Bias correcting climate model simulations using unpaired image-to-image translation networks
Bias correcting climate model simulations using unpaired image-to-image translation networks
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DOI：
10.1175/aies-d-22-0031.1
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
D. J. Fulton;Ben J. Clarke;G. Hegerl
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文献类型：
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作者：
D. J. Fulton;Ben J. Clarke;G. Hegerl
我们评估了不成对的图像到图像转换网络对全球大气环流模型模拟的偏差校正数据的适用性。我们使用UNIT神经网络架构来映射HadGEM 3-A-N216模型的数据和以南亚季风为中心的地理区域中的ERA 5再分析数据，该模型中存在有充分记录的严重偏差。UNIT网络校正交叉变量相关性和空间结构，但创建比目标分布更少极端值的偏差校正。通过将UNIT神经网络与分位数映射的经典技术相结合，我们可以产生比单独使用更好的偏差校正。UNIT+QM方案被证明是正确的交叉变量的相关性，空间模式，和所有的边缘分布的单变量。这种联合分布的仔细校正对于复合极值研究是非常重要的。
We assess the suitability of unpaired image-to-image translation networks for bias correcting data simulated by global atmospheric circulation models. We use the UNIT neural network architecture to map between data from the HadGEM3-A-N216 model and ERA5 reanalysis data in a geographical area centred on the South Asian monsoon, which has well-documented serious biases in this model. The UNIT network corrects cross-variable correlations and spatial structures but creates bias corrections with less extreme values than the target distribution. By combining the UNIT neural network with the classical technique of quantile mapping, we can produce bias corrections that are better than either alone. The UNIT+QM scheme is shown to correct cross-variable correlations, spatial patterns, and all marginal distributions of single variables. The careful correction of such joint distributions is of high importance for compound extremes research.