Cloud Cover Nowcasting with Deep Learning
This work addresses short-term weather forecasting for applications like satellite optimization and photovoltaic energy production, representing an incremental advance using deep learning on existing data.
The paper tackled cloud cover nowcasting by applying deep convolutional neural networks to Meteosat satellite images, achieving significant improvements over persistence and surpassing the AROME physical model.
Nowcasting is a field of meteorology which aims at forecasting weather on a short term of up to a few hours. In the meteorology landscape, this field is rather specific as it requires particular techniques, such as data extrapolation, where conventional meteorology is generally based on physical modeling. In this paper, we focus on cloud cover nowcasting, which has various application areas such as satellite shots optimisation and photovoltaic energy production forecast. Following recent deep learning successes on multiple imagery tasks, we applied deep convolutionnal neural networks on Meteosat satellite images for cloud cover nowcasting. We present the results of several architectures specialized in image segmentation and time series prediction. We selected the best models according to machine learning metrics as well as meteorological metrics. All selected architectures showed significant improvements over persistence and the well-known U-Net surpasses AROME physical model.