Sea Surface Height Estimation Research Articles

A Post-Launch Update on the Effects of Instrumental Measurement Errors on SWOT Estimates of Sea-Surface Height, Velocity and Vorticity

A Post-Launch Update on the Effects of Instrumental Measurement Errors on SWOT Estimates of Sea-Surface Height, Velocity and Vorticity

An adaptive fusion algorithm for coastal sea altimetry based on dual-frequency Beidou-R carrier phase.

This article proposes an integer ambiguity determination method based on Beidou system-reflectometry (Beidou-R) observations of the carrier phase at the B1I and B3I frequencies. To enhance the accuracy of sea surface height (SSH) estimation, this study introduces a parallel filtering algorithm and an adaptive iterative fusion algorithm, enabling data fusion based on the variance at B1I and B3I frequencies. To validate and evaluate the proposed method, a coastal experiment was conducted at the Shenxian River. In this experiment, reflected signals from GEO and IGSO satellites were collected. Data analysis reveals that the method is effective, demonstrating that the root mean square error (RMSE) of SSH achieves 2.85 cm and 2.89 cm for PRN 04 and PRN 33, respectively. Furthermore, the impact of the elevation angle on measurement accuracy is analyzed. This study aims to propose a method to enhance coastal sea surface height estimation, offering potential advancements in sea surface altimetry.

Application of Deep Learning in Sea Surface Height Estimation of GNSS Data Sets

Application of Deep Learning in Sea Surface Height Estimation of GNSS Data Sets

Towards the Mitigation of Discrepancies in Sea Surface Parameters Estimated from Low- and High-Resolution Satellite Altimetry

In this study, we present an extension to existing numerical retrackers of synthetic-aperture radar (SAR) altimetry signals. To our knowledge at the time of writing this manuscript, it offers the most consistent retrieval of geophysical parameters compared to low-resolution mode (LRM) retracking results. We achieve this by additionally estimating the standard deviation of vertical wave-particle velocities σv and a new parameter ux, linked to a residual Doppler in the returned radar echoes, which can be related to wind speed and direction. Including this new parameter into the SAR stack retracker mitigates sea surface height estimation errors by up to two centimeters for Sentinel-6MF SAR mode results. Additionally, we found a closed-form equation to describe ux as a function of eastward and northward wind variables, which allows mitigating the effects of this parameter on a SAR stack within level 1B processing and generating a lookup table to correct sea surface height estimates in SAR mode. This additionally opens up the door to estimating the wind speed and direction from SAR altimetry stacks. Additionally, we discuss how this new retracker performs with respect to different planned future baseline processor changes of Sentinel-6MF, namely F09 and F10, by attempting to imitate their level 2 processing. This is achieved by processing cycles 017 to 051 (nearly a full year) of Sentinel-6MF level 1A data on a global scale. We observe that the new retracking method is, on average, more accurate with respect to LRM. However, there is a slight increase in measurement noise due to the introduction of an additional parameter. To ensure that the results of the new retracker are not biased, we retrack using both the new method and the SINCS-OV ZSK retracker on Sentinel-6MF stack data produced in a Monte Carlo simulation. We analyze the simulation results with respect to accuracy, precision, and correlations between estimated parameters. We show that the accuracy of the new retracker is better than SINCS-OV ZSK but less precise, which could be related to higher correlation coefficients—especially with respect to the new parameter ux—between estimated parameters.

A New Method to Combine Coastal Sea Surface Height Estimates from Multiple Retrackers by Using the Dijkstra Algorithm

To increase data availability and accuracy in the coastal zone, especially in the last 5 km to the coast, we present a SCMR (Seamless Combination of Multiple Retrackers) processing strategy to combine sea surface height (SSH) estimates from waveform retrackers of SGDR MLE4, ALES, WLS3 and MB4 for Jason-3 and Saral missions, and of SAMOSA and SAMOSA+ for Sentinel-3A mission in the Australian coastal zone. The SCMR does not require the waveform classification result. It includes two steps: (1) estimating and removing the SSH bias due mainly to the significant wave height (SWH) difference-dependent height differences, and (2) determining the optimal along-track SSH profile by using the Dijkstra algorithm. In the study region, the results show that the SCMR increases the data availability by up to 15% in the last 5 km to the coast and reduces the noise level by 28–34% at the spatial scales < 2.5 km. The validation results against tide gauges show that SCMR-derived SSH estimates achieve a better accuracy than that from any single retracker, with the improvement percentage of 6.26% and 4.94% over 0–10 km and 20–100 km distance bands, respectively.

