Feel free to add any paper on Deep Learning for Climate Super-resolution. Just create a pull request.
| Title | Input data | Target data | Model(s) | Baselines | Metrics | Paper | Code | Year | Month |
|---|---|---|---|---|---|---|---|---|---|
| Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches | ERA_Interim | EWEMBI | CNN | - | - | https://arxiv.org/pdf/2302.01771.pdf | - | 2023 | Febr |
| Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales | - | GAN | - | - | - | https://arxiv.org/abs/2302.08720 | - | 2023 | Febr |
| Physics-Constrained Deep Learning for Climate Downscaling | ERA5 downsampled, WRF T2, NorESM LM TAS | ERA5, WRF T2, NorESM MM TAS | constr. CNN, GAN, ConvGRU, DeepVoxel | Bicubic, CNN, GAN | MSE, MAE, bias, SSIM, MS-SSIm, CRPS, Pearcon corr. | https://arxiv.org/abs/2208.05424 | https://github.com/RolnickLab/constrained-downscaling | 2023 | Febr |
| ClimaX: A foundation model for weather and climate | MPI-ESM | ERA5 | ClimaX - Pretrained ViT | UNet, ResNet | RMSE, Pearson, bias | https://arxiv.org/abs/2301.10343 | https://github.com/microsoft/climax | 2023 | Jan |
| DL4DS—Deep learning for empirical downscaling | CAMSRA NO2, ERA5, elevation, land mask | CAMS NO2 | CNN, GAN | - | MAE, SSIM, Pearson cor., PSNR | https://www.cambridge.org/core/journals/environmental-data-science/article/dl4dsdeep-learning-for-empirical-downscaling | https://github.com/carlos-gg/dl4ds | 2023 | Jan |
| Title | Input data | Target data | Model(s) | Baselines | Metrics | Paper | Code | Year | Month |
|---|---|---|---|---|---|---|---|---|---|
| High-resolution downscaling with interpretable deep learning: Rainfall extremes over New Zealand | - | - | - | - | - | https://www.sciencedirect.com/science/article/pii/S2212094722001049 | - | 2022 | Dec |
| Downscaling Extreme Rainfall Using Physical-Statistical Generative Adversarial Learning | - | - | - | - | - | https://arxiv.org/pdf/2212.01446.pdfhttps://arxiv.org/pdf/2212.01446.pdf | 2022 | Dec | |
| Downscaled hyper-resolution (400 m) gridded datasets of daily precipitation and temperature (2008–2019) for the East–Taylor subbasin (western United States) | - | - | - | - | - | https://essd.copernicus.org/articles/14/4949/2022/ | 2022 | Nov | |
| ResDeepD: A residual super-resolution network for deep downscaling of daily precipitation over India | - | - | - | - | - | https://www.cambridge.org/core/journals/environmental-data-science/article/resdeepd-a-residual-superresolution-network-for-deep-downscaling-of-daily-precipitation-over-india | - | 2022 | Nov |
| Contrastive Learning for Climate Model Bias Correction and Super-Resolution | - | - | - | - | - | https://arxiv.org/abs/2211.07555 | - | 2022 | Nov |
| A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts | - | - | - | - | - | https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022MS003120 | - | 2022 | Oct |
| Physically constrained generative adversarial networks for improving precipitation fields from Earth system models | - | - | - | - | - | https://www.nature.com/articles/s42256-022-00540-1 | - | 2022 | Oct |
| RainNet: A Large-Scale Dataset for Spatial Precipitation Downscaling | - | - | - | - | - | https://arxiv.org/pdf/2012.09700.pdf | https://github.com/neuralchen/RainNet | 2022 | Oct |
| Downscaling Atmospheric Chemistry Simulations with Physically Consistent Deep Learning | - | - | - | - | - | https://gmd.copernicus.org/preprints/gmd-2022-76/gmd-2022-76.pdf | - | 2022 | Sept |
| Downscaling multi-model climate projection ensembles with deep learning (DeepESD): contribution to CORDEX EUR-44 | - | - | - | - | - | https://gmd.copernicus.org/articles/15/6747/2022/ | - | 2022 | Sept |
