论文标题
气候模型偏见校正和超分辨率的对比度学习
Contrastive Learning for Climate Model Bias Correction and Super-Resolution
论文作者
论文摘要
气候模型通常需要后处理才能准确估算当地气候风险。最常见的后处理是偏置校正和空间分辨率增强。但是，通常用于此的统计方法不仅无法捕获多元空间相关信息，而且还依赖于发达国家以外的丰富观察数据，从而限制了其潜力。在这里，我们根据图像超级分辨率（SR）和对比度学习生成对抗网络（GAN）的组合提出了一种替代方法来应对这一挑战。我们对NASA的旗舰后处理的CMIP6气候模型产品NEX-GDDP进行了基准性能。我们发现，我们的模型成功达到了NASA产品的空间分辨率两倍，同时还达到了每日降水和温度的可比或改善偏置校正水平。当前和前瞻性气候的较高忠诚模拟可以使更多的局部，准确的危害模型，例如洪水，干旱和热浪。
Climate models often require post-processing in order to make accurate estimates of local climate risk. The most common post-processing applied is bias-correction and spatial resolution enhancement. However, the statistical methods typically used for this not only are incapable of capturing multivariate spatial correlation information but are also reliant on rich observational data often not available outside of developed countries, limiting their potential. Here we propose an alternative approach to this challenge based on a combination of image super resolution (SR) and contrastive learning generative adversarial networks (GANs). We benchmark performance against NASA's flagship post-processed CMIP6 climate model product, NEX-GDDP. We find that our model successfully reaches a spatial resolution double that of NASA's product while also achieving comparable or improved levels of bias correction in both daily precipitation and temperature. The resulting higher fidelity simulations of present and forward-looking climate can enable more local, accurate models of hazards like flooding, drought, and heatwaves.
