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Sea level rise is one of the greatest socio-economic impacts of climate change in the 21st Century. Whilst global mean sea level is an essential climate variable (ECV) for assessing the integrated response of the Earth system to climate change, regional sea level variability is of primary concern for policy-making decisions and the development of adaptation strategies in coastal localities. Redistribution of terrestrial mass, in the form of hydrological and land ice mass fluxes, partly drives this regional sea level variability due to its impact on the Earth’s gravity, rotation and deformation (GRD), termed ‘Sea Level Fingerprints’ or Barystatic-GRD fingerprints. With increasing mass losses projected from ice sheets and glaciers over the coming centuries, the magnitude and relative contribution of these Barystatic-GRD fingerprints to regional sea level change are expected to increase. As a result, accurately quantifying this phenomenon and its uncertainty is critical when assessing contemporary and future regional sea level variability.Current contemporary Barystatic-GRD fingerprints are typically either calculated using a single mass loading observation source or provide discontinuous coverage since 1992 (the satellite altimetry era). Here, we present a continuous monthly Barystatic-GRD fingerprint product from 1992-2017, computed from an ensemble of mass loadings derived from differing observation techniques. To achieve this, we use the Ice Sheet and Sea Level Model (ISSM) sea level equation solver, which uses a finite element approach to solving the sea level equation at high spatial-temporal resolution, whilst maintaining computational efficiency. This enables us to use an ensemble modelling framework, ensuring the computed Barystatic-GRD fingerprint encompasses the variability between differing observation techniques. Additionally, it allows us to propagate the observation uncertainties into the fingerprint uncertainty in a robust manner. As well as the total Barystatic-GRD fingerprint, we assess the contribution of individual terrestrial components (Antarctica, Greenland, Glaciers, and hydrological mass change). This work is part of the Fingerprinting Approach to Close Regional Sea Level Budgets using ESA-CCI (FACTORS), a European Space Agency Climate Change Initiative Research Fellowship.

The Earth energy imbalance (EEI) at the top of the atmosphere is responsible for the accumulation of energy in the climate system. While necessary to better understand the Earth’s warming climate, measuring the EEI is challenging as it is a globally integrated variable whose variations are small (0.5-1 W.m−2) compared to the amount of energy entering and leaving the climate system (~ 340 W.m-2). Accuracies better than 0.1 W.m−2 are needed to evaluate the temporal variations of the EEI at decadal and longer time-scales. The CERES experiment provides EEI time variations with a typical uncertainty of ± 0.1 W.m−2 and shows a trend in EEI of 0.50 +/- 0.47 W.m−2 per decade over the period 2005-2019. The combination of space altimetry and space gravimetry measurements provides an estimate of the ocean heat content (OHC) change which is an accurate proxy of EEI (because >90% of the excess of energy stored by the planet in response to the EEI is accumulated in the ocean in the form of heat). In Marti et al. (2021), the global OHC was estimated at global scales based on the combination of space altimetry and space gravimetry measurements over 2002-2016. Changes in the EEI were then derived with realistic estimates of its uncertainty. Here we present the improvements brought to the global OGC and EEI over an extended period (2002-2021), such as the calculation of the expansion efficiency of heat over the total water column, the improvement of ocean mass solution, the empirical correction of the wet tropospheric correction of Jason-3 altimeter measurements (Barnoud et al., 2022). The space geodetic GOHC-EEI product based on space altimetry and space gravimetry is available on the AVSIO website at https://doi.org/10.24400/527896/a01-2020.003. References: Barnoud A., Picard B., Meyssignac B., Marti F., Ablain M., Roca R. Reducing the uncertainty in the satellite altimetry estimates of global mean sea level trends using highly stable water vapour climate data records. Submitted to JGR: Oceans. Marti, F., Blazquez, A., Meyssignac, B., Ablain, M., Barnoud, A., Fraudeau, R., Jugier, R., Chenal, J., Larnicol, G., Pfeffer, J., Restano, M., and Benveniste, J.: Monitoring the ocean heat content change and the Earth energy imbalance from space altimetry and space gravimetry, Earth Syst. Sci. Data, 14, 229–249, https://doi.org/10.5194/essd-14-229-2022, 2022.

This presentation provides an update on the ESA radar altimetry processing services portfolio, known as SARvatore, for the exploitation of CryoSat-2 (CS-2) and Sentinel-3 (S-3) data from L1A (FBR) data products up to SAR/SARin L2 geophysical data products. The following on-line & on-demand services compose the portfolio, now hosted in the ESA Altimetry Virtual Lab at the EarthConsole® (https://earthconsole.eu): The ESA-ESRIN SARvatore (SAR Versatile Altimetric Toolkit for Research & Exploitation) for CS-2 and S-3 services. These processor prototypes allow the users to customize the processing at L1b & L2 by setting a list of configurable options, including those not available in the operational processing chains (e.g., SAMOSA+ and ALES+ SAR retrackers). The TUDaBo SAR-RDSAR (TU Darmstadt – U Bonn SAR-Reduced SAR) for CS-2 and S-3 service. It allows users to generate reduced SAR, unfocused SAR & LRMC data. Several configurable L1b & L2 processing options and retrackers (BMLE3, SINC2, TALES, SINCS, SINCS OV) are available. The TU München ALES+ SAR for CS-2 and S-3 service. It allows users to process L1b data applying the empirical ALES+ SAR subwaveform retracker, including a dedicated SSB solution. The Aresys FF-SAR (Fully-Focused SAR) for CS-2 & S-3 service. It provides the capability to produce L1b products with several configurable options and with the possibility of appending the ALES+ FFSAR output to the L1b products. In the future, the service will be extended to process Sentinel-6 data. The following new services will be made available: the CLS SMAP S-3 FF-SAR processor (s-3-smap, http://doi.org/10.5270/esa-cnes.sentinel-3.smap) and the ESA-ESTEC/isardSAT L1 Sentinel-6 Ground Prototype Processor. All output data products are generated in standard netCDF format and are therefore also compatible with the multi-mission “Broadview Radar Altimetry Toolbox” (BRAT, http://www.altimetry.info). The Altimetry Virtual Lab is a community space for simplified processing services and knowledge-sharing, hosted on the EarthConsole®, a powerful EO data processing platform now on the ESA Network of Resources. This enables SARvatore Services to remain open for worldwide scientific applications, including for R&D studies on the retrieval of radar altimetry measured variables contributing to Inland Water monitoring (write to altimetry.info@esa.int for further information).

