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dc.contributor.advisorAnderson, Charles W.
dc.contributor.advisorChandra, Chandrasekar V.
dc.contributor.authorTan, Haiming
dc.contributor.committeememberRay, Indrajit
dc.contributor.committeememberChavez, Jose L.
dc.date.accessioned2019-06-14T17:06:11Z
dc.date.available2019-06-14T17:06:11Z
dc.date.issued2019
dc.description2019 Spring.
dc.descriptionIncludes bibliographical references.
dc.description.abstractPrecipitation measurement by satellite radar plays a significant role in researching the water circle and forecasting extreme weather event. Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) has capability of providing a high-resolution vertical profile of precipitation over the tropics regions. Its successor, Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR), can provide detailed information on the microphysical properties of precipitation particles, quantify particle size distribution and quantitatively measure light rain and falling snow. This thesis presents a novel Machine Learning system for ground-based and space borne radar rainfall estimation. The system first trains ground radar data for rainfall estimation using rainfall measurements from gauges and subsequently uses the ground radar based rainfall estimates to train spaceborne radar data in order to get space based rainfall product. Therein, data alignment between spaceborne and ground radar is conducted using the methodology proposed by Bolen and Chandrasekar (2013), which can minimize the effects of potential geometric distortion of spaceborne radar observations. For demonstration purposes, rainfall measurements from three rain gauge networks near Melbourne, Florida, are used for training and validation purposes. These three gauge networks, which are located in Kennedy Space Center (KSC), South Florida Water Management District (SFL), and St. Johns Water Management District (STJ), include 33, 46, and 99 rain gauge stations, respectively. Collocated ground radar observations from the National Weather Service (NWS) Weather Surveillance Radar – 1988 Doppler (WSR-88D) in Melbourne (i.e., KMLB radar) are trained with the gauge measurements. The trained model is then used to derive KMLB radar based rainfall product, which is used to train both TRMM PR and GPM DPR data collected from coincident overpasses events. The machine learning based rainfall product is compared against the standard satellite products, which shows great potential of the machine learning concept in satellite radar rainfall estimation. Also, the local rain maps generated by machine learning system at KMLB area are demonstrate the application potential.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierTan_colostate_0053N_15365.pdf
dc.identifier.urihttps://hdl.handle.net/10217/195325
dc.languageEnglish
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019 - CSU Theses and Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectrainfall estimation
dc.subjectmachine learning
dc.subjectspaceborne radar
dc.titleMachine learning-based fusion studies of rainfall estimation from spaceborne and ground-based radars
dc.typeText
dcterms.rights.dplaThe copyright and related rights status of this Item has not been evaluated (https://rightsstatements.org/vocab/CNE/1.0/). Please refer to the organization that has made the Item available for more information.
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)


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