Sea Surface Height Estimation from Improved Modified, and Decontaminated Sub-Waveform Retracking Methods over Coastal Areas

Coastal zones are challenging areas for sensing by satellite altimeters because reflected signals from non-water surfaces and from calm sea surfaces in small bays and ports inside the radar footprint lead to erroneous powers in return waveforms. Accordingly, these contaminated waveforms do not follow the so-called Brown model in conventional retracking algorithms and fail to derive qualified ranges. Consequently, the estimated water level is erroneous as well. Therefore, selecting an optimized retracker for post-processing waveforms is significantly important to achieve a qualified water level estimation. To find the optimized retracker, we employed a methodology to minimize the effect of erroneous powers on retracked range corrections. To this end, two new approaches were presented, one based on a waveform decontamination method and the other based on a waveform modification method. We considered the first meaningful sub-waveforms in the decontaminated waveforms and in the modified waveforms to be processed with a threshold retracker. To assess their performance, we also retracked the decontaminated and modified full-waveforms. The first meaningful sub-waveform and full-waveform in the original waveforms were retracked to compare the performance of the modified and decontaminated waveform retracking with the original waveform retracking. To compare the results of our sub-waveform retracking algorithms with those of external sub-waveform retracking algorithms, the (Adaptive Leading Edge Sub-waveform) ALES database was also used. In our retracking scenarios, we used the Sentinel-3A SRAL Altimeter to estimate the water levels over the study area within 10 km from the coastlines in both the Persian Gulf and the Bay of Biscay from June 2016 to October 2020. The water levels from processing L2 products were estimated as well. We evaluated our retracking scenarios and L2, as well as the ALES processing results, against the tide gauges. Our analysis showed that within 0–10 km from the coast, the first meaningful sub-waveform of the decontaminated waveforms had the best performance. We reached maximum RMS improvements in this scenario of 53% and 86% over the Persian Gulf and the Bay of Biscay, respectively, in comparison with L2 processing. Over these distances from the coast, the first sub-waveform from the original waveforms and the modified waveforms stayed in the second and third order of performance. The ALES database with an RMS ranging from 13 to 51 cm had a worse performance than all of our sub-waveform retracking scenarios.

Sea Level Rise Estimation on the Pacific Coast from Southern California to Vancouver Island

Previous studies have estimated the sea level rise (SLR) at various locations on the west coast of the USA and Vancouver Island in Canada. Here, we construct an entire SLR profile from Vancouver Island in the Pacific Northwest to San Diego in Southern California. First, we process global navigation satellite system (GNSS) measurements at 405 stations blanketing the whole coast to generate a profile of vertical land motion (VLM) known to bias century-long tide gauge (TG) measurements recording relative SLR (RSLR). We are then able to estimate the absolute SLR (ASLR) by correcting the SLR with the VLM. Our study emphasizes the relationship between the various tectonic movements (i.e., the Cascadia subduction zone, the San Andreas strike-slip fault system) along the Pacific coast which renders it difficult to accurately estimate the SLR. That is why we precisely model the stochastic noise of both GNSS and tide gauge time series using a combination of various models and information criterions (ICs). We also use the latest altimetry products and sea surface height (SSH) to compare it with ASLR at the same location as the TGs. This study supports previous analysis that the power law + white noise and generalized Gauss–Markov + white noise models are the best stochastic noise models for the GNSS time series. The new coastal profile confirms the large variability of VLM estimates in the Pacific Northwest around the Cascadia subduction zone in agreement with previous studies, and a similar result when the San Andreas fault comes onshore in Central California (San Francisco Bay). Negative RSLR values are mostly located in the Pacific Northwest (Vancouver Island and Olympic Peninsula). We also observe a much bigger variation (about 90–150%) of the ASLR in the Pacific Northwest which is predominantly due to glacial isostatic adjustment (GIA). Moreover, the comparison between the ASLR and the SSH estimates shows similarities in the center of the studied area (South Washington, Oregon planes, and some parts of Southern California) where the tectonic activity does not significantly influence the TG measurements. Finally, the twentieth-century satellite geocentric ocean height rates show a global mean of 1.5 to 1.9 mm/yr. Our estimates based on ASLR and SSH are within this interval.

