“…The interferences between the reflected and the directed QZSS signals were recorded by SNR interferograms. In order to obtain the error in multipath, a low-order polynomial (second degree) is utilized to attain SNR detrended time series 2 , 9 . After removing the SNR trend, SNR plots (linear scale units of volts/volts) against the elevation angles are studied.…”
Section: Resultsmentioning
confidence: 99%
“…The QZSS-TG measurement based on the SNR measurement ( dB-Hz ) and the prior reflector height ( H 0 ) can be calculated as follow 28 : where A is the amplitude that is deviated from zero (unit: m), ϕ is the phase offset, θ is the elevation angle (unit: radian), and λ is the signal wavelength (unit: m). A detailed study of the whole analytical solution can be found in our previous work 2 . Furthermore, the Lomb–Scargle periodogram (LSP) is used to estimate the dominant frequency or f m,t (no unit) as proposed by Press et al 29 , and the f m,t is related to the effective reflector height ( H eff ) with the following equation: Figure 2 Screenshots of first Fresnel zones for the P109 site with elevation angles of 5° to 25° and azimuth range 30° to 330° projected on a Google Earth map.…”
Section: Data Collection and Methods Of Modelingmentioning
The tide gauge measurements from global navigation satellite system reflectometry (GNSS-R) observables are considered to be a promising alternative to the traditional tide gauges in the present days. In the present paper, we deliver a comparative analysis of tide-gauge (TG) measurements retrieved by quasi-zenith satellite system-reflectometry (QZSS-R) and the legacy TG recordings with additional observables from other constellations viz. GPS-R and GLONASS-R. The signal-to-noise ratio data of QZSS (L1, L2, and L5 signals) retrieved at the P109 site of GNSS Earth Observation Network in Japan (37.815° N; 138.281° E; 44.70 m elevation in ellipsoidal height) during 01 October 2019 to 31 December 2019. The results from QZSS observations at L1, L2, and L5 signals show respective correlation coefficients of 0.8712, 0.6998, and 0.8763 with observed TG measurements whereas the corresponding root means square errors were 4.84 cm, 4.26 cm, and 4.24 cm. The QZSS-R signals revealed almost equivalent precise results to that of GPS-R (L1, L2, and L5 signals) and GLONASS-R (L1 and L2 signals). To reconstruct the tidal variability for QZSS-R measurements, a machine learning technique, i.e., kernel extreme learning machine (KELM) is implemented that is based on variational mode decomposition of the parameters. These KELM reconstructed outcomes from QZSS-R L1, L2, and L5 observables provide the respective correlation coefficients of 0.9252, 0.7895, and 0.9146 with TG measurements. The mean errors between the KELM reconstructed outcomes and observed TG measurements for QZSS-R, GPS-R, and GLONASS-R very often lies close to the zero line, confirming that the KELM-based estimates from GNSS-R observations can provide alternative unbiased estimations to the traditional TG measurement. The proposed method seems to be effective, foreseeing a dense tide gauge estimations with the available QZSS-R along with other GNSS-R observables.
“…The interferences between the reflected and the directed QZSS signals were recorded by SNR interferograms. In order to obtain the error in multipath, a low-order polynomial (second degree) is utilized to attain SNR detrended time series 2 , 9 . After removing the SNR trend, SNR plots (linear scale units of volts/volts) against the elevation angles are studied.…”
Section: Resultsmentioning
confidence: 99%
“…The QZSS-TG measurement based on the SNR measurement ( dB-Hz ) and the prior reflector height ( H 0 ) can be calculated as follow 28 : where A is the amplitude that is deviated from zero (unit: m), ϕ is the phase offset, θ is the elevation angle (unit: radian), and λ is the signal wavelength (unit: m). A detailed study of the whole analytical solution can be found in our previous work 2 . Furthermore, the Lomb–Scargle periodogram (LSP) is used to estimate the dominant frequency or f m,t (no unit) as proposed by Press et al 29 , and the f m,t is related to the effective reflector height ( H eff ) with the following equation: Figure 2 Screenshots of first Fresnel zones for the P109 site with elevation angles of 5° to 25° and azimuth range 30° to 330° projected on a Google Earth map.…”
Section: Data Collection and Methods Of Modelingmentioning
The tide gauge measurements from global navigation satellite system reflectometry (GNSS-R) observables are considered to be a promising alternative to the traditional tide gauges in the present days. In the present paper, we deliver a comparative analysis of tide-gauge (TG) measurements retrieved by quasi-zenith satellite system-reflectometry (QZSS-R) and the legacy TG recordings with additional observables from other constellations viz. GPS-R and GLONASS-R. The signal-to-noise ratio data of QZSS (L1, L2, and L5 signals) retrieved at the P109 site of GNSS Earth Observation Network in Japan (37.815° N; 138.281° E; 44.70 m elevation in ellipsoidal height) during 01 October 2019 to 31 December 2019. The results from QZSS observations at L1, L2, and L5 signals show respective correlation coefficients of 0.8712, 0.6998, and 0.8763 with observed TG measurements whereas the corresponding root means square errors were 4.84 cm, 4.26 cm, and 4.24 cm. The QZSS-R signals revealed almost equivalent precise results to that of GPS-R (L1, L2, and L5 signals) and GLONASS-R (L1 and L2 signals). To reconstruct the tidal variability for QZSS-R measurements, a machine learning technique, i.e., kernel extreme learning machine (KELM) is implemented that is based on variational mode decomposition of the parameters. These KELM reconstructed outcomes from QZSS-R L1, L2, and L5 observables provide the respective correlation coefficients of 0.9252, 0.7895, and 0.9146 with TG measurements. The mean errors between the KELM reconstructed outcomes and observed TG measurements for QZSS-R, GPS-R, and GLONASS-R very often lies close to the zero line, confirming that the KELM-based estimates from GNSS-R observations can provide alternative unbiased estimations to the traditional TG measurement. The proposed method seems to be effective, foreseeing a dense tide gauge estimations with the available QZSS-R along with other GNSS-R observables.
