Abstract: A general framework of dynamic models for space-time radar observations has been developed in the current research. There exist three difficulties in modeling space-time radar observations: 1) high dimensionality due to the high-resolution radar measurements over a large area, 2) non-stationarity due to the storm motion, and 3) non-stationarity due to the dynamic evolution (growth and decay). These difficulties are addressed in this research. To deal with the storm motion, a novel and efficient radar storm tracking algorithm is developed in the spectral domain. Based on this new technique, the Dynamic and Adaptive Radar Tracking System (DARTS) is developed and evaluated using synthesized and observed radar reflectivity. To tackle the high dimensionality and model the spatial variability of radar observations, a general modeling framework is formulated and the singular value decomposition (SVD) is used for dimension reduction. To deal with the dynamic evolution and model the temporal variability of radar observations, the motion-compensated temporal alignment (MCTA) transformation is developed. In this analysis the evolution of radar storm fields is modeled by the linear dynamic system (LDS) in the low-dimensional subspace. The applications of the dynamic modeling for space-time radar observations are further demonstrated. Spatial and dynamic characteristics are obtained based on the estimated model parameters using three months of radar observations. The characteristic temporal scales are quantified for this dataset. Results have revealed the consistent dependency between the temporal characterization and the spatial characterization of observed radar fields. In general the storms of the larger spatial scales possess the larger temporal scales. Stochastic simulation of different spatiotemporal radar reflectivity fields is demonstrated. The short-term prediction of radar reflectivity fields based on the space-time dynamic model is evaluated using observed radar data. The simulations of the DARTS for real-time applications are also conducted and analyzed.

Adviser: Prof. V Chandrasekar (ECE Department)
Co-Adviser:
Non-ECE Member: Prof. Paul W. Mielke, Jr. (Statistics Department)
Member 3: Prof. V N Bringi (ECE Department)
Addional Members:

Publications:

Program of Study: