TemperatureGAN: Generative Modeling of Regional Atmospheric Temperatures
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 61 references that are in the map. First posted on arXiv 29 Jun 2023.
Builds on
21 of 61 references.
References · 21
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
- 2022Generative Adversarial Networkscited by 5,627
- 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,626
- 2017Improved Training of Wasserstein GANscited by 2,235
- 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
- 2018Spectral Normalization for Generative Adversarial Networkscited by 1,636
- 2017Generative Adversarial Networks: An Overviewcited by 590
- 2018Regularisation of Neural Networks by Enforcing Lipschitz Continuitycited by 314
- 2022GraphCast: Learning skillful medium-range global weather forecastingcited by 267
- 2017GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibriumcited by 213
- 2016Temporal Generative Adversarial Nets with Singular Value Clippingcited by 198
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- 2022Forecasting Global Weather with Graph Neural Networkscited by 168
- 2022Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecastcited by 166
- 2020Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Samplecited by 43
- 2020ExGAN: Adversarial Generation of Extreme Samplescited by 29
- 2020A generative adversarial network approach to (ensemble) weather predictioncited by 18
- 2021Deep Extrapolation for Attribute-Enhanced Generationcited by 18
- 2020DeepClimGAN: A High-Resolution Climate Data Generatorcited by 9
- 2021Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Predictioncited by 7
- 2021EVGen: Adversarial Networks for Learning Electric Vehicle Charging Loads and Hidden Representationscited by 4
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Citing papers per year: 2023: 1.
1 paper.