Performance Assessment of GNSS-R Polarimetric Observations for Sea Level Monitoring

2021 ◽  
Author(s):  
Mahmoud Rajabi ◽  
Mstafa Hoseini ◽  
Hossein Nahavandchi ◽  
Maximilian Semmling ◽  
Markus Ramatschi ◽  
...  
Keyword(s):  
Time Series ◽  
Circular Polarization ◽  
Sea Level ◽  
Tide Gauge ◽  
Satellite System ◽  
Sea Surface ◽  
Analysis Tool ◽  
Lower Accuracy ◽  
Coastal Sea ◽  
Global Navigation Satellite

<p>Determination and monitoring of the mean sea level especially in the coastal areas are essential, environmentally, and as a vertical datum. Ground-based Global Navigation Satellite System Reflectometry (GNSS-R) is an innovative way which is becoming a reliable alternative for coastal sea-level altimetry. Comparing to traditional tide gauges, GNSS-R can offer different parameters of sea surface, one of which is the sea level. The measurements derived from this technique can cover wider areas of the sea surface in contrast to point-wise observations of a tide gauge.  </p><p>We use long-term ground-based GNSS-R observations to estimate sea level. The dataset includes one-year data from January to December 2016. The data was collected by a coastal GNSS-R experiment at the Onsala space observatory in Sweden. The experiment utilizes three antennas with different polarization designs and orientations. The setup has one up-looking, and two sea-looking antennas at about 3 meters above the sea surface level. The up-looking antenna is Right-Handed Circular Polarization (RHCP). The sea-looking antennas with RHCP and Left-Handed Circular Polarization (LHCP) are used for capturing sea reflected Global Positioning System (GPS) signals. A dedicated reflectometry receiver (GORS type) provides In-phase and Quadrature (I/Q) correlation sums for each antenna based on the captured interferometric signal. The generated time series of I/Q samples from different satellites are analyzed using the Least Squares Harmonic Estimation (LSHE) method. This method is a multivariate analysis tool which can flexibly retrieve the frequencies of a time series regardless of possible gaps or unevenly spaced sampling. The interferometric frequency, which is related to the reflection geometry and sea level, is obtained by LSHE with a temporal resolution of 15 minutes. The sea level is calculated based on this frequency in six modes from the three antennas in GPS L1 and L2 signals.</p><p>Our investigation shows that the sea-looking antennas perform better compared to the up-looking antenna. The highest accuracy is achieved using the sea-looking LHCP antenna and GPS L1 signal. The annual Root Mean Square Error (RMSE) of 15-min GNSS-R water level time series compared to tide gauge observations is 3.7 (L1) and 5.2 (L2) cm for sea-looking LHCP, 5.8 (L1) and 9.1 (L2) cm for sea-looking RHCP, 6.2 (L1) and 8.5 (L2) cm for up-looking RHCP. It is worth noting that the GPS IIR block satellites show lower accuracy due to the lack of L2C code. Therefore, the L2 observations from this block are eliminated.</p>

scholarly journals A comparison of methods to estimate vertical land motion trends from GNSS and altimetry at tide gauge stations

Ocean Science ◽  
2018 ◽  
Vol 14 (2) ◽  
pp. 187-204 ◽  
Author(s):  
Marcel Kleinherenbrink ◽  
Riccardo Riva ◽  
Thomas Frederikse
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Tide Gauge ◽  
Satellite System ◽  
Vertical Land Motion ◽  
Correlation Threshold ◽  
The Mean ◽  
Highly Correlated ◽  
Global Navigation Satellite ◽  
Along Track

