论文标题
使用深神经网络进行沉淀
Precipitaion Nowcasting using Deep Neural Network
论文作者
论文摘要
对于天气预报用户来说，降水为降水非常重要，从户外活动和体育比赛到机场交通管理的活动不等。与传统上从数值模型中获得的长期降水预测相反，降水现象需要非常快。因此，由于这个时间限制，获得更具挑战性。最近，已经提出了许多基于机器学习的方法。我们建议使用三种流行的深度学习模型（U-NET，ConvlstM和SVG-LP），该模型在二维降水图上训练了降水。我们提出了一种用于提取贴片的算法，以获得高分辨率沉淀图。我们提出了一个解决模糊图像问题并减少降水图中零值像素的影响的损失函数。
Precipitation nowcasting is of great importance for weather forecast users, for activities ranging from outdoor activities and sports competitions to airport traffic management. In contrast to long-term precipitation forecasts which are traditionally obtained from numerical models, precipitation nowcasting needs to be very fast. It is therefore more challenging to obtain because of this time constraint. Recently, many machine learning based methods had been proposed. We propose the use three popular deep learning models (U-net, ConvLSTM and SVG-LP) trained on two-dimensional precipitation maps for precipitation nowcasting. We proposed an algorithm for patch extraction to obtain high resolution precipitation maps. We proposed a loss function to solve the blurry image issue and to reduce the influence of zero value pixels in precipitation maps.
