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Towards Climate Variable Prediction with Conditioned Spatio-Temporal Normalizing Flows

arXiv 2311.06958 ↗First posted 12 Nov 2023cs.LG cs.AIcited by 0

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Abstract (arXiv)

This study investigates how conditional normalizing flows can be applied to remote sensing data products in climate science for spatio-temporal prediction. The method is chosen due to its desired properties such as exact likelihood computation, predictive uncertainty estimation and efficient inference and sampling which facilitates faster exploration of climate scenarios. Experimental findings reveal that the conditioned spatio-temporal flow surpasses both deterministic and stochastic baselines in prolonged rollout scenarios. It exhibits stable extrapolation beyond the training time horizon for extended rollout durations. These findings contribute valuable insights to the field of spatio-temporal modeling, with potential applications spanning diverse scientific disciplines.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 31 references that are in the map. First posted on arXiv 12 Nov 2023.

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References · 12

  1. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,254
  2. 2016Language Modeling with Gated Convolutional Networkscited by 702
  3. 2021Skillful Precipitation Nowcasting using Deep Generative Models of Radarcited by 311
  4. 2019Learning Likelihoods with Conditional Normalizing Flowscited by 172
  5. 2018Deep learning to represent sub-grid processes in climate modelscited by 156
  6. 2018Neural networks for post-processing ensemble weather forecastscited by 95
  7. 2020Generative Adversarial Networks for Spatio-temporal Data: A Surveycited by 35
  8. 2020ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flowscited by 25
  9. 2021SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with an Autoregressive Embedding Losscited by 13
  10. 2018Convolutional LSTMs for Cloud-Robust Segmentation of Remote Sensing Imagerycited by 11
  11. 2021Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-resolutioncited by 10
  12. 2019D-GAN: Deep Generative Adversarial Nets for Spatio-Temporal Predictioncited by 8

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