The scope of this presentation is to provide an update on the ESA radar altimetry services portfolio for the exploitation of CryoSat-2 (CS-2) and Sentinel-3 (S-3) data from L1A (FBR) data products up to SAR/SARin L2 geophysical data products. At present, the following on-line & on-demand services compose the portfolio:The ESA-ESRIN SARvatore (SAR Versatile Altimetric TOolkit for Research & Exploitation) for CS-2 and S-3 services. These processor prototypes allow the users to customize the processing at L1b & L2 by setting a list of configurable options, including those not available in the operational processing chains (e.g. SAMOSA+ and ALES+ SAR retrackers). The TUDaBo SAR-RDSAR (TU Darmstadt – U Bonn SAR-Reduced SAR) for CS-2 and S-3 service. It allows users to generate reduced SAR, unfocused SAR & LRMC data. Several configurable L1b & L2 processing options and retrackers (BMLE3, SINC2, TALES, SINCS, SINCS OV) are available. The TU München ALES+ SAR for CS-2 and S-3 service. It allows users to process official L1b data and produces L2 products by applying the empirical ALES+ SAR subwaveform retracker, including a dedicated SSB solution. The Aresys FF-SAR (Fully-Focused SAR) for CS-2 service. Currently under development, it will provide the capability to produce L1b products with several configurable options and with the possibility of appending the ALES+ FFSAR output to the L1b products. In the future, these services will be extended and the following new services will be made available: the Aresys FF-SAR services for S-3 & Sentinel-6, the CLS SMAP S-3 FF-SAR processor (s-3--smap) and the ESA-ESTEC/isardSAT L1 Sentinel-6 Ground Prototype Processor. All output data products are generated in standard netCDF format, and are therefore also compatible with the multi-mission “Broadview Radar Altimetry Toolbox” (BRAT, http://www.altimetry.info).The SARvatore Services are being migrated from the ESA G-POD (https://gpod.eo.esa.int/) to the Altimetry Virtual Lab, a community space for simplified services access and knowledge-sharing. It will be hosted on EarthConsole (https://earthconsole.eu), a powerful EO data processing platform now also on the ESA Network of Resources. This enables SARvatore Services to remain open for worldwide scientific applications (info at altimetry.info@esa.int).

HYDROCOASTAL is a two-year project funded by ESA, with the objective to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland water, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. Optical data from Sentinel-2 MSI and Sentinel-3 OLCI instruments will also be used in generating River Discharge products. New SAR and SARin processing algorithms for the coastal zone and inland waters will be developed and implemented and evaluated through an initial Test Data Set for selected regions. From the results of this evaluation a processing scheme will be implemented to generate global coastal zone and river discharge data sets. A series of case studies will assess these products in terms of their scientific impacts. All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms will be provided.The scientific objectives of HYDROCOASTAL are to enhance our understanding of interactions between the inland water and coastal zone, between the coastal zone and the open ocean, and the small-scale processes that govern these interactions. Also, the project aims to improve our capability to characterize the variation at different time scales of inland water storage, exchanges with the ocean and the impact on regional sea-level changes.The technical objectives are to develop and evaluate new SAR and SARin altimetry processing techniques in support of the scientific objectives, including stack processing, and filtering, and retracking. Also, an improved Wet Troposphere Correction will be developed and evaluated.The presentation will describe the different SAR altimeter processing algorithms that are being evaluated in the first phase of the project, and present results from the evaluation of the initial test data set. It will focus particularly on the performance of the new algorithms over inland water.

Given the major role of the Atlantic Ocean in the climate system, it is essential to characterize the temporal and spatial variations of its heat content. The 4DATLANTIC-OHC Project (https://eo4society.esa.int/projects/4datlantic-ohc/) aims at developing and testing space geodetic methods to estimate the local ocean heat content (OHC) changes over the Atlantic Ocean from satellite altimetry and gravimetry. The strategy developed in the frame of the ESA MOHeaCAN Project (https://eo4society.esa.int/projects/moheacan/) is pursued and refined at local scales both for the data generation and the uncertainty estimate. At two test sites, OHC derived from in situ data (RAPID and OVIDE-AR7W) are used to evaluate the accuracy and reliability of the new space geodetic based OHC change. The Atlantic OHC product will be used to better understand the complexity of the Earth’s climate system. In particular, the project aims at better understanding the role played by the Atlantic Meridional Overturning Circulation (AMOC) in regional and global climate change, and the variability of the Meridional Heat transport in the North Atlantic. In addition, improving our knowledge on the Atlantic OHC change will help to better assess the global ocean heat uptake and thus estimate the Earth’s energy imbalance more accurately as the oceans absorb about 90% of the excess energy stored by the Earth system.The objectives of the 4DATLANTIC-OHC Project will be presented. The scientific requirements and data used to generate the OHC change products over the Atlantic Ocean and the first results in terms of development will be detailed. At a later stage, early adopters are expected to assess the OHC products strengths and limitations for the implementation of new solutions for Society. The project started in June 2021 for a 2-year duration.Visit https://www.4datlantic-ohc.org to follow the main steps of the project.

