Articles | Volume 11, issue 8
https://doi.org/10.5194/amt-11-4627-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-11-4627-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Aug 2018
Research article |
| 09 Aug 2018
| 09 Aug 2018
A neural network approach to estimating a posteriori distributions of Bayesian retrieval problems
Simon Pfreundschuh
CORRESPONDING AUTHOR
Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
Patrick Eriksson
Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
David Duncan
Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
Bengt Rydberg
Möller Data Workflow Systems AB, Gothenburg, Sweden
Nina Håkansson
Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden
Anke Thoss
Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden
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Cited
24 citations as recorded by crossref.
- An improved near-real-time precipitation retrieval for Brazil S. Pfreundschuh et al. 10.5194/amt-15-6907-2022
- A New Deep-Learning-Based Framework for Ice Water Path Retrieval From Microwave Humidity Sounder-II Aboard FengYun-3D Satellite W. Wang et al. 10.1109/TGRS.2024.3352654
- Synergistic radar and radiometer retrievals of ice hydrometeors S. Pfreundschuh et al. 10.5194/amt-13-4219-2020
- Neural Bayes Estimators for Irregular Spatial Data Using Graph Neural Networks M. Sainsbury-Dale et al. 10.1080/10618600.2024.2433671
- Towards an operational Ice Cloud Imager (ICI) retrieval product P. Eriksson et al. 10.5194/amt-13-53-2020
- Contrail altitude estimation using GOES-16 ABI data and deep learning V. Meijer et al. 10.5194/amt-17-6145-2024
- Retrieval of ice water path from the Microwave Humidity Sounder (MWHS) aboard FengYun-3B (FY-3B) satellite polarimetric measurements based on a deep neural network W. Wang et al. 10.5194/amt-15-6489-2022
- CLAAS-3: the third edition of the CM SAF cloud data record based on SEVIRI observations N. Benas et al. 10.5194/essd-15-5153-2023
- Overview: Estimating and reporting uncertainties in remotely sensed atmospheric composition and temperature T. von Clarmann et al. 10.5194/amt-13-4393-2020
- Synergistic Retrievals of Ice Cloud Microphysics by Spaceborne Submillimeter and Infrared Observations S. Li et al. 10.1109/TGRS.2024.3453248
- Fast Radiative Transfer Approximating Ice Hydrometeor Orientation and Its Implication on IWP Retrievals I. Kaur et al. 10.3390/rs14071594
- Durability prognostication of ferroconcrete structures on the basis of neural indistinct networks S. Tkalich & O. Taratynov 10.1088/1757-899X/537/2/022038
- Probing the Explainability of Neural Network Cloud-Top Pressure Models for LEO and GEO Imagers C. White et al. 10.1175/AIES-D-21-0001.1
- GPROF-NN: a neural-network-based implementation of the Goddard Profiling Algorithm S. Pfreundschuh et al. 10.5194/amt-15-5033-2022
- Can machine learning correct microwave humidity radiances for the influence of clouds? I. Kaur et al. 10.5194/amt-14-2957-2021
- CLARA-A3: The third edition of the AVHRR-based CM SAF climate data record on clouds, radiation and surface albedo covering the period 1979 to 2023 K. Karlsson et al. 10.5194/essd-15-4901-2023
- Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP) Using Passive Microwave and Infrared Data V. Gorooh et al. 10.1175/JHM-D-21-0194.1
- Ice water path retrievals from Meteosat-9 using quantile regression neural networks A. Amell et al. 10.5194/amt-15-5701-2022
- An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing A. Doicu et al. 10.3390/rs13245061
- The atmospheric radiative transfer simulator ARTS, version 2.6 — Deep python integration S. Buehler et al. 10.1016/j.jqsrt.2025.109443
- A benchmark for testing the accuracy and computational cost of shortwave top-of-atmosphere reflectance calculations in clear-sky aerosol-laden atmospheres J. Escribano et al. 10.5194/gmd-12-805-2019
- The Chalmers Cloud Ice Climatology: retrieval implementation and validation A. Amell et al. 10.5194/amt-17-4337-2024
- The Ice Cloud Imager: retrieval of frozen water column properties E. May et al. 10.5194/amt-17-5957-2024
- Tomographic reconstruction algorithms for retrieving two-dimensional ice cloud microphysical parameters using along-track (sub)millimeter-wave radiometer observations Y. Liu & I. Adams 10.5194/amt-18-1659-2025
24 citations as recorded by crossref.
