Effects of the passage of Comet C/2013 A1 (Siding Spring) observed by the Shallow Radar (SHARAD) on Mars reconnaissance orbiter

The close passage of Comet C/2013 A1 (Siding Spring) to Mars provided a unique opportunity to observe the interaction of cometary materials with the Martian ionosphere and atmosphere using the sounding radar SHARAD (SHAllow RADar) aboard Mars Reconnaissance Orbiter. In two nightside observations, acquired in the 10 h following the closest approach, the SHARAD data reveal a significant increase of the total electron content (TEC). The observed TEC values are typical for daylight hours just after dawn or before sunset but are unprecedented this deep into the night. Results support two predictions indicating that cometary pickup O+ ions, or ions generated from the ablation of cometary dust, are responsible for the creation of an additional ion layer

Primary-productivity in Upwelling Systems (PRIMUS)

Conferencia sobre los Sistemas de Afloramiento de Borde Oriental (EBUS): Pasado, Presente y Futuro & Segunda Conferencia Internacional sobre el Sistema de Corrientes de Humboldt, 19-23 de Septiembre de 2022, Lima, PerúThe ESA-supported Primary-productivity in Upwelling Systems (PRIMUS) project aims to provide the best possible characterisation of net primary productivity (NPP) and its relationship to upwelling in Atlantic Eastern Boundary Upwelling Systems (EBUS), including the Iberian/Canary and Benguela systems. It will create a 25-year time series of 1-km satellite-derived NPP over the Atlantic, and, experimentally, at higher-resolution (300m) using the unique capabilities of the MERIS and OLCI satellite sensors. PRIMUS will use these data to advance analyses of Atlantic EBUS including temporal and spatial variability in NPP and its statistical relationship to upwelling and climate indices (such as the North Atlantic Oscillation). PRIMUS will also conduct eight further science cases in specific science áreas / regional settings: aquaculture in Galicia; fisheries and eutrophication in the Portuguese upwelling region; potential EBUS impacts on ocean carbón pools; Lagrangian estimates of NPP; and air-sea interaction and acidification impacts. Science cases will make use of EO and in situ data, as well as numerical model outputs (freely available through the EU’s Copernicus and elsewhere) to investigate the 4D character of EBUS, for example linking Lagrangian NPP with sediment traps samples at depth. PRIMUS will also conduct demonstrations that transfer science into solutions for society, working together with scientific, agency, policy and commercial “early-adopters”, building on three science case studies (EBUS and aquaculture; fisheries; and eutrophication monitoring). Furthermore, evaluating transition of data production to operational initiatives such as Copernicus and GMES and Africa and the potential for data exploitation by the European and international ecosystem modelling community. This communication will present initial results from the 25-year NPP time series and high resolution NPP computations as well as selected science casesN

Radar Evidence of Subglacial Liquid Water on Mars

Strong radar echoes from the bottom of the martian southern polar deposits are interpreted as being due to the presence of liquid water under 1.5 km of ice

Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation

Studies of the global sea-level budget (SLB) and the global ocean-mass budget (OMB) are essential to assess the reliability of our knowledge of sea-level change and its contributors. Here we present datasets for times series of the SLB and OMB elements developed in the framework of ESA's Climate Change Initiative. We use these datasets to assess the SLB and the OMB simultaneously, utilising a consistent framework of uncertainty characterisation. The time series, given at monthly sampling and available at https://doi.org/10.5285/17c2ce31784048de93996275ee976fff (Horwath et al., 2021), include global mean sea-level (GMSL) anomalies from satellite altimetry, the global mean steric component from Argo drifter data with incorporation of sea surface temperature data, the ocean-mass component from Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry, the contribution from global glacier mass changes assessed by a global glacier model, the contribution from Greenland Ice Sheet and Antarctic Ice Sheet mass changes assessed by satellite radar altimetry and by GRACE, and the contribution from land water storage anomalies assessed by the global hydrological model WaterGAP (Water Global Assessment and Prognosis). Over the period January 1993–December 2016 (P1, covered by the satellite altimetry records), the mean rate (linear trend) of GMSL is 3.05 ± 0.24 mm yr−1. The steric component is 1.15 ± 0.12 mm yr−1 (38 % of the GMSL trend), and the mass component is 1.75 ± 0.12 mm yr−1 (57 %). The mass component includes 0.64  ± 0.03 mm yr−1 (21 % of the GMSL trend) from glaciers outside Greenland and Antarctica, 0.60 ± 0.04 mm yr−1 (20 %) from Greenland, 0.19 ± 0.04 mm yr−1 (6 %) from Antarctica, and 0.32 ± 0.10 mm yr−1 (10 %) from changes of land water storage. In the period January 2003–August 2016 (P2, covered by GRACE and the Argo drifter system), GMSL rise is higher than in P1 at 3.64 ± 0.26 mm yr−1. This is due to an increase of the mass contributions, now about 2.40 ± 0.13 mm yr−1 (66 % of the GMSL trend), with the largest increase contributed from Greenland, while the steric contribution remained similar at 1.19 ± 0.17 mm yr−1 (now 33 %). The SLB of linear trends is closed for P1 and P2; that is, the GMSL trend agrees with the sum of the steric and mass components within their combined uncertainties. The OMB, which can be evaluated only for P2, shows that our preferred GRACE-based estimate of the ocean-mass trend agrees with the sum of mass contributions within 1.5 times or 0.8 times the combined 1σ uncertainties, depending on the way of assessing the mass contributions. Combined uncertainties (1σ) of the elements involved in the budgets are between 0.29 and 0.42 mm yr−1, on the order of 10 % of GMSL rise. Interannual variations that overlie the long-term trends are coherently represented by the elements of the SLB and the OMB. Even at the level of monthly anomalies the budgets are closed within uncertainties, while also indicating possible origins of remaining misclosures