River discharge monitoring is crucial for many activities ranging from the management of water resources to flood risk mitigation. Due to the limitations of the in situ stations (e.g., low station density, incomplete temporal coverage as well as delays in data access), the river discharge is not always continuously monitored in time and in space. This prompted researchers and space agencies, among others, in developing new methods based on satellite observations for the river discharge estimation.In the last decade, ESA has funded the SaTellite based Runoff Evaluation And Mapping and River Discharge Estimation (STREAMRIDE) project, which proposes the combination of two innovative and complementary approaches, STREAM and RIDESAT, for estimating river discharge. The innovative aspect of the two approaches is an almost exclusive use of satellite data. In particular, precipitation, soil moisture and terrestrial water storage observations are used within a simple and conceptual parsimonious approach (STREAM) to estimate runoff, whereas altimeter and Near InfraRed (NIR) sensors are jointly exploited to derive river discharge within RIDESAT. By modelling different processes that act at the basin or at local scale, the combination of STREAM and RIDESAT is able to provide less than 3-day temporal resolution river discharge estimates in many large rivers of the world (e.g., Mississippi, Amazon, Danube, Po), where the single approaches fail. Indeed, even if both the approaches demonstrated high capability to estimate accurate river discharge at multiple cross sections, they are not optimal under certain conditions such as in presence of densely vegetated and mountainous areas or in non-natural basins with high anthropogenic impact (i.e., in basin where the flow is regulated by the presence of dams, reservoirs or floodplains along the river; or in highly irrigated areas).Here, we present some new advancements of both STREAM and RIDESAT approaches which help to overcome the limitations encountered. In particular, specific modules (e.g., reservoir or irrigation modules for STREAM approach) as well as algorithm retrieval improvements (e.g., to take into account the sediment and the vegetation for RIDESAT algorithm) were implemented. Furthermore, in order to exploit the complementarity of the two approaches, the two river discharge estimates were also integrated within a simple data integration framework and evaluated over sites located on the Amazon and Mississippi river basins. Results demonstrated the added-value of a complementary river discharge estimate with respect to a stand-alone estimate.

The Earth energy imbalance (EEI) at the top of the atmosphere is responsible for the accumulation of heat in the climate system. Monitoring the EEI is therefore necessary to better understand the Earth's warming climate. Measuring the EEI is challenging as it is a globally integrated variable whose variations are small (0.5–1 W m−2) compared to the amount of energy entering and leaving the climate system (∼340 W m−2). Since the ocean absorbs more than 90 % of the excess energy stored by the Earth system, estimating the ocean heat content (OHC) change provides an accurate proxy of the EEI. This study provides a space geodetic estimation of the OHC changes at global and regional scales based on the combination of space altimetry and space gravimetry measurements. From this estimate, the global variations in the EEI are derived with realistic estimates of its uncertainty. The mean EEI value is estimated at +0.74±0.22 W m−2 (90 % confidence level) between August 2002 and August 2016. Comparisons against estimates based on Argo data and on CERES measurements show good agreement within the error bars of the global mean and the time variations in EEI. Further improvements are needed to reduce uncertainties and to improve the time series, especially at interannual timescales. The space geodetic OHC-EEI product (version 2.1) is freely available at https://doi.org/10.24400/527896/a01-2020.003 (Magellium/LEGOS, 2020).

The launch of Sentinel-3A in February 2016 represented the beginning of a new long-term series of operational satellite radar altimeters, which will provide Delay-Doppler altimetry measurements over ice sheets for decades to come. Given the potential benefits that these satellites can offer to a range of glaciological applications, it is important to establish their capacity to monitor ice sheet elevation and elevation change. Here, we present the first analysis of Sentinel-3 Delay-Doppler altimetry over the Antarctic ice sheet, and assess the accuracy and precision of retrievals of ice sheet elevation across a range of topographic regimes. Over the low-slope regions of the ice sheet interior, we find that the instrument achieves both an accuracy and a precision of the order of 10 cm, with ∼98 % of the data validated being within 50 cm of co-located airborne measurements. Across the steeper and more complex topography of the ice sheet margin, the accuracy decreases, although analysis at two coastal sites with densely surveyed airborne campaigns shows that ∼60 %–85 % of validated data are still within 1 m of co-located airborne elevation measurements. We then explore the utility of the Sentinel-3A Delay-Doppler altimeter for mapping ice sheet elevation change. We show that with only 2 years of available data, it is possible to resolve known signals of ice dynamic imbalance and to detect evidence of subglacial lake drainage activity. Our analysis demonstrates a new, long-term source of measurements of ice sheet elevation and elevation change, and the early potential of this operational system for monitoring ice sheet imbalance for decades to come.

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 contributions. 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, 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 from satellite radar altimetry and from GRACE; and the contribution from land water storage anomalies assessed by the WaterGAP global hydrological model. Over the period Jan 1993–Dec 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 Jan 2003–Aug 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 2.22 ± 0.15 mm yr−1, 61 % of the GMSL trend), with the largest increase contributed from Greenland. 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 budget, which can be evaluated only for P2, is also closed, that is, the GRACE-based ocean-mass trend agrees with the sum of assessed mass contributions within uncertainties. Combined uncertainties (1-sigma) of the elements involved in the budgets are between 0.26 and 0.40 mm yr−1, about 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.

Information on sea level and its temporal and spatial variability is of great importance for various scientific, societal and economic issues. This article reports about a new sea level dataset for the North Sea (named NorthSEAL) of monthly sea level anomalies (SLA), absolute sea level trends and sea level mean annual amplitudes over the period 1995–2019. Uncertainties and quality flags are provided together with the data. The dataset has been created from multi-mission cross-calibrated altimetry data, preprocessed 5 with coastal dedicated approaches and gridded with innovative methods to a 6–8 km wide triangular mesh. The comparison of SLA and tide gauge time series shows a good consistency with average correlations of 0.85 and maximum correlations of 0.93. The improvement with respect to existing global gridded altimetry solutions amounts to 8–10 %, and it is most pronounced in complicated coastal environments such as river mouths or regions sheltered by islands. The differences in trends at tide gauge locations depend on the vertical land motion model used to correct relative sea level trends. The best 10 consistency with a median difference of 0.04 ± 1.15 mm/year is reached by applying a recent glacial isostatic adjustment (GIA) model. With the presented sea level dataset, for the first time, a regionally optimized product for the entire North Sea is made available. It will enable further investigations of ocean processes, sea level projections and studies on coastal adaptation measures. The NorthSEAL data is available at https://doi.org/10.17882/79673 (Müller et al., 2021).

