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：随机生成器对于评估气候变化对各行业的影响具有重要价值。为量化能源系统等领域的气候风险，相关生成器需兼具精确性（与真实数据的统计相似性）、可靠性（不产生错误样本）与高效性。基于北美陆面数据同化系统数据，我们提出TemperatureGAN——一种以月份、地理位置及时间周期为条件的生成对抗网络，用于生成逐小时分辨率的2米高度大气温度。我们提出了评估方法与指标以衡量生成样本质量，结果表明TemperatureGAN能够生成高保真样本，其空间表征准确、时间动态特征与已知日循环规律一致。