2024

Generative Diffusion-based Downscaling for Climate

Watt, Robbie A., Mansfield, Laura A.

Understand

Downscaling, or super-resolution, provides decision-makers with detailed, high-resolution information about the potential risks and impacts of climate change, based on climate model output.

Machine learning algorithms are proving themselves to be efficient and accurate approaches to downscaling.
Here, we show how a generative, diffusion-based approach to downscaling gives accurate downscaled results.
We focus on an idealised setting where we recover ERA5 at $0.25\degree$~resolution from coarse grained version at $2\degree$~resolution.

Built on

Machine Learning for Precipitation Nowcasting from Radar Images
Original
Agrawal, S., Barrington, L., Bromberg, C., Burge, J., Gazen, C., and Hickey, J. (2019) · 1912
Earlier work this paper cites.
Reverse-time diffusion equation models
Anderson, B. D. O. (1982) · 1982
Earlier work this paper cites.
Downscaling local extreme temperature changes in south-eastern Australia from the CSIRO Mark2 GCM
Schubert, S. (1998) · 1998
Earlier work this paper cites.
Statistical downscaling of general circulation model output: A comparison of methods
Wilby, R. L., Wigley, T. M. L., Conway, D., Jones, P. D., Hewitson, B. C., Main, J., and Wilks, D. S. (1998) · 1998
Earlier work this paper cites.
Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems
Hersbach, H. (2000) · 2000
Earlier work this paper cites.
Denoising Diffusion Probabilistic Models
Original
Ho, J., Jain, A., and Abbeel, P. (2020) · 2006
Earlier work this paper cites.
Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling
Fowler, H. J., Blenkinsop, S., and Tebaldi, C. (2007) · 2007
Earlier work this paper cites.
Strictly Proper Scoring Rules, Prediction, and Estimation
Gneiting, T. and Raftery, A. E. (2007) · 2007
Earlier work this paper cites.
Super-resolution from a single image
Glasner, D., Bagon, S., and Irani, M. (2009) · 2009
Earlier work this paper cites.
The Potential to Narrow Uncertainty in Regional Climate Predictions
Hawkins, E. and Sutton, R. (2009) · 2009
Earlier work this paper cites.
A Connection Between Score Matching and Denoising Autoencoders
Vincent, P. (2011) · 2011
Earlier work this paper cites.
A comparison of different regional climate models and statistical downscaling methods for extreme rainfall estimation under climate change
Sunyer, M., Madsen, H., and Ang, P. (2012) · 2012
Earlier work this paper cites.
Regional Dynamical Downscaling and the CORDEX Initiative
Giorgi, F. and Jr, W. J. G. (2015) · 2015
Earlier work this paper cites.
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., and Brox, T. (2015) · 2015
Earlier work this paper cites.
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., and Ganguli, S. (2015) · 2015
Earlier work this paper cites.
Towards process-informed bias correction of climate change simulations
Maraun, D., Shepherd, T. G., Widmann, M., Zappa, G., Walton, D., Gutiérrez, J. M., Hagemann, S., Richter, I., Soares, P. M. M., Hall, A., and Mearns, L. O. (2017) · 2017
Earlier work this paper cites.
DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution
Vandal, T., Kodra, E., Ganguly, S., Michaelis, A., Nemani, R., and Ganguly, A. R. (2017) · 2017
Earlier work this paper cites.
Evaluation of rainfall simulations over West Africa in dynamically downscaled CMIP5 global circulation models
Akinsanola, A., Ajayi, V., Adejare, A., Adeyeri, O., Gbode, I., Ogunjobi, K., Nikulin, G., and Abolude, A. (2018) · 2018
Earlier work this paper cites.
Could Machine Learning Break the Convection Parameterization Deadlock?
Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., and Yacalis, G. (2018) · 2018
Earlier work this paper cites.
Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events
O’Gorman, P. A. and Dwyer, J. G. (2018) · 2018
Earlier work this paper cites.
Model output statistics downscaling using support vector machine for the projection of spatial and temporal changes in rainfall of Bangladesh
Pour, S. H., Shahid, S., Chung, E.-S., and Wang, X.-J. (2018) · 2018
Earlier work this paper cites.
Thirty Years of Regional Climate Modeling: Where Are We and Where Are We Going next?
Giorgi, F. (2019) · 2019
Earlier work this paper cites.
RUNet: A Robust UNet Architecture for Image Super-Resolution
Hu, X., Naiel, M. A., Wong, A., Lamm, M., and Fieguth, P. (2019) · 2019
Earlier work this paper cites.
Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation
Vandal, T., Kodra, E., and Ganguly, A. R. (2019) · 2019
Earlier work this paper cites.
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks
Wang, Y., Wang, L., Wang, H., and Li, P. (2019) · 2019
Earlier work this paper cites.
ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows
Groenke, B., Madaus, L., and Monteleoni, C. (2020) · 2020
Earlier work this paper cites.
The Ongoing Need for High-Resolution Regional Climate Models: Process Understanding and Stakeholder Information
Gutowski, W. J., Ullrich, P. A., Hall, A., Leung, L. R., O’Brien, T. A., Patricola, C. M., Arritt, R. W., Bukovsky, M. S., Calvin, K. V., Feng, Z., Jones, A. D., Kooperman, G. J., Monier, E., Pritchard, M. S., Pryor, S. C., Qian, Y., Rhoades, A. M., Roberts, A. F., Sakaguchi, K., Urban, N., and Zarzycki, C. (2020) · 2020
Earlier work this paper cites.
Predicting global patterns of long-term climate change from short-term simulations using machine learning
Mansfield, L. A., Nowack, P. J., Kasoar, M., Everitt, R. G., Collins, W. J., and Voulgarakis, A. (2020) · 2020
Earlier work this paper cites.

