Radiative Transfer Speed-Up Combining Optimal Spectral Sampling With a Machine Learning Approach
The Orbiting Carbon Observatories-2 and -3 make space-based measurements in the oxygen A-band and the weak and strong carbon dioxide (CO2) bands using the Atmospheric Carbon Observations from Space (ACOS) retrieval. Within ACOS, a Bayesian optimal estimation approach is employed to retrieve the colu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Remote Sensing |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frsen.2022.932548/full |
| _version_ | 1828074737727700992 |
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| author | Steffen Mauceri Christopher W. O’Dell Gregory McGarragh Vijay Natraj |
| author_facet | Steffen Mauceri Christopher W. O’Dell Gregory McGarragh Vijay Natraj |
| author_sort | Steffen Mauceri |
| collection | DOAJ |
| description | The Orbiting Carbon Observatories-2 and -3 make space-based measurements in the oxygen A-band and the weak and strong carbon dioxide (CO2) bands using the Atmospheric Carbon Observations from Space (ACOS) retrieval. Within ACOS, a Bayesian optimal estimation approach is employed to retrieve the column-averaged CO2 dry air mole fraction from these measurements. This retrieval requires a large number of polarized, multiple-scattering radiative transfer calculations for each iteration. These calculations take up the majority of the processing time for each retrieval and slow down the algorithm to the point that reprocessing data from the mission over multiple years becomes especially time consuming. To accelerate the radiative transfer model and, thereby, ease this bottleneck, we have developed a novel approach that enables modeling of the full spectra for the three OCO-2/3 instrument bands from radiances calculated at a small subset of monochromatic wavelengths. This allows for a reduction of the number of monochromatic calculations by a factor of 10, which can be achieved with radiance errors of less than 0.01% with respect to the existing algorithm and is easily tunable to a desired accuracy-speed trade-off. For the ACOS retrieval, this speeds up the over-retrievals by about a factor of two. The technique may be applicable to similar retrieval algorithms for other greenhouse gas sensors with large data volumes, such as GeoCarb, GOSAT-3, and CO2M. |
| first_indexed | 2024-04-11T01:48:20Z |
| format | Article |
| id | doaj.art-080421c69f6c4a58b9a4a0c960c8772c |
| institution | Directory Open Access Journal |
| issn | 2673-6187 |
| language | English |
| last_indexed | 2024-04-11T01:48:20Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Remote Sensing |
| spelling | doaj.art-080421c69f6c4a58b9a4a0c960c8772c2023-01-03T07:24:43ZengFrontiers Media S.A.Frontiers in Remote Sensing2673-61872022-07-01310.3389/frsen.2022.932548932548Radiative Transfer Speed-Up Combining Optimal Spectral Sampling With a Machine Learning ApproachSteffen Mauceri0Christopher W. O’Dell1Gregory McGarragh2Vijay Natraj3Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United StatesCooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, United StatesCooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, United StatesJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United StatesThe Orbiting Carbon Observatories-2 and -3 make space-based measurements in the oxygen A-band and the weak and strong carbon dioxide (CO2) bands using the Atmospheric Carbon Observations from Space (ACOS) retrieval. Within ACOS, a Bayesian optimal estimation approach is employed to retrieve the column-averaged CO2 dry air mole fraction from these measurements. This retrieval requires a large number of polarized, multiple-scattering radiative transfer calculations for each iteration. These calculations take up the majority of the processing time for each retrieval and slow down the algorithm to the point that reprocessing data from the mission over multiple years becomes especially time consuming. To accelerate the radiative transfer model and, thereby, ease this bottleneck, we have developed a novel approach that enables modeling of the full spectra for the three OCO-2/3 instrument bands from radiances calculated at a small subset of monochromatic wavelengths. This allows for a reduction of the number of monochromatic calculations by a factor of 10, which can be achieved with radiance errors of less than 0.01% with respect to the existing algorithm and is easily tunable to a desired accuracy-speed trade-off. For the ACOS retrieval, this speeds up the over-retrievals by about a factor of two. The technique may be applicable to similar retrieval algorithms for other greenhouse gas sensors with large data volumes, such as GeoCarb, GOSAT-3, and CO2M.https://www.frontiersin.org/articles/10.3389/frsen.2022.932548/fullradiative transfermachine learningoptimal estimation (OE)speed-upOCO-2 retrievals |
| spellingShingle | Steffen Mauceri Christopher W. O’Dell Gregory McGarragh Vijay Natraj Radiative Transfer Speed-Up Combining Optimal Spectral Sampling With a Machine Learning Approach Frontiers in Remote Sensing radiative transfer machine learning optimal estimation (OE) speed-up OCO-2 retrievals |
| title | Radiative Transfer Speed-Up Combining Optimal Spectral Sampling With a Machine Learning Approach |
| title_full | Radiative Transfer Speed-Up Combining Optimal Spectral Sampling With a Machine Learning Approach |
| title_fullStr | Radiative Transfer Speed-Up Combining Optimal Spectral Sampling With a Machine Learning Approach |
| title_full_unstemmed | Radiative Transfer Speed-Up Combining Optimal Spectral Sampling With a Machine Learning Approach |
| title_short | Radiative Transfer Speed-Up Combining Optimal Spectral Sampling With a Machine Learning Approach |
| title_sort | radiative transfer speed up combining optimal spectral sampling with a machine learning approach |
| topic | radiative transfer machine learning optimal estimation (OE) speed-up OCO-2 retrievals |
| url | https://www.frontiersin.org/articles/10.3389/frsen.2022.932548/full |
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