Sea State Bias Research Articles

Exploring Siamese network to estimate sea state bias of synthetic aperture radar altimeter

Sea state bias (SSB) is a crucial error of satellite radar altimetry over the ocean surface. For operational nonparametric SSB (NPSSB) models, such as two-dimensional (2D) or three-dimensional (3D) NPSSB, the solution process becomes increasingly complex and the construction of their regression functions pose challenges as the dimensionality of relevant variables increases. And most current SSB correction models for altimeters still follow those of traditional nadir radar altimeters, which limits their applicability to Synthetic Aperture Radar altimeters. Therefore, to improve this situation, this study has explored the influence of multi-dimensional SSB models on Synthetic Aperture Radar altimeters. This paper proposes a deep learning-based SSB estimation model called SNSSB, which employs a Siamese network framework, takes various multi-dimensional variables related to sea state as inputs, and uses the difference in sea surface height (SSH) at self-crossover points as the label. Experiments were conducted using Sentinel-6 self-crossover data from 2021 to 2023, and the model is evaluated using three main metrics: the variance of the SSH difference, the explained variance, and the SSH difference variance index (SVDI). The experimental results demonstrate that the proposed SNSSB model can further improve the accuracy of SSB estimation. On a global scale, compared to the traditional NPSSB, the multi-dimensional SNSSB not only decreases the variance of the SSH difference by over 11%, but also improves the explained variance by 5-10 cm2 in mid- and low-latitude regions. And the regional SNSSB also performs well, reducing the variance of the SSH difference by over 10% compared to the NPSSB. Additionally, the SNSSB model improves the computational efficiency by approximately 100 times. The favorable results highlight the potential of the multi-dimensional SNSSB in constructing SSB models, particularly the five-dimensional (5D) SNSSB, representing a breakthrough in overcoming the limitations of traditional NPSSB for constructing high-dimensional models. This study provides a novel approach to exploring the multiple influencing factors of SSB.

Determination of the Geoid Heights of Awka and Environ from the Satellite Altimetry data using Broadview RADAR Altimeter Toolbox (BRAT) and Sentinel-3 Missions

Satellite altimetry has revolutionized our ability to measure Earth's surface with unprecedented accuracy, offering invaluable insights into various geophysical phenomena. This study presents the determination of geoid heights (Ns) of Awka and Environ utilizing the Broadview RADAR Altimeter Toolbox (BRAT) in conjunction with data from the Sentinel-3 missions. The geoid, a surface of constant gravitational potential representing mean sea level, is a fundamental reference surface for geodetic measurements and understanding Earth's gravity field. The methodology involves processing raw altimetry data acquired by the Sentinel-3 missions using BRAT, followed by precise corrections for various factors affecting the altimeter measurements, such as atmospheric delays, sea state bias, and orbit errors. Subsequently, the derived SSH data are combined with precise geoid models to compute the geoid heights at different spatial resolutions. The results demonstrate the effectiveness of the approach in determining geoid height (N) with high precision and spatial resolution, offering valuable contributions to geodetic research and applications. The utilization of Sentinel-3 data combined with BRAT facilitates robust and accurate geoid determination, which is essential for a wide range of geospatial applications, including oceanography, geophysics, and climate studies. The integration of BRAT and Sentinel-3 missions offers a powerful tool for geodetic research and applications, contributing to our understanding of Earth's dynamic processes and improving the accuracy of geospatial measurements.

CryoSat Long-Term Ocean Data Analysis and Validation: Final Words on GOP Baseline-C

ESA’s Earth explorer mission CryoSat-2 has an ice-monitoring objective, but it has proven to also be a valuable source of observations for measuring impacts of climate change over oceans. In this paper, we report on our long-term ocean data analysis and validation and give our final words on CryoSat-2’s Geophysical Ocean Products (GOP) Baseline-C. The validation is based on a cross comparison with concurrent altimetry and with in situ tide gauges. The highlights of our findings include GOP Baseline-C showing issues with the ionosphere and pole tide correction. The latter gives rise to an east–west pattern in range bias. Between Synthetic Aperture Radar (SAR) and Low-Resolution Mode (LRM), a 1.4 cm jump in range bias is explained by a 0.5 cm jump in sea state bias, which relates to a significant wave height SAR-LRM jump of 10.5 cm. The remaining 0.9 cm is due to a range bias between ascending and descending passes, exhibiting a clear north–south pattern and ascribed to a timing bias of +0.367 ms, affecting both time-tag and elevation. The overall range bias of GOP Baseline-C is established at −2.9 cm, referenced to all calibrated concurrent altimeter missions. The bias drift does not exceed 0.2 mm/yr, leading to the conclusion that GOP Baseline-C is substantially stable and measures up to the altimeter reference missions. This is confirmed by tide gauge comparison with a selected set of 309 PSMSL tide gauges over 2010–2022: we determined a correlation of R = 0.82, a mean standard deviation of σ=5.7 cm (common reference and GIA corrected), and a drift of 0.17 mm/yr. In conclusion, the quality, continuity, and reference of GOP Baseline-C is exceptionally good and stable over time, and no proof of any deterioration or platform aging has been found. Any improvements for the next CryoSat-2 Baselines could come from sea state bias optimization, ionosphere and pole tide correction improvement, and applying a calibrated value for any timing biases.

Polar Ocean Tides—Revisited Using Cryosat-2

With the availability of more than 9 years of Cryosat-2, it is possible to revisit polar ocean tides, which have traditionally been difficult to determine from satellite altimetry. The SAMOSA+ physical retracker is a stable retracker developed particularly for Cryosat-2. Being a physical retracker, it enables the determination of the sea state bias. Correcting for the sea state bias enables more reliable sea level estimates compared with traditional empirical retrackers used before. Cryosat-2 data have been analyzed for residual ocean tides to the FES2014 ocean tide model in the Arctic Ocean and Antarctic Ocean using the response formalism. We utilize data from the sub-cycle of Cryosat-2, which follows a repeating pattern of approximately 28.33 days. This sub-repeat period makes it an advantageous alias period for the majority of significant constituents. This allowed for the estimation and mapping of the major tidal constituents in the open ocean and also in floating ice shelves from data extracted from leads in the sea ice. A novel empirical ocean tide model designed specifically for the polar region, DTU22, is introduced. Our findings reveal substantial enhancements in semi-diurnal tides within the Arctic Ocean and improvement in diurnal constituents within the Southern Ocean. In the Southern Ocean, the diurnal constituents are particularly improved using the empirical model by more than a factor of two to around 3 cm for both constituents compared with FES2014b. These outcomes underscore the significance of incorporating the reprocessed and retracted Cryosat-2 data into tidal modeling, highlighting its pivotal role in advancing the field.

Estimation of the Sea State Bias Using the Interpolation Method and Applications to Inter-Mission Calibration

An alternative approach to empirical, non-parametric sea state bias (SSB) modeling for satellite altimeter measurements was developed with the intention of providing a simple, transparent, and efficient means to derive both a raw and smoothed SSB solution. This alternative approach, referred to as the interpolation method, maintains the flexibility to generate 2-D or 3-D models using either direct or difference measurements of the sea level anomaly uncorrected for SSB (uSLA). The final, smoothed SSB solution derived using the interpolation method is obtained over three steps, with a supplemental fourth step that consists of estimating a model-dependent dual-frequency ionosphere calibration bias to correct for a relative range + SSB error. A tandem phase analysis for all Topex/Poseidon, Jason 1-3 and Sentinel-6 Michael Freilich satellite altimeter inter-calibration periods reveals that the ionosphere calibration bias removes an ionosphere-related component from intermission bias calculations required to generate the long-term sea level record.