Altimetry for the future: Building on 25 years of progress

In 2018 we celebrated 25 years of development of radar altimetry, and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences. Many symbolic major events have celebrated these developments, e.g., in Venice, Italy, the 15th (2006) and 20th (2012) years of progress and more recently, in 2018, in Ponta Delgada, Portugal, 25 Years of Progress in Radar Altimetry. On this latter occasion it was decided to collect contributions of scientists, engineers and managers involved in the worldwide altimetry community to depict the state of altimetry and propose recommendations for the altimetry of the future. This paper summarizes contributions and recommendations that were collected and provides guidance for future mission design, research activities, and sustainable operational radar altimetry data exploitation. Recommendations provided are fundamental for optimizing further scientific and operational advances of oceanographic observations by altimetry, including requirements for spatial and temporal resolution of altimetric measurements, their accuracy and continuity. There are also new challenges and new openings mentioned in the paper that are particularly crucial for observations at higher latitudes, for coastal oceanography, for cryospheric studies and for hydrology. The paper starts with a general introduction followed by a section on Earth System Science including Ocean Dynamics, Sea Level, the Coastal Ocean, Hydrology, the Cryosphere and Polar Oceans and the ‘‘Green” Ocean, extending the frontier from biogeochemistry to marine ecology. Applications are described in a subsequent section, which covers Operational Oceanography, Weather, Hurricane Wave and Wind Forecasting, Climate projection. Instruments’ development and satellite missions’ evolutions are described in a fourth section. A fifth section covers the key observations that altimeters provide and their potential complements, from other Earth observation measurements to in situ data. Section 6 identifies the data and methods and provides some accuracy and resolution requirements for the wet tropospheric correction, the orbit and other geodetic requirements, the Mean Sea Surface, Geoid and Mean Dynamic Topography, Calibration and Validation, data accuracy, data access and handling (including the DUACS system). Section 7 brings a transversal view on scales, integration, artificial intelligence, and capacity building (education and training). Section 8 reviews the programmatic issues followed by a conclusion

Standalone Multi-mission Altimetry Processor (SMAP)

SMAP is a standalone altimeter data processor written in Python 3 (3.7.3). It implements in particular the fully-focused SAR (FF-SAR) processing (both time-domain and frequency-domain algorithms). SMAP is currently able to process Sentinel-3 L1a Ground Segment products. This processor has been developed though studies and projects funded by ESA and CNES.</jats:p

