Ocean Remote Sensing


Advanced remote sensing capabilities have provided unprecedented opportunities for monitoring and studying the ocean environment as well as improving ocean and climate predictions. Synthesis of remote sensing data with in situ measurements and ocean models have further enhanced the values of oceanic remote sensing measurements. This session provides a forum for interdisciplinary discussions of the latest advances in oceanographic remote sensing and the related applications and to promote collaborations.

We welcome contributions on all aspects of the oceanic remote sensing and the related applications. Topics for this session include but are not limited to: physical oceanography, marine biology and biogeochemistry, biophysical interaction, marine gravity and space geodesy, linkages of the ocean with the atmosphere, cryosphere, and hydrology, new instruments and techniques in ocean remote sensing, new mission concepts, development and evaluation of remote sensing products of the ocean, and improvements of models and forecasts using remote sensing data. Applications of multi-sensor observations to study ocean and climate processes and applications using international (virtual) constellations of satellites are particularly welcome.

Convener: Aida Alvera-Azcárate | Co-conveners: Craig Donlon, Christine Gommenginger, Guoqi Han, Tong Lee
vPICO presentations
| Fri, 30 Apr, 13:30–17:00 (CEST)

vPICO presentations: Fri, 30 Apr

Chairpersons: Aida Alvera-Azcárate, Craig Donlon
Adrien Martin, Sébastien Guimbard, Jacqueline Boutin, Nicolas Reul, Rafael Catany, Paolo Cipollini, and Esa Cci+sss consortium

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%).

How to cite: Martin, A., Guimbard, S., Boutin, J., Reul, N., Catany, R., Cipollini, P., and Cci+sss consortium, E.: Validation of the ESA CCI+SSS products, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14902, https://doi.org/10.5194/egusphere-egu21-14902, 2021.

Xavier Perrot, Jacqueline Boutin, Jean Luc Vergely, Frédéric Rouffi, Adrien Martin, Sébastien Guimbard, Julia Koehler, Nicolas Reul, Rafael Catany, and Paolo Cipollini

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.


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

How to cite: Perrot, X., Boutin, J., Vergely, J. L., Rouffi, F., Martin, A., Guimbard, S., Koehler, J., Reul, N., Catany, R., and Cipollini, P.: Towards an improved temporal stability of CCI+SSS time series, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1231, https://doi.org/10.5194/egusphere-egu21-1231, 2021.

Jacqueline Boutin, Jean-Luc Vergely, Emmanuel Dinnat, Philippe Waldteufel, Francesco D'Amico, Nicolas Reul, Alexandre Supply, and Clovis Thouvenin-Masson

We derived a new parametrisation for the dielectric constant of the ocean (Boutin et al. 2020). Earlier studies have pointed out systematic differences between Sea Surface Salinity retrieved from L-band radiometric measurements and measured in situ, that depend on Sea Surface Temperature (SST). We investigate how to cope with these differences given existing physically based radiative transfer models. In order to study differences coming from seawater dielectric constant parametrization, we consider the model of Somaraju and Trumpf (2006) (ST) which is built on sound physical bases and close to a single relaxation term Debye equation. While ST model uses fewer empirically adjusted parameters than other dielectric constant models currently used in salinity retrievals, ST dielectric constants are found close to those obtained using the Meissner and Wentz (2012) (MW) model. The ST parametrization is then slightly modified in order to achieve a better fit with seawater dielectric constant inferred from SMOS data. Upgraded dielectric constant model is intermediate between KS and MW models. Systematic differences between SMOS and in situ salinity are reduced to less than +/-0.2 above 0°C and within +/-0.05 between 7 and 28°C. Aquarius salinity becomes closer to in situ salinity, and within +/-0.1. The order of magnitude of remaining differences is very similar to the one achieved with the Aquarius version 5 empirical adjustment of wind model SST dependency. The upgraded parametrization is recommended for use in processing the SMOS data. 

The rationale for this new parametrisation, results obtained with this new parametrisation in recent SMOS reprocessings and comparisons with other parametrisations will be discussed.


Boutin, J.,et al. (2020), Correcting Sea Surface Temperature Spurious Effects in Salinity Retrieved From Spaceborne L-Band Radiometer Measurements, IEEE TGRSS, doi:10.1109/tgrs.2020.3030488.

How to cite: Boutin, J., Vergely, J.-L., Dinnat, E., Waldteufel, P., D'Amico, F., Reul, N., Supply, A., and Thouvenin-Masson, C.: Correcting sea surface temperature spurious effects in salinity retrieved from spaceborne L-band Radiometer measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-655, https://doi.org/10.5194/egusphere-egu21-655, 2021.