| Urban precipitation downscaling using deep learning: a smart city application over Austin, Texas, USA | - | - | - | - | - | https://arxiv.org/abs/2209.06848 | - | 2022 | Aug |
| Repeatable high-resolution statistical downscaling through deep learning | - | - | - | - | - | https://gmd.copernicus.org/articles/15/7353/2022/gmd-15-7353-2022.pdf | - | 2022 | Aug |
| Downscaling Earth System Models with Deep Learning | - | - | - | - | - | https://dl.acm.org/doi/pdf/10.1145/3534678.3539031 | - | 2022 | Aug |
| Machine Learning as a Downscaling Approach for Prediction of Wind Characteristics under Future Climate Change Scenarios | - | - | - | - | - | https://www.hindawi.com/journals/complexity/2022/8451812/ | - | 2022 | Aug |
| Regional climate model emulator based on deep learning: concept and first evaluation of a novel hybrid downscaling approach | - | - | - | - | - | https://link.springer.com/article/10.1007/s00382-022-06343-9 | - | 2022 | Jul |
| On the modern deep learning approaches for precipitation downscaling | - | - | - | - | - | https://arxiv.org/abs/2207.00808 | - | 2022 | Jul |
| A Novel Bayesian Deep Learning Approach to the Downscaling of Wind Speed with Uncertainty Quantification | - | - | - | - | - | https://link.springer.com/chapter/10.1007/978-3-031-05981-0_5 | - | 2022 | May |
| On deep learning-based bias correction and downscaling of multiple climate models simulations | - | - | - | - | - | https://link.springer.com/article/10.1007/s00382-022-06277-2 | - | 2022 | Apr |
| Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning | - | - | - | - | - | https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/qj.4265 | - | 2022 | Mar |
| Increasing the accuracy and resolution of precipitation forecasts using deep generative models | - | - | - | - | - | https://arxiv.org/pdf/2203.12297.pdf | https://github.com/raspstephan/nwp-downscale | - | 2022 |
| Super-resolution of near-surface temperature utilizing physical quantities for real-time prediction of urban micrometeorology | - | - | - | - | - | https://www.sciencedirect.com/science/article/pii/S0360132321009884 | - | 2022 | Febr |
| Reconstructing High Resolution ESM Data Through a Novel Fast Super Resolution Convolutional Neural Network (FSRCNN) | - | - | - | - | - | https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021GL097571 | - | 2022 | Febr |
| Convolutional conditional neural processes for local climate downscaling | - | - | - | - | - | https://gmd.copernicus.org/articles/15/251/2022/gmd-15-251-2022.pdf | https://github.com/annavaughan/convCNPClimate | 2022 | Jan |
| A Novel Deep Learning Approach to the Statistical Downscaling of Temperatures for Monitoring Climate Change | - | - | - | - | - | https://dl.acm.org/doi/10.1145/3523150.3523151 | - | 2022 | Jan |
| Deep Learning-Based Downscaling of Temperatures for Monitoring Local Climate Change Using Global Climate Simulation Data | - | - | - | - | - | https://doi.org/10.1142/S2811032322500011 | - | 2022 | Sept |
| A Novel Reference-Based and Gradient-Guided Deep Learning Model for Daily Precipitation Downscaling | - | - | - | - | - | https://www.mdpi.com/2073-4433/13/4/511 | - | 2022 | Mar |
| Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning | - | - | - | - | - | https://doi.org/10.1002/qj.4265 | - | 2022 | Mar |
| Title | Input data | Target data | Model(s) | Baselines | Metrics | Paper | Code | Year | Month |
|---|---|---|---|---|---|---|---|---|---|
