mlsanityData to 7 Oct 2026

Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models

arXiv 2401.15469 ↗First posted 27 Jan 2024cs.LG cs.AI physics.ao-phcited by 15

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

The Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution regional reanalysis dataset for the European domain. In recent years it has shown significant utility across various climate-related tasks, ranging from forecasting and climate change research to renewable energy prediction, resource management, air quality risk assessment, and the forecasting of rare events, among others. Unfortunately, the availability of CERRA is lagging two years behind the current date, due to constraints in acquiring the requisite external data and the intensive computational demands inherent in its generation. As a solution, this paper introduces a novel method using diffusion models to approximate CERRA downscaling in a data-driven manner, without additional informations. By leveraging the lower resolution ERA5 dataset, which provides boundary conditions for CERRA, we approach this as a super-resolution task. Focusing on wind speed around Italy, our model, trained on existing CERRA data, shows promising results, closely mirroring original CERRA data. Validation with in-situ observations further confirms the model's accuracy in approximating ground measurements.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 33 of its 99 references that are in the map. First posted on arXiv 27 Jan 2024.

Builds on

33 of 99 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Precipitation nowcasting with generative diffusion models13 Aug

Other references · 32

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  5. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  6. 2020Denoising Diffusion Implicit Modelscited by 9,655
  7. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  8. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  9. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  10. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,869
  11. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,543
  12. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,256
20 more
  1. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
  2. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,218
  3. 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,199
  4. 2019An Introduction to Variational Autoencoderscited by 997
  5. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
  6. 2021SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Modelscited by 415
  7. 2018Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Networkcited by 263
  8. 2018Deep Back-Projection Networks For Super-Resolutioncited by 224
  9. 2019Meta-SR: A Magnification-Arbitrary Network for Super-Resolutioncited by 180
  10. 2019Feedback Network for Image Super-Resolutioncited by 155
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  14. 2022Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restorationcited by 72
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  16. 2023SwinLSTM:Improving Spatiotemporal Prediction Accuracy using Swin Transformer and LSTMcited by 32
  17. 2021A Comprehensive Review of Deep Learning-based Single Image Super-resolutioncited by 23
  18. 2021A survey on Variational Autoencoders from a GreenAI perspectivecited by 22
  19. 2021WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Datacited by 11
  20. 2018Single Image Super-Resolution via Cascaded Multi-Scale Cross Networkcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 9; 2026: 2.

15 papers.

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  1. 2024Can AI be enabled to dynamical downscaling? A Latent Diffusion Model to mimic km-scale COSMO5.0CLM9 simulationscited by 14
  2. 2026Universal Diffusion-Based Probabilistic Downscalingcited by 5
  3. 2024Transformer based super-resolution downscaling for regional reanalysis: Full domain vs tiling approachescited by 4
  4. 2025DiffScale: Continuous Downscaling and Bias Correction of Subseasonal Wind Speed Forecasts using Diffusion Modelscited by 3
  5. 2025MODS: Multi-source Observations Conditional Diffusion Model for Meteorological State Downscalingcited by 3
  6. 2025Assessing the Geographic Generalization and Physical Consistency of Generative Models for Climate Downscalingcited by 3
  7. 2024TAUDiff: Highly efficient kilometer-scale downscaling using generative diffusion modelscited by 2
  8. 2025Controlling Ensemble Variance in Diffusion Models: An Application for Reanalyses Downscalingcited by 1
  9. 2025DEF: Diffusion-augmented Ensemble Forecastingcited by 1
  10. 2025Multidimensional Distributional Neural Network Output Demonstrated in Super-Resolution of Surface Wind Speedcited by 1
  11. 2025Sparse Local Implicit Image Function for sub-km Weather Downscalingcited by 1
  12. 2024Deep Learning for Sea Surface Temperature Reconstruction under Cloud Occlusioncited by 0
3 more
  1. 2025Vision Transformers for Multi-Variable Climate Downscaling: Emulating Regional Climate Models with a Shared Encoder and Multi-Decoder Architecturecited by 0
  2. 2025Km-scale dynamical downscaling through conformalized latent diffusion modelscited by 0
  3. 2026MotifGen: Spatiotemporal interpolation of misaligned satellite images via multi-source generative modeling, in an application to tropical cyclonescited by 0

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  2. 2024Enhancing Weather Predictions: Super-Resolution via Deep Diffusion Models
  3. 2025Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulence
  4. 2026Apeliotes: A Diffusion-Based Modeling Framework for km-scale Multi-Level Atmospheric Fields
  5. 2026Steering Diffusion Priors with Sparse Observations for High-Resolution Temperature Downscaling
  6. 2024A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation
  7. 2024Dynamical-generative downscaling of climate model ensembles
  8. 2023Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models