A nonlinear data-driven approach to bias correction of XCO 2 for NASA's OCO-2 ACOS version 10 Article Swipe
William Keely
,
Steffen Mauceri
,
Sean Crowell
,
C. O’Dell
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5194/amt-16-5725-2023
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5194/amt-16-5725-2023
Measurements of column-averaged dry air mole fraction of CO2 (termed XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) contain systematic errors and regional-scale biases, often induced by forward model error or nonlinearity in the retrieval. Operationally, these biases are corrected for by a multiple linear regression model fit to co-retrieved variables that are highly correlated with XCO2 error. The operational bias correction is fit in tandem with a hand-tuned quality filter which limits error variance and reduces the regime of interaction between state variables and error to one that is largely linear. While the operational correction and filter are successful in reducing biases in retrievals, they do not allow for throughput or correction of data in which biases become nonlinear in predictors or features. In this paper, we demonstrate a clear improvement in the reduction in error variance over the operational correction by using a set of nonlinear machine learning models, one for land and one for ocean soundings. We further illustrate how the operational quality filter can be relaxed when used in conjunction with a nonlinear bias correction, which allows for an increase in sounding throughput by 14 % while maintaining the residual error in the operational correction. The method can readily be applied to future Atmospheric CO2 Observations from Space (ACOS) algorithm updates, to OCO-2's companion instrument OCO-3, and to other retrieved atmospheric state variables of interest.
Related Topics
Concepts
Nonlinear system
Residual
Filter (signal processing)
Error detection and correction
Computer science
Errors-in-variables models
Algorithm
Observational error
Variance (accounting)
Environmental science
Meteorology
Statistics
Remote sensing
Mathematics
Physics
Geology
Accounting
Business
Computer vision
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/amt-16-5725-2023
- OA Status
- gold
- Cited By
- 11
- References
- 64
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389217958
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4389217958Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/amt-16-5725-2023Digital Object Identifier
- Title
-
A nonlinear data-driven approach to bias correction of XCO 2 for NASA's OCO-2 ACOS version 10Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-29Full publication date if available
- Authors
-
William Keely, Steffen Mauceri, Sean Crowell, C. O’DellList of authors in order
- Landing page
-
https://doi.org/10.5194/amt-16-5725-2023Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5194/amt-16-5725-2023Direct OA link when available
- Concepts
-
Nonlinear system, Residual, Filter (signal processing), Error detection and correction, Computer science, Errors-in-variables models, Algorithm, Observational error, Variance (accounting), Environmental science, Meteorology, Statistics, Remote sensing, Mathematics, Physics, Geology, Accounting, Business, Computer vision, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 4, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
64Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4389217958 |
|---|---|
| doi | https://doi.org/10.5194/amt-16-5725-2023 |
| ids.doi | https://doi.org/10.5194/amt-16-5725-2023 |
| ids.openalex | https://openalex.org/W4389217958 |
| fwci | 2.23307709 |
| type | article |
| title | A nonlinear data-driven approach to bias correction of XCO 2 for NASA's OCO-2 ACOS version 10 |
| awards[0].id | https://openalex.org/G3083281986 |
| awards[0].funder_id | https://openalex.org/F4320306101 |
| awards[0].display_name | |
| awards[0].funder_award_id | 80LARC17C0001 |
| awards[0].funder_display_name | National Aeronautics and Space Administration |
| awards[1].id | https://openalex.org/G3648211085 |
| awards[1].funder_id | https://openalex.org/F4320306101 |
| awards[1].display_name | |
| awards[1].funder_award_id | 80NM0018D0004 |
| awards[1].funder_display_name | National Aeronautics and Space Administration |
| biblio.issue | 23 |
| biblio.volume | 16 |
| biblio.last_page | 5748 |
| biblio.first_page | 5725 |
| grants[0].funder | https://openalex.org/F4320306101 |
| grants[0].award_id | 80LARC17C0001 |
| grants[0].funder_display_name | National Aeronautics and Space Administration |
| grants[1].funder | https://openalex.org/F4320306101 |
| grants[1].award_id | 80NM0018D0004 |
| grants[1].funder_display_name | National Aeronautics and Space Administration |
| topics[0].id | https://openalex.org/T11588 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2306 |
| topics[0].subfield.display_name | Global and Planetary Change |
| topics[0].display_name | Atmospheric and Environmental Gas Dynamics |
| topics[1].id | https://openalex.org/T12157 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.977400004863739 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Geochemistry and Geologic Mapping |
| topics[2].id | https://openalex.org/T11320 |
| topics[2].field.id | https://openalex.org/fields/19 |
| topics[2].field.display_name | Earth and Planetary Sciences |
| topics[2].score | 0.9735000133514404 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1902 |
| topics[2].subfield.display_name | Atmospheric Science |
| topics[2].display_name | Atmospheric Ozone and Climate |
| funders[0].id | https://openalex.org/F4320306101 |
| funders[0].ror | https://ror.org/027ka1x80 |
| funders[0].display_name | National Aeronautics and Space Administration |
| is_xpac | False |
| apc_list.value | 1470 |
| apc_list.currency | EUR |
| apc_list.value_usd | 1585 |
| apc_paid.value | 1470 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 1585 |
| concepts[0].id | https://openalex.org/C158622935 |
| concepts[0].level | 2 |
| concepts[0].score | 0.5919485092163086 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q660848 |
| concepts[0].display_name | Nonlinear system |
| concepts[1].id | https://openalex.org/C155512373 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5198022127151489 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q287450 |
| concepts[1].display_name | Residual |
| concepts[2].id | https://openalex.org/C106131492 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5089807510375977 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q3072260 |
| concepts[2].display_name | Filter (signal processing) |
| concepts[3].id | https://openalex.org/C103088060 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5009961128234863 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1062839 |
| concepts[3].display_name | Error detection and correction |
| concepts[4].id | https://openalex.org/C41008148 |
| concepts[4].level | 0 |
| concepts[4].score | 0.48076745867729187 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[4].display_name | Computer science |
| concepts[5].id | https://openalex.org/C179024874 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4782511293888092 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q5395728 |
| concepts[5].display_name | Errors-in-variables models |
| concepts[6].id | https://openalex.org/C11413529 |
| concepts[6].level | 1 |
| concepts[6].score | 0.47496792674064636 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[6].display_name | Algorithm |
| concepts[7].id | https://openalex.org/C19619285 |
| concepts[7].level | 2 |
| concepts[7].score | 0.45569390058517456 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q196372 |
| concepts[7].display_name | Observational error |
| concepts[8].id | https://openalex.org/C196083921 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4127384126186371 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7915758 |
| concepts[8].display_name | Variance (accounting) |
| concepts[9].id | https://openalex.org/C39432304 |
| concepts[9].level | 0 |
| concepts[9].score | 0.37640732526779175 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[9].display_name | Environmental science |
| concepts[10].id | https://openalex.org/C153294291 |
| concepts[10].level | 1 |
| concepts[10].score | 0.34264713525772095 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q25261 |
| concepts[10].display_name | Meteorology |
| concepts[11].id | https://openalex.org/C105795698 |
| concepts[11].level | 1 |
| concepts[11].score | 0.3400529623031616 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[11].display_name | Statistics |