Abstract. Tide gauge (TG) records are affected by vertical land motion (VLM), causing them to observe relative instead of geocentric sea level. VLM can be estimated from global navigation satellite system (GNSS) time series, but only a few TGs are equipped with a GNSS receiver. Hence, (multiple) neighboring GNSS stations can be used to estimate VLM at the TG. This study compares eight approaches to estimate VLM trends at 570 TG stations using GNSS by taking into account all GNSS trends with an uncertainty smaller than 1 mm yr−1 within 50 km. The range between the methods is comparable with the formal uncertainties of the GNSS trends. Taking the median of the surrounding GNSS trends shows the best agreement with differenced altimetry–tide gauge (ALT–TG) trends. An attempt is also made to improve VLM trends from ALT–TG time series. Only using highly correlated along-track altimetry and TG time series reduces the SD of ALT–TG time series by up to 10 %. As a result, there are spatially coherent changes in the trends, but the reduction in the root mean square (RMS) of differences between ALT–TG and GNSS trends is insignificant. However, setting correlation thresholds also acts like a filter to remove problematic TG time series. This results in sets of ALT–TG VLM trends at 344–663 TG locations, depending on the correlation threshold. Compared to other studies, we decrease the RMS of differences between GNSS and ALT–TG trends (from 1.47 to 1.22 mm yr−1), while we increase the number of locations (from 109 to 155), Depending on the methods the mean of differences between ALT–TG and GNSS trends vary between 0.1 and 0.2 mm yr−1. We reduce the mean of the differences by taking into account the effect of elastic deformation due to present-day mass redistribution. At varying ALT–TG correlation thresholds, we provide new sets of trends for 759 to 939 different TG stations. If both GNSS and ALT–TG trend estimates are available, we recommend using the GNSS trend estimates because residual ocean signals might correlate over long distances. However, if large discrepancies ( > 3 mm yr−1) between the two methods are present, local VLM differences between the TG and the GNSS station are likely the culprit and therefore it is better to take the ALT–TG trend estimate. GNSS estimates for which only a single GNSS station and no ALT–TG estimate are available might still require some inspection before they are used in sea level studies.

scholarly journals An open-source low-cost sensor for SNR-based GNSS reflectometry: design and long-term validation towards sea-level altimetry

GPS Solutions ◽  
2021 ◽  
Vol 25 (2) ◽  
Author(s):  
M. A. R. Fagundes ◽  
I. Mendonça-Tinti ◽  
A. L. Iescheck ◽  
D. M. Akos ◽  
F. Geremia-Nievinski
Keyword(s):  
Open Source ◽  
Sea Level ◽  
Tide Gauge ◽  
Signal To Noise Ratio ◽  
Low Cost ◽  
Satellite System ◽  
Open Science ◽  
Coastal Sea ◽  
Commercial Off The Shelf ◽  
Global Navigation Satellite

AbstractMonitoring sea level is critical due to climate change observed over the years. Global Navigation Satellite System Reflectometry (GNSS-R) has been widely demonstrated for coastal sea-level monitoring. The use of signal-to-noise ratio (SNR) observations from ground-based stations has been especially productive for altimetry applications. SNR records an interference pattern whose oscillation frequency allows retrieving the unknown reflector height. Here we report the development and validation of a complete hardware and software system for SNR-based GNSS-R. We make it available as open source based on the Arduino platform. It costs about US$200 (including solar power supply) and requires minimal assembly of commercial off-the-shelf components. As an initial validation towards applications in coastal regions, we have evaluated the system over approximately 1 year by the Guaíba Lake in Brazil. We have compared water-level altimetry retrievals with independent measurements from a co-located radar tide gauge (within 10 m). The GNSS-R device ran practically uninterruptedly, while the reference radar gauge suffered two malfunctioning periods, resulting in gaps lasting for 44 and 38 days. The stability of GNSS-R altimetry results enabled the detection of miscalibration steps (10 cm and 15 cm) inadvertently introduced in the radar gauge after it underwent maintenance. Excluding the radar gaps and its malfunctioning periods (reducing the time series duration from 317 to 147 days), we have found a correlation of 0.989 and RMSE of 2.9 cm in daily means. To foster open science and lower the barriers for entry in SNR-based GNSS-R research and applications, we make a complete bill of materials and build tutorials freely available on the Internet so that interested researchers can  replicate the system.

