Articles | Volume 16, issue 2
https://doi.org/10.5194/gmd-16-535-2023
© Author(s) 2023. 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-16-535-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
25 Jan 2023
Development and technical paper |
| 25 Jan 2023
| 25 Jan 2023
Customized deep learning for precipitation bias correction and downscaling
Fang Wang
Department of Crop, Soil, and Environmental Sciences, Auburn
University, Auburn, AL 36849, USA
Department of Crop, Soil, and Environmental Sciences, Auburn
University, Auburn, AL 36849, USA
Mark Carroll
Computational and Information Science Technology Office, NASA Goddard
Space Flight Center, Greenbelt, MD 20771, USA
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- Successful Precipitation Downscaling Through an Innovative Transformer-Based Model F. Yang et al. 10.3390/rs16224292
- Accurate and efficient AI-assisted paradigm for adding granularity to ERA5 precipitation reanalysis M. Cavaiola et al. 10.1038/s41598-024-77542-z
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- Deep Learning Improves GFS Wintertime Precipitation Forecast Over Southeastern China D. Sun et al. 10.1029/2023GL104406
- Toward an improved ensemble of multi-source daily precipitation via joint machine learning classification and regression H. Chen et al. 10.1016/j.atmosres.2024.107385
- Performance of Kilometer-Scale CARAS Precipitation Product Against Ground-based Observations During 2008–2021 over Hubei, China C. ZHU et al. 10.3724/j.1006-8775.2024.036
- SPATIAL DOWNSCALING OF DAILY PRECIPITATION d4PDF DATA USING ANN ALGORITHM I. KHATEEB et al. 10.2208/journalofjsce.24-00156
- Current progress in subseasonal-to-decadal prediction based on machine learning Z. Shen et al. 10.1016/j.acags.2024.100201
- Statistical spatial downscaling of significant wave height in a regional sea from the global ERA5 dataset B. Yuan et al. 10.1016/j.oceaneng.2025.121100
- Multivariate bias correction and downscaling of climate models with trend-preserving deep learning F. Wang & D. Tian 10.1007/s00382-024-07406-9
- Impact of deep learning-driven precipitation corrected data using near real-time satellite-based observations and model forecast in an integrated hydrological model K. Patakchi Yousefi et al. 10.3389/frwa.2024.1439906
- Downscaling sea surface height and currents in coastal regions using convolutional neural network B. Yuan et al. 10.1016/j.apor.2024.104153
- Deep Learning for Bias‐Correcting CMIP6‐Class Earth System Models P. Hess et al. 10.1029/2023EF004002
- Deep learning-based bias correction of ISMR simulated by GCM S. Sharma et al. 10.1016/j.atmosres.2024.107589
- Assessing the effectiveness of ANN model in spatial downscaling of d4PDF hourly precipitation data: a case study in Japan I. Khateeb et al. 10.1007/s44367-025-00003-5
- A deep learning-based bias correction model for Arctic sea ice concentration towards MITgcm S. Yuan et al. 10.1016/j.ocemod.2024.102326
- Machine Learning-Based Bias Correction of Precipitation Measurements at High Altitude H. Li et al. 10.3390/rs15082180
- Enhancing precipitation intensity estimation using ERA5-land reanalysis with statistical and machine learning approaches A. Abdolmanafi et al. 10.1016/j.rineng.2025.104928
- A Data-Driven Approach to Improve Cocoa Crop Establishment in Colombia: Insights and Agricultural Practice Recommendations from an Ensemble Machine Learning Model L. Talero-Sarmiento et al. 10.3390/agriengineering7010006
- Intercomparison of Deep Learning Architectures for the Prediction of Precipitation Fields With a Focus on Extremes N. Otero & P. Horton 10.1029/2023WR035088
- A CMIP6-based multi-model downscaling ensemble to underpin climate change services in Australia M. Grose et al. 10.1016/j.cliser.2023.100368
24 citations as recorded by crossref.