| Extension of Convolutional Neural Network along Temporal and Vertical Directions for Precipitation Downscaling | - | - | - | - | - | https://arxiv.org/abs/2112.06571 | - | 2021 | Dez |
| Deconditional Downscaling with Gaussian Processes | - | - | - | - | - | https://proceedings.neurips.cc/paper/2021/file/94aef38441efa3380a3bed3faf1f9d5d-Paper.pdf | - | 2021 | Dec |
| MSG-GAN-SD: A Multi-Scale Gradients GAN for Statistical Downscaling of 2-Meter Temperature over the EURO-CORDEX Domain | - | - | - | - | - | https://www.mdpi.com/2673-2688/2/4/36 | - | 2021 | Nov |
| Fast and accurate learned multiresolution dynamical downscaling for precipitation | WRF precip., T2, IWV, SLP, topography | WRF Precip. | cGAN | CNN, bilinear | MSE, J-S distance, pattern corr. | https://gmd.copernicus.org/articles/14/6355/2021/gmd-14-6355-2021.pdf | https://github.com/lzhengchun/DSGAN | 2021 | Oct |
| WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data | - | - | - | - | - | https://arxiv.org/pdf/2109.08770.pdf | https://github.com/RupaKurinchiVendhan/WiSoSuper | 2021 | Sept |
| Super-resolution data assimilation | - | - | - | - | - | https://arxiv.org/pdf/2109.08017.pdf | - | 2021 | Sept |
| Deep Learning-Based Super-Resolution Climate Simulator-Emulator Framework for Urban Heat Studies | - | - | - | - | - | https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021GL094737 | - | 2021 | Sept |
| Towards Representation Learning of Atmospheric Dynamics | - | - | - | - | - | https://arxiv.org/abs/2109.09076 | https://github.com/sehoffmann/atmodist | 2021 | Sept |
| Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields With a Generative Adversarial Network | - | - | - | - | - | https://ieeexplore.ieee.org/document/9246532 | https://github.com/jleinonen/downscaling-rnn-gan | 2021 | sept |
| A downscaling approach for constructing high-resolution precipitation dataset over the Tibetan Plateau from ERA5 reanalysis | - | - | - | - | - | https://www.sciencedirect.com/science/article/pii/S0169809521001265 | - | 2021 | Jul |
| Comparisons of Machine Learning Methods of Statistical Downscaling Method: Case Studies of Daily Climate Anomalies in Thailand | - | - | - | - | - | https://journals.riverpublishers.com/index.php/JWE/article/view/4869 | - | 2021 | Jul |
| Adjusting spatial dependence of climate model outputs with cycle-consistent adversarial networks | - | - | - | - | - | https://link.springer.com/article/10.1007/s00382-021-05869-8 | - | 2021 | Jul |
| On the suitability of deep convolutional neural networks for continental-wide downscaling of climate change projections | - | - | - | - | - | https://link.springer.com/article/10.1007/s00382-021-05847-0 | - | 2021 | Jun |
| Spatio-\ Downscaling of Climate Data Using Convolutional and Error-Predicting Neural Networks | - | - | - | - | - | https://www.frontiersin.org/articles/10.3389/fclim.2021.656479/full | https://github.com/aserifi/convolutional-downscaling | 2021 | Apr |
| Deep Learning for Daily Precipitation and Temperature Downscaling | - | - | - | - | - | https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020WR029308 | - | 2021 | April |
| Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change Projections | - | - | - | - | - | https://ieeexplore.ieee.org/document/9348885 | https://github.com/cryptonymous9/Augmented-ConvLSTM | 2021 | Febr |
| ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows | - | - | - | - | - | https://dl.acm.org/doi/10.1145/3429309.3429318 | https://github.com/bgroenks96/generative-downscaling | 2021 | Jan |
| Deep learning-based downscaling of summer monsoon rainfall data over Indian region | - | - | - | - | - | https://link.springer.com/article/10.1007/s00704-020-03489-6 | - | 2021 | Jan |
