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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 24, issue 3
Nonlin. Processes Geophys., 24, 553–567, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 24, 553–567, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 06 Sep 2017

Research article | 06 Sep 2017

Non-Gaussian data assimilation of satellite-based leaf area index observations with an individual-based dynamic global vegetation model

Hazuki Arakida et al.

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Cited articles

Anderson, J. L. and Anderson, S. L.: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts, Mon. Weather Rev., 127, 2741–2758, 1999.
Atlas, R.: Atmospheric observations and experiments to assess their usefulness in data assimilation, J. Meteorol. Soc. Jpn., 75, 111–130, 1997.
Bickel, P., Li, B., and Bengtsson, T.: Sharp failure rates for the bootstrap particle filter in high dimensions, in: Pushing the Limits of Contemporary Statistics: Contributions in Honor of Jayanta K. Ghosh, IMS Collections, 3, edited by: Clarke, B. and Ghosal, S., Institute of Mathematical Statistics, Beachwood, Ohio, USA, 318–329, 2008.
Cheaib, A., Badeau, V., Boe, J., Chuine, I., Delire, C., Dufrêne, E., François, C., Gritti, E. S., Legay, M., Pagé, C., Thuiller, W., Viovy, N., and Leadley, P.: Climate change impacts on tree ranges: model intercomparison facilitates understanding and quantification of uncertainty, Ecol. Lett., 15, 533–544, 2012.
Demarty, J., Chevallier, F., Friend, A. D., Viovy, N., Piao, S., and Ciais, P.: Assimilation of global MODIS leaf area index retrievals within a terrestrial biosphere model, Geophys. Res. Lett., 34, L15402,, 2007.
Publications Copernicus
Short summary
This is the first study assimilating the satellite-based leaf area index observations every 4 days into a numerical model simulating the growth and death of individual plants. The newly developed data assimilation system successfully reduced the uncertainties of the model parameters related to phenology and carbon dynamics. It also provides better estimates of the present vegetation structure which can be used as the initial states for the simulation of the future vegetation change.
This is the first study assimilating the satellite-based leaf area index observations every 4...