar radiation ensemble forecasts using convolutional neural network
- 1Météo-France, Toulouse, France
- 2Forschungszentrum Jülich GmbH, Institute for Energy and climate research - Troposphere (IEK-8), Jülich, Germany
- 3Fraunhofer Institute for Wind Energy and Energy System Technology, Bremerhaven, Germany
Ensemble forecast approaches have become state-of-the-art for the quantification of weather forecast uncertainty. However, ensemble forecasts from numerical weather prediction models (NWPs) still tend to be biased and underdispersed, hence justifying the use of statistical post-processing techniques to improve forecast skill.
In this study, ensemble forecasts are post-processed using a convolutional neural network (CNN). CNNs are the most popular machine learning tool to deal with images. In our case, CNNs allow to integrate information from spatial patterns contained in NWP outputs.
We focus on solar radiation forecasts for 48 hours ahead over Europe from the 35-members ARPEGE (Météo-France global NWP) and a 512-members WRF (Weather Research and Forecasting) ensembles. We used a U-Net (a special kind of CNN) designed to produce a probabilistic forecast (quantiles) using as ground truth the CAMS (Copernicus Atmosphere Monitoring System) radiation service dataset with a spatial resolution of 0.2°.
How to cite: Dupuy, F., Lu, Y.-S., Good, G., and Zamo, M.: Calibration of solar radiation ensemble forecasts using convolutional neural network, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7359, https://doi.org/10.5194/egusphere-egu21-7359, 2021.