| concepts[12].id | https://openalex.org/C62649853 |
| concepts[12].level | 1 |
| concepts[12].score | 0.333742618560791 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[12].display_name | Remote sensing |
| concepts[13].id | https://openalex.org/C33923547 |
| concepts[13].level | 0 |
| concepts[13].score | 0.3034983277320862 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[13].display_name | Mathematics |
| concepts[14].id | https://openalex.org/C121332964 |
| concepts[14].level | 0 |
| concepts[14].score | 0.16633260250091553 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[14].display_name | Physics |
| concepts[15].id | https://openalex.org/C127313418 |
| concepts[15].level | 0 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[15].display_name | Geology |
| concepts[16].id | https://openalex.org/C121955636 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q4116214 |
| concepts[16].display_name | Accounting |
| concepts[17].id | https://openalex.org/C144133560 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q4830453 |
| concepts[17].display_name | Business |
| concepts[18].id | https://openalex.org/C31972630 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[18].display_name | Computer vision |
| concepts[19].id | https://openalex.org/C62520636 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[19].display_name | Quantum mechanics |
| keywords[0].id | https://openalex.org/keywords/nonlinear-system |
| keywords[0].score | 0.5919485092163086 |
| keywords[0].display_name | Nonlinear system |
| keywords[1].id | https://openalex.org/keywords/residual |
| keywords[1].score | 0.5198022127151489 |
| keywords[1].display_name | Residual |
| keywords[2].id | https://openalex.org/keywords/filter |
| keywords[2].score | 0.5089807510375977 |
| keywords[2].display_name | Filter (signal processing) |
| keywords[3].id | https://openalex.org/keywords/error-detection-and-correction |
| keywords[3].score | 0.5009961128234863 |
| keywords[3].display_name | Error detection and correction |
| keywords[4].id | https://openalex.org/keywords/computer-science |
| keywords[4].score | 0.48076745867729187 |
| keywords[4].display_name | Computer science |
| keywords[5].id | https://openalex.org/keywords/errors-in-variables-models |
| keywords[5].score | 0.4782511293888092 |
| keywords[5].display_name | Errors-in-variables models |
| keywords[6].id | https://openalex.org/keywords/algorithm |
| keywords[6].score | 0.47496792674064636 |
| keywords[6].display_name | Algorithm |
| keywords[7].id | https://openalex.org/keywords/observational-error |
| keywords[7].score | 0.45569390058517456 |
| keywords[7].display_name | Observational error |
| keywords[8].id | https://openalex.org/keywords/variance |
| keywords[8].score | 0.4127384126186371 |
| keywords[8].display_name | Variance (accounting) |
| keywords[9].id | https://openalex.org/keywords/environmental-science |
| keywords[9].score | 0.37640732526779175 |
| keywords[9].display_name | Environmental science |
| keywords[10].id | https://openalex.org/keywords/meteorology |
| keywords[10].score | 0.34264713525772095 |
| keywords[10].display_name | Meteorology |
| keywords[11].id | https://openalex.org/keywords/statistics |
| keywords[11].score | 0.3400529623031616 |
| keywords[11].display_name | Statistics |
| keywords[12].id | https://openalex.org/keywords/remote-sensing |
| keywords[12].score | 0.333742618560791 |
| keywords[12].display_name | Remote sensing |
| keywords[13].id | https://openalex.org/keywords/mathematics |
| keywords[13].score | 0.3034983277320862 |
| keywords[13].display_name | Mathematics |
| keywords[14].id | https://openalex.org/keywords/physics |
| keywords[14].score | 0.16633260250091553 |
| keywords[14].display_name | Physics |
| language | en |
| locations[0].id | doi:10.5194/amt-16-5725-2023 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S70769875 |
| locations[0].source.issn | 1867-1381, 1867-8548 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1867-1381 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Atmospheric measurement techniques |
| locations[0].source.host_organization | https://openalex.org/P4310313756 |
| locations[0].source.host_organization_name | Copernicus Publications |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310313756 |
