Stochastic generators are useful for estimating climate impacts on various sectors. Projecting climate risk in various sectors, e.g. energy systems, requires generators that are accurate (statistical resemblance to ground-truth), reliable (do not produce erroneous examples), and efficient. Leveraging data from the North American Land Data Assimilation System, we introduce TemperatureGAN, a Generative Adversarial Network conditioned on months, locations, and time periods, to generate 2m above ground atmospheric temperatures at an hourly resolution. We propose evaluation methods and metrics to measure the quality of generated samples. We show that TemperatureGAN produces high-fidelity examples with good spatial representation and temporal dynamics consistent with known diurnal cycles.

翻译：随机生成器对于估算气候变化对各领域的影响十分有用。为评估能源系统等不同领域的气候风险，需要具备准确性（与真实数据统计相似）、可靠性（不生成错误样本）和高效性的生成器。利用北美陆面数据同化系统的数据，我们提出TemperatureGAN——一种以月份、位置和时间段为条件的生成对抗网络，用于生成小时分辨率的地表以上2米处大气温度。我们提出了评估方法和指标来衡量生成样本的质量，并表明TemperatureGAN能够生成高保真样本，具有良好空间表征且时间动态符合已知的日循环规律。