Quantifying Multifrequency Ocean Altimeter Wind Speed Error Due to Sea Surface Temperature and Resulting Impacts on Satellite Sea Level Measurements

Surface wind speed measurements from a satellite radar altimeter are used to adjust altimeter sea level measurements via sea state bias range correction. We focus here on previously neglected ocean radar backscatter and subsequent wind speed variations due to sea surface temperature (SST) change that may impact these sea level estimates. The expected error depends on the radar operating frequency and may be significant at the Ka band (36 GHz) frequency chosen for the new Surface Water and Ocean Topography (SWOT) satellite launched in December 2022. SWOT is expected to revolutionize oceanography by providing wide-swath Ka band observations and enhanced spatial resolution compared to conventional Ku band (14 GHz) altimetry. The change to the Ka band suggests a reconsideration of SST impact on wind and sea level estimates, and we investigate this in advance of SWOT using existing long-term Ku and Ka band satellite altimeter datasets. This study finds errors up to 1.5 m/s in wind speed estimation and 1.0 cm in sea level for AltiKa altimeter data. Future SWOT data analyses may require consideration of this dependence prior to using its radar backscatter data in its sea level estimation.

Estimation of the sea state bias error budget for pulse-limited satellite altimetry

Using an empirical, non-parametric sea state bias (SSB) modeling method, which was developed as a tool for SSB error analysis (Putnam, Alexa Forthcoming), we provide an error budget for overall SSB error, as well as the contributing sources of this error budget. The error analysis compares methods used to derive SSB models from observed altimeter measurements, collinear differences of measurements from adjacent repeat cycles, and methods using both collinear and crossover differences of measurements. Our error analysis reveals systematic error caused by ionosphere correction uncertainty in SSB models obtained from direct measurements, and wet troposphere correction uncertainty in SSB models generated using difference measurements. Results also expose a correlation to altimeter measurement error, with the backscatter coefficient accounting for over 20% of the SSB evaluation error and SWH accounting for approximately 50-60%. The error analysis presented here suggests SSB errors are lower than the often-used approximation of SSB error as 1% of SWH, except at SWH values less than 2 m where errors are likely larger. We find that increasing the pulse repetition frequency of the altimeter reduces SSB errors. The future for improving empirical, nonparametric SSB estimation primarily depends on improving measured SWH.

On Modelling Sea State Bias of Jason-2 Altimeter Data Based on Significant Wave Heights and Wind Speeds

Altimeter data processing is very important to improve the quality of sea surface height (SSH) measurements. Sea state bias (SSB) correction is a relatively uncertain error correction due to the lack of a clear theoretical model. At present, the commonly used methods for SSB correction are polynomial models (parametric models) and non-parametric models. The non-parametric model usually was constructed by collinear data. However, the amount of collinear data was enormous, and it contained redundant information. In this study, the non-parametric regression estimation model was optimized by using the parameter replacement method of ascending and descending tracks based on the crossover data. In this method, significant wave heights from the Jason-2 altimeter data during cycles 200–301 and wind speed from the ERA5 reanalysis data were used. The non-parametric regression estimation model of Jason-2 was constructed by combining it with local linear regression, Epanechnikov kernel function and local window width. At the same time, based on the significant wave height and wind speed at the crossover points, the SSB polynomial model containing six parameters was constructed by using the Taylor series expansion, and the model was optimized. By comparing polynomial model construction with different parameters, the optimized model was obtained. The SSH of the crossover points and the tide gauge records were used to validate these results derived from two models and GDR. Compared with the crossover discrepancies of SSH corrected by the polynomial model, the RMS of the crossover discrepancies of SSH corrected by the non-parametric regression estimation model was reduced by 7.9%. Compared with the crossover discrepancies of SSH corrected by the conventional non-parametric model from GDR, the RMS of the crossover discrepancies of SSH corrected by the non-parametric regression estimation model was reduced by 4.1%. This shows that the precision of the SSHs derived by after the SSB correction, as calculated by the non-parametric regression estimation model, was better than that of the polynomial model and the SSB correction from GDR. Using the Jason-2 altimeter data, the along-track geoid gradient and the sea level change rate of the global ocean were determined by using two models to correct the SSB. By comparing the results of the two models, the accuracy of the geoid gradient along the orbit that was obtained by the non-parametric regression estimation model was better than that of the polynomial model and GDR. The global average sea level change rate after the non-parametric regression estimation model correction was 3.47 ± 0.09 mm/y, which was the closest to the average sea level change rate that has been published in the international literature within this field.

