Evaluation of Functional Forms of Rain Gage — Radar (NEXRAD) Data Relationships

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 generated 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 are often used to capture 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 data modeling (inductive) techniques and develop optimal functional approximation methods for this purpose. The radar based 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.