조위 변동성이 큰 연안 환경을 위한 GNSS-R 기반 조위 추정 기법의 타당성 평가
Feasibility Assessment of GNSS Reflectometry–Based Tide Estimation in Coastal Environments with Large Tidal Variability
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초록
This study evaluates the performance of Global Navigation Satellites System, Reflectometry (GNSS-R)–based tide estimation techniques in coastal environments such as the Korean coastline, where the spatial and temporal variability of the sea level is large. GNSS-R estimates sea surface heights by analyzing multipath interference patterns in the signal-to-noise ratio (SNR) of GNSS signals. To enable the retrieval of sea level estimates with higher spatial and temporal resolution, several signal processing methods were applied, including a time-dependent phase model, optimal sliding-window spectral analysis, and multi-frequency consistency checking. The methods were tested at two coastal GNSS stations in the United States: CALC in Louisiana, which experienced Hurricane Harvey in 2017, and AT01 in Alaska, which was affected by a storm surge in 2019. GNSS-R–derived sea level estimates were compared with tide gauge observations at each site. At CALC, correlation coefficients reached 0.99 during the full period and 0.97 during the hurricane, with mean differences of 2.7 cm and 3.7 cm, respectively. At AT01, correlations were 0.96 and 0.87, with larger mean differences due to the spatial offset from the reference gauge. The results demonstrate that GNSS-R can effectively monitor tidal changes even during extreme weather conditions and can serve as a practical complement to traditional tide gauges. For implementation in Korea, further in-situ validation to account for local tidal characteristics, infrastructure compatibility, and vertical datum alignment should be performed.
Abstract
This study evaluates the performance of Global Navigation Satellites System, Reflectometry (GNSS-R)–based tide estimation techniques in coastal environments such as the Korean coastline, where the spatial and temporal variability of the sea level is large. GNSS-R estimates sea surface heights by analyzing multipath interference patterns in the signal-to-noise ratio (SNR) of GNSS signals. To enable the retrieval of sea level estimates with higher spatial and temporal resolution, several signal processing methods were applied, including a time-dependent phase model, optimal sliding-window spectral analysis, and multi-frequency consistency checking. The methods were tested at two coastal GNSS stations in the United States: CALC in Louisiana, which experienced Hurricane Harvey in 2017, and AT01 in Alaska, which was affected by a storm surge in 2019. GNSS-R–derived sea level estimates were compared with tide gauge observations at each site. At CALC, correlation coefficients reached 0.99 during the full period and 0.97 during the hurricane, with mean differences of 2.7 cm and 3.7 cm, respectively. At AT01, correlations were 0.96 and 0.87, with larger mean differences due to the spatial offset from the reference gauge. The results demonstrate that GNSS-R can effectively monitor tidal changes even during extreme weather conditions and can serve as a practical complement to traditional tide gauges. For implementation in Korea, further in-situ validation to account for local tidal characteristics, infrastructure compatibility, and vertical datum alignment should be performed.