2011 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.
International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies http://www.TuEngr.com, http://go.to/Research
A Land Data Assimilation System Utilizing Low Frequency Passive Microwave Remote Sensing: A Case Study of the Tibetan Plateau a*
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David Kuria , Toshio Koike , Moses Gachari , and Souhail Boussetta
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Department of Geomatic Engineering and Geospatial Information Science, Kimathi University College of Technology, KENYA b River and Environmental Engineering Laboratory, Department of Civil Engineering, Faculty of Engineering, The University of Tokyo, JAPAN c Currently at: European Centre for Medium-Range Weather Forecasts, UNITED KINGDOM ARTICLEINFO
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Article history: Received 15 March 2011 Received in revised form 2 June 2011 Accepted 3 June 2011 Available online 6 June 2011 Keywords: Soil moisture retrieval, Land Surface Modelling, Data assimilation, Passive microwaves, AMSR-E, Tibetan Plateau, Surface emission model
To address the gap in bridging global and smaller modelling scales, downscaling approaches have been reported as an appropriate solution. Downscaling on its own is not wholly adequate in the quest to produce local phenomena, and in this paper we use a physical downscaling method combined with data assimilation strategies, to obtain physically consistent land surface condition prediction. Using data assimilation strategies, it has been demonstrated that by minimizing a cost function, a solution utilizing imperfect models and observation data including observation errors is feasible. We demonstrate that by assimilating lower frequency passive microwave brightness temperature data using a validated theoretical radiative transfer model, we can obtain very good predictions that agree well with observed conditions. 2011 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Some Rights Reserved.
1. Introduction While General Circulation Models (GCMs) are best at simulating evolving and future changes in climate systems, they are unable to produce mesoscale and local atmospheric *Corresponding author (David Kuria). Tel: +254-727-399208. E-mail addresses: 2011. International Transaction Journal of Engineering, Management, dn.kuria@gmail.com. & Applied Sciences & Technologies. Volume 2 No.3. ISSN 2228-9860. eISSN 1906-9642. Online Available at http://TuEngr.com/V02/303-324.pdf
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