Search

Radar altimeters have so far had limited use in the coastal zone, the area with most societal impact. This is due to both lack of, or insufficient accuracy in the necessary corrections, and more complicated altimeter signals. This letter examines waveform data from the Envisat RA-2 as it passes regularly over Pianosa (a 10-km2 island in the northwestern Mediterranean). Forty-six repeat passes were analyzed, with most showing a reduction in signal upon passing over the island, with weak early returns corresponding to the reflections from land. Intriguingly, one third of cases showed an anomalously bright hyperbolic feature. This feature may be due to extremely calm waters in the Golfo della Botte (northern side of the island), but the cause of its intermittency is not clear. The modeling of waveforms in such a complex land/sea environment demonstrates the potential for sea surface height retrievals much closer to the coast than is achieved by routine processing. The long-term development of altimetric records in the coastal zone will not only improve the calibration of altimetric data with coastal tide gauges but also greatly enhance the study of storm surges and other coastal phenomena.

Radar altimetry is a remote sensing technique with a wide range of practical and scientific applications for observing the global ocean, land surface waters, and the Earth’s ice sheets. Understanding complex and technical satellite radar altimetry systems and using the data and information products derived from them can be immensely challenging. Nonexpert users, such as the general public and students in particular, may find that this complexity prevents them from reaping the benefits of the considerable information that satellites can provide. Over the past 28 years and more, experts and amateurs in altimetry outreach have developed programs, activities and information products using a variety of media, targeting a broad spectrum of users in order to demonstrate the significant value of satellite altimetry missions to society. The goal of outreach professionals and nonprofessionals is to convey complex scientific and technical concepts in an understandable and engaging way to a variety of audiences. Outreach people themselves also come from a wide variety of backgrounds: from employees of international space agencies building and launching satellites, through to Mission and Science Team members validating and using the data for research purposes, and science communicators —all with a wide range of motivations. This article reviews the challenges and achievements of altimetry outreach and is largely based on the records of the Outreach and Data Services splinter session of the Ocean Surface Topography Science Team meetings. It includes minutes of splinter sessions, agendas, posters, and collected showcase presentations.

28 pages, 9 figures, 6 tables, supporting information https://doi.org/10.1029/2021JC017676.-- Data Availability Statement: SSS CCI datasets are freely available at https://catalogue.ceda.ac.uk/uuid/4ce685bff631459fb2a30faa699f3fc5. The PI-MEP MDB are freely available as NetCDF files at https://www.salinity-pimep.org/data/mdb.html; corresponding validation reports are available on https://www.salinity-pimep.org/reports/mdb.html. SMAP salinity data are produced by Remote Sensing Systems and sponsored by the NASA Ocean Salinity Science Team. Data are available at www.remss.com. Aquarius data are available at https://podaac.jpl.nasa.gov/dataset/AQUARIUS_L3_SSS_SMID_ANNUAL_V5. CATDS SSS are available at the CATDS Production Data Center (CPDC), www.catds.fr. ISAS and Glorys fields are taken from (https://catalogue.marine.copernicus.eu/; INSITU_GLO_TS_OA_REP_OBSERVATIONS_013_002 and GLOBAL-REANALYSIS-PHY-001-030 products, respectively). The GOSUD RV data set is available at https://doi.org/10.17882/39475, the ships of opportunity data set is available at http://www.legos.obs-mip.fr/observations/sss/datadelivery/dmdata. Argo data are taken from http://www.coriolis.eu.org/, Sea Surface Salinity (SSS) is an increasingly used Essential Ocean and Climate Variable. The Soil Moisture and Ocean Salinity (SMOS), Aquarius, and Soil Moisture Active Passive (SMAP) satellite missions all provide SSS measurements, with very different instrumental features leading to specific measurement characteristics. The Climate Change Initiative Salinity project (CCI + SSS) aims to produce a SSS Climate Data Record (CDR) that addresses well-established user needs based on those satellite measurements. To generate a homogeneous CDR, instrumental differences are carefully adjusted based on in-depth analysis of the measurements themselves, together with some limited use of independent reference data. An optimal interpolation in the time domain without temporal relaxation to reference data or spatial smoothing is applied. This allows preserving the original datasets variability. SSS CCI fields are well suited for monitoring weekly to interannual signals, at spatial scales ranging from 50 km to the basin scale. They display large year-to-year seasonal variations over the 2010–2019 decade, sometimes by more than ±0.4 over large regions. The robust standard deviation of the monthly CCI SSS minus in situ Argo salinities is 0.15 globally, while it is at least 0.20 with individual satellite SSS fields. r2 is 0.97, similar or better than with original datasets. The correlation with independent ship thermosalinographs SSS further highlights the CCI data set excellent performance, especially near land areas. During the SMOS-Aquarius period, when the representativity uncertainties are the largest, r2 is 0.84 with CCI while it is 0.48 with the Aquarius original data set. SSS CCI data are freely available and will be updated and extended as more satellite data become available, This study was funded by ESA CCI contract 4000123663/18/I-NB, With the institutional support of the ‘Severo OchoaCentre of Excellence’ accreditation (CEX2019-000928-S)