The scope of this presentation is to feature and provide an update on the ESA G-POD/SARvatore family of altimetry services portfolio for the exploitation of CryoSat-2 and Sentinel-3 data from L1A (FBR) data products up to SAR/SARin Level-2 geophysical data products. At present, the following on-line & on-demand services compose the portfolio:- The SARvatore (SAR Versatile Altimetric TOolkit for Research & Exploitation) for CryoSat-2 and Sentinel-3 services developed by the Altimetry Team in the R&D division at ESA-ESRIN. These processor prototypes are versatile and allow the users to customize and adapt the processing at L1b & L2 according to their specific requirements by setting a list of configurable options. The scope is to provide users with specific processing options not available in the operational processing chains (e.g. range walk correction, stack sub-setting, extended receiving window, zero padding, high-posting rate and burst weighting at L1b & SAMOSA+, SAMOSA++ and ALES+ SAR retrackers at L2). AJoin & Share Forum (https://wiki.services.eoportal.org/tiki-custom_home.php) allows users to post questions and report issues. A data repository is also available to the Community to avoid the redundant reprocessing of already processed data (https://wiki.services.eoportal.org/tiki-index.php?page=SARvatore+Data+Repository&highlight=repository).- The TUDaBo SAR-RDSAR (Technical University Darmstadt – University Bonn SAR-Reduced SAR) for CryoSat-2 and Sentinel-3 service. It allows users to generate reduced SAR, unfocused SAR & LRMC data. Several configurable L1b & L2 processing options and retrackers (BMLE3, SINC2, TALES, SINCS) are available. The processor will be extended during an additional activity related to the ESA HYDROCOASTAL Project (https://www.satoc.eu/projects/hydrocoastal/) to account in the open ocean for the vertical motion of the wave particles (VMWP) in unfocused SAR and in a simplified form of the fully focused SAR called here Low Resolution Range Cell Migration Correction-Focused (LRMC-F). - The ALES+ SAR for CryoSat-2 and Sentinel-3 service. It allows users to process official L1b data and produces L2 NetCDF products by applying the empirical ALES+ SAR subwaveform retracker, including a dedicated SSB solution, developed by the Technische Universität München in the frame of the ESA Sea Level CCI (http://www.esa-sealevel-cci.org/) & BALTIC+ SEAL Projects (http://balticseal.eu/).- The Aresys Fully Focused SAR for CryoSat-2 service. Currently under development, it will provide the capability to produce CS-2 FF-SAR L1b products thanks to the Aresys 2D transformed frequency domain AREALT-FF1 processor prototype. Output products will also include geophysical corrections and threshold peak & ALES-like subwaveform retracker estimates.The G-POD graphical interface allows users to select, in all the services, a geographical area of interest within the time-frame related to the L1A (FBR) & L1b data products availability in the service catalogue. After the task submission, users can follow, in real time, the status of the processing. The output data products are generated in standard NetCDF format, therefore being compatible with the multi-mission “Broadview Radar Altimetry Toolbox” (BRAT, http://www.altimetry.info) and typical tools.Services are open, free of charge (supported by ESA) for worldwide scientific applications and available, after registration and activation (to be requested for each chosen service to eo-gpod@esa.int), at https://gpod.eo.esa.int.

Climate-related sea level changes in the world coastal zones result from the superposition of the global mean rise due to ocean warming and land ice melt, regional changes mostly caused by non-uniform ocean thermal expansion and salinity changes, and small-scale coastal processes (e.g., shelf currents, wind & waves changes, fresh water input from rivers, etc.). So far, satellite altimetry has provided global gridded sea level time series up to 10-15 km to the coast only, preventing estimation of sea level changes very close to the coast. In the context of the ESA Climate Change Initiative coastal sea level project, we have developed a complete reprocessing of high-resolution (20 Hz) Jason-1, 2 and 3 altimetry data along the world coastal zones using the ALES (Adaptative Leading Edge Subwaveform) retracker combined with the XTRACK system dedicated to improve geophysical corrections at the coast. Here we present coastal sea level trends over the period 2002-2020 along the whole African continent. Different coastal sea level trend behaviors are observed over the study period. We compare the computed coastal trends in Africa with results we previously obtained in other regions (Mediterranean Sea, Northeastern Europe, north Indian Sea, southeast Asia and Australia).

STREAM -SaTellite based Runoff Evaluation And Mapping- is a conceptual hydrological model able to derive daily river discharge and runoff estimates from satellite soil moisture, precipitation and terrestrial water storage anomalies observations. The model is very simple and versatile: It requires a limited number of parameters (only eight) to simulate river discharge.The model simulates river discharge and gridded runoff at daily time scale with a 25 km spatial resolution. Forced by TRMM 3B42 rainfall data and ESA CCI soil moisture data and GRACE over five pilot large basins (Mississippi, Amazon, Niger, Danube and Murray Darling) the model already provided good runoff estimates especially over Amazon basin, with a Kling-Gupta efficiency (KGE) index greater than 0.92 both at the basin outlet and over several inner stations in the basin. Good results have been also obtained for Mississippi, Niger and Danube with KGE index greater than 0.75 for all the gauging stations.By considering the good performances of the STREAM model and by the continuous availability (in space and time) of satellite observations, this work presents an attempt to regionalize the STREAM model parameters. The Mississippi river basin has been taken as case study and specific relationships between model parameters and different predictors (climate variables such as precipitation and evaporation, soil vegetation and topography characteristics) have been developed. By using these relationships, STREAM parameter values have been directly obtained from readily available climatic and physiographic basin characteristics and model performances are still satisfactory (median KGE over the basin equal to 0.60). The capability to use these relationships in other hydrologically similar catchments will be investigated for the Danube and Amazon river basins. The final target is to obtain global relationships as to provide to provide daily, 25 km, global runoff maps from the STREAM approach.

