Identification

Title

Uncertaintyâ€aware machine learning bias correction and filtering for OCOâ€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 (XCO 2 ). However, biases are present in the retrieved XCO 2 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.

Resource type

document

Resource locator

Unique resource identifier

code

https://n2t.net/ark:/85065/d7mw2nkx

codeSpace

Dataset language

eng

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

geoscientificInformation

Keywords

Keyword set

keyword value

Text

originating controlled vocabulary

title

Resource Type

reference date

date type

publication

effective date

2016-01-01T00:00:00Z

Geographic location

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Temporal reference

Temporal extent

Begin position

End position

Dataset reference date

date type

publication

effective date

2025-07-01T00:00:00Z

Frequency of update

Quality and validity

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Conformity

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Use constraints

<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>

Limitations on public access

None

Responsible organisations

Responsible party

contact position

OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact