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

Abstract

Abstract. The land monitoring service of the EuropeanCopernicus programme has developed a set of satellitebased biogeophysical products, including surface soil moisture (SSM) and leaf area index (LAI). This study investigates the impact of joint assimilation of remotely sensed SSM derived from Advanced Scatterometer (ASCAT) backscatter data and the Copernicus Global Land GEOV1 satellitebased LAI product into the the vegetation growth version of the Interactions between Soil Biosphere Atmosphere (ISBA-A-gs) land surface model within the the externalised surface model (SURFEX) modelling platform of Météo-France. The ASCAT data were bias corrected with respect to the model climatology by using a seasonal-based CDF (Cumulative Distribution Function) matching technique. A multivariate multi-scale land data assimilation system (LDAS) based on the extended Kalman Filter (EKF) is used for monitoring the soil moisture, terrestrial vegetation, surface carbon and energy fluxes across the domain of France at a spatial resolution of 8 km. Each model grid box is divided into a number of land covers, each having its own set of prognostic variables. The filter algorithm is designed to provide a distinct analysis for each land cover while using one observation per grid box. The updated values are aggregated by computing a weighted average.In this study, it is demonstrated that the assimilation scheme works effectively within the ISBA-A-gs model over a four-year period (2008)(2009)(2010)(2011). The EKF is able to extract useful information from the data signal at the grid scale and distribute the root-zone soil moisture and LAI increments throughout the mosaic structure of the model. The impact of the assimilation on the vegetation phenology and on the water and carbon fluxes varies from one season to another. The spring drought of 2011 is an interesting case study of the potential of the assimilation to improve drought monitoring. A comparison between simulated and in situ soil moisture gathered at the twelve SMOSMANIA (Soil Moisture Observing System-Meteorological Automatic Network Integrated Application) stations shows improved anomaly correlations for eight stations.

SMOS near-real-time soil moisture product: processor overview and first validation results

“…Meteorological agencies such as the European Centre for Medium-Range Weather Forecasts (ECMWF) and the United Kingdom Met Office assimilate ASCAT surface SM into their operational numerical weather prediction models Dharssi et al, 2011). The approach has also been tested in offline mode at Meteo France (Barbu et al, 2014). To be useful for operational applications, remotely sensed data should be available in near-real-time (typically less than 3-4 h after sensing, hereafter NRT).…”

Assimilation of surface soil moisture into a multilayer soil model: design and evaluation at local scale

“…Several authors have performed CDF matching over the entire period of interest (Reichle and Koster, 2004), whereas others have used seasonal-based CDF matching (Barbu et al, 2014;Draper et al, 2009). As shown in Sect.…”

Observation de la végétation depuis l'espace

“…Des recherches ont été menées af in d'évaluer l'impact de l'intégration de données satellitaires dans Surfex sur le suivi des sécheresses. Ainsi, un système d'assimilation de données a été mis en place (Barbu et al, 2014). Il permet de simuler la biomasse et le LAI des céréales à paille et des prairies naturelles sur la France.…”