The Impact of Altimetry Corrections of Sentinel-3A Sea Surface Height in the Coastal Zone of the Northwest Atlantic

Corrections to altimeter-measured sea surface height anomalies (SSHA) have a larger proportional effect for synthetic aperture radar (SAR) altimetry than conventional, pulse-limited altimetry because of lower range noise. Here, we quantified the impact of the current generation of altimeter corrections in the coastal zone of the Northwest Atlantic, a region with significant dynamic activity. In this study, we used the sea level variance analysis to determine the change in variance for the altimeter corrections—range, geophysical, and mean surface—compared to the baseline. We also evaluated the performance of two coastal retrackers, ALES (empirical) and SAMOSA++ (fully analytical), against the SSHA from the Radar Altimeter Database System (RADS), which uses the standard SAR retracker. Tide corrections caused the largest change in sea level variance, followed by wet tropospheric corrections and sea state bias. Most non-standard altimeter corrections failed to reduce the sea level variance and performed markedly worse closer to the coast. Coastal retrackers showed a higher deviation from the standard SSHA closer to the coast, especially when the backscatter coefficient was high and the significant wave height was low. We conclude that further development of coastal corrections is needed. Contrary to our prior expectation, we found that standard altimetry corrections appear to perform as well as alternative more advanced/tailored corrections.

Seasonality of the Sub‐Mesoscale to Mesoscale Sea Surface Variability From Multi‐Year Satellite Altimetry

AbstractSea surface slope (SSS) varies in response to a range of physical processes: tides, geostrophic flows, surface and internal waves, etc. We present the sea surface variation in the form of the SSS variability using 30 years of heterogeneous satellite altimetry measurements. We apply band‐pass filters to the along‐track SSS, and derive the mean and seasonal (annual and semi‐annual) components of SSS variability in multiple wavelength sub‐bands from 10 to 1,000 km. We show that the seasonal components are generally small (<10% in amplitude) compared to the mean variability. Through correlation analysis, we show evidence that SSS variability with wavelengths less than 30 km is dominated by wave height noise. At sub‐mesoscale to mesoscale (30–100 km) wavelengths, we identify high variability over western boundary currents and regions of rough topography. In this band, the high‐latitude Northern Hemisphere and the south Indian Ocean are associated with large annual cycles. The variability is higher in local wintertime except for a few regions, for example, the Bay of Bengal, which shows high variability in the boreal spring and fall. Through power spectral density analysis of the seasonal SSS variability, we find that the energy differences between local winter and summer are stronger at smaller scales (<100 km). The Ka‐band radar interferometry instrument on the Surface Water and Ocean Topography satellite mission will allow observation of ocean surface activities down to ∼20 km at submonthly time scales, but wave‐related errors (sea state bias, aliasing, wind‐driven activities, etc.) will still be a major challenge.

Impact of Errors in Environmental Correction on Gravity Field Recovery Using Interferometric Radar Altimeter Observations

As a new type of altimeter, interferometric radar altimeter (InRA) has significant potential in marine gravity field recovery due to its high spatial resolution. However, errors in environmental correction on gravity field recovery using InRA observations are unclear. In this study, four kinds of these errors, including wet and dry troposphere, ionosphere, and sea state bias (SSB) correction errors, are simulated. The impact of these errors on gravity field recovery are analyzed and discussed. The results show that, among the four types of errors in environmental correction, the wet troposphere and SSB have a more significant impact on the accuracy of sea surface height computing, and the wet troposphere has the most significant impact on the accuracy of gravity field recovery. The maximum error of gravity anomaly caused by the wet troposphere residual errors is nearly 2 mGal, and the relative error of the recovered gravity anomaly is around 6.42%. We can also find that SSB has a little more significant impact than dry troposphere and ionosphere, where dry troposphere and ionosphere have an almost identical impact, on DV and GA inversion accuracy.

