Assimilation of Synthetic Remotely Sensed Soil Moisture in Environment Canada's MESH Model

作者：Xu Xiaoyong; Tolson Bryan A; Li Jonathan^*; Davison Bruce

来源：IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(4): 1317-1327.

DOI：10.1109/JSTARS.2016.2626256

• Data assimilation
• ensemble Kalman filter (EnKF)
• Modelisation Environmentale-Surface et Hydrologie (MESH)
• satellite soil moisture
• synthetic experiment

With recent advances in satellite microwave soil moisture estimation, particularly the launch of the Soil Moisture and Ocean Salinity satellite and the soil moisture active passive mission, there is an increased demand for exploiting the potential of satellite microwave soil moisture observations to improve the predictive capability of hydrologic and land surface models. This study presents the implementation of the 1-D version of the ensemble Kalman filter scheme to assimilate satellite soil moisture into Environment Canada's Standalone Modelisation Environmentale-Surface et Hydrologie (MESH) model that couples the Canadian land surface scheme with a distributed hydrological model. This paper examines the performance of the established assimilation scheme by conducting a series of synthetic assimilation experiments in which the satellite soil moisture and the reference ("true") solutions were derived from the MESH model simulations. The synthetic analyses have demonstrated the capability of the assimilation system, given the synthetic satellite soil moisture and the intentionally degraded model estimates, to accurately approximate the "true" surface layer and root-zone soil moisture solutions. The experiments have also revealed the impacts of a series of factors (ensemble size, vegetation cover, observing frequency, specification of observation, and model input error parameters) upon the quality of the assimilation estimates, which can provide an important guidance for the practical application of the assimilation scheme.

• 出版日期2017-4
• 单位Saskatoon