论文标题
使用条件生成对抗网络对云反射率字段进行建模
Modeling Cloud Reflectance Fields using Conditional Generative Adversarial Networks
论文作者
论文摘要
我们介绍了一种有条件的生成对抗网络（CGAN）方法，以生成以大规模气象变量（例如海面温度和相对湿度）为条件的云反射场（CRF）。我们表明，训练有素的模型可以从相应的气象观察中生成现实的CRF，这代表了迈向数据驱动的随机云参数化框架的一步。
We introduce a conditional Generative Adversarial Network (cGAN) approach to generate cloud reflectance fields (CRFs) conditioned on large scale meteorological variables such as sea surface temperature and relative humidity. We show that our trained model can generate realistic CRFs from the corresponding meteorological observations, which represents a step towards a data-driven framework for stochastic cloud parameterization.
