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The SWOT (Surface Water Ocean Topography) mission will provide high-resolution and two-dimensional measurements of sea surface height (SSH). However, despite its unprecedented precision, SWOT's Ka-band Radar Interferometer (KaRIn) still exhibits a substantial amount of random noise. In turn, the random noise limits the ability of SWOT to capture the smallest scales of the ocean's topography and its derivatives. In that context, this paper explores the feasibility, strengths and limits of a noise-reduction algorithm based on a convolutional neural network. The model is based on a U-Net architecture and is trained and tested with simulated data from the North Atlantic. Our results are compared to classical smoothing methods: a median filter, a Lanczos kernel smoother and the SWOT de-noising algorithm developed by Gomez-Navarro et al. Our U-Net model yields better results for all the evaluation metrics: 2 mm root mean square error, sub-millimetric bias, variance reduction by factor of 44 (16 dB) and an accurate power spectral density down to 10–20 km wavelengths. We also tested various scenarios to infer the robustness and the stability of the U-Net. The U-Net always exhibits good performance and can be further improved with retraining if necessary. This robustness in simulation is very encouraging: our findings show that the U-Net architecture is likely one of the best candidates to reduce the noise of flight data from KaRIn. [ABSTRACT FROM AUTHOR]

This paper describes the demonstration of a regional high-resolution level-3 (L3) altimeter data unification and altimeter combination system (DUACS) developed with support from the French space agency (CNES). Deduced from full-rate (20 Hz to 40 Hz) level-2 (L2) altimeter measurements, this product provides sea level anomalies (SLA) and other essential physical variables at a spatial resolution of one sample every ~1 km over the North Atlantic Ocean. This allows us to resolve wavelengths from ~35 km to ~55 km depending on the altimeter considered. This was made possible by recent advances in radar altimeter processing for both synthetic aperture radar (SAR) and low-resolution-mode (LRM) measurements, as well as improvements made to different stages of the DUACS processing chain. Firstly, the new adaptive and low-resolution with range migration correction (LR-RMC) processing techniques were considered for Jason and Sentinel-3 (S3A), respectively. They significantly reduce errors at short wavelengths, and the adaptive processing also reduces possible land contamination near the coast. Next, up-to-date geophysical and environmental corrections were selected for this production. This includes specific corrections intended to reduce the measurement noise on LRM measurements and thus enhance the observability at short wavelengths. Compared with the 1 Hz product, the observable wavelengths reached with the demonstration high-resolution product are reduced by up to one third, or up to half in the northeast Atlantic region. The residual noises were optimally filtered from full-rate measurements, taking into consideration the different observing capabilities of the altimeters processed. A specific data recovery strategy was applied, significantly optimizing the data availability, both in the coastal and open ocean areas. This demonstration L3 product is thus better resolved than the conventional 1 Hz product, especially near the coast, where it is defined up to ~5 km against ~10 km for the 1 Hz version. Multi-mission cross-calibration processing was also optimized with an improved long-wavelength error (LWE) correction, leading to a better consistency between tracks, with a 9–15% reduction in SLA variance at cross-overs. The new L3 product improves the overall consistency with tide gauge measurements, with a reduction in SLA differences variance by 5 and 17% compared with the 1 Hz product from the S3A and Jason-3 (J3) measurements, respectively. Primarily intended for regional applications, this product can significantly contribute to improving high-resolution numerical model output via data assimilation. It also opens new perspectives for a better understanding of regional sea-surface dynamics, with an improved representation of the coastal currents and a refined spectral content revealing the unbalanced signal. [ABSTRACT FROM AUTHOR]

The Surface Water and Ocean Topography (SWOT) mission will be affected by various sources of systematic errors, which are correlated in space and in time. Their amplitude before calibration might be as large as tens of centimeters, i.e., able to dominate the mission error budget. To reduce their magnitude, we developed so-called data-driven (or empirical) calibration algorithms. This paper provided a summary of the overall problem, and then presented the calibration framework used for SWOT, as well as the pre-launch performance simulations. We presented two complete algorithm sequences that use ocean measurements to calibrate KaRIN globally. The simple and robust Level-2 algorithm was implemented in the ground segment to control the main source of error of SWOT's hydrology products. In contrast, the more sophisticated Level-3 (multi-mission) algorithm was developed to improve the accuracy of ocean products, as well as the one-day orbit of the SWOT mission. The Level-2 algorithm yielded a mean inland error of 3–6 cm, i.e., a margin of 25–80% (of the signal variance) with respect to the error budget requirements. The Level-3 algorithm yielded ocean residuals of 1 cm, i.e., a variance reduction of 60–80% with respect to the Level-2 algorithm. [ABSTRACT FROM AUTHOR]

