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dc.contributor.advisorSalawitch, Ross Jen_US
dc.contributor.advisorPlatnick, Stevenen_US
dc.contributor.authorWind, Galinaen_US
dc.date.accessioned2016-09-08T05:41:38Z
dc.date.available2016-09-08T05:41:38Z
dc.date.issued2016en_US
dc.identifierhttps://doi.org/10.13016/M2TV47
dc.identifier.urihttp://hdl.handle.net/1903/18775
dc.description.abstractExecuting a cloud or aerosol physical properties retrieval algorithm from controlled synthetic data is an important step in retrieval algorithm development. Synthetic data can help answer questions about the sensitivity and performance of the algorithm or aid in determining how an existing retrieval algorithm may perform with a planned sensor. Synthetic data can also help in solving issues that may have surfaced in the retrieval results. Synthetic data become very important when other validation methods, such as field campaigns,are of limited scope. These tend to be of relatively short duration and often are costly. Ground stations have limited spatial coverage whilesynthetic data can cover large spatial and temporal scales and a wide variety of conditions at a low cost. In this work I develop an advanced cloud and aerosol retrieval simulator for the MODIS instrument, also known as Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS). In a close collaboration with the modeling community I have seamlessly combined the GEOS-5 global climate model with the DISORT radiative transfer code, widely used by the remote sensing community, with the observations from the MODIS instrument to create the simulator. With the MCARS simulator it was then possible to solve the long standing issue with the MODIS aerosol optical depth retrievals that had a low bias for smoke aerosols. MODIS aerosol retrieval did not account for effects of humidity on smoke aerosols. The MCARS simulator also revealed an issue that has not been recognized previously, namely,the value of fine mode fraction could create a linear dependence between retrieved aerosol optical depth and land surface reflectance. MCARS provided the ability to examine aerosol retrievals against “ground truth” for hundreds of thousands of simultaneous samples for an area covered by only three AERONET ground stations. Findings from MCARS are already being used to improve the performance of operational MODIS aerosol properties retrieval algorithms. The modeling community will use the MCARS data to create new parameterizations for aerosol properties as a function of properties of the atmospheric column and gain the ability to correct any assimilated retrieval data that may display similar dependencies in comparisons with ground measurements.en_US
dc.language.isoenen_US
dc.titleMulti-sensor Cloud and Aerosol Retrieval Simulator and Its Applicationsen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentAtmospheric and Oceanic Sciencesen_US
dc.subject.pqcontrolledAtmospheric sciencesen_US
dc.subject.pqcontrolledRemote sensingen_US
dc.subject.pqcontrolledInformation technologyen_US
dc.subject.pquncontrolledaerosolsen_US
dc.subject.pquncontrolledcloudsen_US
dc.subject.pquncontrolledhigh performance computingen_US
dc.subject.pquncontrolledmodelingen_US
dc.subject.pquncontrolledradiative transferen_US
dc.subject.pquncontrolledremote sensingen_US


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