Dynamic Sea Level Variation From GNSS: 2020 Shumagin Earthquake Tsunami Resonance and Hurricane Laura

Larson, Kristine M.; Lay, Thorne; Yamazaki, Yoshiki; Cheung, Kwok Fai; Ye, Lingling; Williams, Simon; Davis, J. L.

doi:10.1029/2020gl091378

Cited by 33 publications

(20 citation statements)

References 27 publications

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“…Sea state not only affects the GPS antenna motion, but also directly influences the roughness of the reflecting surface. Previous applications of storm surge detections show that high winds downgrade the performance of GNSS‐IR for height estimation (Larson et al., 2021; Peng et al., 2019). In our case, during extreme weather events (e.g., hurricanes) fewer satellite tracks fulfilled the quality control and the uncertainty in the sea level estimate is larger compared to days with calm sea state.…”

Section: Discussionmentioning

confidence: 99%

Offshore Sea Levels Measured With an Anchored Spar‐Buoy System Using GPS Interferometric Reflectometry

et al. 2021

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Coastal sea level exhibits significant spatial and temporal variations, reflecting the influence of tides, winds, coastal currents, varying salinity, and local bathymetry. Measuring, predicting, and understanding these variations in the often densely populated coastal regions are important for a number of economic and natural hazard reasons (Barnard et al., 2015;Hallegatte et al., 2013). Robust measurement of coastal sea level requires both high temporal resolution (e.g., coastal tide gauges and the system described here) and the high spatial resolution afforded by satellites. Pulse-limited satellite radar altimeters have been challenged in coastal areas mainly due to land contaminations. Improved retracking algorithms and new generation altimeters with delay-Doppler/Synthetic Aperture Radar (SAR) mode such as Cryosat-2, Sentinel-3, and Sentinel-6 promise significant advances (International Altimetry Team, 2021), though performance can still be problematic within 5 km of the coast (e.g., Peng & Deng, 2020). Satellite altimetry data require calibration, often performed by comparing to tide gauge observations. However, tide gauge distribution is uneven, may lack simultaneous vertical land motion measurements, and typically does not sample shallow offshore regions. These limitations can affect the calibration, hampering applications of satellite altimetry in coastal waters.Global navigation satellite systems (GNSS), including the Global Positioning System (GPS), have been widely used in Earth science studies, such as crustal deformation (e.g., Dixon, 1991), atmospheric water vapor variation (e.g., Bevis et al., 1992), ionosphere perturbation (e.g., Ho et al., 1996), tide gauge calibration (Watson et al., 2008), ice motion (Zhang et al., 2008), and volcanic plume detection (Larson, 2013). One of the error sources for precise positioning, multipath, can be used to measure the height and other characteristics of the reflecting surface using a technique called interferometric reflectometry (

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Section: Discussionmentioning

confidence: 99%

Offshore Sea Levels Measured With an Anchored Spar‐Buoy System Using GPS Interferometric Reflectometry

et al. 2021

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“…E data (D) is the data term of disparity map D, representing the sum of all pixels' cost ∑ p C(p, d p ). E smooth (D) is the smooth term, d p is the disparity between pixel p on the left image and its suspected matching point on the right image, p is the adjacent pixel point of p and T[•] equals 1 if the argument is true and 0 otherwise, N p represents the set of neighboring points of p. The u(p, p ) multiplier can be interpreted as the penalty of a discontinuity between p and p , and in this paper, it is the combination of the intensity difference and space distance, as Equation (9) shows:…”

Section: Fast Dense Matchingmentioning

confidence: 99%

“…In this paper, we utilize the Semi-global matching algorithm to minimize the energy function; it is similar to [24]. However, different from [24], which adds a constant penalty P 1 for all pixels in the neighborhood of p when the disparity changes a little bit (that is, 1 pixel) and adds a larger constant penalty P 2 for all larger disparity changes, we calculate a dynamic penalty for each pixel in the neighborhood of p based on space and intensity differences, as (9) shows. Thus, we first need to calculate the penalty volume for the image.…”