| locations[0].source.host_organization_lineage_names | Copernicus Publications |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Atmospheric Measurement Techniques |
| locations[0].landing_page_url | https://doi.org/10.5194/amt-16-5725-2023 |
| locations[1].id | pmh:oai:doaj.org/article:7f50d66bcaa147b09a695f304f399aeb |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Atmospheric Measurement Techniques, Vol 16, Pp 5725-5748 (2023) |
| locations[1].landing_page_url | https://doaj.org/article/7f50d66bcaa147b09a695f304f399aeb |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5060659688 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-8782-0762 |
| authorships[0].author.display_name | William Keely |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | William R. Keely |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5023725319 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-2371-8843 |
| authorships[1].author.display_name | Steffen Mauceri |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Steffen Mauceri |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5055004173 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-8353-3707 |
| authorships[2].author.display_name | Sean Crowell |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Sean Crowell |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5089387502 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-0271-8433 |
| authorships[3].author.display_name | C. O’Dell |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Christopher W. O'Dell |
| authorships[3].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.5194/amt-16-5725-2023 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A nonlinear data-driven approach to bias correction of XCO 2 for NASA's OCO-2 ACOS version 10 |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-25T14:43:58.451035 |
| primary_topic.id | https://openalex.org/T11588 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2306 |
| primary_topic.subfield.display_name | Global and Planetary Change |
| primary_topic.display_name | Atmospheric and Environmental Gas Dynamics |
| related_works | https://openalex.org/W2068341603, https://openalex.org/W2473790374, https://openalex.org/W4384519788, https://openalex.org/W4241998182, https://openalex.org/W4244757547, https://openalex.org/W4251324013, https://openalex.org/W2961206477, https://openalex.org/W4243183972, https://openalex.org/W4250366701, https://openalex.org/W2808790646 |
| cited_by_count | 11 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 6 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 4 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.5194/amt-16-5725-2023 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S70769875 |
| best_oa_location.source.issn | 1867-1381, 1867-8548 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1867-1381 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Atmospheric measurement techniques |
| best_oa_location.source.host_organization | https://openalex.org/P4310313756 |
| best_oa_location.source.host_organization_name | Copernicus Publications |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310313756 |
| best_oa_location.source.host_organization_lineage_names | Copernicus Publications |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Atmospheric Measurement Techniques |
| best_oa_location.landing_page_url | https://doi.org/10.5194/amt-16-5725-2023 |
| primary_location.id | doi:10.5194/amt-16-5725-2023 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S70769875 |
| primary_location.source.issn | 1867-1381, 1867-8548 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1867-1381 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Atmospheric measurement techniques |
| primary_location.source.host_organization | https://openalex.org/P4310313756 |
| primary_location.source.host_organization_name | Copernicus Publications |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310313756 |
| primary_location.source.host_organization_lineage_names | Copernicus Publications |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Atmospheric Measurement Techniques |
| primary_location.landing_page_url | https://doi.org/10.5194/amt-16-5725-2023 |