The ALtimetry for BIOMass (ALBIOM) project is an ESA-funded Permanent Open Call Project that aims to retrieve forest biomass using Copernicus Sentinel-3 (S3) altimeter data. The overall goal of ALBIOM is to estimate biomass with sufficient accuracy to be able to increase existing satellite data for biomass retrieval, as well as to improve the global mapping and monitoring of this fundamental variable. The project core tasks consist of 1) an analysis of the sensitivity of altimetry backscatter data on land parameters; 2) the development and validation of a Sentinel-3 altimeter backscatter simulator, including the effect of both topography and vegetation and 3) the development and validation of a machine-learning biomass estimation algorithm.Here we present a summary of the results obtained from the project. The sensitivity analysis reveals that both the altimetric waveforms and the corresponding Normalised Radar Cross Sections (NRCSs) can be sensitive to the presence of biomass in the order of 100-400 tons/ha, but they are also influenced by topography and water bodies. Different sensitivities with respect to the different frequencies and resolution modes are observed, highlighting non-linear behaviours of the NRCSs. The use of differential NRCSs, defined as the difference among those calculated over two different bandwidths, was demonstrated to be not necessarily more sensitive to vegetation, as it was instead highlighted by previous studies like [Papa et al., 2003].The tracking window often appears partly or completely misplaced, when the tracking mode is in open-loop mode prescribing a predetermined range, and its size is often not long enough when collecting data over land, especially over regions with complex topography. The length and correct positioning of the tracking window over land represent therefore critical aspects for a study like ALBIOM.The modelling work has been focused on the development of a merged model approach to simulate altimeter waveforms over vegetated areas. The merging is obtained via the simultaneous use of the modifiedTor Vergata Scattering Model (TOVSM) [Ferrazzoli and Guerriero, 1995, 1996] to simulate the waveform of a flat surface covered by forest vegetation, and the use of the Soil And Vegetation Reflection Simulator (SAVERS) [Pierdicca et al., 2014], originally conceived for GNSS-Reflectometry, and here adapted to the Altimetry system. The simulator developed within ALBIOM shows promising ability to reproduce the general characteristics of the S3 waveforms. The simulations related to forested surfaces present at least two peaks, due to the top of canopy and to the ground, but the presence of topography may introduce other peaks in the waveforms, making the identification of vegetation and topographic effects challenging.Initial results on the algorithm development using Artificial Neural Networks (ANN) highlight some promising biomass estimates over specific areas (e.g. Central Africa) but also differences in algorithm performances among different regions. The corrected “ice” backscatter coefficient showed the highest sensitivity to biomass, but its values are often invalid over land, which limits the number of meaningful retrievals. The different altimeter tracking mode of Sentinel-3 over different areas of the globe (i.e., open loop and closed loop) could also be responsible for the differences in results.

Satellite altimetry missions provide a quasi-global synoptic view of sea level variations over more than 25 years and provide regional sea level (SL) indicators such as trends and accelerations. Estimating realistic uncertainties on these quantities is crucial to address current climate science questions. While uncertainty estimates are available for the global mean sea level (GMSL), information is not available at local scales so far. We estimate a local satellite altimetry error budget and use it to derive local error variance-covariance matrices, and estimate confidence intervals on trends and accelerations at the 90% confidence level. Over 1993–2019, we find that the average local sea level trend uncertainty is 0.83 mm.yr−1 with values ranging from 0.78 to 1.22 mm.yr−1. For accelerations, uncertainties range from 0.057 to 0.12 mm.yr−2, with a mean value of 0.062. We also perform a sensitivity study to investigate a range of plausible error budgets.A dataset consisiting of a single NetCDF file containing local error levels, error variance-covariance matrices, SL trends and accelerations, along with corresponding uncertainties is provided (https://doi.org/10.17882/74862). Code to reproduce the study is also distributed (https://github.com/pierre-prandi/rsl). With this information, users should be able to reuse these error levels to derive uncertainties on any metric (e.g. inter annual variability) or time period.

Information on sea level and its temporal and spatial variability is of great importance for various scientific, societal, and economic issues. This article reports about a new sea level dataset for the North Sea (named North SEAL) of monthly sea level anomalies (SLAs), absolute sea level trends, and amplitudes of the mean annual sea level cycle over the period 1995–2019. Uncertainties and quality flags are provided together with the data. The dataset has been created from multi-mission cross-calibrated altimetry data preprocessed with coastal dedicated approaches and gridded with an innovative least-squares procedure including an advanced outlier detection to a 6–8 km wide triangular mesh. The comparison of SLAs and tide gauge time series shows good consistency, with average correlations of 0.85 and maximum correlations of 0.93. The improvement with respect to existing global gridded altimetry solutions amounts to 8 %–10 %, and it is most pronounced in complicated coastal environments such as river mouths or regions sheltered by islands. The differences in trends at tide gauge locations depend on the vertical land motion model used to correct relative sea level trends. The best consistency with a median difference of 0.04±1.15 mm yr−1 is reached by applying a recent glacial isostatic adjustment (GIA) model. With the presented sea level dataset, for the first time, a regionally optimized product for the entire North Sea is made available. It will enable further investigations of ocean processes, sea level projections, and studies on coastal adaptation measures. The North SEAL data are available at https://doi.org/10.17882/79673 (Müller et al., 2021).

The Earth Energy Imbalance (EEI) is a key indicator to understand the Earth’s changing. However, measuring this indicator is challenging since it is a globally integrated variable whose variations are small, of the order of several tenth of W.m-2, compared to the amount of energy entering and leaving the climate system of ~340 W.m-2. Recent studies suggest that the EEI response to anthropogenic GHG and aerosols emissions is 0.5-1 W.m-2. It implies that an accuracy of the direct measurement of in situ temperature based on temperature/Salinity profiles (e.g. ARGO floats), the measurement of the net ocean surface heat fluxes from space (CERES), the estimate from ocean reanalyses that assimilate observations from both satellite and in situ instruments, the measurement of the thermal expansion of the ocean from space based on differences between the total sea-level content derived from altimetry measurements and the mass content derived from GRACE data (noted “Altimetry-GRACE”). To date, the best results are given by the first method based on ARGO network. However ARGO measurements do no sample deep ocean below 2000 m depth and marginal seas as well as the ocean below sea ice. Re-analysis provides a more complete estimation but large biases in the polar oceans and spurious drifts in the deep ocean mask a significant part of the OHC signal related to EEI. The method based on estimation of ocean net heat fluxes (CERES) is not appropriate for OHC calculation due to a too strong uncertainty (±15 W.m-2). In the MOHeaCAN project supported by ESA, we are being developed the “Altimetry-GRACE” approach which is promising since it provides consistent spatial and temporal sampling of the ocean, it samples the entire global ocean, except for polar regions, and it provides estimates of the OHC over the ocean’s entire depth. Consequently, it complements the OHC estimation from ARGO. However, to date the uncertainty in OHC from this method is close to 0.5 W.m-2, and thus greater than the requirement of 0.3 W.m-2 needed to a good EEI estimation. Therefore the scientific objective of the MOHeaCan project is to improve these estimates :by developing novel algorithms in order to reach the challenging target for the uncertainty quantification of 0.3 W. m−2; by estimating realistic OHC uncertainties thanks to an error budget of measurements applying a rigorous mathematical formalism; by developing a software prototype systems that allow to perform sensitivities studies and OHC product and its uncertainty generation; by assessing our estimation by performing comparison against independent estimates based on ARGO network, and based on the Clouds and the Earth’s Radiant energy System (CERES) measurements at the top of the atmosphere.

Water is at the centre of economic and social development; it is vital to maintain health, grow food, manage the environment, produce renewable energy, support industrial processes and create jobs. Despite the importance of water, to date over one third of the world's population still lacks access to drinking water resources and this number is expected to increase due to climate change and outdated water management. As over half of the world’s potable water supply is extracted from rivers, either directly or from reservoirs, understanding the variability of the stored water on and below landmasses, i.e., runoff, is of primary importance. Apart from river discharge observation networks that suffer from many known limitations (e.g., low station density and often incomplete temporal coverage, substantial delay in data access and large decline in monitoring capacity), runoff can be estimated through model-based or observation-based approaches whose outputs can be highly model or data dependent and characterised by large uncertainties. On this basis, developing innovative methods able to maximize the recovery of information on runoff contained in current satellite observations of climatic and environmental variables (i.e., precipitation, soil moisture, terrestrial water storage anomalies and land cover) becomes mandatory and urgent. In this respect, within the European Space Agency (ESA) STREAM Project (SaTellite based Runoff Evaluation And Mapping), a solid “observational” approach, exploiting space-only observations of Precipitation (P), Soil Moisture (SM) and Terrestrial Water Storage Anomalies (TWSA) to derive total runoff has been developed and validated. Different P and SM products have been considered. For P, both in situ and satellite-based (e.g., Tropical Rainfall Measuring Mission, TRMM 3B42) datasets have been collected; for SM, Advanced SCATterometer, ASCAT, and ESA Climate Change Initiative, ESA CCI, soil moisture products have been extracted. TWSA time series are obtained from the latest Goddard Space Flight Center’s global mascon model, which provides storage anomalies and their uncertainties in the form of monthly surface mass densities per approximately 1°x1° blocks. Total runoff estimates have been simulated for the period 2003-2017 at 5 pilot basins across the world (Mississippi, Amazon, Niger, Danube and Murray Darling) characterised by different physiographic/climatic features. Results proved the potentiality of satellite observations to estimate runoff at daily time scale and at spatial resolution better than GRACE spatial sampling. In particular, by using satellite TRMM 3B42 rainfall data and ESA CCI soil moisture data, very good runoff estimates have been obtained over Amazon basin, with a Kling-Gupta efficiency (KGE) index greater than 0.92 both at the closure and over several inner stations in the basin. Good results found for Mississippi and Danube are also encouraging with KGE index greater than 0.75 for both the basins.

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.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 (https://gpod.eo.esa.int/services/SENTINEL3_SAR/) and obtained by using the SAMOSA2, SAMOSA+ & SAMOSA++ retrackers. The selected posting rate of measurements is 80 Hz to optimize the location of data points over the Ohio and Columbia River (an estimate every 80 m along-track), however a comparison with the 20 Hz posting rate is being made. Empirical retrackers outputs, available in the official 20 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.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…) 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.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.

The unique contribution of satellite radar altimetry to river monitoring is well understood, with ‘ altimeter virtual gauge’ heights increasingly ingested into river basin models. However, altimeters gather a wealth of additional information. Waveform shapes reflect underlying topographic variation, surface composition and roughness, and distribution of surface water within the footprint. Backscatter measurements allow soil surface moisture under the satellite track to be determined, using DRy EArth ModelS (DREAMS) crafted from multi-mission altimeter data and ground truth. Initially developed over desert areas, DREAMs are now being built over river basins to extend the scope of altimeter soil moisture measurement.This paper investigates the potential contribution of these additional data to river basin analysis and modelling. The following key questions are addressed. 1) How useful are the data encoded in complex waveform shapes? 2) Can altimeter soil moisture estimates contribute to modelling in river basins?A series of example river basins were chosen in different topographic and climate situations, including the Amazon, Orinoco, Nile, Niger and Congo basins, and wetlands including the Okavango delta.This paper presents outcomes from analysis of multi-mission altimetry, with ERS-1/2, Envisat, Topex, Jason-1/2, Cryosat-2 and Sentinel-3A/B, plus a database of over 86,000 river and lake timeseries.The analysis outcomes demonstrate the value of altimeter soil surface moisture estimates, both as co-temporal and co-spatial data with inland water height measurements, and as an independent validation dataset to assess soil moisture estimates derived from other remote sensing techniques. The precise backscatter cross-calibration of altimeters on successive missions allows derivation of long soil moisture time series. The ability of nadir-pointing altimeters to penetrate vegetation canopy gives a unique perspective in rainforest areas, with information on underlying water height and extent as well as surface soil moisture. Waveform shape classification allows diverse information to be gleaned, particularly at the higher pulse repetition frequencies of the new generation of SAR Altimeters. In conclusion, satellite radar altimeters collect a wealth of information over river basins; this valuable resource is not yet fully exploited.