- An improved near-real-time precipitation retrieval for Brazil S. Pfreundschuh et al. 10.5194/amt-15-6907-2022
- A New Deep-Learning-Based Framework for Ice Water Path Retrieval From Microwave Humidity Sounder-II Aboard FengYun-3D Satellite W. Wang et al. 10.1109/TGRS.2024.3352654
- Synergistic radar and radiometer retrievals of ice hydrometeors S. Pfreundschuh et al. 10.5194/amt-13-4219-2020
- Neural Bayes Estimators for Irregular Spatial Data Using Graph Neural Networks M. Sainsbury-Dale et al. 10.1080/10618600.2024.2433671
- Towards an operational Ice Cloud Imager (ICI) retrieval product P. Eriksson et al. 10.5194/amt-13-53-2020
- Contrail altitude estimation using GOES-16 ABI data and deep learning V. Meijer et al. 10.5194/amt-17-6145-2024
- Retrieval of ice water path from the Microwave Humidity Sounder (MWHS) aboard FengYun-3B (FY-3B) satellite polarimetric measurements based on a deep neural network W. Wang et al. 10.5194/amt-15-6489-2022
- CLAAS-3: the third edition of the CM SAF cloud data record based on SEVIRI observations N. Benas et al. 10.5194/essd-15-5153-2023
- Overview: Estimating and reporting uncertainties in remotely sensed atmospheric composition and temperature T. von Clarmann et al. 10.5194/amt-13-4393-2020
- Synergistic Retrievals of Ice Cloud Microphysics by Spaceborne Submillimeter and Infrared Observations S. Li et al. 10.1109/TGRS.2024.3453248
- Fast Radiative Transfer Approximating Ice Hydrometeor Orientation and Its Implication on IWP Retrievals I. Kaur et al. 10.3390/rs14071594
- Durability prognostication of ferroconcrete structures on the basis of neural indistinct networks S. Tkalich & O. Taratynov 10.1088/1757-899X/537/2/022038
- Probing the Explainability of Neural Network Cloud-Top Pressure Models for LEO and GEO Imagers C. White et al. 10.1175/AIES-D-21-0001.1
- GPROF-NN: a neural-network-based implementation of the Goddard Profiling Algorithm S. Pfreundschuh et al. 10.5194/amt-15-5033-2022
- Can machine learning correct microwave humidity radiances for the influence of clouds? I. Kaur et al. 10.5194/amt-14-2957-2021
- CLARA-A3: The third edition of the AVHRR-based CM SAF climate data record on clouds, radiation and surface albedo covering the period 1979 to 2023 K. Karlsson et al. 10.5194/essd-15-4901-2023
- Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP) Using Passive Microwave and Infrared Data V. Gorooh et al. 10.1175/JHM-D-21-0194.1
- Ice water path retrievals from Meteosat-9 using quantile regression neural networks A. Amell et al. 10.5194/amt-15-5701-2022
- An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing A. Doicu et al. 10.3390/rs13245061
- The atmospheric radiative transfer simulator ARTS, version 2.6 — Deep python integration S. Buehler et al. 10.1016/j.jqsrt.2025.109443
- A benchmark for testing the accuracy and computational cost of shortwave top-of-atmosphere reflectance calculations in clear-sky aerosol-laden atmospheres J. Escribano et al. 10.5194/gmd-12-805-2019
- The Chalmers Cloud Ice Climatology: retrieval implementation and validation A. Amell et al. 10.5194/amt-17-4337-2024
- The Ice Cloud Imager: retrieval of frozen water column properties E. May et al. 10.5194/amt-17-5957-2024
- Tomographic reconstruction algorithms for retrieving two-dimensional ice cloud microphysical parameters using along-track (sub)millimeter-wave radiometer observations Y. Liu & I. Adams 10.5194/amt-18-1659-2025
Latest update: 29 Jul 2025
Short summary
A novel neural-network-based retrieval method is proposed that combines the flexibility and computational efficiency of machine learning retrievals with the consistent treatment of uncertainties of Bayesian methods. Numerical experiments are presented that show the consistency of the proposed method with the Bayesian formulation as well as its ability to represent non-Gaussian retrieval errors. With this, the proposed method overcomes important limitations of traditional methods.
A novel neural-network-based retrieval method is proposed that combines the flexibility and...
