Articles | Volume 18, issue 2
https://doi.org/10.5194/bg-18-509-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/bg-18-509-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assimilating synthetic Biogeochemical-Argo and ocean colour observations into a global ocean model to inform observing system design
David Ford
CORRESPONDING AUTHOR
Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
Viewed
Total article views: 3,652 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 May 2020)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
2,742 | 851 | 59 | 3,652 | 66 | 65 |
- HTML: 2,742
- PDF: 851
- XML: 59
- Total: 3,652
- BibTeX: 66
- EndNote: 65
Total article views: 3,096 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 21 Jan 2021)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
2,472 | 573 | 51 | 3,096 | 52 | 54 |
- HTML: 2,472
- PDF: 573
- XML: 51
- Total: 3,096
- BibTeX: 52
- EndNote: 54
Total article views: 556 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 May 2020)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
270 | 278 | 8 | 556 | 14 | 11 |
- HTML: 270
- PDF: 278
- XML: 8
- Total: 556
- BibTeX: 14
- EndNote: 11
Viewed (geographical distribution)
Total article views: 3,652 (including HTML, PDF, and XML)
Thereof 3,298 with geography defined
and 354 with unknown origin.
Total article views: 3,096 (including HTML, PDF, and XML)
Thereof 2,855 with geography defined
and 241 with unknown origin.
Total article views: 556 (including HTML, PDF, and XML)
Thereof 443 with geography defined
and 113 with unknown origin.
Country | # | Views | % |
---|
Country | # | Views | % |
---|
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Cited
19 citations as recorded by crossref.
- Towards a Multi‐Platform Assimilative System for North Sea Biogeochemistry J. Skákala et al. 10.1029/2020JC016649
- Determination of discrete sampling locations minimizing both the number of samples and the maximum interpolation error: Application to measurements of carbonate chemistry in surface ocean V. Guglielmi et al. 10.1016/j.seares.2023.102336
- An observing system simulation experiment for Indian Ocean surface pCO2 measurements V. Valsala et al. 10.1016/j.pocean.2021.102570
- LIGHT-bgcArgo-1.0: using synthetic float capabilities in E3SMv2 to assess spatiotemporal variability in ocean physics and biogeochemistry C. Nissen et al. 10.5194/gmd-17-6415-2024
- Delayed-Mode Quality Control of Oxygen, Nitrate, and pH Data on SOCCOM Biogeochemical Profiling Floats T. Maurer et al. 10.3389/fmars.2021.683207
- Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design A. Mignot et al. 10.5194/bg-20-1405-2023
- The impact of ocean biogeochemistry on physics and its consequences for modelling shelf seas J. Skákala et al. 10.1016/j.ocemod.2022.101976
- Investigating ecosystem connections in the shelf sea environment using complex networks I. Higgs et al. 10.5194/bg-21-731-2024
- The Southern Ocean carbon and climate observations and modeling (SOCCOM) project: A review J. Sarmiento et al. 10.1016/j.pocean.2023.103130
- EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters J. Bruggeman et al. 10.5194/gmd-17-5619-2024
- Combining neural networks and data assimilation to enhance the spatial impact of Argo floats in the Copernicus Mediterranean biogeochemical model C. Amadio et al. 10.5194/os-20-689-2024
- PPCon 1.0: Biogeochemical-Argo profile prediction with 1D convolutional networks G. Pietropolli et al. 10.5194/gmd-17-7347-2024
- A solution for autonomous, adaptive monitoring of coastal ocean ecosystems: Integrating ocean robots and operational forecasts D. Ford et al. 10.3389/fmars.2022.1067174
- How uncertain and observable are marine ecosystem indicators in shelf seas? J. Skákala et al. 10.1016/j.pocean.2024.103249
- Deep chlorophyll maximum and nutricline in the Mediterranean Sea: emerging properties from a multi-platform assimilated biogeochemical model experiment A. Teruzzi et al. 10.5194/bg-18-6147-2021
- Mechanisms driving ESM-based marine ecosystem predictive skill on the east African coast W. Jeon et al. 10.1088/1748-9326/ac7d63
- Can assimilation of satellite observations improve subsurface biological properties in a numerical model? A case study for the Gulf of Mexico B. Wang et al. 10.5194/os-17-1141-2021
- Modelling considerations for research on ocean alkalinity enhancement (OAE) K. Fennel et al. 10.5194/sp-2-oae2023-9-2023
- Ocean biogeochemical modelling K. Fennel et al. 10.1038/s43586-022-00154-2
19 citations as recorded by crossref.
