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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2018-232
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2018-232
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 03 Jan 2019

Development and technical paper | 03 Jan 2019

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This discussion paper is a preprint. A revision of the manuscript is under review for the journal Geoscientific Model Development (GMD).

Comparison of Different Sequential Assimilation Algorithms for Satellite-derived Leaf Area Index Using the Data Assimilation Research Testbed (lanai)

Xiao-Lu Ling1,2,3, Cong-Bin Fu1,2, Zong-Liang Yang3, and Wei-Dong Guo1,2 Xiao-Lu Ling et al.
  • 1Institute for Climate and Global Change Research & School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
  • 2Joint International Research Laboratory of Atmospheric and Earth System Sciences of Ministry of Education, Nanjing 210023, China
  • 3Department of Geological Sciences, John A. and Katherine G. Jackson School of Geosciences, University of Texas at Austin, Austin, TX 78705, USA

Abstract. The leaf area index (LAI) is a crucial parameter for understanding the exchanges of momentum, carbon, energy, and water between terrestrial ecosystems and the atmosphere. To improve the ability to simulate land surface water and energy balances, the Data Assimilation Research Testbed (DART) has been successfully coupled to the Community Land Model (CLM) by assimilating global remotely sensed LAI data with explicit carbon and nitrogen components (CLM4CN). The purpose of this paper is to determine the best algorithm for LAI assimilation. Within this framework, four sequential assimilation algorithms, i.e., the Kalman Filter (KF), the Ensemble Kalman Filter (EnKF), the Ensemble Adjust Kalman Filter (EAKF), and the Particle Filter (PF), are applied, thoroughly analyzed and compared. The results show that assimilating remotely sensed LAI data into the CLM4CN is an effective method for improving model performance. In detail, the assimilation accuracies of the ensemble filter algorithms (EnKF and EAKF) are better than that of the KF algorithm because the KF is based on the linear model error assumption. The PF algorithm performs worse than the EAKF and EnKF algorithms because of the gradually reduced acceptance of observations with assimilation steps. In other words, the contribution of the observations to the posterior probability during the assimilation process is reduced. The EAKF algorithm is the best method because the matrix is adjusted at each time step during the assimilation procedure.

Xiao-Lu Ling et al.
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Latest update: 23 May 2019
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Short summary
Both observation and simulation can provide the temporal and spatial variation of vegetation characteristic, 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.The results show that the Ensemble Adjust Kalman Filter (EAKF) is the optical algorithm. In addition, models perform better when the DA accept more proportion of observation.
Both observation and simulation can provide the temporal and spatial variation of vegetation...
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