The Effects of Uncorrelated Measurement Noise on SWOT Estimates of Sea Surface Height, Velocity, and Vorticity

Abstract The Ka-band Radar Interferometer (KaRIn) on the Surface Water and Ocean Topography (SWOT) satellite will revolutionize satellite altimetry by measuring sea surface height (SSH) with unprecedented accuracy and resolution across two 50-km swaths separated by a 20-km gap. The original plan to provide an SSH product with a footprint diameter of 1 km has changed to providing two SSH data products with footprint diameters of 0.5 and 2 km. The swath-averaged standard deviations and wavenumber spectra of the uncorrelated measurement errors for these footprints are derived from the SWOT science requirements that are expressed in terms of the wavenumber spectrum of SSH after smoothing with a filter cutoff wavelength of 15 km. The availability of two-dimensional fields of SSH within the measurement swaths will provide the first spaceborne estimates of instantaneous surface velocity and vorticity through the geostrophic equations. The swath-averaged standard deviations of the noise in estimates of velocity and vorticity derived by propagation of the uncorrelated SSH measurement noise through the finite difference approximations of the derivatives are shown to be too large for the SWOT data products to be used directly in most applications, even for the coarsest footprint diameter of 2 km. It is shown from wavenumber spectra and maps constructed from simulated SWOT data that additional smoothing will be required for most applications of SWOT estimates of velocity and vorticity. Equations are presented for the swath-averaged standard deviations and wavenumber spectra of residual noise in SSH and geostrophically computed velocity and vorticity after isotropic two-dimensional smoothing for any user-defined smoother and filter cutoff wavelength of the smoothing.

Nemo-Nordic 2.0: operational marine forecast model for the Baltic Sea

Abstract. This paper describes Nemo-Nordic 2.0, an operational marine model for the Baltic Sea. The model is used for both near-real-time forecasts and hindcast purposes. It provides estimates of sea surface height, water temperature, salinity, and velocity, as well as sea ice concentration and thickness. The model is based on the NEMO (Nucleus for European Modelling of the Ocean) circulation model and the previous Nemo-Nordic 1.0 configuration by Hordoir et al. (2019). The most notable updates include the switch from NEMO version 3.6 to 4.0, updated model bathymetry, and revised bottom friction formulation. The model domain covers the Baltic Sea and the North Sea with approximately 1 nmi resolution. Vertical grid resolution has been increased from 3 to 1 m in the surface layer. In addition, the numerical solver configuration has been revised to reduce artificial mixing to improve the representation of inflow events. Sea ice is modeled with the SI3 model instead of LIM3. The model is validated against sea level, water temperature, and salinity observations, as well as Baltic Sea ice chart data for a 2-year hindcast simulation (October 2014 to September 2016). Sea level root mean square deviation (RMSD) is typically within 10 cm throughout the Baltic basin. Seasonal sea surface temperature variation is well captured, although the model exhibits a negative bias of approximately −0.5 ∘C. Salinity RMSD is typically below 1.5 g kg−1. The model captures the 2014 major Baltic inflow event and its propagation to the Gotland Deep. The model assessment demonstrates that Nemo-Nordic 2.0 can reproduce the hydrographic features of the Baltic Sea.

Retrieval of Sea Surface Height from CYGNSS Data with Tropospheric Delay

An analysis of the delay Doppler maps (DDMs) data from the CYGNSS satellites is implemented to derive the sea surface height (SSH). An SSH estimation algorithm, the leading edge derivation (LED) method which is applied to the delay waveforms, is applied to the DDMs, while the tropospheric delay methods, the Saastamoinen method (SM) and the numerical method (NM) are used. The results show that when the SSH from Jason-2 is referred to as the truth, if the tropospheric delay is corrected, the SSH bias can decrease. The resulted SSH bias from the Jason-2 SSH by the LED retrieval method is of order meter. The resulted SSH deviation from the truth by the NM scheme is half as small as that by the SM scheme. Since the SM scheme is not applicable to the nonhydrostatical condition, the resulted bias is larger. The work can be applied to the Beidou system in the future.

