The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) is one of the six Copernicus Expansion Missions currently being implemented by the European Space Agency and the European Commission. The CRISTAL Mission will provide enhanced retrievals of sea ice thickness and freeboard, land ice sheet/glacier elevation, inland water level and ocean surface topography, wave-height and wind speed by measurements from sensors with improved spatial resolution and performance. Throughout this paper, the current status for the design, development and validation of the ESA CRISTAL Level-2 products is described.

The concept of in situ multisatellite altimetry calibration and validation in the absolute sense using ocean autonomous surface vehicles as global navigation satellite systems (GNSS) platforms is demonstrated through an experiment in the North Sea during 2016. A Wave Glider (WG) equipped with geodetic GNSS traveled to locations ranging from 21 to 78 km from the coast to be directly under four Jason-series tracks and two CryoSat-2 tracks. 5-Hz sea surface heights (SSHs) were estimated from precise point positioning (PPP) mode processing of GPS+GLONASS data, together with hourly zenith wet tropospheric delays (ZWDs), and used as reference values for altimetry satellite measured SSH, tropospheric delay, and significant wave height (SWH). SSH biases obtained were −30 to −8 mm for Jason-2 using geophysical data record (GDR)-D products, −40 to +1 mm for Jason-3 using GDR-F products, and −29 and +18 mm for CryoSat-2 using SAR mode GOP baseline C products. These biases are almost commensurate with results from previous studies in other regions that used GNSS buoys or onshore GNSS reference stations with geoid and tide extrapolation. The Jason-2 and Jason-3 microwave radiometer (MWR)-measured ZWDs differed, respectively, by −15 and −10 mm on average from those measured by the GNSS WG. Root-mean-square SWH differences of 2–6 cm were obtained between Jason-2/3 and the co-located GNSS WG, and equivalent differences of 19–21 cm for CryoSat-2.

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.

The European Space Agency (ESA) Climate Change Initiative (CCI+) for Sea Surface Salinity (SSS) aims at generating global SSS fields from all available satellite L-band radiometer measurements over the longest possible period with a great stability (including Soil Moisture and Ocean Salinity, SMOS). Version 1 and 2 of CCI+SSS level 4 fields combine SSS form the three satellite L-Band radiometer missions and have been found to be in a very good agreement with in situ measurements (global rms difference of 0,16 pss). Nevertheless, some systematic differences still remain between CCI+SSS and in situ SSS. We study here to which extent some errors coming from the SMOS SSS processing are reduced, when making some key changes in the SMOS level 2 OS processing. Then, we discuss the contribution of each change in the preliminary results we obtain.

Altimetry data of two and a half years (June 2016-November 2018) of Sentinel-3A SRAL (S3A-SRAL) were validated at the sampling frequency of 80 Hz. The data were obtained from the European Space Agency (ESA) Grid Processing On Demand (GPOD) service over three coastal sites in Spain: Huelva (HU) (Gulf of Cádiz), Barcelona (BA) (Western Mediterranean Sea), and Bilbao (BI) (Bay of Biscay). Two tracks were selected in each site: one ascending and one descending. Data were validated using in situ tide gauge (TG) data provided by the Spanish Puertos del Estado. The altimetry sea level anomaly time series were obtained using the corrections available in GPOD with the exception of the sea state bias (SSB) correction, not available at 80 Hz. Hence, the SSB was approximated to 5% of the significant wave height (SWH). The validation was performed using two statistical parameters, the Pearson correlation coefficient (r) and the root mean square error (rmse). In the 5-20-km segment with respect to the coastline, the results were 6-8 cm (rmse) and 0.7-0.8 (r) for all the tracks. The 0-5-km segment was also analyzed in detail to study the land effect on the altimetry data quality. The results showed that the track orientation, the angle of intersection with the coast, and the land topography concur to determine the nearest distance to the coast at which the data retain a similar level of accuracy than in the 5-20-km segment. This “distance of good quality” to shore reaches a minimum of 3 km for the tracks at HU and the descending track at BA.

The Cyclone Global Navigation Satellite System (CYGNSS) mission was designed to study hurricane intensification by measuring wind speeds in tropical cyclones. However, the delay-Doppler maps (DDM) that are produced can be used to estimate the sea surface height (SSH) at the specular reflection point on the ocean surface. Proof-of-concept studies that DDMs are suitable to solve for SSH have been recently reported (Clarizia et al., 2016; Mashburn et al., 2018), based on data acquired by the demonstration satellite experiment Tech Demo Sat -1 (TDS-1) carrying a GNSS-R receiver similar to the ones onboard CYGNSS. Although the precision of each 1-sec averaged SSH is considerably lower than that of the existing satellite altimeters, by virtue of the dense coverage and frequent revisit time exhibited by the constellation of 8 microsats, the error may be smoothed down considerably by optimal interpolation (Li et al., 2016). Hence the CYGNSS dataset presents a potential opportunity to sample the tropical oceans, and investigate the sensitivity of the SSH measurements to mesoscale eddies.

This paper analyzes the availability and accuracy of coastal altimetry sea level products in the Strait of Gibraltar. All possible repeats of two sections of the Envisat and AltiKa ground-tracks were used in the eastern and western portions of the strait. For Envisat, along-track sea level anomalies (SLAs) at 18-Hz posting rate were computed using ranges from two sources, namely, the official Sensor Geophysical Data Records (SGDRs) and the outputs of a coastal waveform retracker, the Adaptive Leading Edge Subwaveform (ALES) retracker; in addition, SLAs at 1 Hz were obtained from the Centre for Topographic studies of the Ocean and Hydrosphere (CTOH). For AltiKa, along-track SLA at 40 Hz was also computed both from SGDR and ALES ranges. The sea state bias correction was recomputed for the ALES-retracked Envisat SLA. The quality of these altimeter products was validated using two tide gauges located on the southern coast of Spain. For Envisat, the availability of data close to the coast depends crucially on the strategy followed for data screening. Most of the rejected data were due to the radar instrument operating in a low-precision nonocean mode. We observed an improvement of about 20% in the accuracy of the Envisat SLAs from ALES compared to the standard (SGDR) and the reprocessed CTOH data sets. AltiKa shows higher accuracy, with no significant differences between SGDR and ALES. The use of products from both missions allows longer times series, leading to a better understanding of the hydrodynamic processes in the study area.

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.

Extending the applications of satellite altimetry to the coastal zone requires validated, quality controlled data. We present a validation study in the Gulf of Cádiz (SW Iberian Peninsula), an area of relevant social, economic, and strategic importance. We compare against in-situ data seven years (Dec. 2002-Jan. 2010) of significant wave height (SWH) measurements by the Envisat RA-2 altimeter from two sources: 1) standard geophysical data records (GDR, 1 Hz) and 2) the processor developed in the COASTALT (development of radar altimetry data processing in the coastal zone) project (18 Hz). The comparison is made along two Envisat passes (one descending, i.e., north to south, one ascending). For the descending pass (land to ocean transition) the COASTALT processor improves SWH retrieval in the coastal fringe. In particular, the COASTALT SWH product displays accuracies in the sub-coastal strip (12-20 km from the coastline) of a very similar magnitude to those further offshore, representing a clear improvement over GDR. The bias and standard deviation of the difference with regard to the in-situ measurements in the coastal fringe is about 60% and 40% lower, respectively, when COASTALT data are used instead of the standard GDR product. For the ascending pass, the differences in the ocean to land transition are less marked, probably due to the altimeter keeping a good lock on the sea surface until relatively close proximity to the coastline. This validation case shows that this new coastal-oriented product gets closer to the shoreline than before, while also making available higher-resolution along-track estimates.

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-km2island 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.

A coastal-oriented processing strategy has been developed in the Northwestern part of the Mediterranean Sea and has showed that improved altimetry in the costal ocean is feasible and could be extended to other regions. In this work, we will provide an overview of current capabilities and challenges of existing altimetry products in Black, Caspian, White and Barents seas, in the prospect of increasing the quantity and quality of data in these regions. With respect to the work done in the project called ALTICORE (ALTImetry for COastal REgions - www.alticore.eu), the obstacles limiting the use of the data and the possible areas of improvement are highlighted and discussed.

In this paper, we describe the ALTICORE (value added satellite ALTImetry in COastal REgions) initiative, a consortium aiming at providing high quality coastal altimetry over some European seas. Taking the Ligurian Sea in the NW Mediterranean as an example, which acts as a test zone for this work, we show the improvement in availability and quality of ENVISAT data, through our processing, when compared with the official altimetric products delivered by AVISO. We also introduce the building concepts of solutions for data search, extraction, update and delivery based on web-services. This grid-type infrastructure is being designed within ALTICORE.

We describe a methodology for tracking individual planetary waves in longitude-time plots of satellite data, based on fitting an elementary wave shape model to subsets of the data by maximum likelihood, then reconstructing the trajectory and evolution of every single wave (where for "single wave" we mean an individual positive or negative westward propagating anomaly) by joining the elementary waves according to their similarity. We then illustrate the potential of the methodology with an example at 34/spl deg/N in the Atlantic Ocean and its adaptability to different cases with a second example on eastward-propagating Kelvin waves in the equatorial Pacific. Although the examples given use sea surface height anomaly data, the technique lends itself to be applied to any space-time plot of any dataset displaying propagation and, in particular, to sea surface temperature data.

The authors present a new methodology for estimating the concentration of sea water optically active constituents from remotely sensed hyperspectral data, based on generalized radial basis function neural networks (GRBF-NNs). This family of NNs is particularly suited to approximate relationships like those between hyperspectral reflectance data and the concentrations of optically active constituents of the water body, which are highly nonlinear, especially in case II waters. Three main water constituents are taken into account: phytoplankton, nonchlorophyllous particles, and yellow substance. Each parameter is estimated by means of a specific multi-input single-output GRBF-NN. The authors adopt a recently proposed network learning strategy based on the combined use of the regression tree procedure and forward selection. The effectiveness of this approach, which is completely general and can be easily applied to any hyperspectral sensor, is proved using data simulated with an ocean color model over the channels of the medium resolution imaging spectrometer (MERIS), the new generation ESA sensor to be launched in 2001. The authors define the estimation algorithms over waters of cases I, II, and I+II and compare their performance with that of classical band-ratio, single-band, and multilinear algorithms. Generally, the GRBF-NN algorithms outperform the classical ones, except for the multilinear over case I waters. A particular improvement Is over case II waters, where the mean square error (MSE) can be reduced by one or two orders of magnitude over the error of multilinear and band-ratio algorithms, respectively.