Development of an ENVISAT Altimetry Processor Providing Sea Level Continuity Between Open Ocean and Arctic Leads

Abstract

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 icecovered regions. This new processing has been used to produce maps of Arctic sea level anomaly from 18-Hz ENVIronment SATellite/RA-2 data.

Arctic Ocean Sea Level Record from the Complete Radar Altimetry Era: 1991–2018

“…Looking at the standard deviation (not shown here-for comparison only) of the bootstrapping realizations the level is between about 2 cm in the interior and about 5 cm outside. This may indicate, that some data are tracked as sea-ice, but the results look similar to Poisson et al [65] (only using Envisat data) which get a transition between the open ocean and the interior of the Arctic Ocean of about 2 to 4 cm, and much better than the former DTU data set by Cheng et al [24].…”

“…Getting a more strict PP threshold would lead to areas with very low data coverage, not being able to get a region wide SLA record relaying on data and not only extrapolation. An other option could be to use more advanced classification schemes and machine learning to get a better control of the leads in the sea-ice cover similar to [63][64][65].…”

Retrieving Sea Level and Freeboard in the Arctic: A Review of Current Radar Altimetry Methodologies and Future Perspectives

“…From this, they generated the first pan-Arctic view of the sea-ice thickness from ERS-1 and ERS-2 [32] and the first accurate mean sea surface for the Arctic [33]. The "multiple criteria" approach used by Poisson et al [34] also included some constraints on PP, LEW and σ 0 . For AltiKa, Zakharova et al [31] used MP, which is akin to a combination of peakiness and σ 0 (as the AGC setting is the altimeter's delayed on-board response to changes in σ 0 ).…”

“…Further classification approaches are developed that adopt machine-learning techniques and data-mining strategies, such as partitional clustering methods like K-Means or Neural Networks (NN). Previous studies [34][35][36][37][38] used fixed thresholds for the echo assignment; machine-learning classification strategies are characterized by a high flexibility and dynamic adaption to various surface characteristics. An analysis by Dettmering et al [39] compares different classification strategies based on CryoSat-2 waveforms and points out their benefits and advantages.…”

“…This is done by the K-nearest neighbour, which is a memory-based classifier method. Another possible classification method is based on the use of a Neural Network (NN), as described in Gommenginger et al [38] and Poisson et al [34]. This approach is a supervised classification, i.e., the neural net is trained to associate particular waveform shapes with user-specified classes ( Figure 6).…”