The India-France SARAL/AltiKa mission is the first Ka-band altimetric mission dedicated primarily to oceanography. The mission objectives were firstly the observation of the oceanic mesoscales but also global and regional sea level monitoring, including the coastal zone, data assimilation, and operational oceanography. SARAL/AltiKa proved also to be a great opportunity for inland waters applications, for observing ice sheet or icebergs, as well as for geodetic investigations. The mission ended its nominal phase after three years in orbit and began a new phase (drifting orbit) in July 2016. The objective of this paper is to highlight some of the most remarkable achievements of the SARAL/AltiKa mission in terms of scientific applications. Compared to the standard Ku-band altimetry measurements, the Ka-band provides substantial improvements in terms of spatial resolution and data accuracy. We show here that this leads to remarkable advances in terms of observation of the mesoscale and coastal ocean, waves, river water levels, ice sheets, icebergs, fine scale bathymetry features as well as for the many related applications. [ABSTRACT FROM AUTHOR]

The impact of large atmospheric attenuation events on data quality and availability is a critical aspect for future altimetry missions based on Ka-band altimetry. The SARAL/AltiKa mission and its Ka-band nadir altimeter offer a unique opportunity to assess this impact. Previous publications (Tournadre et al., 2009, 2015) already analyzed the impact of rain on the waveforms at Ka-band and proposed a definition of an elaborate rain flag. This notion tends to give a simpler black and white view of the atmospheric attenuation when the effect on the altimeter measurement is intense. However, in practice, there is a continuum of measurements that may be partially distorted or corrupted by rain events. The present study proposes a wider point of view, directly using the timeseries of the Ka-band altimeter backscattering coefficient for the first time, when previous studies relied on microwave radiometer (MWR) observations or model analyses with coarser resolutions. As guidelines for future Ka-band missions concerning the impact of the atmosphere, the Attenuation CElls Characterization ALgorithm (ACECAL) approach not only provides more representative statistics on rain cells (occurrences, amplitude, size), but also describes the internal structure of the cells. The actual atmospheric attenuation retrieved with ACECAL is about four times larger than the attenuation retrieved from the MWR. At a global scale, 1% of the measurements are affected by an attenuation larger than 23 dB and 10% of the atmospheric attenuation events have a size larger than 40 km. At regional scale, some areas of particular interest for oceanography as Gulf Stream, North Pacific and Brazil currents are more systematically affected compared with global statistics, with atmospheric attenuation up to 8 dB and cell size larger than 25 km when rain occurs. This study also opens some perspectives on the benefits that the community could be drawn from the systematic distribution of the rain cells parameters as secondary products of altimetry missions. [ABSTRACT FROM AUTHOR]

The French/Indian altimeter project Satellite with ARGOS and AltiKa (SARAL) completed its nominal 3-year mission on the historical European Remote-sensing Satellite (ERS) orbit in Spring 2016. In order to extend the lifetime of the satellite as much as possible, the agencies in charge of SARAL decided to initiate a so-called drifting phase where the satellite altitude is no longer maintained. In this paper we describe how the ocean mesoscale sampling capability of SARAL has been preserved during the drifting phase by initiating it at a specific altitude: the optimal starting point was approximately 1 km above the historical ERS/ENVIronment SATellite (ENVISAT) orbit. This strategy secured the ocean mesoscale sampling capability of SARAL drifting phase (DP) for 6 years or more. We also generalize these findings: any altimeter could follow SARAL's drifting phase strategy if their maneuvering capability is limited. Lastly, we explain how an altimetry mission or an entire altimeter constellation could be operated without any form of altitude control: some specific altitude bands (e.g., near 1230 km) guarantee a high-quality mesoscale sampling for years or decades even if the altitude is not maintained. [ABSTRACT FROM AUTHOR]