Articles | Volume 15, issue 11
https://doi.org/10.5194/gmd-15-4569-2022
© 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-15-4569-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
14 Jun 2022
Model description paper |
| 14 Jun 2022
| 14 Jun 2022
CLIMFILL v0.9: a framework for intelligently gap filling Earth observations
Verena Bessenbacher
CORRESPONDING AUTHOR
Institute for Atmospheric and Climate Science, ETH Zurich, Rämistrasse 101, 8092 Zurich, Switzerland
Sonia Isabelle Seneviratne
Institute for Atmospheric and Climate Science, ETH Zurich, Rämistrasse 101, 8092 Zurich, Switzerland
Lukas Gudmundsson
Institute for Atmospheric and Climate Science, ETH Zurich, Rämistrasse 101, 8092 Zurich, Switzerland
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Cited
15 citations as recorded by crossref.
- ESA CCI Soil Moisture GAPFILLED: an independent global gap-free satellite climate data record with uncertainty estimates W. Preimesberger et al. 10.5194/essd-17-4305-2025
- Gap‐Filled Multivariate Observations of Global Land–Climate Interactions V. Bessenbacher et al. 10.1029/2023JD039099
- Machine learning for the physics of climate A. Bracco et al. 10.1038/s42254-024-00776-3
- Validation of ERA5 rainfall data over the South Pacific Region: case study of Fiji Islands P. Sagero et al. 10.1007/s00703-024-01025-z
- A comparative analysis of machine learning approaches to gap filling meteorological datasets B. Lalic et al. 10.1007/s12665-024-11982-8
- On the use of distributed hydrologic model for filling large gaps at different parts of the streamflow data E. Ergün & M. Demirel 10.1016/j.jestch.2022.101321
- A daily reconstructed chlorophyll-a dataset in the South China Sea from MODIS using OI-SwinUnet H. Ye et al. 10.5194/essd-16-3125-2024
- ClimateFiller: A Python framework for climate time series gap-filling and diagnosis based on artificial intelligence and multi-source reanalysis data C. El Hachimi et al. 10.1016/j.simpa.2023.100575
- Development of Continuous AMSR-E/2 Soil Moisture Time Series by Hybrid Deep Learning Model (ConvLSTM2D and Conv2D) and Transfer Learning for Reanalyses V. Sivaprasad et al. 10.1109/JSTARS.2025.3557956
- A robust gap-filling approach for European Space Agency Climate Change Initiative (ESA CCI) soil moisture integrating satellite observations, model-driven knowledge, and spatiotemporal machine learning K. Liu et al. 10.5194/hess-27-577-2023
- Detecting the human fingerprint in the summer 2022 western–central European soil drought D. Schumacher et al. 10.5194/esd-15-131-2024
- Enhancing long-term vegetation monitoring in Australia: a new approach for harmonising the Advanced Very High Resolution Radiometer normalised-difference vegetation index (NDVI) with MODIS NDVI C. Burton et al. 10.5194/essd-16-4389-2024
- Technical note: A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set S. Walther et al. 10.5194/bg-19-2805-2022
- Annual and seasonal rainfall trend analysis using gridded dataset in the Wabe Shebele River Basin, Ethiopia M. Gurara et al. 10.1007/s00704-022-04164-8
- 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
12 citations as recorded by crossref.
- ESA CCI Soil Moisture GAPFILLED: an independent global gap-free satellite climate data record with uncertainty estimates W. Preimesberger et al. 10.5194/essd-17-4305-2025
- Gap‐Filled Multivariate Observations of Global Land–Climate Interactions V. Bessenbacher et al. 10.1029/2023JD039099
- Machine learning for the physics of climate A. Bracco et al. 10.1038/s42254-024-00776-3
- Validation of ERA5 rainfall data over the South Pacific Region: case study of Fiji Islands P. Sagero et al. 10.1007/s00703-024-01025-z
- A comparative analysis of machine learning approaches to gap filling meteorological datasets B. Lalic et al. 10.1007/s12665-024-11982-8
- On the use of distributed hydrologic model for filling large gaps at different parts of the streamflow data E. Ergün & M. Demirel 10.1016/j.jestch.2022.101321
- A daily reconstructed chlorophyll-a dataset in the South China Sea from MODIS using OI-SwinUnet H. Ye et al. 10.5194/essd-16-3125-2024
- ClimateFiller: A Python framework for climate time series gap-filling and diagnosis based on artificial intelligence and multi-source reanalysis data C. El Hachimi et al. 10.1016/j.simpa.2023.100575
- Development of Continuous AMSR-E/2 Soil Moisture Time Series by Hybrid Deep Learning Model (ConvLSTM2D and Conv2D) and Transfer Learning for Reanalyses V. Sivaprasad et al. 10.1109/JSTARS.2025.3557956
- A robust gap-filling approach for European Space Agency Climate Change Initiative (ESA CCI) soil moisture integrating satellite observations, model-driven knowledge, and spatiotemporal machine learning K. Liu et al. 10.5194/hess-27-577-2023
- Detecting the human fingerprint in the summer 2022 western–central European soil drought D. Schumacher et al. 10.5194/esd-15-131-2024
- Enhancing long-term vegetation monitoring in Australia: a new approach for harmonising the Advanced Very High Resolution Radiometer normalised-difference vegetation index (NDVI) with MODIS NDVI C. Burton et al. 10.5194/essd-16-4389-2024
3 citations as recorded by crossref.
- Technical note: A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set S. Walther et al. 10.5194/bg-19-2805-2022
- Annual and seasonal rainfall trend analysis using gridded dataset in the Wabe Shebele River Basin, Ethiopia M. Gurara et al. 10.1007/s00704-022-04164-8
- 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
Latest update: 18 Sep 2025
Short summary
Earth observations have many missing values. They are often filled using information from spatial and temporal contexts that mostly ignore information from related observed variables. We propose the gap-filling method CLIMFILL that additionally uses information from related variables. We test CLIMFILL using gap-free reanalysis data of variables related to soil–moisture climate interactions. CLIMFILL creates estimates for the missing values that recover the original dependence structure.
Earth observations have many missing values. They are often filled using information from...
