edu.ucar.opensky::articles:44004 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T17:46:34.460327 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.net/ark:/85065/d7mw2nkx https://doi.org/10.14291/TCCON.GGG2020 https Related Dataset #1 2020 TCCON Data Release https://doi.org/10.5067/8E4VLCK16O6Q https Related Dataset #2 OCO-2 Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files, Retrospective processing V11.1r https://doi.org/10.5281/zenodo.15085179 https Related Dataset #3 Uncertainty-aware Machine Learning Bias Correction and Filtering for OCO-2 | 2014-2024 Uncertainty‐aware machine learning bias correction and filtering for OCO‐2: 1 2025-07-01T00:00:00Z publication https://n2t.net/ark:/85065/d7mw2nkx https://doi.org/10.1029/2025EA004328 Mauceri, S. author Keely, W. author Laughner, J. author O’Dell, C. W. author Massie, S. author Nelson, R. author Baker, D. author Kiel, M. author Lamminpää, O. author Hobbs, J. author Chatterjee, A. author Taylor, T. author Wennberg, P. author Crowell, S. author Stephens, Britton author Payne, V. H. author UCAR/NCAR - Library publisher The Orbiting Carbon Observatory&#8208;2 (OCO&#8208;2) makes space&#8208;based radiance measurements of reflected sunlight. Using a physics&#8208;based retrieval algorithm, these measurements are inverted to estimate column&#8208;averaged atmospheric carbon dioxide dry&#8208;air mole fractions (XCO 2 ). However, biases are present in the retrieved XCO 2 due to sensor calibration errors and discrepancies between the physics&#8208;based retrieval and nature. We propose a Random Forest (RF), a non&#8208;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&#8208;based observations from the Total Carbon Column Observing Network. The RF&#8208;bias correction is suitable for integration into the operational processing pipeline for the next version of OCO&#8208;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. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication <span style="font-family:Arial;font-size:10pt;font-style:normal;font-weight:normal;" data-sheets-root="1">Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</span> None eng geoscientificInformation