The European Space Agency (ESA) leads on observing the Earth's changing climate from space. Its flagship programme, the Climate Change Initiative (CCI), draws together over 40 years of data from ESA's own satellite missions and those from other space agencies - from past as well as currently active in -orbit ins trumentation. The CCI science teams focus on R&D activities to generate long -term, global climate data records that describe the evolution of key components of the Earth's climate system, as defined by the Global Climate Observing System (GCOS) (see https://public.wmo.int/en/programmes/global-climate-observing-system) in support of the United Nations Framework Convention on Climate Change (see https://unfccc.int/). Currently more than 20 Essential Climate Variables (ECV) of the 54 GCOS defined ECVs have been addressed by science teams involved in the CCI. All ECV datasets are fully validated and have high levels of traceability and consistency, including quantitative estimates of uncertainty required by both climate science and modelling communities. In its contribution to climate and Earth system science, this programme has published over 700 peer-reviewed articles, and supported the Intergovernmental Panel on Climate Change's (IPCC) headline statements on climate in both its fifth Assessment Report and subsequent reports, such as the ‘Special Report on Oceans and Cryosphere in a Changing Climate’, with ongoing involvement in the IPCC's sixth assessment cycle. Besides providing an overview on CCI, this presentation will make the link between CCI and ESA's Soil Moisture and Ocean Salinity (SMOS) mission, demonstrating the contribution that SMOS data can make in the creation of climate data records (CDR). Several CDRs are already including SMOS data on a regular basis, such as the sea surface salinity and soil moisture long-term data sets. There is also potential for using SMOS data for sea ice, biomass and vegetation climate data records.

Sea Surface Salinity (SSS) is an Essential Ocean and Climate Variable, which is increasingly used as part of climate studies. SSS measurements are available from three satellite missions, SMOS, Aqu...

The European Space Agency (ESA) Climate Change Initiative (CCI+) for Sea Surface Salinity (CCI+SSS) project aims at generating long-term, improved, calibrated global SSS fields from space. The project started in mid-2018 and in its second year (version 2) has produced a 10-year dataset (2010-2019) from the three available L-band radiometer satellites (SMOS: Soil Moisture and Ocean Salinity; Aquarius; SMAP: Soil Moisture Active Passive) and validated it against in situ references (Argo and ISAS: In Situ Analysis System). The comparisons with in situ ground truth indicate much better performances than the ones obtained with a single satellite data product, with global precision against in situ references of 0.15 pss. CCI SSS version 2 products show similar performance than version 1 but is one year longer. There is a very good agreement between the CCI dataset and references, including long-term stability, with differences within +-0.05 pss for global ocean within [40°S-20°N]. At higher latitude, we observe seasonal oscillation of the CCI SSS difference against references. The uncertainty provided in the CCI SSS product are in good agreement with observations (within +-25%).