Waveform re-tracking analyses with Fuzzy Logic on altimetry satellite data in Natuna Waters

Waveform re-tracking analyses have been proven to increase the accuracy of sea surface height (SSH) estimation from satellite altimeters specifically in coastal areas. However, each re-tracking algorithm has its strengths and weaknesses so that no dominant algorithm can be applied to any water condition. The study purpose was to obtain the best SSH estimation from altimeter satellite data using waveform re-tracking analyses with fuzzy logic system. The fuzzy logic system was used to select the best SSH values from the results of waveform re-tracking analyses. The data used in this study were level-2 SGRD data from Jason-2 and Jason-3 in Natuna Waters in 2016-2018. Waveform re-tracking with fuzzy logic system can reduce standard deviation of SSH up to 23.3 cm from the on-board (oceanic) algorithm standard deviation. The highest Improvement Percentage (IMP) value from each observation track was constantly generated by re-tracking with fuzzy logic system up to 70.3%. The result of this study showed that this analysis can produce SSH values with the best accuracy in each track observation.

Sea Surface Height Estimation with Multi-GNSS and Wavelet De-noising

This paper presents a new sea surface height (SSH) estimation using GNSS reflectometry (GNSS-R). It is a cost-effective remote sensing technique and owns long-term stability besides high temporal and spatial resolution. Initial in-situ SSH estimates are first produced by using the SNR data of BDS (L1, L5, L7), GPS (L1, L2, L5), and GLONASS (L1, L2), of MAYG station, which is located in Mayotte, France near the Indian Ocean. The results of observation data over a period of seven days showed that the root mean square error (RMSE) of SSH estimation is about 32 cm and the correlation coefficient is about 0.83. The tidal waveform is reconstructed based on the initial SSH estimates by utilizing the wavelet de-noising technique. By comparing the tide gauge measurements with the reconstructed tidal waveform at SSH estimation instants, the SSH estimation errors can be obtained. The results demonstrate that the correlation coefficient and RMSE of the wavelet de-noising based SSH estimation is 0.95 and 19 cm, respectively. Compared with the initial estimation results, the correlation coefficient is improved by about 14.5%, while the RMSE is reduced by 40.6%.

Waveform identification and retracking analyses of Jason-2 altimeter satellite data for improving sea surface height estimation in Southern Java Island Waters and Java Sea, Indonesia

Indonesian waters containing many small islands and shallow waters leads to a less accurate of sea surface height (SSH) estimation from satellite altimetry. Little efforts are also given for the validation of SSH estimation from the satellite in Indonesian waters. The purpose of this research was to identify and retrack waveforms of Jason-2 altimeter satellite data in southern Java island waters and Java Sea using several retrackers and performed improvement percentage analyses for new SSH estimation. The study used data of the Sensor Geophysical Data Record type D (SGDR-D) of Jason-2 satellite altimeter of the year 2010 in the southern Java island waters and 2012-2014 in Java Sea. Waveform retracking analyses were conducted using several retrackers (Offset Center of Gravity, Ice, Threshold, and Improved Threshold) and examined using a world reference undulation geoid of EGM08 and Oceanic retracker. Result showed that shape and pattern of waveforms were varied in all passes, seasons, and locations specifically along the coastal regions. In general, non-Brownish and complex waveforms were identified along coastal region specifically within the distance of 0-10 km from the shoreline. In contrary, generally Brownish waveforms were found in offshore. However, Brownish waveform can also be found within coastal region and non-Brownish waveforms within offshore region. The results were also showed that the four retrackers produced a better SSH estimation in coastal region. However, there was no dominant retracker to improve the accuracy of the SSH estimate.

A Complete Picture of Southern Ocean Surface Circulation

For the first time, researchers combine estimates of sea surface height and circulation patterns in both ice-covered and ice-free regions of the Southern Ocean.

