Abstract
Abstract. The leaf area index (LAI) is a crucial parameter for understanding the exchanges of mass and energy between terrestrial ecosystems and the atmosphere. In this study, the Data Assimilation Research Testbed (DART) has been successfully coupled to the Community Land Model with explicit carbon and nitrogen components (CLM4CN) by assimilating Global Land Surface Satellite (GLASS) LAI data. Within this framework, four sequential assimilation algorithms, including the kernel filter (KF), the ensemble Kalman filter (EnKF), the ensemble adjust Kalman filter (EAKF), and the particle filter (PF), are thoroughly analyzed and compared. The results show that assimilating GLASS LAI into the CLM4CN is an effective method for improving model performance. In detail, the assimilation accuracies of the EnKF 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 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. If all the observations are accepted, the analyzed LAI seem to be better than that when some observations are rejected, especially in low-latitude regions.
Highlights
Land surface processes play an important role in the earth system because all the physical, biochemical, and ecological processes occurring in the soil, vegetation, and hydrosphere influence the mass and energy exchanges during land– atmosphere interactions (Bonan, 1995; Pitman, 2003; Pitman et al, 2009, 2012)
The assimilation with the ensemble adjust Kalman filter (EAKF) and ensemble Kalman filter (EnKF) algorithms displays a lower bias than the kernel filter (KF) and particle filter (PF) algorithms compared to GEOV2 leaf area index (LAI), especially in the northern and eastern Amazon, central Africa, southern Eurasia, and Southeast Asia
The results indicate that the EAKF and EnKF assimilation algorithms are better than the KF and PF algorithms in November
Summary
Land surface processes play an important role in the earth system because all the physical, biochemical, and ecological processes occurring in the soil, vegetation, and hydrosphere influence the mass and energy exchanges during land– atmosphere interactions (Bonan, 1995; Pitman, 2003; Pitman et al, 2009, 2012). The maximum likelihood ensemble filter (MLEF, Zupanski, 2005), the combination of 3DVAR and PF algorithms (Leng and Song, 2013), and the hybrid variational-ensemble data assimilation methods, i.e., the 4DEnKF (Hunt et al, 2004; Fertig et al, 2007; Zhang et al, 2009) and the DrEnKF (Wan et al, 2009), have been developed at NCEP and applied to improve model predictions (Whitaker et al, 2008). The ability to simulate river discharge, land evapotranspiration, and gross primary production has been improved in Europe (Barbu et al, 2011; Albergel et al, 2017) To date, such studies have been conducted using a single sequential algorithm at a single site or on regional scales (Montzka et al, 2012; Sawada, 2018).
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