Articles | Volume 12, issue 7
https://doi.org/10.5194/gmd-12-3119-2019
https://doi.org/10.5194/gmd-12-3119-2019
Development and technical paper
 | 
22 Jul 2019
Development and technical paper |  | 22 Jul 2019

Comparison of different sequential assimilation algorithms for satellite-derived leaf area index using the Data Assimilation Research Testbed (version Lanai)

Xiao-Lu Ling, Cong-Bin Fu, Zong-Liang Yang, and Wei-Dong Guo

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Cited articles

Albergel, C., Munier, S., Leroux, D. J., Dewaele, H., Fairbairn, D., Barbu, A. L., Gelati, E., Dorigo, W., Faroux, S., Meurey, C., Moigne, P. L., Decharme, B., Mahfouf, J. F., and Calvet, J. C.: Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area, Geosci. Model Develop., 10, 3889–3912, https://doi.org/10.5194/gmd-10-3889-2017, 2017. 
Anderson, J. L.: An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2, 2001. 
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Anderson, J. L.: An adaptive covariance inflation error correction algorithm for ensemble filters, Tellus, 59, 210–224, https://doi.org/10.1111/j.1600-0870.2006.00216.x, 2007. 
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, https://doi.org/10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2, 1999. 
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Short summary
Observation and simulation can provide the temporal and spatial variation of vegetation characteristics, while they are not satisfactory for understanding the mechanism of the exchange between ecosystems and atmosphere. Data assimilation (DA) can combine the observation and models via mathematical statistical analysis. Results show that the ensemble adjust Kalman filter (EAKF) is the optimal algorithm. In addition, models perform better when the DA accepts a higher proportion of observations.