Uncertainty‐Aware Machine Learning Bias Correction and Filtering for OCO‐2: 1
Abstract The Orbiting Carbon Observatory‐2 (OCO‐2) makes space‐based radiance measurements of reflected sunlight. Using a physics‐based retrieval algorithm, these measurements are inverted to estimate column‐averaged atmospheric carbon dioxide dry‐air mole fractions (XCO2). However, biases are prese...
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| Format: | Article |
| Language: | English |
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American Geophysical Union (AGU)
2025-07-01
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| Series: | Earth and Space Science |
| Online Access: | https://doi.org/10.1029/2025EA004328 |
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| author | Steffen Mauceri William Keely Josh Laughner Christopher W. O’Dell Steven Massie Robert Nelson David Baker Matthäus Kiel Otto Lamminpää Jonathan Hobbs Abhishek Chatterjee Tommy Taylor Paul Wennberg Sean Crowell Britton Stephens Vivienne H. Payne |
| author_facet | Steffen Mauceri William Keely Josh Laughner Christopher W. O’Dell Steven Massie Robert Nelson David Baker Matthäus Kiel Otto Lamminpää Jonathan Hobbs Abhishek Chatterjee Tommy Taylor Paul Wennberg Sean Crowell Britton Stephens Vivienne H. Payne |
| author_sort | Steffen Mauceri |
| collection | DOAJ |
| description | Abstract The Orbiting Carbon Observatory‐2 (OCO‐2) makes space‐based radiance measurements of reflected sunlight. Using a physics‐based retrieval algorithm, these measurements are inverted to estimate column‐averaged atmospheric carbon dioxide dry‐air mole fractions (XCO2). However, biases are present in the retrieved XCO2 due to sensor calibration errors and discrepancies between the physics‐based retrieval and nature. We propose a Random Forest (RF), a non‐linear, interpretable machine learning (ML) technique, to correct these biases. The approach is rigorously validated, comes with quantified uncertainties, and is derived independent of carbon flux models. Compared to the operational approach, our method reduces unphysical variability over land and ocean and shows closer agreement with independent ground‐based observations from the Total Carbon Column Observing Network. The RF‐bias correction is suitable for integration into the operational processing pipeline for the next version of OCO‐2 products, pending additional testing and validation. It is inherently generalizable to other existing and planned greenhouse gas monitoring missions. This paper (Part 1) describes the RF bias correction, while a second paper (Part 2) describes the development of a data filtering strategy specifically designed for a subset of retrievals exhibiting irreducible errors that remain inadequately corrected by the ML bias correction. |
| format | Article |
| id | doaj-art-a82d2ee958c84b6f905bf74f308c1234 |
| institution | DOAJ |
| issn | 2333-5084 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Earth and Space Science |
| spelling | doaj-art-a82d2ee958c84b6f905bf74f308c12342025-08-20T02:45:27ZengAmerican Geophysical Union (AGU)Earth and Space Science2333-50842025-07-01127n/an/a10.1029/2025EA004328Uncertainty‐Aware Machine Learning Bias Correction and Filtering for OCO‐2: 1Steffen Mauceri0William Keely1Josh Laughner2Christopher W. O’Dell3Steven Massie4Robert Nelson5David Baker6Matthäus Kiel7Otto Lamminpää8Jonathan Hobbs9Abhishek Chatterjee10Tommy Taylor11Paul Wennberg12Sean Crowell13Britton Stephens14Vivienne H. Payne15Jet Propulsion Laboratory California Institute of Technology Pasadena CA USAJet Propulsion Laboratory California Institute of Technology Pasadena CA USAJet Propulsion Laboratory California Institute of Technology Pasadena CA USACooperative Institute for Research in the Atmosphere Colorado State University Fort Collins CO USALaboratory for Atmospheric and Space Physics University of Colorado Boulder CO USAJet Propulsion Laboratory California Institute of Technology Pasadena CA USACooperative Institute for Research in the Atmosphere Colorado State University Fort Collins CO USAJet Propulsion Laboratory California Institute of Technology Pasadena CA USAJet Propulsion Laboratory California Institute of Technology Pasadena CA USAJet Propulsion Laboratory California Institute of Technology Pasadena CA USAJet Propulsion Laboratory California Institute of Technology Pasadena CA USACooperative Institute for Research in the Atmosphere Colorado State University Fort Collins CO USACalifornia Institute of Technology Pasadena CA USALumenUs Scientific LLC Oklahoma City OK USANSF National Center for Atmospheric Research Boulder CO USAJet Propulsion Laboratory California Institute of Technology Pasadena CA USAAbstract The Orbiting Carbon Observatory‐2 (OCO‐2) makes space‐based radiance measurements of reflected sunlight. Using a physics‐based retrieval algorithm, these measurements are inverted to estimate column‐averaged atmospheric carbon dioxide dry‐air mole fractions (XCO2). However, biases are present in the retrieved XCO2 due to sensor calibration errors and discrepancies between the physics‐based retrieval and nature. We propose a Random Forest (RF), a non‐linear, interpretable machine learning (ML) technique, to correct these biases. The approach is rigorously validated, comes with quantified uncertainties, and is derived independent of carbon flux models. Compared to the operational approach, our method reduces unphysical variability over land and ocean and shows closer agreement with independent ground‐based observations from the Total Carbon Column Observing Network. The RF‐bias correction is suitable for integration into the operational processing pipeline for the next version of OCO‐2 products, pending additional testing and validation. It is inherently generalizable to other existing and planned greenhouse gas monitoring missions. This paper (Part 1) describes the RF bias correction, while a second paper (Part 2) describes the development of a data filtering strategy specifically designed for a subset of retrievals exhibiting irreducible errors that remain inadequately corrected by the ML bias correction.https://doi.org/10.1029/2025EA004328 |
| spellingShingle | Steffen Mauceri William Keely Josh Laughner Christopher W. O’Dell Steven Massie Robert Nelson David Baker Matthäus Kiel Otto Lamminpää Jonathan Hobbs Abhishek Chatterjee Tommy Taylor Paul Wennberg Sean Crowell Britton Stephens Vivienne H. Payne Uncertainty‐Aware Machine Learning Bias Correction and Filtering for OCO‐2: 1 Earth and Space Science |
| title | Uncertainty‐Aware Machine Learning Bias Correction and Filtering for OCO‐2: 1 |
| title_full | Uncertainty‐Aware Machine Learning Bias Correction and Filtering for OCO‐2: 1 |
| title_fullStr | Uncertainty‐Aware Machine Learning Bias Correction and Filtering for OCO‐2: 1 |
| title_full_unstemmed | Uncertainty‐Aware Machine Learning Bias Correction and Filtering for OCO‐2: 1 |
| title_short | Uncertainty‐Aware Machine Learning Bias Correction and Filtering for OCO‐2: 1 |
| title_sort | uncertainty aware machine learning bias correction and filtering for oco 2 1 |
| url | https://doi.org/10.1029/2025EA004328 |
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