| Regional downscaling of climate data using deep learning and applications for drought and rainfall forecasting | - | - | - | - | - | https://eresearchnz.figshare.com/articles/presentation/Regional_downscaling_of_climate_data_using_deep_learning_and_applications_for_drought_and_rainfall_forecasting/14110157/1 | - | 2021 | Feb |
| Deep learning-based downscaling of seasonal forecasts over the Iberian Peninsula | - | - | - | - | - | https://meetingorganizer.copernicus.org/EGU21/EGU21-12253.html | - | 2021 | Mar |
| Deep learning-based downscaling of tropospheric nitrogen dioxide using ground-level and satellite observations | - | - | - | - | - | https://pubmed.ncbi.nlm.nih.gov/33940718/ | - | 2021 | Jun |
| Title | Input data | Target data | Model(s) | Baselines | Metrics | Paper | Code | Year | Month |
|---|---|---|---|---|---|---|---|---|---|
| Investigating two super-resolution methods for downscaling precipitation: ESRGAN and CAR | - | - | - | - | - | https://arxiv.org/pdf/2012.01233.pdf | - | 2020 | Dec |
| Deep-Learning-Based Gridded Downscaling of Surface Meteorological Variables in Complex Terrain. Part I: Daily Maximum and Minimum 2-m Temperature | Downsampled PRISM, elevation | TMAX/TMIN PRISM | UNet | bicubic, regression | MAE, pearson, corr. | https://journals.ametsoc.org/view/journals/apme/59/12/jamc-d-20-0057.1.xml | https://github.com/yingkaisha/JAMC_20_0057 | 2020 | Nov |
| Deep-Learning-Based Gridded Downscaling of Surface Meteorological Variables in Complex Terrain. Part II: Daily Precipitation | - | - | - | - | - | https://journals.ametsoc.org/view/journals/apme/59/12/jamc-d-20-0058.1.xml | https://github.com/yingkaisha/JAMC_20_0057 | 2020 | Nov |
| CliGAN: A structurally sensitive convolutional neural network model for statistical downscaling of precipitation from multi-model ensembles | - | - | - | - | - | https://www.mdpi.com/2073-4441/12/12/3353 | - | 2020 | Nov |
| Radar Super Resolution Using a Deep Convolutional Neural Network | - | - | - | - | - | https://journals.ametsoc.org/view/journals/atot/37/12/jtech-d-20-0074.1.xml?tab_body=pdf | - | 2020 | Nov |
| Deep-Learning based climate downscaling using the super-resolution method: a case study over the western US | - | - | - | - | - | https://gmd.copernicus.org/preprints/gmd-2020-214/gmd-2020-214.pdf | - | 2020 | Sept |
| A comparative study of convolutional neural network models for wind field downscaling | - | - | - | - | - | https://arxiv.org/ftp/arxiv/papers/2008/2008.12257.pdf | - | 2020 | Sept |
| Climate Downscaling Using YNet: A Deep Convolutional Network with Skip Connections and Fusion | - | - | - | - | - | https://dl.acm.org/doi/pdf/10.1145/3394486.3403366 | https://github.com/yuminliu/Downscaling | 2020 | Aug |
| Statistical Downscaling of Temperature Distributions from the Synoptic Scale to the Mesoscale Using Deep Convolutional Neural Networks | - | - | - | - | - | https://arxiv.org/abs/2007.10839 | - | 2020 | Jul |
| Adversarial super-resolution of climatological wind and solar data | NREL Wind toolkit + NSRDB downsampled (train), NCAR CCSM (test) | NREL Wind toolkit + NSRDB | GAN | bicubic, CNN | MSE | https://www.pnas.org/doi/10.1073/pnas.1918964117 | https://github.com/NREL/PhIRE | 2020 | Jul |
| Statistical downscaling of daily temperature and precipitation over China using deep learning neural models: Localization and comparison with other methods | - | - | - | - | - | https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.6769 | - | 2020 | Jul |