Clovis Thouvenin-Masson, Jacqueline Boutin, Jean-Luc Vergely, Dimitry Khvorostyanov, Xavier Perrot, and Gilles Reverdin

Sea Surface Salinity (SSS) are retrieved from SMOS and SMAP L-band radiometers at a spatial resolution of about 50km.


Traditionally, satellite SSS products validation is based on comparisons with in-situ near surface salinity measurements.


In-situ measurements are performed on moorings, argo floats and along ship tracks[JB1] , which provide punctual or one-dimensional (along ship tracks) estimations of the SSS.


The sampling difference between one-dimensional or punctual in-situ measurements and two-dimensional satellite products results in a sampling error that must be separated from measurement errors for the validation of satellite products.


We use a small-scale resolution field (1/12° Mercator Global Ocean Physics Analysis and Forecast) to estimate the expected sampling error of each kind of in-situ measurements, by comparing punctual, [JB2] one-dimensional and two-dimensional SSS variability.


The better understanding of sampling errors allows a more accurate validation of satellite SSS and of the errors estimated by satellite retrieval algorithms. The improvement is quantified by considering the standard deviation of satellite minus in-situ salinities differences normalized by the sampling and retrieval errors. This quantity should be equal to one if all the error contributions are correctly considered. This methodology will be applied to SMOS SSS and to merged SMOS and SMAP SSS products.

How to cite: Thouvenin-Masson, C., Boutin, J., Vergely, J.-L., Khvorostyanov, D., Perrot, X., and Reverdin, G.: Salinity variability in satellite subpixels: impact on satellite in-situ comparisons., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1205, https://doi.org/10.5194/egusphere-egu21-1205, 2021.

Veronica Gonzalez Gambau, Estrella Olmedo, Cristina Gonzalez Haro, Antonio Turiel, Aina Garcia, Carolina Gabarro, Justino Martinez, Pekka Alenius, Laura Tuomi, Petra Roiha, Manuel Arias, Rafael Catany, Diego Fernandez, and Roberto Sabia

The Baltic Sea is a strongly stratified semi-enclosed sea with a large freshwater supply from rivers, net precipitation and water exchange and high-saline water from the North Sea through the Kattegat Strait. In the Danish Straits the water exchange is hampered by bathymetric constraints , such as narrow and shallow sills, and by hydrodynamic restrictions, such as fronts and mixing. The shallow depth of the Baltic Sea (i.e. 54 m in average) yields to highly variable ocean dynamics controlled mainly by local atmospheric forcing. The water exchange between the Baltic Sea and the North Atlantic Ocean is restricted by the narrows and sills of the Danish Straits (i.e. via Kattergat Strait at the East of the Baltic Sea) and by different river outflows distributed across the Baltic Sea. The bottom water in the deep sub-basins is ventilated mainly by large perturbations, so-called major Baltic saltwater inflows. The occurrence of these events needs still further investigation. The description of the complex oceanographic conditions within the Baltic Sea in current model simulations could also be developed. Furthermore, model simulations of the Baltic Sea are constrained to the initialization of the model (i.e. parametrization of the initial surface atmospheric and ocean conditions).

For this, the Earth Observation salinity measurements have a great potential to help in the understanding of the dynamics in the basin and to improve the regional models there. However, the Baltic Sea is one of the most challenging regions for the sea surface salinity (SSS) retrieval from satellite measurements. The available EO-based SSS products are quite limited over this region both in terms of spatio-temporal coverage and quality. This is mainly due to several technical limitations that strongly affect the satellite brightness temperatures (TB) measurements, particularly over semi-enclosed seas, such as the high contamination by Radio-Frequency Interferences (RFI) and the contamination close to land and ice edges. Besides, the sensitivity of TB to SSS changes is very low in cold waters and much larger errors are expected compared to temperate oceans.

As a main result of the ESA Baltic+ Salinity Dynamics project (), a new regional SSS product derived from the measurements provided by the European Soil Moisture and Ocean Salinity (SMOS) mission has been developed. In this work, first, we describe briefly the enhanced algorithms used in the generation of SMOS SSS fields. Second, we show a complete quality assessment by comparing the satellite and the in situ salinity measurements. For this, we use in situ measurements provided by SeaDataNet and Helcom and Ferry box lines. Finally, we compare the satellite salinity measurements with the salinity fields provided by a model. We focus our analysis in two aspects: i) the description of the freswater fluxes coming from continental discharge and sea-ice melting; and ii) the capability of describing the dynamics of the saltier Atlantic water that enters into the basin through the Kattegat strait.