| publication_date | 2023-11-29 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2149662495, https://openalex.org/W6942155255, https://openalex.org/W1977184325, https://openalex.org/W4297957988, https://openalex.org/W2295598076, https://openalex.org/W2081136852, https://openalex.org/W1630629781, https://openalex.org/W2075828063, https://openalex.org/W3176510132, https://openalex.org/W1974105424, https://openalex.org/W2529373853, https://openalex.org/W2912861686, https://openalex.org/W6923577898, https://openalex.org/W6942268118, https://openalex.org/W2509279774, https://openalex.org/W2969476445, https://openalex.org/W2095774458, https://openalex.org/W2466707795, https://openalex.org/W2495955835, https://openalex.org/W2468299449, https://openalex.org/W6886021616, https://openalex.org/W3090495048, https://openalex.org/W6963011042, https://openalex.org/W2900766309, https://openalex.org/W2474376136, https://openalex.org/W2900783296, https://openalex.org/W2081498147, https://openalex.org/W2760848730, https://openalex.org/W6886096079, https://openalex.org/W6886058305, https://openalex.org/W2145605303, https://openalex.org/W2672663592, https://openalex.org/W4328090534, https://openalex.org/W4200114671, https://openalex.org/W6961075232, https://openalex.org/W2756223094, https://openalex.org/W3184827121, https://openalex.org/W4281668795, https://openalex.org/W6905015774, https://openalex.org/W2128217321, https://openalex.org/W2886397182, https://openalex.org/W6929463735, https://openalex.org/W2968631756, https://openalex.org/W4207068078, https://openalex.org/W2166165736, https://openalex.org/W6886068009, https://openalex.org/W2784856930, https://openalex.org/W4240733927, https://openalex.org/W2950888176, https://openalex.org/W2522254802, https://openalex.org/W6923635780, https://openalex.org/W2041242327, https://openalex.org/W2187862109, https://openalex.org/W3081932258, https://openalex.org/W4382279505, https://openalex.org/W2523294909, https://openalex.org/W6942327501, https://openalex.org/W2891561649, https://openalex.org/W6942264124, https://openalex.org/W6923682818, https://openalex.org/W2487264755, https://openalex.org/W2138504084, https://openalex.org/W1990194058, https://openalex.org/W2508427873 |
| referenced_works_count | 64 |
| abstract_inverted_index.% | 189 |
| abstract_inverted_index.a | 42, 67, 129, 144, 175 |
| abstract_inverted_index.14 | 188 |
| abstract_inverted_index.In | 124 |
| abstract_inverted_index.We | 159 |
| abstract_inverted_index.an | 182 |
| abstract_inverted_index.be | 168, 203 |
| abstract_inverted_index.by | 26, 41, 142, 187 |
| abstract_inverted_index.do | 106 |
| abstract_inverted_index.in | 32, 64, 100, 103, 115, 120, 132, 135, 172, 184, 195 |
| abstract_inverted_index.is | 62, 89 |
| abstract_inverted_index.of | 2, 8, 79, 113, 146, 227 |
| abstract_inverted_index.or | 30, 111, 122 |
| abstract_inverted_index.to | 48, 86, 205, 215, 221 |
| abstract_inverted_index.we | 127 |
| abstract_inverted_index.CO2 | 9, 208 |
| abstract_inverted_index.The | 58, 199 |
| abstract_inverted_index.air | 5 |
| abstract_inverted_index.and | 21, 75, 84, 96, 154, 220 |
| abstract_inverted_index.are | 38, 52, 98 |
| abstract_inverted_index.can | 167, 201 |
| abstract_inverted_index.dry | 4 |
| abstract_inverted_index.fit | 47, 63 |
| abstract_inverted_index.for | 40, 109, 152, 156, 181 |
| abstract_inverted_index.how | 162 |
| abstract_inverted_index.not | 107 |
| abstract_inverted_index.one | 87, 151, 155 |
| abstract_inverted_index.set | 145 |
| abstract_inverted_index.the | 13, 33, 77, 93, 133, 139, 163, 192, 196 |
| abstract_inverted_index.XCO2 | 56 |
| abstract_inverted_index.bias | 60, 177 |
| abstract_inverted_index.data | 114 |
| abstract_inverted_index.from | 12, 210 |
| abstract_inverted_index.land | 153 |
| abstract_inverted_index.mole | 6 |
| abstract_inverted_index.over | 138 |
| abstract_inverted_index.that | 51, 88 |
| abstract_inverted_index.they | 105 |
| abstract_inverted_index.this | 125 |
| abstract_inverted_index.used | 171 |
| abstract_inverted_index.when | 170 |
| abstract_inverted_index.with | 55, 66, 174 |
| abstract_inverted_index.Space | 211 |
| abstract_inverted_index.While | 92 |
| abstract_inverted_index.XCO2) | 11 |
| abstract_inverted_index.allow | 108 |
| abstract_inverted_index.clear | 130 |
| abstract_inverted_index.error | 29, 73, 85, 136, 194 |
| abstract_inverted_index.model | 28, 46 |
| abstract_inverted_index.ocean | 157 |
| abstract_inverted_index.often | 24 |