Section: Fast Dense Matchingmentioning

confidence: 99%

“…The German-Indonesian Tsunami Early Warning System takes an "end-to-end" approach to cover the complete tsunami warning chain; its working mechanism is similar to the submarine cable system and the Tsunami Warning systems in India and Australia [6][7][8]. Other methods include rapid determination of sea level variations caused by tsunamis and tsunami parameter estimation from Global Navigation Satellite Systems, tsunami detection and forecasting by radar on unconventional airborne crafts [9][10][11]. The sparseness and cost limit their capabilities.…”

Section: Introductionmentioning

confidence: 99%

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A Stereo Matching Method for 3D Image Measurement of Long-Distance Sea Surface

Yang

2021

JMSE

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Tsunamis are some of the most destructive natural disasters. Some proposed tsunami measurement and arrival prediction systems use a limited number of instruments, then judge the occurrence of the tsunami, forecast its arrival time, location and scale. Since there are a limited number of measurement instruments, there is a possibility that large prediction errors will occur. In order to solve this problem, a long-distance tsunami measurement system based on the binocular stereo vision principle is proposed in this paper. The measuring range is 4–20 km away from the system deployment site. In this paper, we will focus on describing the stereo matching method for the proposed system. This paper proposes a two-step matching method. It first performs fast sparse matching, and then complete high precision dense matching based on the results of the sparse matching. A matching descriptor based on the physical features of sea waves is proposed to solve the matching difficulty caused by the similarity of sea surface image textures. The relationship between disparity and the y coordinate is built to reduce the matching search range. Experiments were conducted on sea surface images with different shooting times and distances; the results verify the effectiveness of the presented method.

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“…GNSS-IR has been proven to be effective for detecting storm surges [16,17], tsunami [18], sea state [19] and astronomical tides [20][21][22]. However, all these previous studies focused on short-term, i.e., sub-daily sea-level variations.…”

Section: Introductionmentioning

confidence: 99%

Measuring Coastal Absolute Sea-Level Changes Using GNSS Interferometric Reflectometry

et al. 2021

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Rising sea levels pose one of the greatest threats to coastal zones. However, sea-level changes near the coast, particularly absolute sea-level changes, have been less well monitored than those in the open ocean. In this study, we aim to investigate the potential of Global Navigation Satellite Systems Interferometric Reflectometry (GNSS-IR) to measure coastal absolute sea-level changes and tie on-land (coastal GNSS) and offshore (satellite altimetry) observations into the same framework. We choose three coastal GNSS stations, one each in regions of subsidence, uplift and stable vertical land motions, to derive both relative sea levels and sea surface heights (SSH) above the satellite altimetry reference ellipsoid from 2008 to 2020. Our results show that the accuracy of daily mean sea levels from GNSS-IR is <1.5 cm compared with co-located tide-gauge records, and amplitudes of annual cycle and linear trends estimated from GNSS-IR measurements and tide-gauge data agree within uncertainty. We also find that the de-seasoned and de-trended SSH time series from GNSS-IR and collocated satellite altimetry are highly correlated and the estimated annual amplitudes and linear trends statistically agree well, indicating that GNSS-IR has the potential to monitor coastal absolute sea-level changes and provide valuable information for coastal sea-level and climate studies.

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Dynamic Sea Level Variation From GNSS: 2020 Shumagin Earthquake Tsunami Resonance and Hurricane Laura

Cited by 33 publications

References 27 publications

Offshore Sea Levels Measured With an Anchored Spar‐Buoy System Using GPS Interferometric Reflectometry

Offshore Sea Levels Measured With an Anchored Spar‐Buoy System Using GPS Interferometric Reflectometry

A Stereo Matching Method for 3D Image Measurement of Long-Distance Sea Surface

Measuring Coastal Absolute Sea-Level Changes Using GNSS Interferometric Reflectometry

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