HR: 15:25h
AN: H23K-08    [Abstracts]
TI: Characterizing Rain Gage – Radar (NEXRAD) Data Relationships Using Inductive Modeling
AU: * Peters, D
EM: dpeter34@fau.edu
AF: Florida Atlantic University, 777 Glades Road, Bldg # 36, Room 217, Boca Raton, FL 33431, United States
AU: Teegavarapu, R S
EM: ramesh@civil.fau.edu
AF: Florida Atlantic University, 777 Glades Road, Bldg # 36, Room 217, Boca Raton, FL 33431, United States
AU: Pathak, C
EM: cpathak@sfwmd.gov
AF: South Florida Water Management District, 3301 Gun Club Road, MSC 4260, West Palm Beach, FL 33406, United States
AB: The use of radar (NEXRAD) estimated rainfall data for providing information about the extreme rainfall amounts resulting from storms, hurricanes and tropical depressions is common today. Often corrections are applied to the RADAR-based rainfall data-based on what was actually measured on the ground by rain gages. Understanding and modeling the relationships between RADAR and rain gage data are essential tasks to confirm the accuracy and reliability of the former surrogate method of rainfall measurement. Conventional regression models in many situations are found to be incapable of capturing these highly variant non-linear spatial and temporal relationships. This study aims to understand and model the relationships between RADAR (NEXRAD) estimated rainfall data and the data measured by conventional rain gages. This study proposes to investigate the use of emerging computational inductive modeling techniques and to develop optimal functional approximation methods for this purpose. The raw and transformed RADAR rainfall data and rain gage data will also be analyzed to understand spatio-temporal associations. The study areas selected from upper and lower Kissimmee basins of south Florida form the test-bed for the proposed approaches and ensure the testing of the validity and operational applicability of these approaches.
DE: 1800 HYDROLOGY
DE: 1853 Precipitation-radar
DE: 1854 Precipitation (3354)
SC: Hydrology [H]
MN: 2007 Fall Meeting