Is this project an undergraduate, graduate, or faculty project?
Graduate
individual
What campus are you from?
Daytona Beach
Authors' Class Standing
Austin Gleydura, Graduate
Lead Presenter's Name
Austin Gleydura
Faculty Mentor Name
Dr. Alan Liu
Abstract
Flash flood prediction in Southern Appalachia is particularly challenging due to steep terrain, narrow valleys, highly localized rainfall patterns, and limited measurement coverage. Traditional remote sensing methods, such as Doppler radar and microwave radiometry, suffer from reduced resolution at extended range and signal blockage by mountains, while satellite instruments like MODIS lack sufficient spatio-temporal resolution for sub-kilometer measurements critical to flash flood nowcasting. GNSS-Meteorology offers an established alternative for measuring precipitable water vapor (PWV) and is currently integrated into several numerical weather models. Recent research demonstrates that GNSS-derived PWV products can be used to accurately predict rainfall intensity and timing without requiring radar or radiometers. Additionally, studies have shown that commercially available GNSS receivers can reproduce PWV measurements comparable to high-end geodetic equipment, making this a cost-effective approach. To validate this methodology for operational nowcasting, a dense network of GNSS stations must be deployed over an extended period. However, current approaches use inefficient single-board computers with commercial modules and lack methods for transmitting data to a central network, limiting scalability. These shortcomings can be addressed by developing a low-cost, self-contained embedded system that processes raw GNSS data in real-time following IoT principles. By consolidating all necessary components into easily deployable units with minimal ground infrastructure and network overhead, this system enables scalable network deployment for research validation and eases the transition to operational use. The resulting dense GNSS-PWV network will provide high-resolution atmospheric water vapor measurements to improve flash flood nowcasting in mountainous regions where traditional methods are inadequate.
Did this research project receive funding support from the Office of Undergraduate Research.
No
Included in
Developing Low-Cost GNSS Remote Sensing Hardware to Measure Precipitable Water Vapor for Flash Flood Nowcasting in Southern Appalachia
Flash flood prediction in Southern Appalachia is particularly challenging due to steep terrain, narrow valleys, highly localized rainfall patterns, and limited measurement coverage. Traditional remote sensing methods, such as Doppler radar and microwave radiometry, suffer from reduced resolution at extended range and signal blockage by mountains, while satellite instruments like MODIS lack sufficient spatio-temporal resolution for sub-kilometer measurements critical to flash flood nowcasting. GNSS-Meteorology offers an established alternative for measuring precipitable water vapor (PWV) and is currently integrated into several numerical weather models. Recent research demonstrates that GNSS-derived PWV products can be used to accurately predict rainfall intensity and timing without requiring radar or radiometers. Additionally, studies have shown that commercially available GNSS receivers can reproduce PWV measurements comparable to high-end geodetic equipment, making this a cost-effective approach. To validate this methodology for operational nowcasting, a dense network of GNSS stations must be deployed over an extended period. However, current approaches use inefficient single-board computers with commercial modules and lack methods for transmitting data to a central network, limiting scalability. These shortcomings can be addressed by developing a low-cost, self-contained embedded system that processes raw GNSS data in real-time following IoT principles. By consolidating all necessary components into easily deployable units with minimal ground infrastructure and network overhead, this system enables scalable network deployment for research validation and eases the transition to operational use. The resulting dense GNSS-PWV network will provide high-resolution atmospheric water vapor measurements to improve flash flood nowcasting in mountainous regions where traditional methods are inadequate.