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oai:mountainscholar.org:10217/2438032026-03-27T11:03:39Zcom_10217_100532com_10217_100000com_10217_100411com_10217_100303col_10217_100538col_10217_100415

Xu, Gang, author Chandrasekar, V., advisor Bringi, V. N., committee member Jayasumana, Anura P., committee member Mielke, Paul W., Jr., committee member 2026-03-26T18:32:13Z 2007 https://hdl.handle.net/10217/243803 https://doi.org/10.25675/3.026490 A general framework of the dynamic model for space-time radar observations has been developed in the current research. There exist three difficulties in modeling spacetime 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 evolution (growth and decay). These difficulties are addressed in this research. To deal with the storm motion, an efficient radar storm tracking algorithm is developed in the spectral domain. Based on this new technique, the Dynamic and Adaptive Radar Tracking of Storms (DARTS) is developed and evaluated using the synthesized and the 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 model 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. The correlation between the temporal characterization and the spatial characterization of observed radar fields are explored. The simulation capability of different spatiotemporal radar reflectivity fields is demonstrated. Evaluation of the space time variability is particularly important in the context of adaptive scanning of storm systems. 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 evaluated. doctoral dissertations English eng Colorado State University. Libraries 2000-2019 Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. Per the terms of a contractual agreement, all use of this item is limited to the non-commercial use of Colorado State University and its authorized users. electrical engineering atmosphere computer science atmospheric sciences Dynamic model for space-time weather radar observation and nowcasting Text Doctor of Philosophy (Ph.D.) Doctoral Electrical and Computer Engineering Colorado State University This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). open.access