Validation of sea surface heights from satellite altimetry along the Indian coast

2021 ◽  
Author(s):  
Milaa Murshan ◽  
Balaji Devaraju ◽  
Nagarajan Balasubramanian ◽  
Onkar Dikshit
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Satellite Altimetry ◽  
Tide Gauge ◽  
Atmospheric Correction ◽  
North Indian Ocean ◽  
Sea Surface ◽  
Indian Coast ◽  
Vertical Land Motion ◽  
And Performance

<p>Satellite altimetry provides measurements of sea surface height of centimeter-level accuracy over open oceans. However, its accuracy reduces when approaching the coastal areas and over land regions. Despite this downside, altimetric measurements are still applied successfully in these areas through altimeter retracking processes. This study aims to calibrate and validate retracted sea level data of Envisat, ERS-2, Topex/Poseidon, Jason-1, 2, SARAL/AltiKa, Cryosat-2 altimetric missions near the Indian coastline. We assessed the reliability, quality, and performance of these missions by comparing eight tide gauge (TG) stations along the Indian coast. These are Okha, Mumbai, Karwar, and Cochin stations in the Arabian Sea, and Nagapattinam, Chennai, Visakhapatnam, and Paradip in the Bay of Bengal. To compare the satellite altimetry and TG sea level time series, both datasets are transformed to the same reference datum. Before the calculation of the bias between the altimetry and TG sea level time series, TG data are corrected for Inverted Barometer (IB) and Dynamic Atmospheric Correction (DAC). Since there are no prior VLM measurements in our study area, VLM is calculated from TG records using the same procedure as in the Technical Report NOS organization CO-OPS 065. </p><p>Keywords— Tide gauge, Sea level, North Indian ocean, satellite altimetry, Vertical land motion</p>

Regional Mean Sea Surface Model over the Eastern Mediterranean Sea

2020 ◽  
Author(s):  
Milaa Murshan ◽  
Balaji Devaraju ◽  
Nagarajan Balasubramanium ◽  
Onkar Dikshit
Keyword(s):  
Time Series ◽  
Mediterranean Sea ◽  
Sea Level ◽  
Tide Gauge ◽  
Eastern Mediterranean ◽  
Sea Surface ◽  
Surface Model ◽  
Eastern Mediterranean Sea ◽  
Mean Sea Level ◽  
Mean Sea Surface

<p>The Mean Sea Level is not an equipotential surface because it is subject to several variations, e.g., the tides, currents, winds, etc. Mean Sea Level can be measured either by tide gauges near to coastlines relative to local datum or by satellite altimeter above the reference ellipsoid. From this observable quantity, one can derive a non-observable quantity at which the potential is constant called geoid and differs from mean sea surface by amount of ±1 m. This separation is called Sea Surface Topography. In this research, the data of nine altimetric Exact Repeat Missions (Envisat, ERS_1 of 35 days (phase C and G), ERS_2, GFO, Jason_1, Jason_2, Jason_3, Topex/Poseidon and SARAL) were used for computing the regional mean sea surface model over the eastern Mediterranean Sea. The data of all missions together span approximately 25 years from September -1992 to January-2017 and referenced to Topex ellipsoid.  Which is later transformed to WGS84 ellipsoid, as it is chosen to be a unified datum in this study. Prior to computing the altimetric MSS,  altimetric sea surface height measurements were validated  by comparing  time series of altimetric-MSL with mean sea level time series calculated from three in-situ tide gauge measurements.  The sea surface heights values of the derived MSS model is between 15.6 and 26.7 m. And the linear trend slope is between -3.02 to 6.53 mm/year.</p><p>Keywords: Mean Sea Level, Satellite Altimetry, Tide Gauge, Exact Repeat Missions</p>

scholarly journals Coastal sea level rise at Senetosa (Corsica) during the Jason altimetry missions

Ocean Science ◽  
2020 ◽  
Vol 16 (5) ◽  
pp. 1165-1182 ◽  
Author(s):  
Yvan Gouzenes ◽  
Fabien Léger ◽  
Anny Cazenave ◽  
Florence Birol ◽  
Pascal Bonnefond ◽  
...  
Keyword(s):  
High Resolution ◽  
Sea Level Rise ◽  
Sea Level ◽  
Leading Edge ◽  
Satellite System ◽  
Careful Examination ◽  
Coastal Zones ◽  
Coastal Sea ◽  
Potential Contributor ◽  
Global Navigation Satellite

