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.

Global Assessments of the HY-2B Measurements and Cross-Calibrations with Jason-3

The HY-2B satellite was successfully launched on 25 October 2018. One of the main payloads of the HY-2B was a radar altimeter. In the present study, the quality of the HY-2B along-track sea surface heights (SSH), significant wave heights (SWH), and sea surface wind speeds (SSWS) were assessed, including their precision and accuracy. In order to achieve this goal, the mono-mission metrics of the HY-2B were analyzed and compared with those of the Jason-2 and Jason-3 over the same periods of time. The results of both direct comparison and cross comparison methods were presented in this study. The comparison results indicated that the quality of the HY-2B satellite’s geophysical data records (GDRs) data was excellent, with 95% of the sea surfaces effectively observed between 82 degrees north and south latitudes. In addition, the standard deviation of the sea level anomalies (SLA) at the single mission crossovers was 4.6 cm to 5.8 cm, and at the dual-crossovers with Jason-3, the standard deviation was determined to be 5.1 cm to 5.8 cm. The accuracy levels of the significant wave heights and products of the HY-2B satellite radar altimeter were observed to be greater than 0.3 m and 1.4 m/s (STD), respectively. Therefore, it was concluded in this study that the data quality and system performance of the HY-2B satellite were excellent and stable, and could be widely utilized in such fields as global sea-level change monitoring, wave numerical assimilation predictions etc.

A revised acceleration rate from the altimetry-derived global mean sea level record

Satellite radar altimetry has been providing estimates of global mean sea level (GMSL) since 1992. The early TOPEX record originates from two identical instruments, which requires the estimation of an intermission bias and careful handling of the problematic first part of the record. Calibration of TOPEX is crucial to obtain a continuous and consistent record, which is needed to quantify any recent acceleration. We propose a novel approach to calibrate TOPEX altimeter data using sea surface height differences at crossovers of TOPEX and ERS. Tide gauges are only used to determine a drift in one of the two datasets. We provide a new and more accurate estimate of the intra-mission bias, which leads to a much reduced GMSL acceleration over the whole record. Hence, the conundrum of an uncertain GMSL acceleration from altimetry is still unsolved, in spite of recent opposite claims, and in contrast to the expected effect of ocean warming and continental freshwater fluxes.

Sea State Bias Variability in Satellite Altimetry Data

Sea State Bias (SSB) contributes to global mean sea level variability and it needs cm-level range adjustment due to the instrumental drift over time. To investigate its variations and correct the global and regional sea level trend precisely, we calculate the temporal and spatial variability of the SSB correction in TOPEX, Jason-1, Jason-2 and Jason-3 missions, separately, as well as in the combined missions over the period 1993–2017. The long-term trend in global mean operational 2D non-parametric SSB correction is about −0.03 ± 0.03 mm/yr, which accounts for 1% of current global mean sea level change rate during 1993–2016. This correction contributes to sea level change rates of −1.27 ± 0.21 mm/yr and −0.26 ± 0.13 mm/yr in TOPEX-A and Jason-2 missions, respectively. The global mean SSB varies up to 7–10 mm during the very strong ENSO events in 1997–1998 and 2015–2016. Furthermore, the TOPEX SSB trend, which is consistent with recently reported sea level trend drift during 1993–1998, may leak into the determined global sea level trend in the period. Moreover, the Jason-1/2 zonal SSB variability is highly correlated with the significant wave height (SWH). On zonal average, SSB correction causes about 1% uncertainty in mean sea level trend. At high SWH regions, the uncertainties grow to 2–4% near the 50°N and 60°S bands. This should be considered in the study of regional sea level variability.

Using a stacked-autoencoder neural network model to estimate sea state bias for a radar altimeter

This paper constructed a stacked-autoencoder neural network model (SAE model) to estimate sea state bias (SSB) based on radar altimeter data. Six cycles of the geophysical data record (GDR) from Jason-1/2 radar altimeters were used as a training dataset, and the other 2 cycles of the GDR from Jason-1/2 were used for testing. The inputs to this SAE model include the significant wave height (SWH), wind speed (U), sea surface height (SSH), backscatter coefficient (σ₀) and automatic gain control (AGC), and the model outputs the SSB. The model includes one input layer, three hidden layers and one output layer. The SSBs in the GDR of Jason-1/2 were obtained from a nonparametric model based on the SWH and U as input variables; thus, the model has high accuracy but low efficiency. The SSBs in the GDR of HY-2A were computed using a four-parameter parametric model that uses the SWH and U as input variables; therefore, this model’s computational speed is high but its accuracy is low. Thus, we used the HY-2A radar altimeter as an unseen validation dataset to evaluate the performance of the SAE model. Then, we analyzed the contrasting results of these methods, including the differences in the SSB, explained variance, residual error and operational efficiency. The results demonstrate not only that the accuracy of the SAE model is superior to that of the conventional parametric model but also that its operational efficiency is better than that of the nonparametric model.

The measurement of surface gravity

This review covers basic theory and techniques behind the use of ground-based gravimetry at the Earth's surface. The orientation is toward modern instrumentation, data processing and interpretation for observing surface, land-based, time-variable changes to the geopotential. The instrumentation side is covered in some detail, with specifications and performance of the most widely used models of the three main types: the absolute gravimeters (FG5, A10 from Micro-g LaCoste), superconducting gravimeters (OSG, iGrav from GWR instruments), and the new generation of spring instruments (Micro-g LaCoste gPhone, Scintrex CG5 and Burris ZLS). A wide range of applications is covered, with selected examples from tides and ocean loading, atmospheric effects on gravity, local and global hydrology, seismology and normal modes, long period and tectonics, volcanology, exploration gravimetry, and some examples of gravimetry connected to fundamental physics. We show that there are only a modest number of very large signals, i.e. hundreds of µGal (10(-8) m s(-2)), that are easy to see with all gravimeters (e.g. tides, volcanic eruptions, large earthquakes, seasonal hydrology). The majority of signals of interest are in the range 0.1-5.0 µGal and occur at a wide range of time scales (minutes to years) and spatial extent (a few meters to global). Here the competing effects require a careful combination of different gravimeter types and measurement strategies to efficiently characterize and distinguish the signals. Gravimeters are sophisticated instruments, with substantial up-front costs, and they place demands on the operators to maximize the results. Nevertheless their performance characteristics such as drift and precision have improved dramatically in recent years, and their data recording ability and ruggedness have seen similar advances. Many subtle signals are now routinely connected with known geophysical effects such as coseismic earthquake displacements, post-glacial rebound, local hydrological mass balances, and detection of non-steric sea level changes.