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ACP | Articles | Volume 19, issue 22
Atmos. Chem. Phys., 19, 13911–13932, 2019
https://doi.org/10.5194/acp-19-13911-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: Data assimilation in carbon/biogeochemical cycles: consistent...

Atmos. Chem. Phys., 19, 13911–13932, 2019
https://doi.org/10.5194/acp-19-13911-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Review article 19 Nov 2019

Review article | 19 Nov 2019

Fundamentals of data assimilation applied to biogeochemistry

Peter J. Rayner et al.
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Ades, M. and van Leeuwen, P. J.: The equivalent-weights particle filter in a high-dimensional system, Q. J. Roy. Meteor. Soc., 141, 484–503, https://doi.org/10.1002/qj.2370, 2015. a
Backus, G. and Gilbert, F.: The resolving power of gross earth data, Geophys. J. Roy. Astr. S., 13, 247–276, 1968. a
Bacour, C., Peylin, P., MacBean, N., Rayner, P. J., Delage, F., Chevallier, F., Weiss, M., Demarty, J., Santaren, D., Baret, F., and Prunet, P.: Joint assimilation of eddy-covariance flux measurements and FAPAR products over temperate forests within a process-oriented biosphere model, J. Geophys. Res., 120, 1839–1857, https://doi.org/10.1002/2015JG002966, 2015. a, b
Baker, D. F., Law, R. M., Gurney, K. R., Rayner, P., Peylin, P., Denning, A. S., Bousquet, P., Bruhwiler, L., Chen, Y.-H., Ciais, P., Fung, I. Y., Heimann, M., John, J., Maki, T., Maksyutov, S., Masarie, K., Prather, M., Pak, B., Taguchi, S., and Zhu, Z.: TransCom 3 inversion intercomparison: Impact of transport model errors on the interannual variability of regional CO2 fluxes, 1988–2003, Global Biogeochem. Cy., 20, GB1002, https://doi.org/10.1029/2004GB002439, 2006. a
Balkanski, Y., Monfray, P., Battle, M., and Heimann, M.: Ocean primary production derived from satellite data: An evaluation with atmospheric oxygen measurements, Global Biogeochem. Cy., 13, 257–271, 1999. a
Publications Copernicus
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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