原创 dl4ds 气象学家

DL4DS—Deep learning for empirical downscaling

框架库地址 https://github.com/carlos-gg/dl4ds

地球科学中的一项常见任务是从全球气候模式中推断局地和区域尺度的气候信息。动力降尺度需要运行高分辨率数值模式机时花费巨大，另外由于模式运行时间也比较长，这些足以让人望而却步。此外，统计降尺度技术提供了另一种方法，以更有效的方式学习大尺度和局地尺度气候之间的联系。近年来，大量基于深度神经网络的统计降尺度方法被提出，这些方法大多基于为计算机视觉和超级分辨率任务开发的卷积架构。本文介绍了用于统计降尺度的深度学习（DL4DS），这是一个Python库，实现了各种最先进的和新颖的算法，采用深度神经网络对格点化地球科学数据降尺度。DL4DS的设计目标是提供一个通用框架，用于训练具有可配置架构和学习策略的卷积神经网络，以促进以强大的方式进行比较和消融研究。我们在地中海西部地区的空气质量哥白尼大气监测服务（CAMS）数据上展示了DL4DS的能力。DL4DS库可以在这个资源库中找到：https://github.com/carlos-gg/dl4ds

Figure 1. General architecture of DL4DS. A low-resolution gridded dataset can be downscaled, with the help of auxiliary predictor and static variables, and a high-resolution reference dataset. The mapping between the low- and high-resolution data is learned with either a supervised or a conditional generative adversarial DL model.

Figure 2. Panel (a) shows the main blocks and layers implemented in DL4DS. Panel (b) shows the structure of the main spatial convolutional block, a succession of two convolutional layers with interleaved regularization operations, such as dropout or normalization. Blocks and operations shown with dashed lines are optional.

Figure 3. DL4DS supervised DL models, as well as generators, are composed of a backbone section (examples in panels [a–d]) and an output module (panel [e]). Panel (a) shows the backbone of models for downscaling pre-upsampled spatial samples using either residual or dense blocks. Panel (b) presents the backbone of a model for downscaling spatial samples using ConvNext-like blocks and one of the post-upsampling blocks described in Section 3.4.1. Panel (c) shows the backbone of a model for downscaling pre-upsampled spatial samples using an encoder-decoder structure. Panel (d) shows the backbone of a model for downscaling spatiotemporal samples using recurrent-convolutional blocks and a post-upsampling block. These backbones are followed by the output module (see Section 3.4.2) shown in panel (e). The color legend for the blocks used here is shown in Figure 2a.

Figure 4. Example of a conditional generative adversarial model for spatiotemporal samples in postupsampling mode (see Section 3.4.1). Two networks, the generator shown in panel (a), and discriminator shown in panel (b), are trained together optimizing an adversarial loss (see Section 3.5). The color legend for the blocks used here is shown in Figure 2a.

Figure 5. A reference NO2 surface concentration field from the low-resolution CAMS global reanalysis is shown in panel (a). In panel (b), we present a resampled version, via bicubic interpolation, of the lowresolution reference field. This interpolated field looks overly smoothed and showcases the inefficiency of simple resampling methods at restoring fine-scale information. Panel(c): the corresponding highresolution field from the CAMS regional reanalysis. Both low- and high-resolution grids were taken from the holdout set for the same time step. The maximum value shown corresponds to the maximum value in the high-resolution grid.