“…Global warming and change of climate are also expected causes of sea-level rise. The impact of sea level rising is of special interest for the population residing in coastal areas or islands, which does not only impact the public safety but also their infrastructure developments, protection of relocation, sustainability and heath ecosystem services, and rising and fall of economical situations (Löfgren et al 2014;Oke et al 2015;Ansari et al 2020). Thus, effective and accurate modeling of sea-level variation and its impact on economic growth is an important mission (Wang et al 2018;Montillet et al 2018;Chen et al 2019;Cardellach et al 2019;Liu et al 2020;Tabibi et al 2020).…”
Section: Introductionmentioning
confidence: 99%
“…There are several kinds of observation models and integration systems that are relevant for the sea-level prediction in Japan's coastal areas as well other places of the world (Jeon et al 2014;Klos et al 2019;Ansari et al 2020;De Biasio et al 2020;Vignudelli De Biasioan 2021). Jeon et al (2014) used 25-month data of real-time sea-level measurements and studied semidiurnal internal tides seasonal variation.…”
This study investigates the real-time sea-level measurement from six tide gauge stations in the coastal area of Japan. Initially, the six co-located global navigation satellite station sites vertical velocity has been observed followed by estimation of the relative tide gauge velocities. The results showed that the estimated relative sea-level changes abruptly at different sites depending on the location of tide gauge stations. To accurately analyse the regional tide gauge patterns at each site, a continuous wavelet transforms based on Morse wavelet as a default transformation has been applied. Analysis showed that the peaks of tide gauges have not occurred at a static time at each site, rather they were repeating with the variation of time. The width of the spectrum peaks varied with different sites and the overall spectrum frequency was not unique. These characteristics variation with time may be due to the variables that affect the stability of the sea level measurements. Finally, the kernel extreme learning machine (KELM) approach based on variational mode decomposition is used to reconstruct the tide gauge measurements. The correlation coefficients between observed and reconstructed data sets are very high varying from 0.91 to 0.96. The estimated root mean square error between two data sets is varying from 0.04 to 0.06 m at the entire region. This means that the KELM reconstructed tide gauge showed good agreement with the observed tidal waveforms. Thus, it can be concluded that the proposed techniques can be implemented for effective real-time sea-level monitoring changes with the available data.
“…The most widely applied technique is to retrieve the characteristics of the earth surface from the reflected GNSS signals, which is called GNSS reflectometry (GNSS-R), and the implementation of the GNSS-R is more convenient and frequent in the ocean environment. So far, the applications of the GNSS-R in ocean remote sensing have covered lots of fields, such as sea state estimation [24], ocean altimetry [25,26], target detection [27], etc. A generalized version of the bistatic radar equation was derived by Voronovich and Zavorotny [28] to simulate the waveforms and delay-Doppler maps (DDMs) of the surface-reflected GNSS signals in various scattering scenarios including partially coherent scattering and strong diffuse scattering.…”
The anomalous propagation conditions, particularly the tropospheric ducts, severely impact the regular operation and performance evaluation of radio devices in the atmospheric boundary layer. Therefore, it is necessary to provide the regional distribution of tropospheric ducts for utilizing or avoiding these abnormal propagation phenomena. As significant uncooperative signal sources, the global navigation satellite systems (GNSS) have been widely applied in the remote sensing of the ocean and atmosphere due to the greater convenience and lower cost. With the completed deployment of the BeiDou Navigation Satellite System (BDS) in 2020, an additional source can be chosen in the relevant studies. Taking the BDS as an example, since the scattered signals from the ocean surface at low satellite elevation angles can be effectively trapped by tropospheric ducts, we propose a method to invert for the regional distribution of tropospheric ducts using the received power of ocean-scattered signals in this paper. Firstly, the propagation model was built to calculate the received power, and a suite of simulations was made in various atmospheric environments. The results suggested that the received power is more sensitive to the surface duct without a base layer. Then, we made a preliminary estimation of the tropospheric ducts on the ocean nearby Qingdao utilizing the Weather Research and Forecasting (WRF) model as well as the echo data measured by a Doppler weather radar. Before the inversion, the actual satellite azimuth and elevation angles should be obtained to evaluate the bistatic scattering coefficients and the received powers of the selected satellite signals. Finally, we presented an inversion example using the proposed method. In absence of the actual measurements, the received powers pre-estimated at different SNRs served as the inputs of the inversion process and the estimated duct parameters were used to verify the validity of the proposed inversion method. For both the received power and modified refractivity profile, the fitness between the values pre-estimated using the estimated duct parameters and calculated by the inverted duct parameters gets better as the elevation angle decreases and the SNR increases. The variation of the fitness between the estimated and inverted values is slightly different for each duct parameter. Moreover, the calculation of inversion errors further explained the above behaviors, including the mean absolute error (MAE) and the root mean square error (RMSE). Despite some certain errors, the inversion results maintain the overall tendencies and most characteristics of the estimated values, thus proving the validity of the inversion method.
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