Computer Science > Computer Vision and Pattern Recognition
[Submitted on 24 Sep 2020 (v1), last revised 17 Dec 2020 (this version, v3)]
Title:Cloud Cover Nowcasting with Deep Learning
View PDFAbstract: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.
Submission history
From: Lea Berthomier [view email][v1] Thu, 24 Sep 2020 09:57:29 UTC (583 KB)
[v2] Mon, 28 Sep 2020 07:23:35 UTC (584 KB)
[v3] Thu, 17 Dec 2020 11:57:43 UTC (583 KB)
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