The scope of this presentation is to feature the G-POD SARvatore service to users for the exploitation of CryoSat-2 and Sentinel-3 data, which was designed and developed by the Altimetry Team in the R&D division at ESA-ESRIN. The G-POD service coined SARvatore (SAR Versatile Altimetric Toolkit for Ocean Research & Exploitation) is a web platform that allows any scientist to process on-line, on-demand and with user-selectable configuration CryoSat-2 SAR/SARin and Sentinel-3 SAR data, from L1A (FBR) data products up to SAR/SARin Level-2 geophysical data products. The G-POD graphical interface allows users to select a geographical area of interest within the time-frame related to the Cryosat-2 SAR/SARin FBR and Sentinel-3 L1A data products availability in the service catalogue. The processor prototype is versatile, allowing users to customize and to adapt the processing according to their specific requirements by setting a list of configurable options. Pre-defined processing configurations (Official CryoSat-2, Official Sentinel-3, Open Ocean, Coastal Zone, Inland Water (20Hz & 80Hz), Ice and Sea-Ice) are available. After the task submission, users can follow, in real time, the status of the processing. The output data products are generated in standard NetCDF format, therefore being compatible with the multi-mission “Broadview Radar Altimetry Toolbox” (BRAT, http://www.altimetry.info) and typical tools.Initially, the processing was designed and optimized uniquely for open ocean studies. It was based on the SAMOSA model developed for the Sentinel-3 Ground Segment. However, since June 2015, the SAMOSA+ retracker is available as a dedicated retracker for coastal zone, inland water and sea-ice/ice-sheet. A new retracker (SAMOSA++) has been recently developed and will be made available in the future. The scope is to maximize the exploitation of CryoSat-2 and Sentinel-3 data over all surfaces providing user with specific processing options not available in the default processing chains.Recent improvements include: 1) A Join & Share Forum to allow users to post questions and report issues (https://wiki.services.eoportal.org/tiki-custom_home.php); 2) A data repository to better support the growing Altimetry Community avoiding the redundant reprocessing of already processed data (https://wiki.services.eoportal.org/tiki-index.php?page=SARvatore+Data+Repository&highlight=repository); 3) A new function in the GUI allowing users to compute the geodetic distance between selected points on the map; 4) A new function in the GUI to filter the products search to a specific RON (Relative Orbit Number) and to a specific pass direction (Ascending or Descending). Furthermore, users will find in the folder SUM_RESDIR of the output data package a short summary report with information on the products that have not been processed and instructions on how to eventually try to re-process the missing data.To respond to the request of hydrologists, and simulate data that a river gauge would provide, SARvatore will soon include a post-processing service to convert water level estimates in L2 data to virtual station water level values, which are typically required by hydrologists. Validation of SARvatore data over river targets will be presented to demonstrate the potential of both the SAMOSA+/++ retrackers and the innovative processing configurations not available in the default CryoSat-2 and Sentinel-3 processing chains.

RIDESAT - RIver flow monitoring and Discharge Estimation by integrating multiple SATellite data, is an ESA-funded Permanent Open Call project aimed to develop a new methodology for estimating river discharge through the combination of radar altimeter, optical and thermal satellite sensors. The combination of multi-sensor measurements can provide significant advantages over single sensors contributing to improve the quality of the final products also in terms of spatial and temporal coverage.The methodology developed in the project includes two phases. First, the single-instrument products (altimeter, optical and thermal sensors) are independently processed to generate a dataset of proxies of hydraulic variables strongly linked with river discharge (e.g. water level, flow velocity, width). Successively, these proxies are implemented as integrated techniques for the final estimation of the river discharge.To test the ability of the approach to retrieve river discharge at global scale, 20 pilot sites are selected all over the world, based on the availability of in-situ measurements and the climatic characteristics of the basins. The availability of large datasets of in situ measurements is used for: 1) the validation of single-instrument products and the river discharge product; 2) the evaluation of the uncertainty attributed to the combination process; 3) the evaluation of the limitation of the procedure.

The use of satellite altimetry at high latitudes and coastal regions is currently limited by the presence of seasonal sea ice coverage, and the proximity to the coast. The semi-enclosed Baltic Sea features seasonal coverage of sea-ice in the northern and coastal regions, and complex jagged coastlines with a huge number of small islands. However, as a semi-enclosed sea with a considerable extent, the Baltic Sea features a much-reduced tidal signal, both open- and coastal- waters, and an extensive multi-national network of tide-gauges. These factors maximise opportunities to drive improvements in sea-level estimations for coastal, and seasonal-ice regions.The ESA Baltic SEAL project, launched in April 2019, aims to exploit these opportunities. It is generating and validating a suite of enhanced multi-mission sea level products. Processing is developed specifically for coastal regions, with the objective of achieving a consistent description of the sea-level variability in terms of long-term trends, seasonal variations and a mean sea-surface. These will advance knowledge on adapting processing algorithms, to account for seasonal ice, and complex coastlines. Best practice approaches will be available to update current state-of-the-art datasets.In order to fulfill these goals, a novel altimeter re-tracking strategy has been developed. This enables the homogeneous determination of sea-surface heights for open-ocean, coastal and sea-ice conditions (ALES+). An unsupervised classification algorithm based on artificial intelligence routines has been developed and tailored to ingest data from all current and past satellite altimetry missions. This identifies radar echoes, reflected by narrow cracks within the sea-ice domain. Finally, the improved altimetry observations are gridded onto a triangulated surface mesh, featuring a spatial resolution greater than 1/4 degree. This is more suitable for utility for coastal areas, and use by coastal stakeholders.In addition to utilizing a wide range of altimetry data (Delay-Doppler and Pulse-Limited systems), the Baltic SEAL initiative harnesses the Baltic Seas unique characteristics to test novel geophysical corrections (e.g. wet troposphere correction), use the latest generation of regional altimetry datasets, and evaluate the benefits of the newest satellite altimetry missions. This presentation outlines the methodology and results achieved to date. These include estimations of a new regional mean sea surface, and insights into the trends of the sea level along the altimetry tracks with the longest records. The transfer of advances to other regions and sea-level initiatives are also highlighted.

