Sea level monitoring and sea state estimate using a single geodetic receiver

Roussel, Nicolas; Ramillien, Guillaume; Frappart, Frédéric; Darrozes, José; Gay, A.M.; Biancale, R.; Striebig, Nicolas; Hanquiez, Vincent; Bertin, Xavier; Allain, Damien

doi:10.1016/j.rse.2015.10.011

Cited by 95 publications

(59 citation statements)

References 21 publications

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“…But for sites with large sea level variations a correction term must be added, for example, based on tidal models [ Löfgren and Haas , ]. Roussel et al [] instead introduced a method based on the Lomb‐Scargle inversion that combines all available GNSS signals, by fitting h and

\frac{normald h}{normald t}

to all satellites visible during a measurement time span. However, their study considers only a correction term for linear temporal changes of the reflector height.…”

Section: Gnss‐r and Sea Levelmentioning

confidence: 99%

Improving GNSS‐R sea level determination through inverse modeling of SNR data

2016

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This paper presents a new method for retrieving sea surface heights from Global Navigation Satellite Systems reflectometry (GNSS‐R) data by inverse modeling of SNR observations from a single geodetic receiver. The method relies on a B‐spline representation of the temporal sea level variations in order to account for its continuity. The corresponding B‐spline coefficients are determined through a nonlinear least squares fit to the SNR data, and a consistent choice of model parameters enables the combination of multiple GNSS in a single inversion process. This leads to a clear increase in precision of the sea level retrievals which can be attributed to a better spatial and temporal sampling of the reflecting surface. Tests with data from two different coastal GNSS sites and comparison with colocated tide gauges show a significant increase in precision when compared to previously used methods, reaching standard deviations of 1.4 cm at Onsala, Sweden, and 3.1 cm at Spring Bay, Tasmania.

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\frac{normald h}{normald t}

to all satellites visible during a measurement time span. However, their study considers only a correction term for linear temporal changes of the reflector height.…”

Section: Gnss‐r and Sea Levelmentioning

confidence: 99%

Improving GNSS‐R sea level determination through inverse modeling of SNR data

2016

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“…They included a tropospheric effect by using an adaptive mapping function [ Gegout et al ., ] to calculate the change in incidence angle (with respect to the vacuum angle) due to bending of the radio waves and found that, for receiver heights greater than 5 m and elevation angles less than 10°, tropospheric error has a significant effect on the specular point position. They did not however calculate the effect on propagation delays or reflector heights, which was also left for future investigation in their follow‐on paper [ Roussel et al ., ]. Santamaria‐Gomez et al ., [] noted an elevation dependence in estimated leveling heights between observations using satellite arcs with mean elevations below 12° compared to those above 12°.…”

Section: Introductionmentioning

confidence: 99%

Tropospheric delays in ground‐based GNSS multipath reflectometry—Experimental evidence from coastal sites

Williams

Geremia‐Nievinski

2017

JGR Solid Earth

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Recent studies have demonstrated the utility of ground‐based Global Navigation Satellite Systems‐Multipath Reflectometry (GNSS‐MR) for sea level studies. Typical root‐mean‐square (RMS) differences of GNSS‐MR‐derived sea level time series with respect to nearby tide gauges are on the order of 6–40 cm, sufficiently accurate to estimate tidal and secular sea level variations but are possibly biased due to delay of the signal through the troposphere. In this study we investigate the tropospheric effect from more than 20 GNSS coastal sites located from several meters up to 280 m above sea level. We find a bias in the estimated heights that is elevation and height dependent and can reach orders of 1 m for a 90 m site. Without correcting for tropospheric delay we find that GNSS‐MR‐estimated tidal coefficients will be smaller than their true amplitudes by around 2% while phases seem unaffected. Correcting for the tropospheric delay also improves leveling results as a function of reflector height. Correcting for the tropospheric delay in GNSS‐MR for sea level studies is therefore highly recommended for all sites no matter the height of the antenna above the sea surface as it manifests as a scale error.

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“…By analyzing the frequency of the SNR oscillation, we can estimate the vertical distance from the water surface to the GNSS antenna phase center. It has been demonstrated that the retrieved water levels from a single geodetic GNSS receiver have comparable accuracy to traditional tide gauges (Larson et al 2013b(Larson et al , 2017Löfgren et al 2014;Roussel et al 2015). GNSS-IR can sense larger areas (tens to hundreds of meters from the station) than traditional tide gauges, and may thus capture sea-level extremes occurring at some distance along the coast.…”

Section: Introductionmentioning

confidence: 99%

Application of GNSS interferometric reflectometry for detecting storm surges

Peng

Hill

et al. 2019

GPS Solut

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A single geodetic GNSS station placed at the coast has the capability of a traditional tide gauge for sea-level measurements, with the additional advantage of simultaneously obtaining vertical land motions. The sea-level measurements are obtained using GNSS signals that have reflected off the water, using analysis of the signal-to-noise ratio (SNR) data. For the first time, we apply this technique to detect extreme weather-induced sea-level fluctuations, i.e., storm surges. We first derive 1-year sea-level measurements under normal weather conditions, for a GNSS station located in Hong Kong, and compare them with traditional tide-gauge data to validate its performance. Our results show that the RMS difference between the individual GNSS sea-level measurements and tide-gauge records is about 12.6 cm. Second, we focus on the two recent extreme events, Typhoon Hato of 2017 and Typhoon Mangkhut of 2018, that are ranked the third and second most powerful typhoons hitting Hong Kong since 1954 in terms of maximum sea level. We use GNSS SNR data from two coastal stations to produce sea-level measurements during the two typhoon events. Referenced to predicted astronomical tides, the storm surges caused by the two events are evident in the sea-level time series generated from the SNR data, and the results also agree with tidegauge records. Our results demonstrate that this technique has the potential to provide a new approach to monitor storm surges that complement existing tide-gauge networks.

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Sea level monitoring and sea state estimate using a single geodetic receiver

Cited by 95 publications

References 21 publications

Improving GNSS‐R sea level determination through inverse modeling of SNR data

Improving GNSS‐R sea level determination through inverse modeling of SNR data

Tropospheric delays in ground‐based GNSS multipath reflectometry—Experimental evidence from coastal sites

Application of GNSS interferometric reflectometry for detecting storm surges

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