Assimilation of Soil Wetness Index and Leaf Area Index into the ISBA-A-gs land surface model: grassland case study

Abstract

Abstract. The performance of the joint assimilation in a land surface model of a Soil Wetness Index (SWI) product provided by an exponential filter together with Leaf Area Index (LAI) is investigated. The data assimilation is evaluated with different setups using the SURFEX modeling platform, for a period of seven years (2001)(2002)(2003)(2004)(2005)(2006)(2007), at the SMOSREX grassland site in southwestern France. The results obtained with a Simplified Extended Kalman Filter demonstrate the effectiveness of a joint data assimilation scheme when both SWI and Leaf Area Index are merged into the ISBA-A-gs land surface model. The assimilation of a retrieved Soil Wetness Index product presents several challenges that are investigated in this study. A significant improvement of around 13 % of the root-zone soil water content is obtained by assimilating dimensionless root-zone SWI data. For comparison, the assimilation of in situ surface soil moisture is considered as well. A lower impact on the root zone is noticed. Under specific conditions, the transfer of the information from the surface to the root zone was found not accurate. Also, our results indicate that the assimilation of in situ LAI data may correct a number of deficiencies in the model, such as low LAI values in the senescence phase by using a seasonal-dependent error definition for background and observations. In order to verify the specification of the errors for SWI and LAI products, a posteriori diagnostics are employed. This approach highlights the importance of the assimilation design on the quality of the analysis. The impact of data assimilation scheme on CO 2 fluxes is also quantified by using measurements of net CO 2 fluxes gathered at the SMOSREX site from 2005 to 2007. An improvement of about 5 % in terms of rms error is obtained.

Modelling LAI, surface water and carbon fluxes at high-resolution over France: comparison of ISBA-A-gs and ORCHIDEE

“…As they will be available in near-real-time, they will permit a continuous quality control of model-based monitoring systems. Ultimately, they could be assimilated in land surface models, as shown by Barbu et al (2011).…”

“…In order to use this information into LSMs, data assimilation techniques were implemented and tested (Demarty et al, 2007;Sabater et al, 2008;Albergel et al, 2010a;Barbu et al, 2011). Data assimilation techniques aim at improving the model simulations, which are affected by uncertainties, mainly caused by the lack of knowledge of some biophysical processes, together with errors in the atmospheric variables used as input to the models (Szczypta et al, 2011;Zhao et al, 2011).…”

Sensitivity of the Weather Research and Forecast/Community Multiscale Air Quality modeling system to MODIS LAI, FPAR, and albedo

“…Their results show dramatic improvement in land surface temperature simulation both spatially and temporally for most of the year. Other studies [Buermann et al, 2001;Masson et al, 2003;Rodell et al, 2004;Baker et al, 2010;Lawrence et al, 2011;Barbu et al, 2011] have also used satellite-derived LAI and vegetation index in LSMs for global and mesoscale atmospheric modeling. However, for operational retrospective meteorology and air quality WRF/CMAQ simulations LAI, VF, and albedo along with other surface and soil parameters are still specified in the LSM tables with some simple seasonal adjustments [Pleim and Xiu, 1995;Chen and Dudhia, 2001;Walko et al, 2000;Xiu and Pleim, 2001].…”

Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France

“…The possibility of combining these two data streams within such models has been explored in several data assimilation applications either by setting observing system simulation experiments (Pauwels et al, 2007;Nearing et al, 2012) or by monitoring real environments (Sabater et al, 2008;Barbu et al, 2011).…”