论文标题
DL4DS-深度学习实证缩小
DL4DS -- Deep Learning for empirical DownScaling
论文作者
论文摘要
地球科学中的一项常见任务是从全球气候模型中推断本地和区域范围内的气候信息。动态降尺度需要以高分辨率运行昂贵的数值模型，这可能会由于较长的模型运行时而过于刺激。另一方面，统计缩减技术为以更有效的方式提供了一种替代方法，用于学习大型气候和地方规模的气候。近年来，已经提出了许多用于统计缩减的深层神经网络方法，主要基于为计算机视觉和超分辨率任务开发的卷积体系结构。本文介绍了DL4DS，经验缩减的深度学习，这是一个python库，它实现了各种各样的最先进的和新颖的算法，用于缩小具有深神经网络的缩小网格的地球科学数据。 DL4DS的设计目的是为培训卷积神经网络提供可配置的架构和学习策略的一般框架，以促进以强大的方式促进比较和消融研究。我们展示了DL4D在地中海西部地区的空气质量凸轮数据中的功能。可以在此存储库中找到DL4DS库：https：//github.com/carlos-gg/dl4ds
A common task in Earth Sciences is to infer climate information at local and regional scales from global climate models. Dynamical downscaling requires running expensive numerical models at high resolution which can be prohibitive due to long model runtimes. On the other hand, statistical downscaling techniques present an alternative approach for learning links between the large- and local-scale climate in a more efficient way. A large number of deep neural network-based approaches for statistical downscaling have been proposed in recent years, mostly based on convolutional architectures developed for computer vision and super-resolution tasks. This paper presents DL4DS, Deep Learning for empirical DownScaling, a python library that implements a wide variety of state-of-the-art and novel algorithms for downscaling gridded Earth Science data with deep neural networks. DL4DS has been designed with the goal of providing a general framework for training convolutional neural networks with configurable architectures and learning strategies to facilitate the conduction of comparative and ablation studies in a robust way. We showcase the capabilities of DL4DS on air quality CAMS data over the western Mediterranean area. The DL4DS library can be found in this repository: https://github.com/carlos-gg/dl4ds
