Lesher-Garcia, Jacob T., authorChandrasekar, V., advisorCheney, Margaret, committee memberPopat, Ketul, committee member2024-01-012025-12-292023https://hdl.handle.net/10217/237354Weather radars are vital to ensuring the safety of society by providing timely, accurate products used to forecast the development of weather phenomena. To this end, high spatiotemporal resolution data is paramount. Collecting high-resolution polarimetric observation data necessitates scan strategies with a slow scan rate. This thesis proposes the use of an established deep learning model in order to augment the current weather radar operational paradigm. Specifically, this thesis focuses on evaluating the efficacy of the super-resolution generative adversarial network (SRGAN) in generating physically realistic, pseudo-high-resolution radar scans – referred to as super-resolution (SR) scans – from low-resolution (LR) weather radar scans. With this, weather radar systems would be able to collect LR scans at faster scan rates while maintaining the quality of high-resolution (HR) scans by using the SRGAN to generate SR scans. This thesis aims to assess the generating capabilities of the SRGAN within the scope of generating SR scans from a pseudo-LR scan, processed from an actual HR scan. In order to accomplish this task, multiple experiments are setup, designed to test the SRGAN's capabilities in conducting SR for different architectural configurations, scan types, resolution scaling factors and downsampling methods, one of which simulates the characteristics of actual LR weather radar scans. The experimental SRGANs' performances are assessed both quantitatively and qualitatively, comparing between the SR scans and the baseline interpolation methods. The results of this thesis have found that the SRGAN model can outperform the baseline methods, specifically for the higher resolution scale factors and especially for the RHI radar scan type. Furthermore, the SRGAN is able to generate a physically representative SR image that reflects the natural features of a HR image. This is significant as it suggests that the SRGAN model is more effective when applied to practical applications.born digitalmasters thesesengCopyright 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.deep learningremote sensingweather radargenerative adversarial networkdata processingsuper-resolutionTextEmbargo expires: 12/29/2025.