Articles | Volume 14, issue 10
https://doi.org/10.5194/gmd-14-6355-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-6355-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
22 Oct 2021
Development and technical paper |
| 22 Oct 2021
| 22 Oct 2021
Fast and accurate learned multiresolution dynamical downscaling for precipitation
Jiali Wang
Environmental Science Division, Argonne National Laboratory, Lemont, IL, USA
Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
Ian Foster
Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
Won Chang
Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, USA
Rajkumar Kettimuthu
Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
Environmental Science Division, Argonne National Laboratory, Lemont, IL, USA
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- Efficient Super‐Resolution of Near‐Surface Climate Modeling Using the Fourier Neural Operator P. Jiang et al. 10.1029/2023MS003800
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- Fast and accurate learned multiresolution dynamical downscaling for precipitation J. Wang et al. 10.5194/gmd-14-6355-2021
27 citations as recorded by crossref.
- Improving the statistical downscaling performance of climatic parameters with convolutional neural networks A. Hosseini Baghanam et al. 10.2166/wcc.2024.592
- Emulating aerosol optics with randomly generated neural networks A. Geiss et al. 10.5194/gmd-16-2355-2023
- Super-resolution of three-dimensional temperature and velocity for building-resolving urban micrometeorology using physics-guided convolutional neural networks with image inpainting techniques Y. Yasuda et al. 10.1016/j.buildenv.2023.110613
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- Efficient Super‐Resolution of Near‐Surface Climate Modeling Using the Fourier Neural Operator P. Jiang et al. 10.1029/2023MS003800
- Successful Precipitation Downscaling Through an Innovative Transformer-Based Model F. Yang et al. 10.3390/rs16224292
- Projected Changes in Extreme Wet and Dry Conditions in Greece E. Kostopoulou & C. Giannakopoulos 10.3390/cli11030049
- A two-stage model for spatial downscaling of daily precipitation data W. Lei et al. 10.1007/s00371-023-03236-8
- Using Machine Learning to Cut the Cost of Dynamical Downscaling S. Hobeichi et al. 10.1029/2022EF003291
- Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities Z. Ma et al. 10.1007/s10462-024-10764-9
- Deep learning approach for downscaling the significant wave height based on CBAM_CGAN M. Yu et al. 10.1016/j.oceaneng.2024.119169
- Downscaling atmospheric chemistry simulations with physically consistent deep learning A. Geiss et al. 10.5194/gmd-15-6677-2022
- Statistical Downscaling of SEVIRI Land Surface Temperature to WRF Near-Surface Air Temperature Using a Deep Learning Model A. Afshari et al. 10.3390/rs15184447
- Spatio‐Temporal Super‐Resolution Data Assimilation (SRDA) Utilizing Deep Neural Networks With Domain Generalization Y. Yasuda & R. Onishi 10.1029/2023MS003658
- Rotationally equivariant super-resolution of velocity fields in two-dimensional flows using convolutional neural networks Y. Yasuda & R. Onishi 10.1063/5.0132326
- A Transfer Learning-Enhanced Generative Adversarial Network for Downscaling Sea Surface Height through Heterogeneous Data Fusion Q. Zhang et al. 10.3390/rs16050763
- A Fast Generative Adversarial Network Combined With Transformer for Downscaling GRACE Terrestrial Water Storage Data in Southwestern China S. Gu et al. 10.1109/TGRS.2024.3349548
- Learn from Simulations, Adapt to Observations: Super-Resolution of Isoprene Emissions via Unpaired Domain Adaptation A. Giganti et al. 10.3390/rs16213963
- On the limitations of deep learning for statistical downscaling of climate change projections: The transferability and the extrapolation issues A. Hernanz et al. 10.1002/asl.1195
- Regional climate model emulator based on deep learning: concept and first evaluation of a novel hybrid downscaling approach A. Doury et al. 10.1007/s00382-022-06343-9
- Medium-term forecasting of global horizontal solar radiation in Brazil using machine learning-based methods A. Weyll et al. 10.1016/j.energy.2024.131549
- Assimilation of surface soil moisture jointly retrieved by multiple microwave satellites into the WRF-Hydro model in ungauged regions: Towards a robust flood simulation and forecasting L. Chao et al. 10.1016/j.envsoft.2022.105421
- An illustration of model agnostic explainability methods applied to environmental data C. Wikle et al. 10.1002/env.2772
- Comparison of machine learning statistical downscaling and regional climate models for temperature, precipitation, wind speed, humidity and radiation over Europe under present conditions A. Hernanz et al. 10.1002/joc.8190
- Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches J. González‐Abad et al. 10.1029/2023MS003641
- Street-level temperature estimation using graph neural networks: Performance, feature embedding and interpretability Y. Yu et al. 10.1016/j.uclim.2024.102003
2 citations as recorded by crossref.
Latest update: 13 Dec 2024
Short summary
Downscaling, the process of generating a higher spatial or time dataset from a coarser observational or model dataset, is a widely used technique. Two common methodologies for performing downscaling are to use either dynamic (physics-based) or statistical (empirical). Here we develop a novel methodology, using a conditional generative adversarial network (CGAN), to perform the downscaling of a model's precipitation forecasts and describe the advantages of this method compared to the others.
Downscaling, the process of generating a higher spatial or time dataset from a coarser...