Uncertainty reduction of Arctic sea ice freeboard from CryoSat-2 interferometric mode

A study by Armitage and Davidson (2014) has shown that the extra information from the CryoSat-2 (CS2) SARIn mode increases the number of valid sea surface height estimates which are usually discarded in the SAR mode due to snagging of the radar signal. As the number of valid detected leads increases, the uncertainty of the freeboard heights decreases.In this study, the freeboard heights estimated by processing CS2 SARIn level 1b waveforms are validated using the information from airborne laser and radar altimetry as well as snow radar measurements acquired during the CryoVEx 2012 and Operation IceBridge 2012 campaigns, respectively. The possible reduction in the random freeboard uncertainty is investigated comparing two scenarios, i.e. a SAR-like and a SARIn acquisition.A very good agreement is found between average airborne and satellite radar freeboards although, at the CS2 footprint scale, they do not show along-track spatial correlation. It is observed that using the extra phase information, CS2 is able to detect leads up to 2300 m off-nadir. A reduction in the total random freeboard uncertainty of ∼40% is observed by taking advantage of the CS2 interferometric capabilities, which enable to include ∼35% of the waveforms discarded in the SAR-like scenario.

Dynamic Topography and Sea Level Anomalies of the Southern Ocean: Variability and Teleconnections

AbstractThis study combines sea surface height (SSH) estimates of the ice‐covered Southern Ocean with conventional open‐ocean SSH estimates from CryoSat‐2 to produce monthly composites of dynamic ocean topography (DOT) and sea level anomaly (SLA) on a 50 km grid spanning 2011–2016. This data set reveals the full Southern Ocean SSH seasonal cycle for the first time; there is an antiphase relationship between sea level on the Antarctic continental shelf and the deeper basins, with coastal SSH highest in autumn and lowest in spring. As a result of this pattern of seasonal SSH variability, the barotropic component of the Antarctic Slope Current (ASC) has speeds that are regionally up to twice as fast in the autumn. Month‐to‐month circulation variability of the Ross and Weddell Gyres is strongly influenced by the local wind field, and is correlated with the local wind curl (Ross: −0.58; Weddell: −0.67). SSH variability is linked to both the Southern Oscillation and the Southern Annular Mode, dominant modes of southern hemisphere climate variability. In particular, during the strong 2015–2016 El Niño, a sustained negative coastal SLA of up to −6 cm, implying a weakening of the ASC, was observed in the Pacific sector of the Southern Ocean. The ability to examine sea level variability in the seasonally ice‐covered regions of the Southern Ocean—climatically important regions with an acute sparsity of data—makes this new merged sea level record of particular interest to the Southern Ocean oceanography and glaciology communities.

Arctic sea surface height variability and change from satellite radar altimetry and GRACE, 2003–2014

AbstractArctic sea surface height (SSH) is poorly observed by radar altimeters due to the poor coverage of the polar oceans provided by conventional altimeter missions and because large areas are perpetually covered by sea ice, requiring specialized data processing. We utilize SSH estimates from both the ice‐covered and ice‐free ocean to present monthly estimates of Arctic Dynamic Ocean Topography (DOT) from radar altimetry south of 81.5°N and combine this with GRACE ocean mass to estimate steric height. Our SSH and steric height estimates show good agreement with tide gauge records and geopotential height derived from Ice‐Tethered Profilers. The large seasonal cycle of Arctic SSH (amplitude ∼5 cm) is dominated by seasonal steric height variation associated with seasonal freshwater fluxes, and peaks in October–November. Overall, the annual mean steric height increased by 2.2 ± 1.4 cm between 2003 and 2012 before falling to circa 2003 levels between 2012 and 2014 due to large reductions on the Siberian shelf seas. The total secular change in SSH between 2003 and 2014 is then dominated by a 2.1 ± 0.7 cm increase in ocean mass. We estimate that by 2010, the Beaufort Gyre had accumulated 4600 km3 of freshwater relative to the 2003–2006 mean. Doming of Arctic DOT in the Beaufort Sea is revealed by Empirical Orthogonal Function analysis to be concurrent with regional reductions in the Siberian Arctic. We estimate that the Siberian shelf seas lost ∼180 km3 of freshwater between 2003 and 2014, associated with an increase in annual mean salinity of 0.15 psu yr−1. Finally, ocean storage flux estimates from altimetry agree well with high‐resolution model results, demonstrating the potential for altimetry to elucidate the Arctic hydrological cycle.