Variability of surface gravity wave field over a realistic cyclonic eddy

Abstract. Recent remote sensing measurements and numerical studies have shown that surface gravity waves interact strongly with small-scale open ocean currents. Through these interactions, the significant wave height, the wave frequency, and the wave direction are modified. In the present paper, we investigate the interactions of surface gravity waves with a large and isolated realistic cyclonic eddy. This eddy is subject to instabilities, leading to the generation of specific features at both the mesoscale and submesoscale ranges. We use the WAVEWATCH III numerical framework to force surface gravity waves in the eddy before and after its destabilization. In the wave simulations the source terms are deactivated, and waves are initialized with different wave intrinsic frequencies. The study of these simulations illustrates how waves respond to the numerous kinds of instabilities in the large cyclonic eddy from a few hundred to a few tens of kilometres. Our findings show that the spatial variability of the wave direction, the mean period, and the significant wave height is very sensitive to the presence of submesoscale structures resulting from the eddy destabilization. The intrinsic frequency of the incident waves is key in the change of the wave direction resulting from the current-induced refraction and in the location, from the boundary where waves are generated, of the maximum values of significant wave height. However, for a given current forcing, the maximum values of the significant wave height are similar regardless of the frequency of the incident waves. In this idealized study it has been shown that the spatial gradients of wave parameters are sharper for simulations forced with the destabilized eddy. Because the signature of currents on waves encodes important information of currents, our findings suggest that the vertical vorticity of the current could be statistically estimated from the significant wave height gradients down to a very fine spatial scale. Furthermore, this paper shows the necessity to include currents in parametric models of sea-state bias; using a coarse-resolution eddy field may severely underestimate the sea-state-induced noise in radar altimeter measurements.

Validation of an Empirical Subwaveform Retracking Strategy for SAR Altimetry

The sea level retrievals from the latest generation of radar altimeters (the SAR altimeters) are still challenging in the coastal zone and areas covered by sea ice and require a dedicated fitting (retracking) strategy for the waveforms. In the framework of the European Space Agency’s Baltic + Sea Level (ESA Baltic SEAL) project, an empirical retracking strategy (ALES + SAR), including a dedicated sea state bias correction, has been designed to improve the sea level observations in the Baltic Sea, characterised by a jagged coastline and seasonal sea ice coverage, without compromising the quality of open ocean data. In this work, the performances of ALES + SAR are validated against in-situ data in the Baltic Sea. Moreover, variance, crossover differences and power spectral density of the open ocean data are evaluated on a global scale. The results show that ALES + SAR performances are of comparable quality to the ones obtained using physical-based retrackers, with relevant advantages in coastal and sea ice areas in terms of quality and quantity of the sea level data.

Simulation and Calculation of Sea State Bias Based on the Electromagnetic Backscattering from the Simulated Nonlinear Rough Sea Surface

Abstract Due to the improvement of satellite orbit determination technology, sea state bias(SSB) has become the main source of altimeter height measurement error. In this paper, the physical mechanism and causes of SSB are simulated and analyzed by electromagnetic scattering, and the SSB correction method and model are given. Firstly, this paper simulates and studies four kinds of nonlinear rough sea surface and their electromagnetic scattering characteristics for the first time, calculates SSB from the physical mechanism of SSB, and analyzes the physical mechanism and process causes of SSB from the electromagnetic scattering characteristics. Secondly, a fitting model to correct SSB is proposed in terms of the electromagnetic simulation. Finally, the paper compares the “true value of SSB” of HY-2B satellite radar altimeter in China’s offshore waters with SSB calculated by the simulating model. The comparison results show that they are consistent with a weak bias, indicating that it is an effective method to correct SSB by simulating nonlinear rough sea surface and its electromagnetic scattering. It is expected to improve the accuracy of SSB correction by the new way of simulating the physical mechanism and causes of SSB.