Abstract. In the context of the ESA Climate Change Initiative project, we are engaged in a regional reprocessing of high-resolution (20 Hz) altimetry data of the classical missions in a number of the world's coastal zones. It is done using the ALES (Adaptive Leading Edge Subwaveform) retracker combined with the X-TRACK system dedicated to improve geophysical corrections at the coast. Using the Jason-1 and Jason-2 satellite data, high-resolution, along-track sea level time series have been generated, and coastal sea level trends have been computed over a 14-year time span (from July 2002 to June 2016). In this paper, we focus on a particular coastal site where the Jason track crosses land, Senetosa, located south of Corsica in the Mediterranean Sea, for two reasons: (1) the rate of sea level rise estimated in this project increases significantly in the last 4–5 km to the coast compared to what is observed further offshore, and (2) Senetosa is the calibration site for the TOPEX/Poseidon and Jason altimetry missions, which are equipped for that purpose with in situ instrumentation, in particular tide gauges and a Global Navigation Satellite System (GNSS) antenna. A careful examination of all the potential errors that could explain the increased rate of sea level rise close to the coast (e.g., spurious trends in the geophysical corrections, imperfect inter-mission bias estimate, decrease of valid data close to the coast and errors in waveform retracking) has been carried out, but none of these effects appear able to explain the trend increase. We further explored the possibility that it results from real physical processes. Change in wave conditions was investigated, but wave setup was excluded as a potential contributor because the magnitude was too low and too localized in the immediate vicinity of the shoreline. A preliminary model-based investigation about the contribution of coastal currents indicates that it could be a plausible explanation of the observed change in sea level trend close to the coast.

Sea Surface Height Predictions from the Global Navy Coastal Ocean Model during 1998–2001*

2004 ◽  
Vol 21 (12) ◽  
pp. 1876-1893 ◽  
Author(s):  
Charlie N. Barron ◽  
A. Birol Kara ◽  
Harley E. Hurlburt ◽  
C. Rowley ◽  
Lucy F. Smedstad
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Tide Gauge ◽  
Ocean Model ◽  
Sea Surface ◽  
Tide Gauges ◽  
Model Data ◽  
Free Running ◽  
R Value ◽  
Coastal Ocean Model

Abstract A ⅛° global version of the Navy Coastal Ocean Model (NCOM), operational at the Naval Oceanographic Office (NAVOCEANO), is used for prediction of sea surface height (SSH) on daily and monthly time scales during 1998–2001. Model simulations that use 3-hourly wind and thermal forcing obtained from the Navy Operational Global Atmospheric Prediction System (NOGAPS) are performed with/without data assimilation to examine indirect/direct effects of atmospheric forcing in predicting SSH. Model–data evaluations are performed using the extensive database of daily averaged SSH values from tide gauges in the Atlantic, Pacific, and Indian Oceans obtained from the Joint Archive for Sea Level (JASL) center during 1998–2001. Model–data comparisons are based on observations from 282 tide gauge locations. An inverse barometer correction was applied to SSH time series from tide gauges for model–data comparisons, and a sensitivity study is undertaken to assess the impact of the inverse barometer correction on the SSH validation. A set of statistical metrics that includes conditional bias (Bcond), root-mean-square (rms) difference, correlation coefficient (R), and nondimensional skill score (SS) is used to evaluate the model performance. It is shown that global NCOM has skill in representing SSH even in a free-running simulation, with general improvement when SSH from satellite altimetry and sea surface temperature (SST) from satellite IR are assimilated via synthetic temperature and salinity profiles derived from climatological correlations. When the model was run from 1998 to 2001 with NOGAPS forcing, daily model SSH comparisons from 612 yearlong daily tide gauge time series gave a median rms difference of 5.98 cm (5.77 cm), an R value of 0.72 (0.76), and an SS value of 0.45 (0.51) for the ⅛° free-running (assimilative) NCOM. Similarly, error statistics based on the 30-day running averages of SSH time series for 591 yearlong daily tide gauge time series over the time frame 1998–2001 give a median rms difference of 3.63 cm (3.36 cm), an R value of 0.83 (0.85), and an SS value of 0.60 (0.64) for the ⅛° free-running (assimilated) NCOM. Model– data comparisons show that skill in 30-day running average SSH time series is as much as 30% higher than skill for daily SSH. Finally, SSH predictions from the free-running and assimilative ⅛° NCOM simulations are validated against sea level data from the tide gauges in two different ways: 1) using original detided sea level time series from tide gauges and 2) using the detided data with an inverse barometer correction derived using daily mean sea level pressure extracted from NOGAPS at each location. Based on comparisons with 612 yearlong daily tide gauge time series during 1998–2001, the inverse barometer correction lowered the median rms difference by about 1 cm (15%–20%). Results presented in this paper reveal that NCOM is able to predict SSH with reasonable accuracies, as evidenced by model simulations performed during 1998–2001. In an extension of the validation over broader ocean regions, the authors find good agreement in amplitude and distribution of SSH variability between NCOM and other operational model products.

