论文标题
深度学习的云封面
Cloud Cover Nowcasting with Deep Learning
论文作者
论文摘要
Nowcasting是气象学领域，目的是在短期内长达几个小时的天气预测天气。在气象景观中，该领域相当具体，因为它需要特定的技术，例如数据外推，而传统的气象通常基于物理建模。在本文中，我们专注于云覆盖现象，该云覆盖物具有各种应用领域，例如卫星镜头优化和光伏能源生产预测。 在最近的多个图像任务上取得了深度学习成功之后，我们在MeteoSat卫星图像上应用了深度卷积神经网络，以进行云覆盖。我们介绍了一些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。所有选定的体系结构均对持久性均有显着改善，而众所周知的U-NET超过了Arome物理模型。
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.