ACCURACY IMPROVEMENT ON SEA SURFACE HEIGHT ESTIMATION BASED ON WAVEFORM RETRACKING ANALYSES OF JASON-2 SATELLITE IN JAVA SEA

A waveform created by the reflected signal from altimeter satellite in offshore is generally in ideal shape (Brown-waveform) and produces an accurate sea surface height (SSH) estimation. However, over coastal waters, the waveform shape becomes complex due to a disruption by reflected signal from land, resulting inaccurate SSH estimation. The objective of this research was to improve the accuracy of SSH estimation employing waveform retracking analyses of Jason-2 altimeter satellite data in the Java Sea during the years of 2012-2014. This study used data from the Sensor Geophysical Data Record type D (SGDR-D) from Jason-2 satellite (cycle 129 - 239) and global geoid undulation data of Earth Gravitational Model 2008 (EGM08). Waveform retracking analyses were conducted using several retracker methods. The performance of the all retrackers were examined using a world reference undulation geoid of EGM08. The results showed that the waveform retracking analyses were able to improve the accuracy of SSH estimation approximately 29.7% in the north coast and 56.4% in the south coast of total non-Brown-waveform in each region. Higher improvement percentage (IMP) of SSH estimation found in the southern coastal areas was due to a relatively smooth coastline formation in this region than in northern coastal region. There was no specific retracker that produce dominant IMP of SSH estimation. However, the threshold 10% retracker produced better SSH estimation than the other retrackers with dominant IMP values of 57.1% (pass 051), 48.1% (pass 064), and 25.7% (pass 127). OCOG retracker the worst retracker to estimate SSH in the Java Sea. Keywords: EGM08, waveform retracking, SSH, Jason-2, ocean retracker, threshold retracker

ACCURACY IMPROVEMENT ON SEA SURFACE HEIGHT ESTIMATION BASED ON WAVEFORM RETRACKING ANALYSES OF JASON-2 SATELLITE IN JAVA SEA

<p><em>A waveform created by the reflected signal from altimeter satellite in offshore is generally in ideal shape (Brown-waveform) and produces an accurate sea surface height (SSH) estimation. However, over coastal waters, the waveform shape becomes complex due to a disruption by reflected signal from land, resulting inaccurate SSH estimation. The objective of this research was to improve the accuracy of SSH estimation employing waveform retracking analyses of Jason-2 altimeter satellite data in the Java Sea during the years of 2012-2014. This study used data from the Sensor Geophysical Data Record type D (SGDR-D) from Jason-2 satellite (cycle 129 - 239) and global geoid undulation data of Earth Gravitational Model 2008 (EGM08). </em><em>Waveform retracking analyses were conducted using several retracker methods</em>. <em>The performance of the all retrackers were examined using a world reference undulation geoid of EGM08. The results showed that the waveform retracking analyses were able to improve the accuracy of SSH estimation approximately 29.7% in the north coast and 56.4% in the south coast of total non-Brown-waveform in each region. Higher improvement percentage (IMP) of SSH estimation found in the southern coastal areas was due to a relatively smooth coastline formation in this region than in northern coastal region. There was no specific retracker that produce dominant IMP of SSH estimation. However, the threshold 10% retracker produced better SSH estimation than the other retrackers with dominant IMP values of 57.1% (pass 051), 48.1% (pass 064), and 25.7% (pass 127). OCOG retracker the worst retracker to estimate SSH in the Java Sea. </em></p><p><strong><em>Keywords: </em></strong><em>EGM08, </em><em>waveform retracking, SSH, Jason-2, ocean retracker, threshold retracker</em></p>

First spaceborne observation of sea surface height using GPS‐Reflectometry

AbstractAn analysis of spaceborne Global Positioning System reflectometry (GPS‐R) data from the TechDemoSat‐1 (TDS‐1) satellite is carried out to image the ocean sea surface height (SSH). An SSH estimation algorithm is applied to GPS‐R delay waveforms over two regions in the South Atlantic and the North Pacific. Estimates made from TDS‐1 overpasses during a 6 month period are aggregated to produce SSH maps of the two regions. The maps generally agree with the global DTU10 mean sea surface height. The GPS‐R instrument is designed to make bistatic measurements of radar cross section for ocean wind observations, and its altimetric performance is not optimized. The differences observed between measured and DTU10 SSH can be attributed to limitations with the GPS‐R instrument and the lack of precision orbit determination by the TDS‐1 platform. These results represent the first observations of SSH by a spaceborne GPS‐R instrument.