scholarly journals A comparison of data weighting methods to derive vertical land motion trends from GNSS and altimetry at tide gauge stations

2017 ◽  
Author(s):  
Marcel Kleinherenbrink ◽  
Riccardo Riva ◽  
Thomas Frederikse
Keyword(s):  
Time Series ◽  
Tide Gauge ◽  
Satellite System ◽  
Vertical Land Motion ◽  
Correlation Threshold ◽  
The Mean ◽  
Highly Correlated ◽  
Global Navigation Satellite ◽  
Along Track ◽  
Weighting Methods

Abstract. This study compares eight weighting techniques for Global Navigation Satellite System (GNSS)-derived Vertical Land Motion (VLM) trends at 570 tide gauge (TG) stations. The spread between the methods has a comparable size as the formal uncertainties of the GNSS trends. Taking the median of the surrounding GNSS trends shows the best agreement with differenced altimetry – tide gauge (ALT-TG) trends. An attempt is also made to improve VLM trends from ALT-TG time series. Only using highly correlated along-track altimetry and TG time series, reduces the standard deviation of ALT-TG time series up to 10 %. As a result, there are spatially coherent changes in the trends, but the reduction in the RMS of differences between ALT-TG and GNSS trends is insignificant. However, setting correlation thresholds also acts like a filter to remove problematic TG stations. This results in sets of ALT-TG VLM trends at 344–663 TG locations, depending on the correlation threshold. Compared to other studies, we decrease the RMS of differences between GNSS and ALT-TG trends (from 1.47 to 1.22 mm/yr), while we increase the number of locations (from 109 to 155), Depending on the weighting methods the mean of differences between ALT-TG and GNSS trends varies between 0.1–0.2 mm/yr. We reduce the mean of differences by taking into account the effect of elastic deformation due to present-day mass redistribution into account.

scholarly journals Mapping Sea Surface Height Using New Concepts of Kinematic GNSS Instruments

2020 ◽  
Vol 12 (16) ◽  
pp. 2656
Author(s):  
Clémence Chupin ◽  
Valérie Ballu ◽  
Laurent Testut ◽  
Yann-Treden Tranchant ◽  
Michel Calzas ◽  
...  
Keyword(s):  
Sea Level ◽  
Complex Dynamics ◽  
Tide Gauge ◽  
Sea Surface Height ◽  
Sea Surface ◽  
Global Navigation Satellite Systems ◽  
Detailed Knowledge ◽  
Satellite Systems ◽  
Global Mean Sea Level ◽  
Global Navigation Satellite

For over 25 years, satellite altimetry observations have provided invaluable information about sea-level variations, from Global Mean Sea-Level to regional meso-scale variability. However, this information remains difficult to extract in coastal areas, where the proximity to land and complex dynamics create complications that are not sufficiently accounted for in current models. Detailed knowledge of local hydrodynamics, as well as reliable sea-surface height measurements, is required to improve and validate altimetry measurements. New kinematic systems based on Global Navigation Satellite Systems (GNSS) have been developed to map the sea surface height in motion. We demonstrate the capacity of two of these systems, designed to measure the height at a centimetric level: (1) A GNSS floating carpet towed by boat (named CalNaGeo); and (2) a combination of GNSS antenna and acoustic altimeter (named Cyclopée) mounted on an unmanned surface vehicle (USV). We show that, at a fixed point, these instruments provide comparable accuracy to the best available tide gauge systems. When moving at up to 7 knots, the instrument velocity does not affect the sea surface height accuracy, and the two instruments agree at a cm-level.