| abstract_inverted_index.other | 222 |
| abstract_inverted_index.state | 82, 225 |
| abstract_inverted_index.these | 36 |
| abstract_inverted_index.using | 143 |
| abstract_inverted_index.which | 71, 116, 179 |
| abstract_inverted_index.while | 190 |
| abstract_inverted_index.(ACOS) | 212 |
| abstract_inverted_index.Carbon | 15 |
| abstract_inverted_index.OCO-3, | 219 |
| abstract_inverted_index.allows | 180 |
| abstract_inverted_index.become | 118 |
| abstract_inverted_index.biases | 37, 102, 117 |
| abstract_inverted_index.error. | 57 |
| abstract_inverted_index.errors | 20 |
| abstract_inverted_index.filter | 70, 97, 166 |
| abstract_inverted_index.future | 206 |
| abstract_inverted_index.highly | 53 |
| abstract_inverted_index.limits | 72 |
| abstract_inverted_index.linear | 44 |
| abstract_inverted_index.method | 200 |
| abstract_inverted_index.paper, | 126 |
| abstract_inverted_index.regime | 78 |
| abstract_inverted_index.tandem | 65 |
| abstract_inverted_index.(OCO-2) | 17 |
| abstract_inverted_index.(termed | 10 |
| abstract_inverted_index.OCO-2's | 216 |
| abstract_inverted_index.applied | 204 |
| abstract_inverted_index.between | 81 |
| abstract_inverted_index.biases, | 23 |
| abstract_inverted_index.contain | 18 |
| abstract_inverted_index.forward | 27 |
| abstract_inverted_index.further | 160 |
| abstract_inverted_index.induced | 25 |
| abstract_inverted_index.largely | 90 |
| abstract_inverted_index.linear. | 91 |
| abstract_inverted_index.machine | 148 |
| abstract_inverted_index.models, | 150 |
| abstract_inverted_index.quality | 69, 165 |
| abstract_inverted_index.readily | 202 |
| abstract_inverted_index.reduces | 76 |
| abstract_inverted_index.relaxed | 169 |
| abstract_inverted_index.Orbiting | 14 |
| abstract_inverted_index.fraction | 7 |
| abstract_inverted_index.increase | 183 |
| abstract_inverted_index.learning | 149 |
| abstract_inverted_index.multiple | 43 |
| abstract_inverted_index.reducing | 101 |
| abstract_inverted_index.residual | 193 |
| abstract_inverted_index.sounding | 185 |
| abstract_inverted_index.updates, | 214 |
| abstract_inverted_index.variance | 74, 137 |
| abstract_inverted_index.Abstract. | 0 |
| abstract_inverted_index.algorithm | 213 |
| abstract_inverted_index.companion | 217 |
| abstract_inverted_index.corrected | 39 |
| abstract_inverted_index.features. | 123 |
| abstract_inverted_index.interest. | 228 |
| abstract_inverted_index.nonlinear | 119, 147, 176 |
| abstract_inverted_index.reduction | 134 |
| abstract_inverted_index.retrieved | 223 |
| abstract_inverted_index.variables | 50, 83, 226 |
| abstract_inverted_index.correction | 61, 95, 112, 141 |
| abstract_inverted_index.correlated | 54 |
| abstract_inverted_index.hand-tuned | 68 |
| abstract_inverted_index.illustrate | 161 |
| abstract_inverted_index.instrument | 218 |
| abstract_inverted_index.predictors | 121 |
| abstract_inverted_index.regression | 45 |
| abstract_inverted_index.retrieval. | 34 |
| abstract_inverted_index.soundings. | 158 |
| abstract_inverted_index.successful | 99 |
| abstract_inverted_index.systematic | 19 |
| abstract_inverted_index.throughput | 110, 186 |
| abstract_inverted_index.Atmospheric | 207 |
| abstract_inverted_index.atmospheric | 224 |
| abstract_inverted_index.conjunction | 173 |
| abstract_inverted_index.correction, | 178 |
| abstract_inverted_index.correction. | 198 |
| abstract_inverted_index.demonstrate | 128 |
| abstract_inverted_index.improvement | 131 |
| abstract_inverted_index.interaction | 80 |
| abstract_inverted_index.maintaining | 191 |
| abstract_inverted_index.operational | 59, 94, 140, 164, 197 |
| abstract_inverted_index.retrievals, | 104 |
| abstract_inverted_index.Measurements | 1 |
| abstract_inverted_index.Observations | 209 |
| abstract_inverted_index.co-retrieved | 49 |
| abstract_inverted_index.nonlinearity | 31 |
| abstract_inverted_index.Observatory-2 | 16 |
| abstract_inverted_index.Operationally, | 35 |
| abstract_inverted_index.regional-scale | 22 |
| abstract_inverted_index.column-averaged | 3 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/14 |
| sustainable_development_goals[0].score | 0.8700000047683716 |
| sustainable_development_goals[0].display_name | Life below water |
| citation_normalized_percentile.value | 0.86893993 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |