Data assimilation experiments with MODIS LAI observations and the dynamic global vegetation model SEIB-DGVM over Siberia
In the previous study, Arakida et al.  developed a data assimilation system based on a particle filter approach with a dynamical global vegetation model known as the SEIB-DGVM, and assimilated the satellite-based MODIS LAI observations successfully. We extend the previous study to a large domain in Siberia and estimate the state variables including carbon flux, water flux, heat flux, vegetation structure, and parameters related to the phenology of the deciduous needle leaved tree and grass. The initial perturbation of the parameters produced much larger LAI than the observed LAI. DA reduced the LAI greatly by optimizing the parameters and made the estimated LAI very close to the observation. This suggests that the DA system work properly at the large domain. Corresponding to the reduction of LAI, the estimated vegetation functions and structures are also reduced greatly. As a result, most of the estimated variables are highly correlated to the observed LAI. This study suggested the potential of the DA system to estimate the vegetation structure as well as vegetation functions in a large area.
Arakida et al. (2017), Non-Gaussian data assimilation of satellite-based leaf area index observations with an individual-based dynamic global vegetation model, Nonlinear Proc. Geoph., 24, 553-567.
Dr. Hazuki Arakida RIKEN