Cross-Track Error Correction and Evaluation of the Tiangong-2 Interferometric Imaging Radar Altimeter

The Interferometric Imaging Radar Altimeter (InIRA) carried on the Tiangong-2 Space Laboratory can observe three-dimensional sea surface topography over a swath tens of kilometers wide. The InIRA represents a test mission that will provide valuable experience for the design and data processing technology of future wide-swath altimeters. In addition to the tropospheric delay, ionospheric delay, and sea state bias that affect traditional altimeters, InIRA was also affected by the cross-track error derived from roll error, baseline length error, and interference phase error, which had to be properly corrected. In this paper, range error correction based on correction models and cross-track error correction based on the reference topography data method were applied to a 15-day InIRA dataset and evaluated using the Jason-2, Jason-3, Saral/AltiKa, and Sentinel-3A. The results showed that the standard deviation between corrected InIRA and the combined dataset was 7.96 cm after eliminating the influence of tides and dynamic atmospheric correction, which was comparable to nadir altimeter measurement accuracy.

New Ku-Band Geophysical Model Function and Wind Speed Retrieval Algorithm Developed for Tiangong-2 Interferometric Imaging Radar Altimeter

A Ku-band geophysical model function for wind speed retrieval named as KuLMOD-H is proposed based on the quasi-specular reflection model for the Chinese Tiangong-2 Interferometric Imaging Radar Altimeter (TG2-InIRA), which can be used to retrieve the wind speed with 2 km resolution. The model is derived by expanding the effective nadir reflection coefficient term and the mean square slope term in the quasi-specular reflection model using quadratic polynomials with wind speed as variable. The model coefficients are obtained by fitting the radar backscattering coefficient data from TG2-InIRA to the collocated sea surface wind speed data from European Center for Medium-Range Weather Forecasts (ECMWF). For solving the problem of potential ambiguous solutions when the incidence angles are relatively large, a regularization approach is further proposed. The retrieved wind speed results have a root-mean-square error (RMSE) of 1.42 m/s compared with the collocated ECMWF wind speed data and at the same time, they are highly consistent with the buoy data. Different from the previous works on TG2-InIRA wind speed retrieval, this article derives a semiphysical model suitable for incidence angles from 1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> to 8 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> , by which better retrieval results are achieved. This work not only explores the wind speed retrieval capability of the instrument under low incidence-angle observation, but also provides high-resolution wind speed data for further correction of sea state bias for TG2-InIRA sea surface height measurements.

Quantifying GNSS-R Delay Sea State Bias and Predicting Its Variation Based on Ship-Borne Observations in China’s Seas

Quantifying GNSS-R Delay Sea State Bias and Predicting Its Variation Based on Ship-Borne Observations in China’s Seas

Improving the Specular Point Positioning Accuracy of Ship-Borne GNSS-R Observations in China Seas Based on Comprehensive Geophysical Correction

The accurate modeled GNSS-R reflection delay, which is indispensable for the quantification, modeling, and correction of the GNSS-R altimetry sea-state bias, can be obtained based on the accurate modeled position of the specular point. At present, the reflection surface model of the specular point positioning still has the mean dynamic topography (MDT) error and the deviation of the vertical (DOV) error relative to the instantaneous sea surface. In this study, the following studies have been carried out. Based on the ship-borne GNSS-R observations in China seas, we introduced various elevation parameters including the MDT to correct the elevation error of the reflection surface. We introduced the DOV based on the elevation correction, and the DOV correction positioning method was proposed to correct the slope error of the reflection surface. The specular point was positioned on the instantaneous sea reflection surface. We verified the instantaneous sea reflection surface model and the specular point positioning results, analyzed the relationship between the position correction distance and the reflection incident angle, and discussed the spatial distribution characteristics of the MDT correction distance. The results showed that the reflection surface modeling and the specular point positioning were accurate. The positioning error increased to varying degrees with the increase of the reflection incident angle. The MDT correction improved the positioning by 0.91 m, and the DOV correction further improved the positioning by 0.12 m. Based on the combined application of the two kinds of correction positioning, the positioning was comprehensively improved by 0.99 m. The MDT correction of China seas gradually increased from the north to south. While in the regional sea areas, it gradually decreased from the north to south and showed randomness. The relative position between the antennas and their random changes introduced uncertainty, which can be reduced by integration. The new instantaneous sea reflection surface model and the corresponding specular point positioning method can provide accurate modeled reflection delay for the sea-state bias correction of ship-borne GNSS-R observations, and they can be extended to satellite-borne global observations.

