Articles | Volume 19, issue 22
https://doi.org/10.5194/acp-19-13911-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-19-13911-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
19 Nov 2019
Review article |
| 19 Nov 2019
| 19 Nov 2019
Fundamentals of data assimilation applied to biogeochemistry
School of Earth Sciences, University of Melbourne,
Melbourne, Australia
Anna M. Michalak
Department of Global Ecology, Carnegie Institution for Science,
Stanford, USA
Frédéric Chevallier
Laboratoire des Sciences du Climat et de l'Environnement,
Gif-sur-Yvette, France
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- The potential of Orbiting Carbon Observatory-2 data to reduce the uncertainties in CO<sub>2</sub> surface fluxes over Australia using a variational assimilation scheme Y. Villalobos et al. 10.5194/acp-20-8473-2020
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- Urban-scale variational flux inversion for CO Using TROPOMI total-column retrievals: A case study of Tehran N. Shahrokhi et al. 10.1016/j.atmosenv.2023.120009
- Regional CO<sub>2</sub> inversions with LUMIA, the Lund University Modular Inversion Algorithm, v1.0 G. Monteil & M. Scholze 10.5194/gmd-14-3383-2021
- CO anthropogenic emissions in Europe from 2011 to 2021: insights from Measurement of Pollution in the Troposphere (MOPITT) satellite data A. Fortems-Cheiney et al. 10.5194/acp-24-4635-2024
- National CO2budgets (2015–2020) inferred from atmospheric CO2observations in support of the global stocktake B. Byrne et al. 10.5194/essd-15-963-2023
- The consolidated European synthesis of CO2 emissions and removals for the European Union and United Kingdom: 1990–2018 A. Petrescu et al. 10.5194/essd-13-2363-2021
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- The CO2 Human Emissions (CHE) Project: First Steps Towards a European Operational Capacity to Monitor Anthropogenic CO2 Emissions G. Balsamo et al. 10.3389/frsen.2021.707247
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- High-resolution modeling of gaseous air pollutants over Tehran and validation with surface and satellite data N. Shahrokhishahraki et al. 10.1016/j.atmosenv.2021.118881
- The regional European atmospheric transport inversion comparison, EUROCOM: first results on European-wide terrestrial carbon fluxes for the period 2006–2015 G. Monteil et al. 10.5194/acp-20-12063-2020
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- The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies A. Berchet et al. 10.5194/gmd-14-5331-2021
- Carbon Flux Variability From a Relatively Simple Ecosystem Model With Assimilated Data Is Consistent With Terrestrial Biosphere Model Estimates G. Quetin et al. 10.1029/2019MS001889
- Evaluation of light atmospheric plume inversion methods using synthetic XCO2 satellite images to compute Paris CO2 emissions A. Danjou et al. 10.1016/j.rse.2023.113900
- 3D modeling of generalized Newtonian fluid flow with data assimilation using the least-squares finite element method S. Averweg et al. 10.1016/j.cma.2022.114668
- Anthropogenic NOx Emission Estimations over East China for 2015 and 2019 Using OMI Satellite Observations and the New Inverse Modeling System CIF-CHIMERE D. Savas et al. 10.3390/atmos14010154
- Assessing the Impact of Atmospheric CO2 and NO2 Measurements From Space on Estimating City-Scale Fossil Fuel CO2 Emissions in a Data Assimilation System T. Kaminski et al. 10.3389/frsen.2022.887456
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- Improved process representation of leaf phenology significantly shifts climate sensitivity of ecosystem carbon balance A. Norton et al. 10.5194/bg-20-2455-2023
- Data assimilation using an ensemble of models: a hierarchical approach P. Rayner 10.5194/acp-20-3725-2020
Latest update: 23 Nov 2024
Short summary
This paper describes the methods for combining models and data to understand how nutrients and pollutants move through natural systems. The methods are analogous to the process of weather forecasting in which previous information is combined with new observations and a model to improve our knowledge of the internal state of the physical system. The methods appear highly diverse but the paper shows that they are all examples of a single underlying formalism.
This paper describes the methods for combining models and data to understand how nutrients and...
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