Informations et ressources scientifiques
sur le développement des zones arides et semi-arides

Accueil du site → Doctorat → Italie → Inversion of geophysical properties from EnKF analysis of satellite data over semi-arid regions

Politecnico di Milano (2014)

Inversion of geophysical properties from EnKF analysis of satellite data over semi-arid regions


Titre : Inversion of geophysical properties from EnKF analysis of satellite data over semi-arid regions


Université de soutenance : Politecnico di Milano

Grade : Tesi di dottorato (2014)

Résumé partiel
As more and more satellites, specifically designed for hydrological monitoring, have been recently launched, the needs of satellite data utilization study are increasingly growing in the fields of hydrology, atmospheric science and geoscience. The development of inverse method is intended for such research needs. Main objective of this thesis is to propose the method inverting geophysical parameters from the measurements after filtering out the measurement errors, by means of data assimilation, specifically Ensemble Kalman Filter (EnKF). Significance of this method lies in overcoming the limitations of empirical formulations. The globally available satellite data-based inversion method appropriately addresses the characteristics in the extreme climatic conditions misestimated by means of empirical formulations. This thesis is organized as follows : EnKF was implemented with Surface Energy Balance System (SEBS)-retrieved sensible heat flux, and Synthetic Aperture Radar (SAR) and Soil Moisture and Ocean Salinity (SMOS)-retrieved surface soil moisture products. These EnKF analyses were further used as the reference data in the inverse method. The inversion of aerodynamic roughness in the SEBS model was conducted with the Tibet- Global Energy and Water cycle Experiment (GEWEX) Asian Monsoon Experiment (GAME) datasets. The inversion of soil hydraulic input variables in the Soil Vegetation Atmosphere Transfer (SVAT) model was implemented with the Tibet-GAME and GEWEX-Analyses Multidisciplinaires de la Mousson Africaine (AMMA) datasets. Prior to an inverse modelling, the EnKF scheme for filtering out satellite errors was explored and assessed because those observation errors may adversely affect the parameter inversion minimizing a mismatch between simulation and observation. Two different schemes of stationary and sequential EnKF were compared to examine whether observation error correction can replace the time-evolution of sequential ensemble. Because the stationary ensemble-based Ensemble Optimal Interpolation (EnOI) scheme is a computationally cost-effective but suboptimal approach, the two-step stationary EnKF scheme empirically defining the observation errors by means of L-band Microwave Emission of the Biosphere (L-MEB) radiative transfer model-based SMOS L2 processor was suggested, in contrast to a sequential EnKF assuming global constant a priori error. The result suggested that the sequential EnKF scheme consuming a longer record of satellite data may not be required if the SMOS brightness temperature errors in EnOI are empirically adjusted.

Mots clés : EnKF ; SVAT ; SMOS ; SAR surface soil moisture ; heat flux ; semi-arid regions


Version intégrale (2,97 Mb)

Page publiée le 9 juillet 2014, mise à jour le 15 mars 2019