论文标题
有条件的云场
Conditional generation of cloud fields
论文作者
论文摘要
与云物理有关的过程构成了气候模型和预测中剩余最大的科学不确定性。这种不确定性源于当前气候模型的粗糙本质，以及对详细物理学的理解缺乏理解。我们训练一个生成的对抗网络，以生成在气候模型输出和卫星图像的计量学重新分析数据上的逼真的云场。尽管我们的网络能够生成逼真的云字段，尤其是它们的大规模模式，但需要更多的工作来完善其准确性，以解决云质量的更细细性细节以改善其预测。
Processes related to cloud physics constitute the largest remaining scientific uncertainty in climate models and projections. This uncertainty stems from the coarse nature of current climate models and relatedly the lack of understanding of detailed physics. We train a generative adversarial network to generate realistic cloud fields conditioned on meterological reanalysis data for both climate model outputs as well as satellite imagery. While our network is able to generate realistic cloud fields, especially their large-scale patterns, more work is needed to refine its accuracy to resolve finer textural details of cloud masses to improve its predictions.
