Uncertainty of remote sensing precipitation estimates

dc.contributor.advisorSharif, Hatim O.
dc.contributor.advisorXie, Hongjie
dc.contributor.authorMazari, Newfel
dc.contributor.committeeMemberSharif, Hatim
dc.contributor.committeeMemberXie, Hongjie
dc.contributor.committeeMemberDutton, Allam R.
dc.contributor.committeeMemberJohnson, Drew W.
dc.contributor.committeeMemberGao, Yongli
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractThis dissertation aims to quantify the uncertainty of remote sensing precipitation estimates. The focus is on radar rainfall estimation and satellite snow cover classification. The first part of this dissertation introduces a new approach to study the spatial, temporal and vertical variability of radar-estimated rainfall using a vertically pointing radar (Micro Rain Radar or MRR) in conjunction with a ground sensor (rain gauge) and rainfall estimates from the nearest weather radar (Next Generation Radar or NEXRAD). The MRR's direct rainfall estimates using the Mie theory has similar values when compared to the collocated gauge rainfall observations. It was found that MRR estimates are sensitive to the height resolution (the size of the vertical radar bin) and that the MRR rainfall may be biased in presence of bright band or other artifacts at higher elevations (above 2100 m). In the second part of the research multiple radar bin integrations are used to investigate reflectivity-derived rainfall accuracy and errors from two NEXRAD radars that cover the same network of 50 gauges in the Upper Guadalupe River Basin. It is found that, in addition to the size of the integration bin, there are other sources of uncertainty such as distance from the radar, amount of rainfall, and type of the rainfall event. The third research is a validation of a new NEXRAD rainfall product called Digital Storm Total Precipitation (DSP) using a dense gauge network in the Hill Country of Texas. The DSP is a product of high temporal and spatial resolutions intended for flash flood forecasting and warning. The validation process is based on three years of rainfall data, using statistical and analytical parameters. The accuracy of DSP is found to be highly dependent of the radar range and is also affected by seasonality, with more accurate measurements in warm season than in cold season. The DSP probability of rainfall detection is found to be always higher than gauges. Finally, the fourth part investigates the daily snow cover product of the Ice Mapping System (IMS) at a nominal resolution of 4 x 4 km. The product's accuracy and robustness are compared against snow depth measurements from a network of 197 meteorological stations in the Colorado Plateau and MODIS satellite estimates. IMS accuracy is found to be similar to MODIS accuracy with slightly lower values during ablation and accumulation periods. IMS classification errors are also significantly comparable to MODIS errors (both at 500 m or 4 km resolutions) with the exception of unstable periods (accumulation and ablation) where IMS errors can be close to 10% higher than MODIS errors.
dc.description.departmentEarth and Environmental Science
dc.format.extent208 pages
dc.subjectremote sensing
dc.subject.classificationEnvironmental science
dc.subject.classificationEnvironmental engineering
dc.titleUncertainty of remote sensing precipitation estimates
thesis.degree.departmentEarth and Environmental Science
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.nameDoctor of Philosophy


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