Search

Over the Arctic regions, current conventional altimetry products suffer from a lack of coverage or from degraded performance due to the inadequacy of the standard processing applied in the ground segments. This paper presents a set of dedicated algorithms able to process consistently returns from open ocean and from sea-ice leads in the Arctic Ocean (detection of water surfaces and derivation of water levels using returns from these surfaces). This processing extends the area over which a precise sea level can be computed. In the frame of the European Space Agency Sea Level Climate Change Initiative (http://cci.esa.int), we have first developed a new surface identification method combining two complementary solutions, one using a multiple-criteria approach (in particular the backscattering coefficient and the peakiness coefficient of the waveforms) and one based on a supervised neural network approach. Then, a new physical model has been developed (modified from the Brown model to include anisotropy in the scattering from calm protected water surfaces) and has been implemented in a maximum likelihood estimation retracker. This allows us to process both sea-ice lead waveforms (characterized by their peaky shapes) and ocean waveforms (more diffuse returns), guaranteeing, by construction, continuity between open ocean and ice-covered regions. This new processing has been used to produce maps of Arctic sea level anomaly from 18-Hz ENVIronment SATellite/RA-2 data. [ABSTRACT FROM AUTHOR]

Abstract. Sea level is an essential climate variable (ECV) that has a direct effect on many people through inundations of coastal areas, and it is also a clear indicator of climate changes due to external forcing factors and internal climate variability. Regional patterns of sea level change inform us on ocean circulation variations in response to natural climate modes such as El Niño and the Pacific Decadal Oscillation, and anthropogenic forcing. Comparing numerical climate models to a consistent set of observations enables us to assess the performance of these models and help us to understand and predict these phenomena, and thereby alleviate some of the environmental conditions associated with them. All such studies rely on the existence of long-term consistent high-accuracy datasets of sea level. The Climate Change Initiative (CCI) of the European Space Agency was established in 2010 to provide improved time series of some ECVs, including sea level, with the purpose of providing such data openly to all to enable the widest possible utilisation of such data. Now in its second phase, the Sea Level CCI project (SL_cci) merges data from nine different altimeter missions in a clear, consistent and well-documented manner, selecting the most appropriate satellite orbits and geophysical corrections in order to further reduce the error budget. This paper summarises the corrections required, the provenance of corrections and the evaluation of options that have been adopted for the recently released v2.0 dataset (https://doi.org/10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612). This information enables scientists and other users to clearly understand which corrections have been applied and their effects on the sea level dataset. The overall result of these changes is that the rate of rise of global mean sea level (GMSL) still equates to ∼ 3.2 mm yr−1 during 1992–2015, but there is now greater confidence in this result as the errors associated with several of the corrections have been reduced. Compared with v1.1 of the SL_cci dataset, the new rate of change is 0.2 mm yr−1 less during 1993 to 2001 and 0.2 mm yr−1 higher during 2002 to 2014. Application of new correction models brought a reduction of altimeter crossover variances for most corrections. [ABSTRACT FROM AUTHOR]

Analysis of the radar echoes from a spaceborne altimeter gives information on sea surface height, wave height and wind speed, as well as other parameters over land and ice. The first spaceborne radar altimeter was pioneered on Skylab in 1974. Since then, there have been about 20 further missions, with several advances in the sophistication of hardware and complexity of processing with the aim of increased accuracy and precision. Because of that, the importance of regular and precise calibration and validation ("cal/val") remains undiminished, especially with efforts to merge altimetric records from multiple missions spanning different domains and time periods. This special issue brings together 19 papers, with a focus on the recent missions (Jason-2, Jason-3, Sentinel-3A and HY-2B) as well as detailing the issues for anticipated future missions such as SWOT. This editorial provides a brief guide to the approaches and issues for cal/val of the various different derived parameters, including a synopsis of the papers in this special issue. [ABSTRACT FROM AUTHOR]

Abstract The sea state bias (SSB) is a large source of uncertainty in the estimation of sea level from satellite altimetry. It is still unclear to what extent it depends on errors in parameter estimations (numerical source) or to the wave physics (physical source). By improving the application of this correction we compute 20-Hz sea level anomalies that are about 30% more precise (i.e. less noisy) than the current standards. The improvement is two-fold: first we prove that the SSB correction should be applied directly to the 20-Hz data (12 to 19% noise decrease); secondly, we show that by recomputing a regional SSB model (based on the 20-Hz estimations) even a simple parametric relation is sufficient to further improve the correction (further 15 to 19% noise decrease). We test our methodology using range, wave height and wind speed estimated with two retrackers applied to Jason-1 waveform data: the MLE4 retracked-data available in the Sensor Geophysical Data Records of the mission and the ALES retracked-data available in the OpenADB repository (https://openadb.dgfi.tum.de/). The regional SSB models are computed parametrically by means of a crossover analysis in the Mediterranean Sea and North Sea. Correcting the high-rate data for the SSB reduces the correlation between retracked parameters. Regional variations in the proposed models might be due to differences in wave climate and remaining sea-state dependent residual errors. The variations in the empirical model with respect to the retracker used recall the need for a specific SSB correction for any retracker. This study, while providing a significantly more precise solution to exploit high-rate sea level data, calls for a re-thinking of the SSB correction in both its physical and numerical component, gives robustness to previous theories and provides an immediate improvement for the application of satellite altimetry in the regions of study. Highlights • We study the sea-state related errors on sea level data (sea state bias correction). • A regional 20-Hz parametric sea state bias correction is derived using Jason-1 data. • The new correction improves the precision of high-frequency sea level data by 30%. • The new correction decreases the correlation between range and wave height estimation. [ABSTRACT FROM AUTHOR]

A regional cross-calibration between the first Delay–Doppler altimetry dataset from CryoSat-2 and a retracked Envisat dataset is here presented, in order to test the benefits of the Delay–Doppler processing and to expand the Envisat time series in the coastal ocean. The Indonesian Seas are chosen for the calibration, since the availability of altimetry data in this region is particularly beneficial due to the lack of in situ measurements and its importance for global ocean circulation. The Envisat data in the region are retracked with the Adaptive Leading Edge Subwaveform (ALES) retracker, which has been previously validated and applied successfully to coastal sea level research. The study demonstrates that CryoSat-2 is able to decrease the 1-Hz noise of sea level estimations by 0.3 cm within 50 km of the coast, when compared to the ALES-reprocessed Envisat dataset. It also shows that Envisat can be confidently used for detailed oceanographic research after the orbit change of October 2010. Cross-calibration at the crossover points indicates that in the region of study a sea state bias correction equal to 5% of the significant wave height is an acceptable approximation for Delay–Doppler altimetry. The analysis of the joint sea level time series reveals the geographic extent of the semiannual signal caused by Kelvin waves during the monsoon transitions, the larger amplitudes of the annual signal due to the Java Coastal Current and the impact of the strong La Niña event of 2010 on rising sea level trends. [ABSTRACT FROM AUTHOR]