Super-resolving the coarse outputs of global climate simulations, termed downscaling, is crucial in making political and social decisions on systems requiring long-term climate change projections. Existing fast super-resolution techniques, however, have yet to preserve the spatially correlated nature of climatological data, which is particularly important when we address systems with spatial expanse, such as the development of transportation infrastructure. Herein, we show an adversarial network-based machine learning enables us to correctly reconstruct the inter-regional spatial correlations in downscaling with high magnification up to fifty, while maintaining the pixel-wise statistical consistency. Direct comparison with the measured meteorological data of temperature and precipitation distributions reveals that integrating climatologically important physical information is essential for the accurate downscaling, which prompts us to call our approach $\pi$SRGAN (Physics Informed Super-Resolution Generative Adversarial Network). The present method has a potential application to the inter-regionally consistent assessment of the climate change impact.
翻译:超大解析全球气候模拟(称为降尺度)的粗粗产出,对于就需要长期气候变化预测的系统作出政治和社会决策至关重要。然而,现有的快速超分辨率技术尚未保持气候学数据的空间关联性,这对于我们处理空间扩展的系统(如运输基础设施的发展)尤其重要。在这里,我们展示了一种以网络为基础的对称机器学习,使我们能够正确地重建区域间空间关系,以高放大度降幅降幅最高至50,同时保持像素统计一致性。与测量的温度和降水分布气象数据进行直接比较表明,整合气候学上的重要物理信息对于准确降尺度至关重要,这促使我们称之为“超分辨率超分辨率基因反转网络 ” 。目前的方法有可能适用于区域间对气候变化影响的一致评估。