This study is performed in the frame of the European Space Agency (ESA) Climate Change Initiative (CCI+) for Sea Surface Salinity (SSS), which aims at generating global SSS fields from all available satellite L-band radiometer measurements over the longest possible period with a great stability. By combining SSS from the Soil Moisture and Ocean Salinity, SMOS, Aquarius and the Soil Moisture Active Passive, SMAP missions, CCI+SSS fields (Boutin et al. 2020) are the only one to provide a 10 year time series of satellite salinity with such quality: global rms difference of weekly 25x25km2 CCI+SSS with respect to in situ Argo SSS of 0.17 pss, correlation coefficient of 0.97 (see https://pimep.ifremer.fr/diffusion/analyses/mdb-database/GO/cci-l4-esa-merged-oi-v2.31-7dr/argo/report/pimep-mdb-report_GO_cci-l4-esa-merged-oi-v2.31-7dr_argo_20201215.pdf). Nevertheless, we found that some systematic biases remained. In this presentation, we will show how they will be reduced in the next CCI+SSS version.The key satellite mission ensuring the longest time period, since 2010, at global scale, is SMOS. We implemented a re-processing of the whole SMOS dataset by changing some key points. Firstly we replace the Klein and Swift (1977) dielectric constant parametrization by the new Boutin et al. (2020) one. Secondly we change the reference dataset used to perform a vicarious calibration over the south east Pacific Ocean (the so-called Ocean Target Transformation), by using Argo interpolated fields (ISAS, Gaillard et al. 2016) contemporaneous to the satellite measurements instead of the World Ocean Atlas climatology. And thirdly the auxiliary data (wind, SST, atmospheric parameters) used as priors in the retrieval scheme, which come in the original SMOS processing from the ECMWF forecast model were replaced by ERA5 reanalysis.Our results are showing a quantitative improvement in the stability of the SMOS CCI+SSS with respect to in situ measurements for all the period as well as a decrease of the spread of the difference between SMOS and in situ salinity measurements.Bibliography:J. Boutin et al. (2020), Correcting Sea Surface Temperature Spurious Effects in Salinity Retrieved From Spaceborne L-Band Radiometer Measurements, IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3030488.F. Gaillard et al. (2016), In Situ–Based Reanalysis of the Global Ocean Temperature and Salinity with ISAS: Variability of the Heat Content and Steric Height, Journal of Climate, vol. 29, no. 4, pp. 1305-1323, doi: 10.1175/JCLI-D-15-0028.1.L. Klein and C. Swift (1977), An improved model for the dielectric constant of sea water at microwave frequencies, IEEE Transactions on Antennas and Propagation, vol. 25, no. 1, pp. 104-111, doi: 10.1109/JOE.1977.1145319.Data reference:J. Boutin et al. (2020): ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product, v2.31, for 2010 to 2019. Centre for Environmental Data Analysis. https://catalogue.ceda.ac.uk/uuid/eacb7580e1b54afeaabb0fd2b0a53828

Our earlier work on assessment of altimeter significant wave height (SWH) algorithms [...]

Radar altimeters have been measuring ocean significant wave height for more than three decades, with their data used to record the severity of storms, the mixing of surface waters and the potential threats to offshore structures and low-lying land, and to improve operational wave forecasting. Understanding climate change and long-term planning for enhanced storm and flooding hazards are imposing more stringent requirements on the robustness, precision, and accuracy of the estimates than have hitherto been needed. Taking advantage of novel retracking algorithms, particularly developed for the coastal zone, the present work aims at establishing an objective baseline processing chain for wave height retrieval that can be adapted to all satellite missions. In order to determine the best performing retracking algorithm for both Low Resolution Mode and Delay-Doppler altimetry, an objective assessment is conducted in the framework of the European Space Agency Sea State Climate Change Initiative project. All algorithms process the same Level-1 input dataset covering a time-period of up to two years. As a reference for validation, an ERA5-based hindcast wave model as well as an in-situ buoy dataset from the Copernicus Marine Environment Monitoring Service In Situ Thematic Centre database are used. Five different metrics are evaluated: percentage and types of outliers, level of measurement noise, wave spectral variability, comparison against wave models, and comparison against in-situ data. The metrics are evaluated as a function of the distance to the nearest coast and the sea state. The results of the assessment show that all novel retracking algorithms perform better in the majority of the metrics than the baseline algorithms currently used for operational generation of the products. Nevertheless, the performance of the retrackers strongly differ depending on the coastal proximity and the sea state. Some retrackers show high correlations with the wave models and in-situ data but significantly under- or overestimate large-scale spectral variability. We propose a weighting scheme to select the most suitable retrackers for the Sea State Climate Change Initiative programme.

