Improving the quality of NEXRAD products in terms of resolution and accuracy

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dc.contributor.advisorXie, Hongjie
dc.contributor.authorYu, Beibei
dc.contributor.committeeMemberSharif, Hatim
dc.contributor.committeeMemberSun, Minghe
dc.date.accessioned2024-03-08T17:34:57Z
dc.date.available2024-03-08T17:34:57Z
dc.date.issued2009
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.abstractThe growing of economy in Central Texas area resulted in the degradation of environment. The pollutants, bacteria loading and water quality assessment is required to evaluate and predict the environment quality. Precipitation is the main source of storm discharge and runoff, which becomes a critical input to several hydrological, ecological, climatic and flood prediction models. The purpose of this study was to improve the spatial resolution as well as the accuracy of the NEXRAD MPE products. The first part of this study was to improve the resolution of the original 4km×4km NEXRAD MPE products by the means of downscaling the radar products into 1km×1km. The downscaling algorithm estimates precipitation distribution without prior knowledge of the atmospheric setting. It auto-searches precipitation spatial structures and atmospheric effects by incorporating a digital elevation model (DEM) map into precipitation maps. The downscaled precipitation fields were examined based on different time scales: hour, day and storm period. Three downscaled precipitation fields are in good agreement with the original 4 km × 4 km NEXRAD precipitation fields. However, the accuracy of the downscaled radar products has not necessarily been improved. The regression algorithm may be an efficient model in capturing the variability of spatial rainfall distribution in mountainous area, but not as efficient in flat area. Incorporating the topological information from DE M may be more effective for mountainous regions. The second part was to improve the accuracy of NEXRAD MPE products in capturing rainfall periods. The major difference between this study and precious study (Wang et al, 2008) is that the validation and correction is based on the fact that the spatial and temporal continuity of precipitation is reserved. For hydrological modeling, the continuous and spatially distributed precipitation data is recognized as a significant input. Thus, this part aimed at conducting continuous hourly spatial and temporal evaluation of the accuracy of NEXRAD and comparing 4 different interpolation methods (Bias Adjustment (BA), Simple Kriging with varying Local Means (SKlm), Kriging with External Drift (KED), and Regression Kriging (RK)) for incorporating raingauge measurements into NEXRAD MPE products. Four evaluation parameters (Percentage Bias, Mean Absolute Error, Coefficient of Determination, and Nash-Sutcliffe efficiency) were used to evaluate the performances using the observed rain gauge data as constraint. The comparison results show that the average performance of SKlm is similar to or better than the other methods. KED is a most vulnerable method and we have to use it carefully. It is worth noting that no one method can consistently outperform the other methods in terms of all evaluation coefficients, for all time steps, and at all rain gauges. In practical application of NEXRAD precipitation products, if there is plenty of time and computational resource, it is suggested to implement multiple methods to correct the original NEXRAD data, and choose the one with best performance for some specific objectives. Otherwise, SKlm is the preferable method for incorporating raingauge measurements into NEXRAD MPE products. Overall, it is clear that incorporating secondary source into the original NEXRAD MPE products can improve the resolution and accuracy of original products. To satisfy the model requirements of high quality precipitation data, combination of incorporating both DEM and rain gauge measurements can be a good approach. SKlm is generally a good method in precipitation interpolation, since it is easy to implement and achieve desire results.
dc.description.departmentEarth and Environmental Science
dc.format.extent103 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9781109124125
dc.identifier.urihttps://hdl.handle.net/20.500.12588/6088
dc.languageen
dc.subjectaccuracy
dc.subjectdownscaling
dc.subjectinterpolation
dc.subjectkriging
dc.subjectNEXRAD
dc.subjectresolution
dc.subject.classificationEnvironmental science
dc.subject.classificationRemote sensing
dc.subject.lcshPrecipitation (Meteorology) -- Measurement
dc.subject.lcshMeteorological stations, Radar
dc.subject.lcshRadar meteorology
dc.titleImproving the quality of NEXRAD products in terms of resolution and accuracy
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentEarth and Environmental Science
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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