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.

中文翻译：

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使用深度学习进行云量临近预报

临近预报是气象学领域，旨在预测长达几个小时的短期天气。在气象领域，该领域相当特殊，因为它需要特定的技术，例如数据外推，而传统的气象学通常基于物理建模。在本文中，我们专注于云覆盖临近预报，它具有各种应用领域，例如卫星镜头优化和光伏能源生产预测。继最近在多个图像任务上的深度学习成功之后，我们在 Meteosat 卫星图像上应用了深度卷积神经网络，用于云层临近预报。我们展示了几种专门用于图像分割和时间序列预测的架构的结果。我们根据机器学习指标和气象指标选择了最佳模型。