CryoSat’s ability to operate in different operating modes over water surfaces led to the first in-orbit evidence of the value of SAR-mode altimetry for oceanography, with the mission continuing to provide high-quality data and information not just over ice but also over the open ocean, polar waters and coastal regions. Approaching ten years in orbit, CryoSat routinely delivers a number of oceanographic products for global ocean applications. A dedicated operational CryoSat ocean processor (COP) has existed since April 2014 generating data products available in near real time (FDM/NOP), within ~3 days (IOP) and a geophysical ocean product (GOP) available within a month. An improved processing baseline was introduced in late 2017 and the same processing chain has now been applied to provide consistent ocean data products from the start of the mission. Within the ESA funded CryOcean-QCV project, the National Oceanography Centre (NOC) in the UK is responsible for routine quality control and validation of CryoSat Ocean Products. Activities include the production of daily and monthly reports containing global assessments and quality control of sea surface height anomaly (SSHA), significant wave height (SWH), backscatter coefficient (Sigma0) and wind speed, as well as a suite of validation protocols involving in situ data, model output and data from other satellite altimetry missions. This presentation will review some of the metrics and results obtained for CryoSat Ocean Products for SSHA, SWH and wind speed when assessed against data from tide gauges, wind and wave buoys, WaveWatch III wave model output, HF radar surface current data and comparisons with Jason-2 and Jason-3. Example metrics include SSHA along-track power spectra and the characterisation of offsets and variability regionally and in different sea states. In this presentation, we demonstrate the quality and scientific value of the CryoSat data in the open ocean where the altimeter operates mainly in conventional low-resolution-mode (LRM) but also over selected ocean regions where CryoSat operates in SAR-mode. Finally, scientific exploitation of the CryoSat data for oceanographic studies will be illustrated, focusing on CryoSat sea surface height anomalies. We will present examples of the benefits of CryoSat ocean products for oceanographic studies based on a dedicated Level 3 gridded product, featuring investigations of propagating ocean features (e.g. Rossby-type wave propagation) and their signatures in CryoSat in comparisons with data from other sources including SMOS, Sentinel 3A and 3B.

Sea state data are of major importance for climate studies, marine engineering, safety at sea and coastal management. However, long-term sea state datasets are sparse and not always consistent, and sea state data users still mostly rely on numerical wave models for research and engineering applications. Facing the urgent need for a sea state climate data record, the Global Climate Observing System has listed “Sea State” as an Essential Climate Variable (ECV), fostering the launch in 2018 of the Sea State Climate Change Initiative (CCI). The CCI is a programme of the European Space Agency, whose objective is to realise the full potential of global Earth observation archives established by ESA and its member states in order to contribute to the ECV database. This paper presents the implementation of the first release of the Sea State CCI dataset, the implementation and benefits of a high-level denoising method, its validation against in situ measurements and numerical model outputs, and the future developments considered within the Sea State CCI project. The Sea State CCI dataset v1 is freely available on the ESA CCI website (http://cci.esa.int/data, last access: 25 August 2020) at ftp://anon-ftp.ceda.ac.uk/neodc/esacci/sea_state/data/v1.1_release/ (last access: 25 August 2020). Three products are available: a multi-mission along-track L2P product (http://dx.doi.org/10.5285/f91cd3ee7b6243d5b7d41b9beaf397e1, Piollé et al., 2020a), a daily merged multi mission along-track L3 product (http://dx.doi.org/10.5285/3ef6a5a66e9947d39b356251909dc12b, Piollé et al., 2020b) and a multi-mission monthly gridded L4 product (http://dx.doi.org/10.5285/47140d618dcc40309e1edbca7e773478, Piollé et al., 2020c).

