Articles | Volume 12, issue 12
https://doi.org/10.5194/amt-12-6619-2019
© Author(s) 2019. 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-12-6619-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Dec 2019
Research article |
| 13 Dec 2019
| 13 Dec 2019
A neural network radiative transfer model approach applied to the Tropospheric Monitoring Instrument aerosol height algorithm
Swadhin Nanda
Royal Netherlands Meteorological Institute (KNMI), Utrechtseweg 297, 3731 GA De Bilt, the Netherlands
Geoscience and Remote Sensing, Delft university of Technology (TU Delft), Mekelweg 2, 2628 CD Delft, the Netherlands
Royal Netherlands Meteorological Institute (KNMI), Utrechtseweg 297, 3731 GA De Bilt, the Netherlands
J. Pepijn Veefkind
Royal Netherlands Meteorological Institute (KNMI), Utrechtseweg 297, 3731 GA De Bilt, the Netherlands
Geoscience and Remote Sensing, Delft university of Technology (TU Delft), Mekelweg 2, 2628 CD Delft, the Netherlands
Mark ter Linden
S[&]T Corp, Delft, the Netherlands
Maarten Sneep
Royal Netherlands Meteorological Institute (KNMI), Utrechtseweg 297, 3731 GA De Bilt, the Netherlands
Johan de Haan
Royal Netherlands Meteorological Institute (KNMI), Utrechtseweg 297, 3731 GA De Bilt, the Netherlands
Pieternel F. Levelt
Royal Netherlands Meteorological Institute (KNMI), Utrechtseweg 297, 3731 GA De Bilt, the Netherlands
Geoscience and Remote Sensing, Delft university of Technology (TU Delft), Mekelweg 2, 2628 CD Delft, the Netherlands
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Cited
21 citations as recorded by crossref.
- Aerosol Shortwave Radiative Heating and Cooling by the 2017 and 2023 Chilean Wildfire Smoke Plumes M. de Graaf et al. 10.1029/2023GL104387
- Study on Influencing Factors of the Information Content of Satellite Remote-Sensing Aerosol Vertical Profiles Using Oxygen A-Band Y. Wang et al. 10.3390/rs15040948
- Direct Retrieval of NO 2 Vertical Columns from UV-Vis (390-495 nm) Spectral Radiances Using a Neural Network C. Li et al. 10.34133/2022/9817134
- Simultaneous retrieval of aerosol and ocean properties from PACE HARP2 with uncertainty assessment using cascading neural network radiative transfer models M. Gao et al. 10.5194/amt-16-5863-2023
- Analytical Prediction of Scattering Properties of Spheroidal Dust Particles With Machine Learning X. Chen et al. 10.1029/2021GL097548
- First Mapping of Monthly and Diurnal Climatology of Saharan Dust Layer Height Over the Atlantic Ocean From EPIC/DSCOVR in Deep Space Z. Lu et al. 10.1029/2022GL102552
- Aerosol Absorption Over Land Derived From the Ultra-Violet Aerosol Index by Deep Learning J. Sun et al. 10.1109/JSTARS.2021.3108669
- Introduction of the DISAMAR radiative transfer model: determining instrument specifications and analysing methods for atmospheric retrieval (version 4.1.5) J. de Haan et al. 10.5194/gmd-15-7031-2022
- Assessment of smoke plume height products derived from multisource satellite observations using lidar-derived height metrics for wildfires in the western US J. Huang et al. 10.5194/acp-24-3673-2024
- Hourly Mapping of the Layer Height of Thick Smoke Plumes Over the Western U.S. in 2020 Severe Fire Season Z. Lu et al. 10.3389/frsen.2021.766628
- Aerosol Parameters Retrieval From TROPOMI/S5P Using Physics-Based Neural Networks L. Rao et al. 10.1109/JSTARS.2022.3196843
- Radiative transfer acceleration based on the principal component analysis and lookup table of corrections: optimization and application to UV ozone profile retrievals J. Bak et al. 10.5194/amt-14-2659-2021
- A new machine-learning-based analysis for improving satellite-retrieved atmospheric composition data: OMI SO2 as an example C. Li et al. 10.5194/amt-15-5497-2022
- A first comparison of TROPOMI aerosol layer height (ALH) to CALIOP data S. Nanda et al. 10.5194/amt-13-3043-2020
- First retrieval of absorbing aerosol height over dark target using TROPOMI oxygen B band: Algorithm development and application for surface particulate matter estimates X. Chen et al. 10.1016/j.rse.2021.112674
- Direct estimates of biomass burning NO<sub><i>x</i></sub> emissions and lifetimes using daily observations from TROPOMI X. Jin et al. 10.5194/acp-21-15569-2021
- The retrieval of aerosol optical properties based on a random forest machine learning approach: Exploration of geostationary satellite images F. Bao et al. 10.1016/j.rse.2022.113426
- The 2018 fire season in North America as seen by TROPOMI: aerosol layer height intercomparisons and evaluation of model-derived plume heights D. Griffin et al. 10.5194/amt-13-1427-2020
- Estimation of Aerosol Layer Height from OLCI Measurements in the O2A-Absorption Band over Oceans L. Jänicke et al. 10.3390/rs15164080
- Effects of clouds on the UV Absorbing Aerosol Index from TROPOMI M. Kooreman et al. 10.5194/amt-13-6407-2020
- Use of A Neural Network-Based Ocean Body Radiative Transfer Model for Aerosol Retrievals from Multi-Angle Polarimetric Measurements C. Fan et al. 10.3390/rs11232877
20 citations as recorded by crossref.
