 |
| Titel |
Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France |
| VerfasserIn |
A. L. Barbu, J.-C. Calvet, J.-F. Mahfouf, S. Lafont |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 18, no. 1 ; Nr. 18, no. 1 (2014-01-14), S.173-192 |
| Datensatznummer |
250120251
|
| Publikation (Nr.) |
 copernicus.org/hess-18-173-2014.pdf |
|
|
|
|
|
| Zusammenfassung |
The land monitoring service of the European Copernicus programme has
developed a set of satellite-based 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 satellite-based 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
(2008–2011). 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. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|