| Generalization Properties of Machine-Learning Based Weather Model Downscaling | - | - | - | - | - | https://ai4earthscience.github.io/iclr-2020-workshop/papers/ai4earth25.pdf | - | 2020 | May |
| Configuration and Intercomparison of Deep Learning Neural Models for Statistical Downscaling | ERA-Interim geopot., zonal + mer. wind, tempt, humidity | E-Obs temp. precip. | CNN | CNN, GLM | bias mean, bias 2-perc., bias 98-perc., RMSE, Pearson correlation, Spearman correlation, ROC sklill score | https://gmd.copernicus.org/preprints/gmd-2019-278/gmd-2019-278.pdf | https://github.com/SantanderMetGroup/DeepDownscaling | 2020 | Apr |
| ResLab: Generating High-Resolution Climate Prediction Through Image Super-Resolution | CMA precip, humidity downsampled, topography | CMA precip | CNN | bilinear, DeepSD, VDSR, ESPCN, RDN, LapSRN | RMSE, prediction correction, prediction ommision, fasle alarm ratio, threat score | https://ieeexplore.ieee.org/document/9001044 | https://github.com/Jianxin-Cheng/SR-Climate-Prediction | 2020 | Febr |
| Performance of statistical and machine learning ensembles for daily temperature downscaling | - | - | - | - | - | https://link.springer.com/article/10.1007/s00704-020-03098-3 | - | 2020 |
| Title | Input data | Target data | Model(s) | Baselines | Metrics | Paper | Code | Year | Month |
|---|---|---|---|---|---|---|---|---|---|
| Downscaling numerical weather models with GANs | - | - | - | - | - | https://alok.github.io/assets/ci-2019.pdf | - | 2019 | Oct |
| Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning | - | - | - | - | - | https://arxiv.org/abs/1802.04742 | - | 2018 | May |
| Downscaling rainfall using deep learning long short-term memory and feedforward neural network | - | - | - | - | - | https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.6066 | - | 2019 | Mar |
| Improving Precipitation Estimation Using Convolutional Neural Network | - | - | - | - | - | https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018WR024090 | - | 2019 | Jan |
| Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation | - | - | - | - | - | https://link.springer.com/article/10.1007/s00704-018-2613-3 | - | 2018 | Sept |
| Generating High Resolution Climate Change Projections through Single Image Super-Resolution: An Abridged Version | - | - | - | - | - | https://www.ijcai.org/proceedings/2018/0759.pdf | - | 2018 | Aug |
| Statistical downscaling of precipitation using long short-term memory recurrent neural networks | - | - | - | - | - | https://link.springer.com/article/10.1007/s00704-017-2307-2 | - | 2017 | Nov |
| A Machine Learning Approach to Non-uniform Spatial Downscaling of Climate Variables | - | - | - | - | - | https://ieeexplore.ieee.org/abstract/document/8215681 | - | 2017 | Nov |
| DeepSD: Generating High-Resolution Climate Change Projections through Single Image Super-Resolution | - | - | - | - | - | https://dl.acm.org/doi/10.1145/3097983.3098004 | https://github.com/tjvandal/deepsd | 2017 | Aug |
| Title | Input data | Target data | Model(s) | Baselines | Metrics | Paper | Code | Year | Month |
|---|---|---|---|---|---|---|---|---|---|
| Spatial Interpolation of Surface Air Temperatures Using Artificial Neural Networks: Evaluating Their Use for Downscaling GCMs | - | - | - | - | - | https://doi.org/10.1175/1520-0442(2000)013%3C0886:SIOSAT%3E2.0.CO;2 | - | 2000 | Mar |
| Simulation of daily temperatures for climate change scenarios over Portugal: a neural network model approach | - | - | - | - | - | https://www.jstor.org/stable/24866022 | - | 1999 | Sep |
At the moment not included: Other ML techniques than Deep Learning and application to satellite imagery.