Browsing by Person "Park, Chang-Hwan"

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Microwave forward model for land surface remote sensing
(2015) Park, Chang-Hwan; Wulfmeyer, Volker
In order to improve hydro-meteorological model prediction using remote-sensing measurements the difference between the model world and the observed world should be identified. The forward model proposed in this study allows us to simulate the BT (brightness temperature) from the land surface model to compare with the observed microwave BT. The proposed dielectric mixing model is the key part of the forward model to properly link the model parameters and the BT observed by remote sensing. In this study, it was established that the physically valid computation of the effective dielectric constant should be based on the arithmetic average with consideration of the proposed universal damping factor. This physically based dielectric mixing model is superior to the refractive mixing model or semi-empirical/calibration model with RMSE values of 0.96 and 0.63 for the predicted real and imaginary parts, respectively, compared to the measured values. The RMSE obtained with the new model is smaller than those obtained by other researchers using refractive mixing models for operational microwave remote sensing. Once we determine the model uncertainty using this forward model, we can update the model state using the values obtained from the remote-sensing measurement. The challenging task in this process is to resolve the ill-posed inversion problem (estimation of multiple model parameters from a single BT measurement). This study proposes a simple partitioning factor based on model physics. Again, the forward model is crucial because these factors are required to be computed in BT space. In the case study involving the Schäfertal catchment area, the proposed forward model, including the new dielectric mixing model, and the proper partitioning factors computed from land surface model physics was able to successfully extract the refined soil texture information from the microwave BT measurements. The highly resolved soil moisture variability based on the refined soil texture will allow us to predict convective precipitation with higher spatial and temporal accuracy in the numerical weather forecasting model. Moreover, microwave remote sensing using the developed forward model, which provides the soil texture, soil moisture, and soil temperature with a fine scale resolution, is expected to open up new possibilities to examine the energy balance closure problem with unprecedented realism.