- Towards a Multi‐Platform Assimilative System for North Sea Biogeochemistry J. Skákala et al. 10.1029/2020JC016649
- Determination of discrete sampling locations minimizing both the number of samples and the maximum interpolation error: Application to measurements of carbonate chemistry in surface ocean V. Guglielmi et al. 10.1016/j.seares.2023.102336
- An observing system simulation experiment for Indian Ocean surface pCO2 measurements V. Valsala et al. 10.1016/j.pocean.2021.102570
- LIGHT-bgcArgo-1.0: using synthetic float capabilities in E3SMv2 to assess spatiotemporal variability in ocean physics and biogeochemistry C. Nissen et al. 10.5194/gmd-17-6415-2024
- Delayed-Mode Quality Control of Oxygen, Nitrate, and pH Data on SOCCOM Biogeochemical Profiling Floats T. Maurer et al. 10.3389/fmars.2021.683207
- Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design A. Mignot et al. 10.5194/bg-20-1405-2023
- The impact of ocean biogeochemistry on physics and its consequences for modelling shelf seas J. Skákala et al. 10.1016/j.ocemod.2022.101976
- Investigating ecosystem connections in the shelf sea environment using complex networks I. Higgs et al. 10.5194/bg-21-731-2024
- The Southern Ocean carbon and climate observations and modeling (SOCCOM) project: A review J. Sarmiento et al. 10.1016/j.pocean.2023.103130
- EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters J. Bruggeman et al. 10.5194/gmd-17-5619-2024
- Combining neural networks and data assimilation to enhance the spatial impact of Argo floats in the Copernicus Mediterranean biogeochemical model C. Amadio et al. 10.5194/os-20-689-2024
- PPCon 1.0: Biogeochemical-Argo profile prediction with 1D convolutional networks G. Pietropolli et al. 10.5194/gmd-17-7347-2024
- A solution for autonomous, adaptive monitoring of coastal ocean ecosystems: Integrating ocean robots and operational forecasts D. Ford et al. 10.3389/fmars.2022.1067174
- How uncertain and observable are marine ecosystem indicators in shelf seas? J. Skákala et al. 10.1016/j.pocean.2024.103249
- Deep chlorophyll maximum and nutricline in the Mediterranean Sea: emerging properties from a multi-platform assimilated biogeochemical model experiment A. Teruzzi et al. 10.5194/bg-18-6147-2021
- Mechanisms driving ESM-based marine ecosystem predictive skill on the east African coast W. Jeon et al. 10.1088/1748-9326/ac7d63
- Can assimilation of satellite observations improve subsurface biological properties in a numerical model? A case study for the Gulf of Mexico B. Wang et al. 10.5194/os-17-1141-2021
- Modelling considerations for research on ocean alkalinity enhancement (OAE) K. Fennel et al. 10.5194/sp-2-oae2023-9-2023
- Ocean biogeochemical modelling K. Fennel et al. 10.1038/s43586-022-00154-2
Latest update: 21 Feb 2025
Short summary
Biogeochemical-Argo floats are starting to routinely measure ocean chlorophyll, nutrients, oxygen, and pH. This study generated synthetic observations representing two potential Biogeochemical-Argo observing system designs and created a data assimilation scheme to combine them with an ocean model. The proposed system of 1000 floats brought clear benefits to model results, with additional floats giving further benefit. Existing satellite ocean colour observations gave complementary information.
Biogeochemical-Argo floats are starting to routinely measure ocean chlorophyll, nutrients,...
Altmetrics
Final-revised paper
Preprint