- Advancing isotope‐enabled hydrological modelling for ungauged calibration of data‐scarce humid tropical catchments A. Watson et al. 10.1002/hyp.15065
- MAUNet: a max-average neural network architecture for precipitation downscaling S. Mishra Sharma & A. Mitra 10.1007/s00521-024-10012-9
- Successful Precipitation Downscaling Through an Innovative Transformer-Based Model F. Yang et al. 10.3390/rs16224292
- Accurate and efficient AI-assisted paradigm for adding granularity to ERA5 precipitation reanalysis M. Cavaiola et al. 10.1038/s41598-024-77542-z
- Deep learning based bias correction of TRMM precipitation estimates using IMD-gridded precipitation as ground observation S. Mishra Sharma & A. Mitra 10.1017/eds.2024.39
- A precipitation downscaling method using a super-resolution deconvolution neural network with step orography P. Reddy et al. 10.1017/eds.2023.18
- Sub-Seasonal Precipitation Bias-Correction in Thailand Using Attention U-Net With Seasonal and Meteorological Effects T. Faijaroenmongkol et al. 10.1109/ACCESS.2023.3337998
- Deep Learning Improves GFS Wintertime Precipitation Forecast Over Southeastern China D. Sun et al. 10.1029/2023GL104406
- Toward an improved ensemble of multi-source daily precipitation via joint machine learning classification and regression H. Chen et al. 10.1016/j.atmosres.2024.107385
- Performance of Kilometer-Scale CARAS Precipitation Product Against Ground-based Observations During 2008–2021 over Hubei, China C. ZHU et al. 10.3724/j.1006-8775.2024.036
- SPATIAL DOWNSCALING OF DAILY PRECIPITATION d4PDF DATA USING ANN ALGORITHM I. KHATEEB et al. 10.2208/journalofjsce.24-00156
- Current progress in subseasonal-to-decadal prediction based on machine learning Z. Shen et al. 10.1016/j.acags.2024.100201
- Statistical spatial downscaling of significant wave height in a regional sea from the global ERA5 dataset B. Yuan et al. 10.1016/j.oceaneng.2025.121100
- Multivariate bias correction and downscaling of climate models with trend-preserving deep learning F. Wang & D. Tian 10.1007/s00382-024-07406-9
- Impact of deep learning-driven precipitation corrected data using near real-time satellite-based observations and model forecast in an integrated hydrological model K. Patakchi Yousefi et al. 10.3389/frwa.2024.1439906
- Downscaling sea surface height and currents in coastal regions using convolutional neural network B. Yuan et al. 10.1016/j.apor.2024.104153
- Deep Learning for Bias‐Correcting CMIP6‐Class Earth System Models P. Hess et al. 10.1029/2023EF004002
- Deep learning-based bias correction of ISMR simulated by GCM S. Sharma et al. 10.1016/j.atmosres.2024.107589
- Assessing the effectiveness of ANN model in spatial downscaling of d4PDF hourly precipitation data: a case study in Japan I. Khateeb et al. 10.1007/s44367-025-00003-5
- A deep learning-based bias correction model for Arctic sea ice concentration towards MITgcm S. Yuan et al. 10.1016/j.ocemod.2024.102326
- Machine Learning-Based Bias Correction of Precipitation Measurements at High Altitude H. Li et al. 10.3390/rs15082180
- Enhancing precipitation intensity estimation using ERA5-land reanalysis with statistical and machine learning approaches A. Abdolmanafi et al. 10.1016/j.rineng.2025.104928
- A Data-Driven Approach to Improve Cocoa Crop Establishment in Colombia: Insights and Agricultural Practice Recommendations from an Ensemble Machine Learning Model L. Talero-Sarmiento et al. 10.3390/agriengineering7010006
- Intercomparison of Deep Learning Architectures for the Prediction of Precipitation Fields With a Focus on Extremes N. Otero & P. Horton 10.1029/2023WR035088
1 citations as recorded by crossref.
Latest update: 23 Apr 2025
Short summary
Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a customized deep learning (DL) model to bias-correct and downscale gridded precipitation data using radar observations. The results showed that the customized DL model can generate improved precipitation at fine resolutions where regular DL and statistical methods experience challenges. The new model can be used to improve precipitation estimates, especially for capturing extremes at smaller scales.
Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a...
