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dc.contributor.advisorBras, Rafael
dc.contributor.authorLin, Liao-Fan
dc.date.accessioned2016-05-27T13:12:46Z
dc.date.available2016-05-27T13:12:46Z
dc.date.created2016-05
dc.date.issued2016-04-01
dc.date.submittedMay 2016
dc.identifier.urihttp://hdl.handle.net/1853/54974
dc.description.abstractEnvironmental monitoring of Earth from space has provided invaluable information for understanding the land-atmosphere water and energy exchanges. However, the use of satellite observations in hydrologic applications is often limited by coarse space-time resolutions. This study aims to develop a data assimilation system that integrates remotely-sensed precipitation and soil moisture observations into physically-based models to produce fine-scale precipitation, soil moisture, and other relevant hydrometeorological variables. This is particularly useful with the active Global Precipitation Measurement and Soil Moisture Active Passive missions. The system consists of two major components: (1) a framework for dynamic downscaling of satellite precipitation products using the Weather Research and Forecasting (WRF) model with four-dimensional variational data assimilation (4D-Var) and (2) a variational data assimilation system using spatio-temporally varying background error covariance for directly assimilating satellite soil moisture data into the Noah land surface model coupled with the WRF model. The WRF 4D-Var system can effectively assimilate and downscale six-hour precipitation products of a spatial resolution of about 20 km (i.e., those derived from the National Centers for Environmental Prediction Stage IV data and the Tropical Rainfall Measuring Mission (TRMM) 3B42 dataset) to hourly precipitation with a spatial resolution of less than 10 km. The system is able to assimilate and downscale daily soil moisture products at a gridded 36-km resolution obtained from the Soil Moisture and Ocean Salinity (SMOS) mission to produce hourly 4-by-4 km surface soil moisture forecasts with a reduction of mean absolute error by 35% on average. The results from the system with coupled components show that assimilation of the TRMM 3B42 precipitation improves the quality of both downscaled precipitation and soil moisture analyses, while the effect of SMOS soil moisture data assimilation is largely on the soil moisture analyses. The downscaled WRF precipitation, with and without assimilation of TRMM precipitation, was preliminarily tested with a spatially distributed simulation of streamflow using the TIN (Triangular Irregular Network)-based Real-time Integrated Basin Simulator (tRIBS).
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectPrecipitation
dc.subjectSoil moisture
dc.subjectData assimilation
dc.subjectDynamical downscaling
dc.subjectWeather research and forecasting
dc.subjectLand-atmosphere interaction
dc.subjectRemote sensing
dc.subjectHydrometeorology
dc.subjectHydrology
dc.titleData assimilation and dynamical downscaling of remotely-sensed precipitation and soil moisture from space
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentCivil and Environmental Engineering
thesis.degree.levelDoctoral
dc.contributor.committeeMemberWang, Jingfeng
dc.contributor.committeeMemberGeorgakakos, Aris
dc.contributor.committeeMemberDi Lorenzo, Emanuele
dc.contributor.committeeMemberFlores, Alejandro
dc.date.updated2016-05-27T13:12:46Z


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