Non-Gaussian data assimilation of satellite-based leaf area index observations with an individual-based dynamic global vegetation model
- 1RIKEN Advanced Institute for Computational Science, Kobe, 650-0047, Japan
- 2Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20742, USA
- 3Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, 236-0001, Japan
- 4Field Science Education and Research Center, Kyoto University, Kyoto, 606-8502, Japan
- 5Graduate School of Simulation Studies, University of Hyogo, Kobe, 650-0047, Japan
Abstract. We developed a data assimilation system based on a particle filter approach with the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM). We first performed an idealized observing system simulation experiment to evaluate the impact of assimilating the leaf area index (LAI) data every 4 days, simulating the satellite-based LAI. Although we assimilated only LAI as a whole, the tree and grass LAIs were estimated separately with high accuracy. Uncertain model parameters and other state variables were also estimated accurately. Therefore, we extended the experiment to the real world using the real Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data and obtained promising results.