scholarly journals Neural Network Model to Predict a Storm Surge

2009 ◽  
Vol 48 (1) ◽  
pp. 143-155 ◽  
Author(s):  
Marilia M. F. de Oliveira ◽  
Nelson Francisco F. Ebecken ◽  
Jorge Luiz Fernandes de Oliveira ◽  
Isimar de Azevedo Santos
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Sea Level ◽  
Storm Surge ◽  
Network Model ◽  
Neural Network Model ◽  
Tide Gauge ◽  
Low Frequency ◽  
Coastal Sea ◽  
The Relationship

Abstract The southeastern coast of Brazil is frequently affected by meteorological disturbances such as cold fronts, which are sometimes associated with intense extratropical cyclones. These disturbances cause oscillations on the sea surface, generating low-frequency motions. The relationship of these meteorologically driven forces in low frequency to the storm-surge event is investigated in this work. A method to predict coastal sea level variations related to meteorological events that use a neural network model (NNM) is presented here. Pressure and wind values from NCEP–NCAR reanalysis data and tide gauge time series from the Cananéia reference station in São Paulo State, Brazil, were used to analyze the relationship between these variables and to use them as input to the model. Meteorological influences in the sea level fluctuations can be verified by filtering the astronomical tide frequencies for periods lower than tidal cycles (periods higher than 24 h). Thus, a low-pass filter was applied in the tide gauge and meteorological time series for periods lower than tides to identify more readily the interactions between coastal sea level response and atmospheric-driven forces. Statistical analyses on time and frequency domain were used. Maxima correlations and coherence between the low-frequency sea level and meteorological series could be defined using the time lag of the NNM input variables. The model was tested for 6-, 12-, 18-, and 24-hourly forecasts, and the results were compared with filtered sea level values. The results show that this model is able to capture the effects of atmospheric and oceanic interactions. It can be considered to be an efficient model for predicting the nontidal residuals and can effectively complement the standard constant harmonic analysis model. A case study of a storm that impacted coastal areas of southeastern Brazil in March 1998 was analyzed and indicates that the neural network model can be effectively utilized in the Cananéia region.

Beach Deployment of a Low-Cost GNSS Buoy for Determining Sea-Level and Wave Characteristics

Geosciences ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 494
Author(s):  
Philip J. Knight ◽  
Cai O. Bird ◽  
Alex Sinclair ◽  
Jonathan Higham ◽  
Andrew J. Plater
Keyword(s):  
Sea Level ◽  
Tide Gauge ◽  
Low Cost ◽  
Satellite System ◽  
Mooring Line ◽  
Level Data ◽  
Elevation Data ◽  
Satellite Signal ◽  
Global Navigation Satellite ◽  
Spatially Explicit Data

Spatially explicit data on tidal and waves are required as part of coastal monitoring applications (e.g., radar monitoring of coastal change) for the design of interventions to mitigate the impacts of climate change. A deployment over two tidal cycles of a low-cost Global Navigation Satellite System (GNSS) buoy at Rossall (near Fleetwood), UK demonstrated the potential to record good quality sea level and wave data within the intertidal zone. During each slack water and the following ebb tide, the sea level data were of good quality and comparable with data from nearby tide gauges on the national tide gauge network. Moreover, the GNSS receiver was able to capture wave information and these compared well with data from a commercial wave buoy situated 9.5 km offshore. Discontinuities were observed in the elevation data during flood tide, coincident with high accelerations and losing satellite signal lock. These were probably due to strong tidal currents, which, combined with spilling waves, would put the mooring line under tension and allow white water to spill over the antenna resulting in the periodic loss of GNSS signals, hence degrading the vertical solutions.

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