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. The leaf area index (LAI) is a crucial parameter forunderstanding the exchanges of mass and energy between terrestrialecosystems and the atmosphere. In this study, the Data Assimilation ResearchTestbed (DART) has been successfully coupled to the Community Land Modelwith explicit carbon and nitrogen components (CLM4CN) by assimilating GlobalLand Surface Satellite (GLASS) LAI data. Within this framework, foursequential assimilation algorithms, including the kernel filter (KF), theensemble Kalman filter (EnKF), the ensemble adjust Kalman filter (EAKF), andthe particle filter (PF), are thoroughly analyzed and compared. The resultsshow that assimilating GLASS LAI into the CLM4CN is an effective method forimproving model performance. In detail, the assimilation accuracies of theEnKF and EAKF algorithms are better than those of the KF and PF algorithm.From the perspective of the average and RMSD, the PF algorithm performs worsethan the EAKF and EnKF algorithms because of the gradually reducedacceptance of observations with assimilation steps. In other words, thecontribution of the observations to the posterior probability during theassimilation process is reduced. The EAKF algorithm is the best methodbecause the matrix is adjusted at each time step during the assimilationprocedure. If all the observations are accepted, the analyzed LAI seem to bebetter than that when some observations are rejected, especially inlow-latitude regions.

geoscientific model development

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