The ALtimetry for BIOMass project (ALBIOM) is ESA-funded Permanent Open Call Project that proposes to derive forest biomass using Copernicus Sentinel-3 (S3) SAR altimeter data. The project targets the need to improve our current global observations of biomass as an Essential Climate Variable (ECV) and crucial for bioenergy, risk mitigation activities, and sustainable management of forests. The overall goal is to estimate biomass with sufficient accuracy to be able to increase the existing satellite data for biomass retrieval and to improve the global mapping and monitoring of this fundamental variable.The project originates from evidence that radar altimetry backscatter over land responds to a variety of land parameters, including vegetation-related parameters, at the different bands used by past and existing altimeters.To achieve the scientific objectives, the project is structured into six conceptual tasks. After a review of the literature and of the existing user needs, a sensitivity analysis is performed to understand the relationship between SAR altimetry backscatter data and land parameters themselves. This is followed by the development of a Sentinel-3 SAR altimeter backscatter simulator over vegetated areas, and then by the development of a biomass inversion algorithm, testing different retrieval methodologies, both theoretical and empirical. A validation task for both the model and the algorithm is carried out over specific test sites of boreal and tropical forests, to finally generate a prototype of biomass product to be reviewed by potential users.The sensitivity analysis allows to understand how the S3 Level 1 backscatter power waveforms change with respect to varying biomass, but also how they are affected by other land parameters such as soil moisture, land cover, topography and roughness. This analysis is carried out considering both the single-looked and the multi-looked waveforms, and considering primarily the high-resolution SAR mode, but also the Pseudo Low Resolution Mode (PLRM). The outcome of the sensitivity analysis provides indication of what waveforms, acquisition mode, observables derived from the waveforms and characteristics of the waveforms themselves respond more strongly to biomass variations, and on the degree of influence of the other auxiliary parameters, informing on the best strategies and approaches to adopt for the development of the retrieval algorithm.Subsequent to the sensitivity analysis, the S3 altimetry backscatter simulator is developed over vegetated areas, reproducing both the coherent scattering component, which represents the echo from the ground, and incoherent scattering component arising from the forest layers between the ground and the top of canopy. The approach followed is similar to that of the SAVERS simulator, developed for GNSS-Reflectometry, with the signal backscatter attenuation introduced by branches, leaves and trunks modelled through the discrete approach of the Tor Vergata Scattering Model.Results from the sensitivity analysis and the initial stages of the simulation development will be presented and discussed at the conference, together with the foreseen approaches for the development of the biomass retrieval algorithm.

The scope of this work is to showcase the BRAT (Broadview Radar Altimetry Toolbox) and GUT (GOCE User Toolbox) toolboxes.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 & Geophysics, Oceanography, Coastal Zone, Atmosphere, Wind & Waves, Hydrology, Land, Ice and Climate, which can also be consulted in http://www.altimetry.info/radar-altimetry-tutorial/.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.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.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 – such as geoid height, gravity anomaly/disturbance, radial gravity gradient, vertical deflections – that may be derived from the GOCE gravity models.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.BRAT and GUT toolboxes can be freely downloaded, along with ancillary material, at https://earth.esa.int/brat and https://earth.esa.int/gut.

SCOOP (SAR Altimetry Coastal & Open Ocean Performance) is a project funded under the ESA SEOM (Scientific Exploitation of Operational Missions) Programme Element, to characterise the expected performance of Sentinel-3 SRAL SAR mode altimeter products, and then to develop and evaluate enhancements to the baseline processing scheme in terms of improvements to ocean measurements. Another objective is to develop and evaluate an improved Wet Troposphere correction for Sentinel-3.The SCOOP studies are based on two 2-year test data sets derived from CryoSat-2 FBR data, produced for 10 regions. The first Test Data Set was processed with algorithms equivalent to the Sentinel-3 baseline, and the second with algorithms expected to provide an improved performance.We present results from the SCOOP project that demonstrate the excellent performance of SRAL at the coast in terms of measurement precision, with noise in Sea Surface Height 20Hz measurements of less than 5cm to within 5km of the coast.We then report the development and testing of new processing approaches designed to improve performance, including, for L1B to L2:Application of zero-padding Application of intra-burst Hamming windowing Exact beam forming in the azimuthal direction Restriction of stack processing to within a specified range of look angles. Along-track antenna compensation And for L1B to L2Application of alternative re-trackers for SAR and RDSAR. Based on the results of this assessment, a second test data set was generated and we present an assessment of the performance of this second Test Data Set generated, and compare it to that of the original Test Data Set.Regarding the WTC for Sentinel-3A, the correction from the on-board MWR has been assessed by means of comparison with independent data sets such as the GPM Microwave Imager (GMI), Jason-2, Jason-3 and Global Navigation Satellite Systems (GNSS) derived WTC at coastal stations. GNSS-derived path Delay Plus (GPD+) corrections have been derived for S3A. Results indicate good overall performance of S3A MWR and GPD+ WTC improvements over MWR-derived WTC, particularly in coastal and polar regions. Based on the outcomes of this study we provide recommendations for improving SAR mode altimeter processing and priorities for future research.