TemperatureGAN: Generative Modeling of Regional Atmospheric Temperatures

Published: 11 June 2024| Version 1 | DOI: 10.17632/9k892pzkfx.1
Emmanuel Balogun


Includes engineered dataset for creating TemperatureGAN and trained models for inference. 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. If you use the data or models, you must cite our paper (paper in related links). The complete dataset used to train the model is too large to upload here, thus we offer a random split of the original data, which should be sufficient to train a good model to reproduce many of our results. Please refer to TemperatureGAN paper for details on data engineering.



Stanford University


Machine Learning, Weather, Climate Data, Deep Learning, Generative Adversarial Network