Similar

Regional climate models: 30 years of dynamical downscaling
Tapiador, F. J., Navarro, A., Moreno, R., Sánchez, J. L., and García-Ortega, E. (2020) · 2020
Cited alongside, same era.
PreciPatch: A Dictionary-based Precipitation Downscaling Method
Xu, M., Liu, Q., Sha, D., Yu, M., Duffy, D. Q., Putman, W. M., Carroll, M., Lee, T., and Yang, C. (2020) · 2020
Cited alongside, same era.
Novel statistical downscaling emulator for precipitation projections using deep Convolutional Autoencoder over Northern Africa
Babaousmail, H., Hou, R., Gnitou, G. T., and Ayugi, B. (2021) · 2021
Cited alongside, same era.
Climbing down Charney’s ladder: machine learning and the post-Dennard era of computational climate science
Balaji, V. (2021) · 2021
Cited alongside, same era.
A generative adversarial network approach to (ensemble) weather prediction
Bihlo, A. (2021) · 2021
Cited alongside, same era.
Framing, Context, and Methods
Chen, D., Rojas, M., Samset, B., Cobb, K., Diongue Niang, A., Edwards, P., Emori, S., Faria, S., Hawkins, E., Hope, P., Huybrechts, P., Meinshausen, M., Mustafa, S., Plattner, G.-K., and Tréguier, A.-M. (2021b) · 2021
Cited alongside, same era.
A downscaling approach for constructing high-resolution precipitation dataset over the Tibetan Plateau from ERA5 reanalysis
Jiang, Y., Yang, K., Shao, C., Zhou, X., Zhao, L., Chen, Y., and Wu, H. (2021) · 2021
Cited alongside, same era.
Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields With a Generative Adversarial Network
Leinonen, J., Nerini, D., and Berne, A. (2021) · 2021
Cited alongside, same era.
Improved Denoising Diffusion Probabilistic Models
Original
Nichol, A. and Dhariwal, P. (2021) · 2021
Cited alongside, same era.
Score-based Generative Modeling through Stochastic Differential Equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B. (2021) · 2021
Cited alongside, same era.
Fast and accurate learned multiresolution dynamical downscaling for precipitation
Wang, J., Liu, Z., Foster, I., Chang, W., Kettimuthu, R., and Kotamarthi, V. R. (2021) · 2021
Cited alongside, same era.
Machine learning emulation of a local-scale UK climate model
Original
Addison, H., Kendon, E., Ravuri, S., Aitchison, L., and Watson, P. A. (2022) · 2022
Cited alongside, same era.

