mlsanityData to 7 Oct 2026

Machine learning emulation of a local-scale UK climate model

arXiv 2211.16116 ↗First posted 29 Nov 2022physics.ao-ph cs.LGcited by 20

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Abstract (arXiv)

Climate change is causing the intensification of rainfall extremes. Precipitation projections with high spatial resolution are important for society to prepare for these changes, e.g. to model flooding impacts. Physics-based simulations for creating such projections are very computationally expensive. This work demonstrates the effectiveness of diffusion models, a form of deep generative models, for generating much more cheaply realistic high resolution rainfall samples for the UK conditioned on data from a low resolution simulation. We show for the first time a machine learning model that is able to produce realistic samples of high-resolution rainfall based on a physical model that resolves atmospheric convection, a key process behind extreme rainfall. By adding self-learnt, location-specific information to low resolution relative vorticity, quantiles and time-mean of the samples match well their counterparts from the high-resolution simulation.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 9 of its 21 references that are in the map. First posted on arXiv 29 Nov 2022.

Builds on

9 of 21 references.

References · 9

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  6. 2016Pixel Recurrent Neural Networkscited by 814
  7. 2021Skillful Precipitation Nowcasting using Deep Generative Models of Radarcited by 308
  8. 2020Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Networkcited by 68
  9. 2017Intercomparison of Machine Learning Methods for Statistical Downscaling: The Case of Daily and Extreme Precipitationcited by 24

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 7; 2025: 4; 2026: 2.

20 papers.

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  1. 2023Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscalingcited by 103
  2. 2023Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantificationcited by 53
  3. 2024Generative Diffusion-based Downscaling for Climatecited by 25
  4. 2023Precipitation Downscaling with Spatiotemporal Video Diffusioncited by 16
  5. 2024Machine learning emulation of precipitation from km-scale UK regional climate simulations using a diffusion modelcited by 15
  6. 2024DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models with 3D Diffusion Modelscited by 15
  7. 2024Can AI be enabled to dynamical downscaling? A Latent Diffusion Model to mimic km-scale COSMO5.0CLM9 simulationscited by 14
  8. 2023Forecasting Tropical Cyclones with Cascaded Diffusion Modelscited by 10
  9. 2025EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rulescited by 10
  10. 2023DiffESM: Conditional Emulation of Earth System Models with Diffusion Modelscited by 9
  11. 2023Stochastic Latent Transformer: Efficient Modelling of Stochastically Forced Zonal Jetscited by 8
  12. 2024Downscaling Extreme Precipitation with Wasserstein Regularized Diffusioncited by 4
8 more
  1. 2024TCP-Diffusion: A Multi-modal Diffusion Model for Global Tropical Cyclone Precipitation Forecasting with Change Awarenesscited by 4
  2. 2025DiffScale: Continuous Downscaling and Bias Correction of Subseasonal Wind Speed Forecasts using Diffusion Modelscited by 3
  3. 2025Generating realistic global precipitation fields from modelled atmospheric circulationcited by 2
  4. 2025China Regional 3km Downscaling Based on Residual Corrective Diffusion Modelcited by 1
  5. 2023Evaluating Loss Functions and Learning Data Pre-Processing for Climate Downscaling Deep Learning Modelscited by 0
  6. 2024Diffusion-Based Joint Temperature and Precipitation Emulation of Earth System Modelscited by 0
  7. 2026Apeliotes: A Diffusion-Based Modeling Framework for km-scale Multi-Level Atmospheric Fieldscited by 0
  8. 2026Pushing the Limits of High-Resolution Weather Forecasting through Data Scalingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Regional Climate Model Emulation with Diffusion Approaches: What is the Added Value of Generative Machine Learning?
  2. 2024Enhancing Weather Predictions: Super-Resolution via Deep Diffusion Models
  3. 2023Generating High-Resolution Regional Precipitation Using Conditional Diffusion Model
  4. 2026Guided Diffusion Sampling for Precipitation Forecast Interventions
  5. 2024Conditional diffusion models for downscaling and bias correction of Earth system model precipitation
  6. 2024Downscaling Precipitation with Bias-informed Conditional Diffusion Model
  7. 2023Precipitation nowcasting with generative diffusion models
  8. 2026Flow Matching for Convective-Scale Precipitation Downscaling