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

Williams, Simon; Geremia‐Nievinski, Felipe

doi:10.1002/2016jb013612

Cited by 85 publications

(70 citation statements)

References 37 publications

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“…6 Standard deviation of the difference between the tide gauges and the Kalman filter solution as a function of time before evaluation for both GTGU and SPBY. Spectral analysis is possible after recording data from a sufficiently long satellite arc, i.e., roughly 20 min (Williams and Nievinski 2017). Least-squares inversion (LSQ) requires significantly more data, and thus, time before analysis is possible.…”

Section: Discussionmentioning

confidence: 99%

“…The data are taken from the GTGU installation at Onsala on day of year 357 in reflector height h . Depending on the height of the antenna above the reflecting surfaces, roughly 20 min of SNR data are enough for a single height retrieval (Williams and Nievinski 2017). This method, which we will refer to as spectral analysis, has been successfully demonstrated to be able to retrieve, for example, snow depth , where the assumption of a static reflector is appropriate.…”

Section: Current State Of Gnss Reflectometrymentioning

confidence: 99%

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Real-time sea-level monitoring using Kalman filtering of GNSS-R data

2019

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Current GNSS-R (GNSS reflectometry) techniques for sea surface measurements require data collection over longer periods, limiting their usability for real-time applications. In this work, we present a new, alternative GNSS-R approach based on the unscented Kalman filter and the so-called inverse modeling approach. The new method makes use of a mathematical description that relates SNR (signal-to-noise ratio) variations to multipath effects and uses a B-spline formalism to obtain time series of reflector height. The presented algorithm can provide results in real time with a precision that is significantly better than spectral inversion methods and almost comparable to results from inverse modeling in post-processing mode. To verify the performance, the method has been tested at station GTGU at the Onsala Space Observatory, Sweden, and at the station SPBY in Spring Bay, Australia. The RMS (root mean square) error with respect to nearby tide gauge data was found to be 2.0 cm at GTGU and 4.8 cm at SPBY when evaluating the output corresponding to real-time analysis. The method can also be applied in post-processing, resulting in RMS errors of 1.5 cm and 3.3 cm for GTGU and SPBY, respectively. Finally, based on SNR data from GTGU, it is also shown that the Kalman filter approach is able to detect the presence of sea ice with a higher temporal resolution than the previous methods and traditional remote sensing techniques which monitor ice in coastal regions.

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Section: Discussionmentioning

confidence: 99%

Section: Current State Of Gnss Reflectometrymentioning

confidence: 99%

Real-time sea-level monitoring using Kalman filtering of GNSS-R data

2019

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“…Due to the tropospheric delay, the measured reflector heights are always smaller than the true geometric heights. Williams and Nievinski (2017) demonstrated that ignoring the tropospheric delay in GNSS-IR induces a scale error in the reflector heights. We use a combination of astronomical…”

Section: Snr Data Analysismentioning

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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“…The complementary angle of the incidence angle ε defers from the elevation angle of the satellite due to the curvature of the reflecting surface and can be calculated according to [6,15]. The tropospheric refraction can be considered by a correction of ε derived from an astronomic refraction model [16][17][18][19].…”

Section: Analysis Of Snr Datamentioning

confidence: 99%

Estimating Wave Direction Using Terrestrial GNSS Reflectometry

2019

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The signal-to-noise ratio (SNR) data are part of the global navigation satellite systems (GNSS) observables. In a marine environment, the oscillation of the SNR data can be used to derive reflector heights. Since the attenuation of the SNR oscillation is related to the roughness of the sea surface, the significant wave height (SWH) of the water surface can be calculated from the analysis of the attenuation. The attenuation depends additionally on the relation between the coherent and the incoherent part of the scattered power. The latter is a function of the correlation length of the surface waves. Since the correlation length changes with respect to the direction of the line of sight relative to the wave direction, the attenuation must show an anisotropic characteristic. In this work, we present a method to derive the wave direction from the anisotropy of the attenuation of the SNR data. The method is investigated based on simulated data, as well by the analysis of experimental data from a GNSS station in the North Sea.

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

Cited by 85 publications

References 37 publications

Real-time sea-level monitoring using Kalman filtering of GNSS-R data

Real-time sea-level monitoring using Kalman filtering of GNSS-R data

Application of GNSS interferometric reflectometry for detecting storm surges

Estimating Wave Direction Using Terrestrial GNSS Reflectometry

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