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Joaquín Muñoz Sabater, Lionel Jarlan, Jean-Christophe Calvet, François Bouyssel, and Patricia De Rosnay


Root-zone soil moisture constitutes an important variable for hydrological and weather forecast models. Microwave radiometers like the L-band instrument on board the European Space Agency’s (ESA) future Soil Moisture and Ocean Salinity (SMOS) mission are being designed to provide estimates of near-surface soil moisture (0–5 cm). This quantity is physically related to root-zone soil moisture through diffusion processes, and both surface and root-zone soil layers are commonly simulated by land surface models (LSMs). Observed time series of surface soil moisture may be used to analyze the root-zone soil moisture using data assimilation systems. In this paper, various assimilation techniques derived from Kalman filters (KFs) and variational methods (VAR) are implemented and tested. The objective is to correct the modeled root-zone soil moisture deficiencies of the newest version of the Interaction between Soil, Biosphere, and Atmosphere scheme (ISBA) LSM, using the observations of the surface soil moisture of the Surface Monitoring of the Soil Reservoir Experiment (SMOSREX) over a 4-yr period (2001–04). This time period includes contrasting climatic conditions. Among the different algorithms, the ensemble Kalman filter (EnKF) and a simplified one-dimensional variational data assimilation (1DVAR) show the best performances. The lower computational cost of the 1DVAR is an advantage for operational root-zone soil moisture analysis based on remotely sensed surface soil moisture observations at a global scale.

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Aaron Boone, Patricia de Rosnay, Gianpaolo Balsamo, Anton Beljaars, Franck Chopin, Bertrand Decharme, Christine Delire, Agnes Ducharne, Simon Gascoin, Manuela Grippa, Françoise Guichard, Yeugeniy Gusev, Phil Harris, Lionel Jarlan, Laurent Kergoat, Eric Mougin, Olga Nasonova, Anette Norgaard, Tristan Orgeval, Catherine Ottlé, Isabelle Poccard-Leclercq, Jan Polcher, Inge Sandholt, Stephane Saux-Picart, Christopher Taylor, and Yongkang Xue

The rainfall over West Africa has been characterized by extreme variability in the last half-century, with prolonged droughts resulting in humanitarian crises. There is, therefore, an urgent need to better understand and predict the West African monsoon (WAM), because social stability in this region depends to a large degree on water resources. The economies are primarily agrarian, and there are issues related to food security and health. In particular, there is a need to better understand land-atmosphere and hydrological processes over West Africa because of their potential feedbacks with the WAM. This is being addressed through a multiscale modeling approach using an ensemble of land surface models that rely on dedicated satellite-based forcing and land surface parameter products, and data from the African Multidisciplinary Monsoon Analysis (AMMA) observational field campaigns. The AMMA land surface model (LSM) Intercomparison Project (ALMIP) offline, multimodel simulations comprise the equivalent of a multimodel reanalysis product. They currently represent the best estimate of the land surface processes over West Africa from 2004 to 2007. An overview of model intercomparison and evaluation is presented. The far-reaching goal of this effort is to obtain better understanding and prediction of the WAM and the feedbacks with the surface. This can be used to improve water management and agricultural practices over this region.

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