Storm surges are abnormal coastal sea level events caused by meteorological conditions such as tropical cyclones. They have the potential to cause widespread loss of life and financial damage and have done so on many occasions in the past. Accurate and timely forecasts are necessary to help mitigate the risks posed by these events. Operational forecasting models use discretisations of the governing equations for fluid flow to model the sea surface, which is then forced by surface stresses derived from a model wind and pressure fields. The wind fields are typically idealised and generated parametrically. In this study, wind field datasets derived from remotely sensed data are used to modify the model parametric wind forcing and investigate potential improvement to operational forecasting. We examine two methods for using analysis wind fields derived from remotely sensed observations of three hurricanes. Our first method simply replaces the parametric wind fields with its corresponding analysis wind field for a period of time. Our second method does this also but takes it further by attempting to use some of the information present in the analysis wind field to estimate future wind fields. We find that our methods do yield some forecast improvement, most notably for our second method where we get improvements of up to 0.29 m on average. Importantly, the spatial structure of the surge is changed in some places such that locations that were previously forecast small surges had their water levels increased. These results were validated by tide gauge data.

The CryoSat-2 satellite, primarily dedicated to precise monitoring of the Cryosphere, is demonstrating its capability to provide valuable altimetric data also over the ocean. Here we present the results of a global assessment and validation of the new Geophysical Ocean Product (GOP) distributed by the European Space Agency (ESA) since April 2014, focusing on the sea surface height anomaly (SSHA), the significant wave height (SWH), and the wind speed. Our assessment involves only Low Resolution Mode (LRM) and Pseudo LRM (PLRM) data, since full SAR processing is not already operationally implemented in the GOP. The global assessment is conducted on the basis of measurement noise and along-track spectral and crossover analysis, whereas the validation is performed against a variety of in situ observations such as tide gauges, buoys and Argo floats as well as data from the WaveWatch III (WWIII) model. The performance of the GOP is compared to that of Jason-2 and CryoSat-2 data from the Radar Altimeter Database System (RADS). The mean value of the 20-Hz SSHA noise at 2 m SWH is 6.3 cm for LRM and 10.2 cm for PLRM, and the standard deviation of the crossovers is ~ 5.4 cm. The mean 20-HZ SWH noise over the global oceans is 49.4 cm and 69.8 cm, for LRM and PLRM respectively. CryoSat-2 and Jason-2 show almost identical performance when SSHAs are validated against tide gauges, with a median correlation and root mean square difference (RMSD) of 0.78 and 7.1 cm for the GOP, 0.76 and 7.3 cm for Jason-2, and 0.79 and 7.8 cm for CryoSat-2 from RADS. The median correlation with Argo-derived steric heights is 0.68 for the GOP, 0.74 for Jason-2, and 0.67 for CryoSat-2 from RADS. However, the correlation shows a strong latitudinal dependence, with higher values at low latitudes (median value larger than 0.80 in the 10°S-10°N band). The median RMSD between the SSHAs and steric heights is 5.3 cm for the GOP, 4.6 cm for Jason-2, and 5.1 cm for CryoSat-2 from RADS. The GOP and Jason-2 show also identical performance when SWHs are compared to buoy data, with a slope and RMS error of 0.98 and 15 cm for GOP, 0.97 and 16 cm for Jason-2, and 1.05 and 17 cm for CryoSat-2 from RADS. On the other hand, the GOP wind speed exhibits a bias of about 2 m/s relative to both Jason-2 and to buoy data. Differences between the GOP and WWIII SWH are smaller than 20% of the SWH almost everywhere. In summary the GOP products are fit for oceanographic applications.

Coastal altimetry products are available and are being extensively validated. Their accuracy has been assessed in many coastal zones around the world and they are ready for exploitation near the shore. This opens a variety of applications of the sea level data obtained from the specific reprocessing of radar altimeter signals in the coastal strip. In this work, we retracked altimeter waveforms of the European Space Agency satellites: ERS-2 RA and Envisat RA-2 from descending track (#0360) over the eastern side of the Strait of Gibraltar using the Adaptive Leading Edge Sub-waveform (ALES) retracker. We estimated along-track Sea Level Anomaly (AT_SLA) profiles (RA-2) at high posting rate (18 Hz) using improved range and geophysical corrections. Tides were removed with a global model (DTU10) that displays a good performance in the study area: the mean root square sum (RSS) of the main constituents obtained with DTU10 and 11 tide gauge stations was 4.3 cm in agreement with the RSS using a high-resolution local hydrodynamic model (UCA2.5D) (4.2 cm). We also estimated a local mean sea surface by reprocessing ERS-2/Envisat waveforms (track #0360) with ALES. The use of this local model gave more realistic AT_SLA than the values obtained with the global model DTU15MSS. Finally, the along-track Absolute Dynamic Topography (AT_ADT) was estimated using a local Mean Dynamic Topography obtained with the local hydrodynamic model UCA2.5D. We analysed the cross-strait variability of the sea level difference between the African/Spanish coasts along the selected track segment. This was compared to the sea level cross-strait difference from the records of two tide gauges located in the African (Ceuta) and Spanish (Tarifa) coasts. The sea level differences from altimetry and tide gauges were linked to the zonal component of the wind. We found a positive and significant (>95% c.l.) correlation between easterlies/westerlies and positive/negative cross-strait sea level differences between the southern and northern coasts of the Strait in both datasets (altimetry: r = 0.54 and in-situ: r = 0.82).

This chapter provides an overview of the satellite altimetry measurement system, how it works and how the water surface elevation is derived. Satellite altimetry had originally been designed for open ocean studies, however a decade of progress has significantly improved the retrieval of data in coastal zones and inland waters. Advances in observational techniques (Ka-band and delay-Doppler/SAR-mode), revisited data processing, and improved corrections led to a higher accuracy in water surface elevation retrieval in these challenging areas. The available data sets with consistent coastal processing as well as the existing products dedicated to inland waters are presented including their potential accuracy for exploitation. The latest developments and capabilities of the various altimetric missions around coasts and inland are illustrated, with an emphasis on describing the benefits to studies of extreme events (heavy river discharge and storm surges). The future satellite altimetry missions such as Sentinel-3 and Sentinel-6/Jason-CS are expected to increase capabilities and coverage and represent a great opportunity to stimulate further exploitation of altimeter data in coastal and inland water targets.

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

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite has been providing data, including sea surface salinity (SSS) measurements, for more than five years. However, the operational ESA Level 2 SSS data are known to have significant spatially and temporally varying biases between measurements from ascending passes (SSSA) and measurements from descending passes (SSSD).This paper demonstrates how these biases are reduced through the use of SSS anomalies. Climatology products are constructed using SMOS Level 2 data to provide daily, one-degree by one-degree climatologies separately for ascending and descending passes using a moving window approach (in time and space). The daily, one-degree products can then be averaged to provide values of climatological SSS at different spatial and/or temporal resolutions.The averaged values of the SMOS climatology products are in good general agreement with data from the World Ocean Atlas 2013. However, there are significant differences at high latitudes, as well as in coastal and dynamic regions, as found by previous studies. Both the mean and standard deviation of the differences between data from ascending passes and data from descending passes for the anomalies are reduced compared with those obtained using the original salinity values.Geophysical signals are clearly visible in the anomaly products and an example is shown in the Southern Indian Ocean of westward-propagating signals that we conclude represent the surface expression of Rossby waves or large-scale non-linear eddies. The signals seen in salinity data agree (in speed) with those from sea surface temperature and sea surface height and are consistent with previous studies.

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS), and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales, their role in marine biogeochemistry, the global carbon cycle, the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean, and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration, since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency, the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA), and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

A 1/6° numerical simulation is used to investigate the vertical structure of westward propagation between 1993 and 2000 in the North Atlantic ocean. The realism of the simulated westward propagating signals, interpreted principally as the signature of first-mode baroclinic Rossby waves (RW), is first assessed by comparing the simulated amplitude and zonal phase speeds of Sea Level Anomalies (SLA) against TOPEX/Poseidon-ERS satellite altimeter data. Then, the (unobserved) subsurface signature of RW phase speeds is investigated from model outputs by means of the Radon Transform which was specifically adapted to focus on first-mode baroclinic RW. The analysis is performed on observed and simulated SLA and along 9 simulated isopycnal displacements spanning the 0–3250 m depth range. Simulated RW phase speeds agree well with their observed counterparts at the surface, although with a slight slow bias. Below the surface, the simulated phase speeds exhibit a systematic deceleration with increasing depth, by a factor that appears to vary geographically. Thus, while the reduction factor is about 15–18% on average at 3250 m over the region considered, it appears to be much weaker (about 5–8%) in the eddy-active Azores Current, where westward propagating structures might be more coherent in the vertical. In the context of linear theories, these results question the often-made normal mode assumption of many WKB-based theories that the phase speed is independent of depth. Alternatively, these results could also suggest that the vertical structure of westward propagating signals may significantly depend on their degree of nonlinearity, with the degree of vertical coherence possibly increasing with the degree of nonlinearity.

The main objectives of this paper are to present the status of the CryoSat ocean products and to give an overview of all associated quality control and validation activities. Launched in 2010, the polar-orbiting European Space Agency’s (ESA) CryoSat mission was primarily developed to measure changes in the thickness of polar sea ice and elevation of the ice sheets. Going beyond its ice-monitoring objective, CryoSat is also a valuable source of data for the oceanographic community. The satellite’s radar altimeter can measure high-resolution geophysical parameters from the open ocean to the coast. To enable their full scientific and operational exploitation, the ocean products continuously evolve and need to be quality-controlled and thoroughly validated via science-oriented diagnostics based on multi-platform in situ data, models and other satellite missions. In support to ESA, the CryoSat ocean validation teams conduct this quality assessment for both the near real time and offline ocean products, both over short time scales (daily and monthly monitoring) and long-term stability (annual trends). Based on the outcomes from these quality analyses and feedback from scientific oceanographic community, ESA intends to upgrade the CryoSat Ocean processing chain for Autumn 2017.

The first digital elevation model (DEM) of the Antarctic ice sheet derived from Global Navigation Satellite Systems-Reflectometry (GNSS-R) data from the UK TechDemoSat-1 satellite is presented. This is obtained using 32 months of data from the mission. This opportunistic and inexpensive method is shown to produce encouraging results from the technology demonstration platform of TechDemoSat-1, with median bias under 18 m and root-mean-square difference under 91 m when compared to the CryoSat-2 1-km DEM v1.0. Discrepancies between the two data sets are explored along with possible causes of such differences and potential improvements to further optimize this technique for future GNSS-R missions.

We present the validation of sea level measurements taken over the eastern shelf of the Gulf of Cadiz (southwestern Iberian Peninsula) by the European Space Agency CryoSat-2 satellite’s SIRAL altimeter in SAR mode. Time series of sea level anomaly (SLA) at 20-Hz posting rate (corresponding to approximately 350 m along the satellite track) are compared against sea level measurements from two tide gauge stations located along the Spanish coast of the study area. Due to the long repeat cycle of CryoSat-2 (369 days), data selection is performed by including all the tracks falling in a radius of 50 km from the location of the tide gauge stations. Then, time series of SLA are formed during the observational time period (August 2010–December 2014) by taking the altimeter measurements located at growing distances from the coast (1, 3, 5, 10, 15 and 20 km) as well as around four isobaths (10, 25, 50 and 75 m). The validation of AltiKa’s SARAL altimeter 20-Hz data, using its two 35-day-repeat ground tracks available in the area is also presented. Overall, CryoSat-2 20-Hz SLAs selected in the coastal strip from 3 to 20 km agree well with data from the western station (Huelva) with rmse ranging from 11.4 to 6.4 cm. The analysis at the eastern station (Bonanza) yields rmse ranging from 16.8 to 14.6 cm. The comparisons made with CryoSat-2 20-Hz SLAs are in agreement with the rmse estimated using AltiKa 20-Hz SLAs at distances to the coast higher than 5 km, and much better in the 1–5 coastal band. The differences in quantity of SAR mode data observed at the two stations and in their agreement with the tide gauges can be explained by (1) the more complex coastal morphology around Bonanza station with the altimetry tracks almost parallel to the coast; (2) the noisier hydrodynamics around Bonanza station due to the presence of the estuary mouth of the Guadalquivir River; and (3) residual (uncorrected) tidal signals in the Bonanza gauge record. We conclude that the quality of SAR altimetry is comparable or even slightly better than conventional altimetry, thus qualifying SAR data for exploitation in oceanographic studies and sea level monitoring. An example is the analysis made of the surface signal due to a heavy Guadalquivir River discharge event.

This chapter describes both the improvements in coastal altimetry on the technical side and the ensuing applications, that is, first improvements in retracking and improvements in corrections. Passaro et al. have reviewed the different approaches taken in literature for the processing of coastal pulse-limited waveforms that do not conform to the Brown model. SAR altimetry is intrinsically promising for coastal applications by virtue of the higher signal-to-noise ratio and along-track resolution. The accurate retrieval of sea surface height from satellite altimetry with centimeter-level accuracy requires the knowledge of all terms involved in the altimeter measurement system with similar or better accuracy. Those are satellite height above a reference ellipsoid from precise orbit determination; altimeter range from dedicated retracking, including all instrument effects; and all range and geophysical corrections. The corrections with particular issues in the coastal regions are mainly the wet tropospheric correction, the sea state bias, and to somewhat lesser extent the dry tropospheric correction.

Significant wave height (SWH) is mapped globally through satellite altimetry. SWH estimation is possible because the shape of a pulse-limited altimetric waveform depends on the sea state. The algorithm for SWH also depends on the width of the point target response (PTR) function. Particularly challenging for SWH detection are coastal data, due to land and calm water interference in the altimeter footprint, and low sea states, due to an extremely sharp leading edge in the waveform that is consequently poorly sampled. Here, Adaptive Leading Edge Subwaveform Retracker (ALES), a new algorithm for reprocessing altimetric waveforms, will be validated for SWH estimation in the German Bight. This challenging region presents both low sea state and coastal issues, and an extended network of buoys of the Bundesamt fuer Seeschiffahrt und Hydrographie is available for the in situ validation. Reprocessed data from Envisat, Jason-1, and Jason-2 missions are validated against the three offshore buoys. The in situ validation is applied both at the point nearest to the buoy and at all other points along track. The skill metrics is based on bias, standard deviation, slope of regression line, and number of cycles with correlation larger than 90%. We also tested the impact of the inclusion of two additional waveform samples that are provided in the Envisat Sensor Geophysical Data Records and the adoption of different values for the width of the PTR. Results show that near coast ALES estimations of SWH are generally better correlated with buoy data than standard processed products.

10th Coastal Altimetry Workshop; Florence, Italy, 21–24 February 2017

The increasing quality and quantity (resolution in space, coverage in time, combinations of sensors in the Sentinel family) of information provided by Copernicus offer the possibility to analyse and predict coastal meteo-oceanography at an unprecedented level. This is a unique opportunity to develop the Copernicus coastal dimension to tackle the pressures of increasing population and activities. The combination of ocean/atmosphere/land observations from the Sentinel (S) 1/2/3, aligned with the availability of an increasing number of high-resolution numerical simulations (e.g. wave and current fields) in the Copernicus Marine Environment Monitoring Service (CMEMS) catalogue, should allow users to access proven representations of the coastal environment at a new level of understanding (e.g. wave diffraction at coastal "obstacles"), coupling (e.g. incorporating the land discharge into the coastal sea) and reliability for applications (e.g. hazards for coastal navigation). By adding periodic bathymetric up-dating and incorporating new assimilation routines it will be possible to achieve a new level of analysis for coastal seas. In the paper we shall present the CEASELESS project that addresses the multiple scales coexisting in littoral areas by developing new shallow water parameterizations, introducing them into coupled model suites (wind-wave-surge-current-land discharge) and producing new standards for coastal simulations and analyses. This will demonstrate the technical feasibility of an operational coastal service. The set of derived products will be ingested into the users' work routines, proving the economic feasibility of such a coastal extension. The level of conflicts in squeezed coastal zones, expected to grow in the face of climate change, will, thus, benefit directly from CEASELESS, establishing tangible contributions for a wide range of economic sectors. The mutual validation of satellite data, numerical results and in-situ observations will generate reciprocal profit for enhanced competiveness of coastal oceanographic products and their future evolution.