The BRAT and GUT Couple: Broadview Radar Altimetry and GOCE User Toolboxes

&amp;lt;p&amp;gt;The scope of this work is to showcase the BRAT (Broadview Radar Altimetry Toolbox) and GUT (GOCE User Toolbox) toolboxes.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The Broadview Radar Altimetry Toolbox (BRAT) is a collection of tools designed to facilitate the processing of radar altimetry data from all previous and current altimetry missions, including Sentinel-3A L1 and L2 products. A tutorial is included providing plenty of use cases on Geodesy &amp;amp; Geophysics, Oceanography, Coastal Zone, Atmosphere, Wind &amp;amp; Waves, Hydrology, Land, Ice and Climate, which can also be consulted in &amp;amp;#160;http://www.altimetry.info/radar-altimetry-tutorial/.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;BRAT's last version (4.2.1) was released in June 2018. Based on the community feedback, the front-end has been further improved and simplified whereas the capability to use BRAT in conjunction with MATLAB/IDL or C/C++/Python/Fortran, allowing users to obtain desired data bypassing the data-formatting hassle, remains unchanged. Several kinds of computations can be done within BRAT involving the combination of data fields, that can be saved for future uses, either by using embedded formulas including those from oceanographic altimetry, or by implementing ad-hoc Python modules created by users to meet their needs. BRAT can also be used to quickly visualise data, or to translate data into other formats, e.g. from NetCDF to raster images.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The GOCE User Toolbox (GUT) is a compilation of tools for the use and the analysis of GOCE gravity field models. It facilitates using, viewing and post-processing GOCE L2 data and allows gravity field data, in conjunction and consistently with any other auxiliary data set, to be pre-processed by beginners in gravity field processing, for oceanographic and hydrologic as well as for solid earth applications at both regional and global scales. Hence, GUT facilitates the extensive use of data acquired during GRACE and GOCE missions.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In the current version (3.2), GUT has been outfitted with a graphical user interface allowing users to visually program data processing workflows. Further enhancements aiming at facilitating the use of gradients, the anisotropic diffusive filtering, and the computation of Bouguer and isostatic gravity anomalies have been introduced. Packaged with GUT is also GUT's Variance/Covariance Matrix (VCM) tool, which enables non-experts to compute and study, with relative ease, the formal errors of quantities &amp;amp;#8211; such as geoid height, gravity anomaly/disturbance, radial gravity gradient, vertical deflections &amp;amp;#8211; that may be derived from the GOCE gravity models.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;On our continuous endeavour to provide better and more useful tools, we intend to integrate BRAT into SNAP (Sentinel Application Platform). This will allow our users to easily explore the synergies between both toolboxes. During 2020 we will start going from separate toolboxes to a single one.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;BRAT and GUT toolboxes can be freely downloaded, along with ancillary material, at https://earth.esa.int/brat and https://earth.esa.int/gut.&amp;lt;/p&amp;gt; </jats:p

Volume scattering influence on MARSIS and SHARAD data inversion

The Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) and the Shallow Radar (SHARAD) are currently operating on Mars transmitting an HF chirp signal to retrieve information regarding the Martian crust structure. An important task to be performed is the data inversion, i.e., the permittivity estimation of the detected subsurface layers. In order to be properly performed, degrading effects on attenuation estimation and geometric term correction have to be understood. This paper aims to simulate a few representative scenarios, using the Finite-Difference Time-Domain formulation, pointing out the volume scattering influence on MARSIS and SHARAD data. Influence on both attenuation estimation and geometric term correction will be discussed

A phase-gradient-autofocus algorithm for the recovery of MARSIS subsurface data

The Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) is currently operating, providing information of the subsurface structure of the planet Mars. The transmitted chirp signals pass through the ionosphere, resulting delayed, attenuated, and, in the phase, distorted. MARSIS signals are routinely phase corrected during ground processing by adopting a predefined model based on the Chapman theory. In this letter, phase distortions are compensated by using a phase-gradient-autofocus algorithm, which is capable of correcting distortions of different nature by exploiting their redundancy along the orbit. For the first time, images presenting saturated regions have been recovered

A Robust Error Characterization Method for SAR Altimetry over the Inland Water Domain

&amp;lt;p&amp;gt;The advent of SAR (delay-Doppler) altimetry allowed the production of data with a high spatial resolution (300 m along-track). Investigations in the inland water domain clearly benefited from SAR data and future processing strategies (e.g. the fully-focused SAR, FF-SAR) are expected to improve further the quantity of data points over water bodies of a reduced size.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The proposed work aims at investigating the quality of Sentinel-3 water level retrievals over three targets of different characteristics: the Ohio River, the Columbia River and the Great Salt Lake. Data are processed through the ESA G-POD SARvatore online and on-demand processing service for the exploitation of CryoSat-2 and Sentinel-3 data (&amp;lt;strong&amp;gt;https://gpod.eo.esa.int/services/SENTINEL3_SAR/&amp;lt;/strong&amp;gt;) and obtained by using the SAMOSA2, SAMOSA+ &amp;amp; SAMOSA++ retrackers. The selected posting rate of measurements is 80&amp;amp;#160;Hz to optimize the location of data points over the Ohio and Columbia River (an estimate every 80&amp;amp;#160;m along-track), however a comparison with the 20&amp;amp;#160;Hz posting rate is being made. Empirical retrackers outputs, available in the official 20&amp;amp;#160;Hz Sentinel-3 LAN products, are also considered for comparison and water masks from (Pekel et al., 2016) are used to select data points acquired over water bodies.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The main goal of this study is to analyse the key parameters characterizing both the L1b SAR waveform and the retracking (e.g. the Pulse Peakiness, the Misfit&amp;amp;#8230;) to define a robust error characterization method that is expected to filter out an increased number of outliers. A validation exercise using in situ data will be presented to demonstrate that the proposed method leads to the definition of a reduced, highly reliable dataset, associated with a realistic error characterization model.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The study is expected to unlock possible synergies with SWOT and support the comparison of SAR estimates to FF-SAR estimates obtained at a comparable along-track resolution.&amp;lt;/p&amp;gt; </jats:p