Articles | Volume 15, issue 1
https://doi.org/10.5194/gmd-15-251-2022
© Author(s) 2022. 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-15-251-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
13 Jan 2022
Model evaluation paper |
| 13 Jan 2022
| 13 Jan 2022
Convolutional conditional neural processes for local climate downscaling
Anna Vaughan
CORRESPONDING AUTHOR
Department of Engineering, University of Cambridge, Cambridge, UK
Will Tebbutt
Department of Engineering, University of Cambridge, Cambridge, UK
J. Scott Hosking
British Antarctic Survey, Cambridge, UK
The Alan Turing Institute, London, UK
Richard E. Turner
Department of Engineering, University of Cambridge, Cambridge, UK
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- Characterization of Subsurface Hydrogeological Structures With Convolutional Conditional Neural Processes on Limited Training Data Z. Cui et al. 10.1029/2022WR033161
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13 citations as recorded by crossref.
- A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts L. Harris et al. 10.1029/2022MS003120
- Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks T. Asfaw & J. Luo 10.1007/s00376-023-3029-2
- Assessing statistical downscaling in Argentina: Daily maximum and minimum temperatures R. Balmaceda‐Huarte & M. Bettolli 10.1002/joc.7733
- spateGAN: Spatio‐Temporal Downscaling of Rainfall Fields Using a cGAN Approach L. Glawion et al. 10.1029/2023EA002906
- Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5 K. Tazi et al. 10.5194/hess-28-4903-2024
- A two-step downscaling method for high-scale super-resolution of daily temperature — a case study of Wei River Basin, China X. Li et al. 10.1007/s11356-022-24422-6
- Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches J. González‐Abad et al. 10.1029/2023MS003641
- 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
- Characterization of Subsurface Hydrogeological Structures With Convolutional Conditional Neural Processes on Limited Training Data Z. Cui et al. 10.1029/2022WR033161
- ResDeepD: A residual super-resolution network for deep downscaling of daily precipitation over India S. Mishra Sharma & A. Mitra 10.1017/eds.2022.23
- POINT-BY-POINT AND COMPLEX QUALITY METRICS IN ATMOSPHERE AND OCEAN RESEARCH: REVIEW OF METHODS AND APPROACHES V. Rezvov et al. 10.29006/1564-2291.JOR-2024.52(4).10
- Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review Y. Sun et al. 10.1016/j.isprsjprs.2023.12.011
- Towards deep-learning solutions for classification of automated snow height measurements (CleanSnow v1.0.2) J. Svoboda et al. 10.5194/gmd-18-1829-2025
1 citations as recorded by crossref.
Latest update: 31 Mar 2025
Short summary
We develop a new method for climate downscaling, i.e. transforming low-resolution climate model output to high-resolution projections, using a deep-learning model known as a convolutional conditional neural process. This model is shown to outperform an ensemble of baseline methods for downscaling daily maximum temperature and precipitation and provides a powerful new downscaling framework for climate impact studies.
We develop a new method for climate downscaling, i.e. transforming low-resolution climate model...