- Aerosol Shortwave Radiative Heating and Cooling by the 2017 and 2023 Chilean Wildfire Smoke Plumes M. de Graaf et al. 10.1029/2023GL104387
- Study on Influencing Factors of the Information Content of Satellite Remote-Sensing Aerosol Vertical Profiles Using Oxygen A-Band Y. Wang et al. 10.3390/rs15040948
- Direct Retrieval of NO 2 Vertical Columns from UV-Vis (390-495 nm) Spectral Radiances Using a Neural Network C. Li et al. 10.34133/2022/9817134
- Simultaneous retrieval of aerosol and ocean properties from PACE HARP2 with uncertainty assessment using cascading neural network radiative transfer models M. Gao et al. 10.5194/amt-16-5863-2023
- Analytical Prediction of Scattering Properties of Spheroidal Dust Particles With Machine Learning X. Chen et al. 10.1029/2021GL097548
- First Mapping of Monthly and Diurnal Climatology of Saharan Dust Layer Height Over the Atlantic Ocean From EPIC/DSCOVR in Deep Space Z. Lu et al. 10.1029/2022GL102552
- Aerosol Absorption Over Land Derived From the Ultra-Violet Aerosol Index by Deep Learning J. Sun et al. 10.1109/JSTARS.2021.3108669
- Introduction of the DISAMAR radiative transfer model: determining instrument specifications and analysing methods for atmospheric retrieval (version 4.1.5) J. de Haan et al. 10.5194/gmd-15-7031-2022
- Assessment of smoke plume height products derived from multisource satellite observations using lidar-derived height metrics for wildfires in the western US J. Huang et al. 10.5194/acp-24-3673-2024
- Hourly Mapping of the Layer Height of Thick Smoke Plumes Over the Western U.S. in 2020 Severe Fire Season Z. Lu et al. 10.3389/frsen.2021.766628
- Aerosol Parameters Retrieval From TROPOMI/S5P Using Physics-Based Neural Networks L. Rao et al. 10.1109/JSTARS.2022.3196843
- Radiative transfer acceleration based on the principal component analysis and lookup table of corrections: optimization and application to UV ozone profile retrievals J. Bak et al. 10.5194/amt-14-2659-2021
- A new machine-learning-based analysis for improving satellite-retrieved atmospheric composition data: OMI SO2 as an example C. Li et al. 10.5194/amt-15-5497-2022
- A first comparison of TROPOMI aerosol layer height (ALH) to CALIOP data S. Nanda et al. 10.5194/amt-13-3043-2020
- First retrieval of absorbing aerosol height over dark target using TROPOMI oxygen B band: Algorithm development and application for surface particulate matter estimates X. Chen et al. 10.1016/j.rse.2021.112674
- Direct estimates of biomass burning NO<sub><i>x</i></sub> emissions and lifetimes using daily observations from TROPOMI X. Jin et al. 10.5194/acp-21-15569-2021
- The retrieval of aerosol optical properties based on a random forest machine learning approach: Exploration of geostationary satellite images F. Bao et al. 10.1016/j.rse.2022.113426
- The 2018 fire season in North America as seen by TROPOMI: aerosol layer height intercomparisons and evaluation of model-derived plume heights D. Griffin et al. 10.5194/amt-13-1427-2020
- Estimation of Aerosol Layer Height from OLCI Measurements in the O2A-Absorption Band over Oceans L. Jänicke et al. 10.3390/rs15164080
- Effects of clouds on the UV Absorbing Aerosol Index from TROPOMI M. Kooreman et al. 10.5194/amt-13-6407-2020
Latest update: 24 Apr 2024
Short summary
This paper discusses a neural network forward model used by the operational aerosol layer height (ALH) retrieval algorithm for the TROPOspheric Monitoring Instrument (TROPOMI) on board the European Sentinel-5 Precursor satellite mission. This model replaces online radiative transfer calculations within the oxygen A-band, improving the speed of the algorithm by 3 orders of magnitude. With this advancement in the algorithm's speed, TROPOMI is set to deliver the ALH product operationally.
This paper discusses a neural network forward model used by the operational aerosol layer height...
