Non-Gaussian data assimilation of satellite-based leaf area index observations with an individual-based dynamic global vegetation model

Abstract

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.

Regional-Scale Data Assimilation with The Spatially Explicit Individual-Based Dynamic Global Vegetation Model (SEIB-DGVM) Over Siberia

“…The SEIB-DGVM This study used a particle lter-based DA system with the SEIB-DGVM (Arakida et al 2017). Refer to Arakida et al (2017) for detailed descriptions.…”

“…For mortality, establishment, and some of the adjustments of crown states, the model time step was a year. We used model version 2.71 (Sato and Ise, 2012) with modi cations described by Arakida et al (2017). In addition, we corrected coding bugs and modi ed some parameters for the experiment presented here (Table 1).…”

“…Individual-based dynamic global vegetation models (DGVMs) simulate vertical forest structure explicitly; undergrowth is simulated at the forest oor separately from overstory. Arakida et al (2017) developed a DA system with the Spatially Explicit Individual-based DGVM (SEIB-DGVM; Sato et al 2007) for the rst time; it utilizes the satellite-observed LAI effectively and estimates overstory phenology separately from undergrowth phenology.…”

“…This study also explores how assimilating LAI improves those unobserved variables at a regional scale. Therefore, in this study, we extended the previous experiment of Arakida et al (2017) to examine the performance of the DA system in estimating tree LAI, plant functional type (PFT) speci c model parameters, and other state variables across Siberia.…”

Regional-Scale Data Assimilation of a Terrestrial Ecosystem Model: Leaf Phenology Parameters Are Dependent on Local Climatic Conditions

“…To this end, data assimilation (DA), which incorporates observation data into models in a systematic manner, has been applied to phenology model in the terrestrial ecosystem models, and uncertainties in the state variables and model parameters have been partially reduced (Viskari et al, 2015;Arakida et al, 2017). In recent years, DA has been used in many fields of ecological research (reviewed by Luo et al, 2011), including palaeology (Peng et al, 2011), ecosystem ecology (Xiao and Friedrichs, 2014;Arakida et al, 2017) and community ecology (Massoud et al, 2018).…”