Then

DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting
Original
Cachay, S. R., Zhao, B., Joren, H., and Yu, R. (2023) · 2023
Later among the works it cites.
FuXi: a cascade machine learning forecasting system for 15-day global weather forecast
Chen, L., Zhong, X., Zhang, F., Cheng, Y., Xu, Y., Qi, Y., and Li, H. (2023) · 2023
Later among the works it cites.
A new statistical downscaling approach for short-term forecasting of summer air temperatures through a fusion of deep learning and spatial interpolation
Cho, D., Im, J., and Jung, S. (2023) · 2023
Later among the works it cites.
Evaluation of near-surface air temperature reanalysis datasets and downscaling with machine learning based Random Forest method for complex terrain of Turkey
Hasan Karaman, C. and Akyürek, Z. (2023) · 2023
Later among the works it cites.
WF-UNet: Weather Data Fusion using 3D-UNet for Precipitation Nowcasting
Kaparakis, C. and Mehrkanoon, S. (2023) · 2023
Later among the works it cites.
Learning skillful medium-range global weather forecasting
Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W., Merose, A., Hoyer, S., Holland, G., Vinyals, O., Stott, J., Pritzel, A., Mohamed, S., and Battaglia, P. (2023) · 2023
Later among the works it cites.
SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models
Original
Li, L., Carver, R., Lopez-Gomez, I., Sha, F., and Anderson, J. (2023) · 2023
Later among the works it cites.
Updates on Model Hierarchies for Understanding and Simulating the Climate System: A Focus on Data-Informed Methods and Climate Change Impacts
Mansfield, L. A., Gupta, A., Burnett, A. C., Green, B., Wilka, C., and Sheshadri, A. (2023) · 2023
Later among the works it cites.
Generative Residual Diffusion Modeling for Km-scale Atmospheric Downscaling
Original
Mardani, M., Brenowitz, N., Cohen, Y., Pathak, J., Chen, C.-Y., Liu, C.-C., Vahdat, A., Kashinath, K., Kautz, J., and Pritchard, M. (2023) · 2023
Later among the works it cites.
Downscaling Daily Satellite-Based Precipitation Estimates Using MODIS Cloud Optical and Microphysical Properties in Machine-Learning Models
Medrano, S. C., Satgé, F., Molina-Carpio, J., Zolá, R. P., and Bonnet, M.-P. (2023) · 2023
Later among the works it cites.
A Review of Recent and Emerging Machine Learning Applications for Climate Variability and Weather Phenomena
Molina, M. J., O’Brien, T. A., Anderson, G., Ashfaq, M., Bennett, K. E., Collins, W. D., Dagon, K., Restrepo, J. M., and Ullrich, P. A. (2023) · 2023
Later among the works it cites.
ClimaX: A foundation model for weather and climate
Original
Nguyen, T., Brandstetter, J., Kapoor, A., Gupta, J. K., and Grover, A. (2023) · 2023
Later among the works it cites.
Deep generative model super-resolves spatially correlated multiregional climate data
Oyama, N., Ishizaki, N. N., Koide, S., and Yoshida, H. (2023) · 2023
Later among the works it cites.
GenCast: Diffusion-based ensemble forecasting for medium-range weather
Original
Price, I., Sanchez-Gonzalez, A., Alet, F., Ewalds, T., El-Kadi, A., Stott, J., Mohamed, S., Battaglia, P., Lam, R., and Willson, M. (2023) · 2023
Later among the works it cites.
Tackling Climate Change with Machine Learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, A. S., Maharaj, T., Sherwin, E. D., Mukkavilli, S. K., Kording, K. P., Gomes, C. P., Ng, A. Y., Hassabis, D., Platt, J. C., Creutzig, F., Chayes, J., and Bengio, Y. (2023) · 2023
Later among the works it cites.
Harnessing AI and computing to advance climate modelling and prediction
Schneider, T., Behera, S., Boccaletti, G., Deser, C., Emanuel, K., Ferrari, R., Leung, L. R., Lin, N., Müller, T., Navarra, A., Ndiaye, O., Stuart, A., Tribbia, J., and Yamagata, T. (2023) · 2023
Later among the works it cites.
Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models
Original
Wan, Z. Y., Baptista, R., Chen, Y.-f., Anderson, J., Boral, A., Sha, F., and Zepeda-Núñez, L. (2023) · 2023
Later among the works it cites.
ACE: A fast, skillful learned global atmospheric model for climate prediction
Original
Watt-Meyer, O., Dresdner, G., McGibbon, J., Clark, S. K., Henn, B., Duncan, J., Brenowitz, N. D., Kashinath, K., Pritchard, M. S., Bonev, B., Peters, M. E., and Bretherton, C. S. (2023) · 2023
Later among the works it cites.
Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling
Original
Yang, Q., Hernandez-Garcia, A., Harder, P., Ramesh, V., Sattegeri, P., Szwarcman, D., Watson, C. D., and Rolnick, D. (2023) · 2023
Later among the works it cites.
Text-to-image Diffusion Models in Generative AI: A Survey
Original
Zhang, C., Zhang, C., Zhang, M., and Kweon, I. S. (2023) · 2023
Later among the works it cites.
Hyper-Diffusion: Estimating Epistemic and Aleatoric Uncertainty with a Single Model
Original
Chan, M. A., Molina, M. J., and Metzler, C. A. (2024) · 2024
Closest in time.
Hard-Constrained Deep Learning for Climate Downscaling
Original
Harder, P., Hernandez-Garcia, A., Ramesh, V., Yang, Q., Sattigeri, P., Szwarcman, D., Watson, C., and Rolnick, D. (2024) · 2024
Closest in time.
DiffDA: a Diffusion model for weather-scale Data Assimilation
Original
Huang, L., Gianinazzi, L., Yu, Y., Dueben, P. D., and Hoefler, T. (2024) · 2024
Closest in time.
Neural General Circulation Models for Weather and Climate
Original
Kochkov, D., Yuval, J., Langmore, I., Norgaard, P., Smith, J., Mooers, G., Klöwer, M., Lottes, J., Rasp, S., Düben, P., Hatfield, S., Battaglia, P., Sanchez-Gonzalez, A., Willson, M., Brenner, M. P., and Hoyer, S. (2024) · 2024
Closest in time.
Forecasting Tropical Cyclones with Cascaded Diffusion Models
Original
Nath, P., Shukla, P., Wang, S., and Quilodrán-Casas, C. (2024) · 2024
Closest in time.
The ERA5 global reanalysis
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N. (2020) · 2049
Closest in time.
TRU-NET: a deep learning approach to high resolution prediction of rainfall
Adewoyin, R. A., Dueben, P., Watson, P., He, Y., and Dutta, R. (2021) · 2062
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…