10.1 Parameter Retrieval from Radar Rainfall Data to Validate a Dynamic Moving Storm (DMS) Generator

Wednesday, 25 January 2017: 4:00 PM
604 (Washington State Convention Center )
Nick Z. Fang, Univ. of Texas, Arlington, TX; and S. Gao

A new tool, Dynamic Moving Storm (DMS) generator aims to address the complex nature of precipitation field: spatial variability, temporal variability, and storm movement. DMS generator is a multivariate rainfall model depending upon parameters governing the spatio-temporal structure of individual storm cells. The authors have investigated the sensitivity of DMS parameters on the associated hydrologic responses by using synthetic events. Two parameter retrieval algorithms were respectively developed for storm cell recognition and storm motion tracking. The storm cell recognition feature used in this study allows for preserving the internal structure of a single storm cells as well as differentiating intense storm cells; while the motion tracking techniques herein combines the advantages of cross-correlation method and storm cell centroid tracking method. Thus two algorithms can provide statistical information on spatio-temporal parameters of any given event. In order to deal with rain cell clustering effectively, a multi-layer technique based upon the superposition of different rain cells was adopted in this study. As validation, several storm events were used to demonstrate how accurately DMS generator can recreate real rainfall fields. Lastly, twelve years of radar rainfall data over the Upper Trinity River (UTR) watershed was used to establish the statistics of DMS parameters. The resulted parameters were further analyzed for perspectives on the regional climatology for UTR. Combined with insights gained from previous work on DMS generator, this study enables us to better understood the flood potentials in UTR based upon the spatio-temporal features in precipitation.
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