Preliminary Significant Wave Height Retrieval from Interferometric Imaging Radar Altimeter Aboard the Chinese Tiangong-2 Space Laboratory

The interferometric imaging radar altimeter (InIRA) aboard the Chinese Tiangong-2 space laboratory is the first spaceborne imaging radar working at low incidence angles. This study focuses on the retrieval of significant wave heights (SWHs) from InIRA data. The retrieved SWHs can be used for correcting the sea state bias of InIRA-derived sea surface heights and can supplement SWH products from other spaceborne sensors. First, we analyzed tilt, range bunching and velocity bunching wave modulations at low incidence angles, and we found clear dependencies between the SWH and two defined factors, range and azimuth integration, for ocean waves in the range and azimuth directions, respectively. These dependencies were further confirmed using InIRA measurements and collocated WaveWatch III (WW3) data. Then, an empirical orthogonal SWH model using the range and azimuth integration factors as model inputs was proposed. The model was segmented by the incidence angle, and the model coefficients were estimated by fitting the collocation at each incidence angle bin. Finally, the SWHs were retrieved from InIRA data using the proposed model. The retrievals were validated using both WW3 and altimeter (JASON2, JASON3, SARAL, and HY2A) SWHs. The validation with WW3 data shows a root mean square error (RMSE) of 0.43 m, while the average RMSE with all traditional altimeter data is 0.48 m. This indicates that the InIRA can be used to measure SWHs.

Cross-Calibrations of the HY-2B Altimeter Using Jason-3 Satellite During the Period of April 2019–September 2020

In 2018, the Haiyang-2B (HY-2B) satellite altimeter was sent to orbit as a follow-up mission of the HY-2A satellite altimeter. The performance of the HY-2B system over the global oceans is considered to be critical. However, its performance is not fully known at the present time. In the present study, the first global quality assessment of the HY-2B Geophysical Data Record (GDR) was presented using comparison and crossover analysis processes of the main parameters and sea level anomalies (SLAs) with Jason-3 GDR data. This study’s assessment results demonstrated that the editing proportion of unqualified data for the HY-2B was 2.67%, which was at a similar level as the Jason-3 (2.86%). In addition, this study’s assessment results of the HY-2B key parameters (mainly the backscatter coefficients, significant wave heights, sea state bias, wet troposphere delays, and ionosphere delays) showed good agreement with the Jason-3, and there were no abnormal trends observed. The mean and standard deviations (STDs) were determined to be (0.21 ± 6.70) cm and (−3.4 ± 6.25) cm for the SLA differences at the self-crossover points of the HY-2B and dual-crossover points between the HY-2B and Jason-3 satellites, respectively. In addition, the SLA crossover analysis results indicated that the accuracy of the sea surface heights for the HY-2B was close to that of the Jason-3 satellite. The spatial distributions of the SLA differences showed no significant errors in the geographic characteristics. The SLA measurements were assessed using a wavenumber spectra method. The obtained results suggested that the power spectrum of the SLAs of the HY-2B satellite followed the regular patterns of the traditional Jason-3 altimeter. Furthermore, based on the spectrum analysis results, it was revealed that the noise level of the HY-2B was lower than that of the Jason-3, indicating a good overall performance of the HY-2B.