Development of the Coupled Atmosphere and Land Data Assimilation System (CALDAS) and Its Application Over the Tibetan Plateau

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dc.contributor.author Kuria, David Ndegwa
dc.contributor.author Mohamed Rasmy
dc.contributor.author Toshio Koike
dc.contributor.author Cyrus Raza Mirza
dc.contributor.author Kun Yang
dc.contributor.author Xin Li
dc.date.accessioned 2020-03-02T07:41:34Z
dc.date.available 2020-03-02T07:41:34Z
dc.date.issued 2012-11
dc.identifier.issn 0196-2892
dc.identifier.uri http://repository.dkut.ac.ke:8080/xmlui/handle/123456789/1095
dc.description.abstract Land surface heterogeneities are important for accurate estimation of land-atmosphere interactions and their feedbacks on water and energy budgets. To physically introduce existing land surface heterogeneities into a mesoscale model, a land data assimilation system was coupled with a mesoscale model (LDAS-A) to assimilate low-frequency satellite microwave observations for soil moisture and the combined system was applied in the Tibetan Plateau. Though the assimilated soil moisture distribution showed high correlation with Advanced Microwave Scanning Radiometer on the Earth Observing System soil moisture retrievals, the assimilated land surface conditions suffered substantial errors and drifts owing to predicted model forcings (i.e., solar radiation and rainfall). To overcome this operational pitfall, the Coupled Land and Atmosphere Data Assimilation System (CALDAS) was developed by coupling the LDAS-A with a cloud microphysics data assimilation. CALDAS assimilated lower frequency microwave data to improve the representation of land surface conditions, and merged them with higher frequency microwave data to improve the representation of atmospheric conditions over land surfaces. The simulation results showed that CALDAS effectively assimilated atmospheric information contained in higher frequency microwave data and significantly improved correlation of cloud, distribution compared with the satellite observation. CALDAS also improved biases in cloud conditions and associated rainfall events, which contaminated land surface conditions in LDAS-A. Improvements in predicted clouds resulted in better land surface model forcings (i.e., solar radiation and rainfall), which maintained assimilated surface conditions in accordance with observed conditions during the model forecast. Improvements in both atmospheric forcings and land surface conditions enhanced land-atmosphere interactions in the CALDAS model, as confirmed by radiosonde observations en_US
dc.language.iso en en_US
dc.publisher IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING en_US
dc.title Development of the Coupled Atmosphere and Land Data Assimilation System (CALDAS) and Its Application Over the Tibetan Plateau en_US
dc.type Article en_US


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