Presentation Type

Poster

Faculty Mentor’s Full Name

Dr. Hilary Martens

Faculty Mentor’s Department

Geoscience

Abstract / Artist's Statement

Thousands of Global Positioning System (GPS) receivers worldwide record signals sent by satellites to infer how each receiver (and the ground they are attached to) moves over time. The motion of GPS receivers are used for many purposes, including studying tectonic deformation and changes in Earth's shape caused by surface loading. In this project, reflected wave arrivals contained within the multipath signal of GPS time series are extracted and analyzed to advance understanding of snow properties in mountainous regions of Montana/Idaho, USA. Analyzing reflected signals in GPS series has potential to reveal properties of local snowpack, such as height, water content, snow surface temperature, dielectric properties, and density. Improving our ability to monitor physical characteristics of snowpack and how they evolve over space and time is essential as properties of snow are key to understanding the slippage of one layer on another, which impacts avalanche hazard. Moreover, snowpack monitoring provides information about availability of water resources and snow hydrology. This project focuses on analyzing the ray paths and attenuation of reflected GPS signals, also using reflections to infer properties of snow. Traditionally, to study snow properties, one must manually dig a snow pit to study the snowpack and/or use expensive remote-sensing technologies (e.g. InSAR). However, digging snow pits can be dangerous due to avalanche risk as well as costly and time inefficient. Relatively low-cost GPS stations that are now widely deployed worldwide present new opportunities to study snow properties, including in developing nations with fewer financial resources. We will use GPS interferometric reflectometry (GPS-IR) software developed by Kristine Larson (CCAR) to infer snow depth data from GPS multipath. Results will be validated with nearby instruments, such as Snow Telemetry (SNOTEL) and a co-located weather station, as well as by visiting the site in person to measure snow properties manually.

Category

Physical Sciences

UMCUR PRESENTATION.mp4 (29052 kB)
Video

Share

COinS
 

Analyzing the multipath of GPS time series to study snow properties

Thousands of Global Positioning System (GPS) receivers worldwide record signals sent by satellites to infer how each receiver (and the ground they are attached to) moves over time. The motion of GPS receivers are used for many purposes, including studying tectonic deformation and changes in Earth's shape caused by surface loading. In this project, reflected wave arrivals contained within the multipath signal of GPS time series are extracted and analyzed to advance understanding of snow properties in mountainous regions of Montana/Idaho, USA. Analyzing reflected signals in GPS series has potential to reveal properties of local snowpack, such as height, water content, snow surface temperature, dielectric properties, and density. Improving our ability to monitor physical characteristics of snowpack and how they evolve over space and time is essential as properties of snow are key to understanding the slippage of one layer on another, which impacts avalanche hazard. Moreover, snowpack monitoring provides information about availability of water resources and snow hydrology. This project focuses on analyzing the ray paths and attenuation of reflected GPS signals, also using reflections to infer properties of snow. Traditionally, to study snow properties, one must manually dig a snow pit to study the snowpack and/or use expensive remote-sensing technologies (e.g. InSAR). However, digging snow pits can be dangerous due to avalanche risk as well as costly and time inefficient. Relatively low-cost GPS stations that are now widely deployed worldwide present new opportunities to study snow properties, including in developing nations with fewer financial resources. We will use GPS interferometric reflectometry (GPS-IR) software developed by Kristine Larson (CCAR) to infer snow depth data from GPS multipath. Results will be validated with nearby instruments, such as Snow Telemetry (SNOTEL) and a co-located weather station, as well as by visiting the site in person to measure snow properties manually.