How to cite: Gonzalez Gambau, V., Olmedo, E., Gonzalez Haro, C., Turiel, A., Garcia, A., Gabarro, C., Martinez, J., Alenius, P., Tuomi, L., Roiha, P., Arias, M., Catany, R., Fernandez, D., and Sabia, R.: First regional SMOS Sea Surface Salinity products over the Baltic Sea: quality assessment and oceanographic added-value, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15254, https://doi.org/10.5194/egusphere-egu21-15254, 2021.

David Garcia-Garcia, Isabel Vigo, Mario Trottini, and Juan Vargas

Global water cycle involves water-mass transport on land, atmosphere, ocean, and among them. Quantification of such transport, and especially its time evolution, is essential to identify footprints of the climate change and helps to constrain and improve climatic models. In the ocean, net water-mass transport among the ocean basins is a key, but poorly estimated parameter presently. We propose a new methodology that incorporates the time-variable gravity observations from the GRACE satellite (2003-2016) to estimate the change of water content, and that overcomes some fundamental limitations of existing approaches. We show that the Pacific and Arctic Oceans receive an average of 1916 (95% confidence interval [1812, 2021]) Gt/month (~0.72 ± 0.02 Sv) of excess freshwater from the atmosphere and the continents that gets discharged into the Atlantic and Indian Oceans, where net evaporation minus precipitation returns the water to complete the cycle. This salty water-mass transport from the Pacific and Arctic Oceans to the Atlantic and Indian Oceans show a clear seasonal variability, with a maximum transport of 3000 Gt/month during boreal summer, a minimum of 1000 Gt/month or less on February, Mars, and November.

This research has been primarily supported by the Spanish Ministerio de Ciencia, Innovación and Universidades research project DEEP-MAPS (RTI2018-093874-B-I00).

How to cite: Garcia-Garcia, D., Vigo, I., Trottini, M., and Vargas, J.: Seasonal variability of the net water-mass transport among the four major basins, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14184, https://doi.org/10.5194/egusphere-egu21-14184, 2021.

Rory Scarrott, Fiona Cawkwell, Mark Jessopp, and Caroline Cusack

The Ocean-surface Heterogeneity MApping (OHMA) algorithm is an objective, replicable approach to map the spatio-temporal heterogeneity of ocean surface waters. It is used to processes hypertemporal, satellite-derived data and produces a single-image surface heterogeneity (SH) dataset for the selected parameter of interest. The product separates regions of dissimilar temporal characteristics. Data validation is challenging because it requires In-situ observations at spatial and temporal resolutions comparable to the hyper-temporal inputs. Validating this spatio-temporal product highlighted the need to optimise existing vessel-based data collection efforts, to maximise exploitation of the rapidly-growing hyper-temporal data resource.

For this study, the SH was created using hyper-temporal 1km resolution satellite derived Sea Surface Temperature (SST) data acquired in 2011. Underway ship observations of near surface temperature collected on multiple scientific surveys off the Irish coast in 2011 were used to validate the results. The most suitable underway ship SST data were identified in ocean areas sampled multiple times and with representative measurements across all seasons.

A 3-stage bias reduction approach was applied to identify suitable ocean areas. The first bias reduction addressed temporal bias, i.e., the temporal spread of available In-situ ship transect data across each satellite image pixel. Only pixels for which In-situ data were obtained at least once in each season were selected; resulting in 14 SH image pixels deemed suitable out of a total of 3,677 SH image pixels with available In-situ data. The second bias reduction addressed spatial bias, to reduce the influence of over-sampled areas in an image pixel with a sub-pixel approach. Statistical dispersion measures and statistical shape measures were calculated for each of the sets of sub-pixel values. This gave heterogeneity estimates for each cruise transit of a pixel area. The third bias reduction addressed bias of temporally oversampled seasons. For each transit-derived heterogeneity measure, the values within each season were averaged, before the annual average value was derived across all four seasons in 2011.

Significant associations were identified between satellite SST-derived SH values, and In-situ heterogeneity measures related to shape; absolute skewness (Spearman’s Rank, n=14, ρ[12]= +0.5755, P<0.05), and kurtosis (Spearman’s Rank, n=14, ρ[12] = 0.5446, P < 0.05). These are a consequence of (i) locally-extreme measurements, and/or (ii) increased presence of sharp transitions detected spatially by In-situ data. However, the number and location of suitable In-situ validation sites precluded a robust validation of the SH dataset (14 pixels located in Irish waters, for a dataset spanning the North Atlantic). This requires more targeted data. The approach would have benefited from more samples over the winter season, which would have enabled more offshore validation sites to be incorporated into the analysis. This is a challenge that faces satellite product developers, who want to deliver spatio-temporal information to new markets. There is a significant opportunity for dedicated, transit-measured (e.g. Ferry box data), validation sites to be established. These could potentially synergise with key nodes in global shipping routes to maximise data collected by vessels of opportunity, and ensure consistent data are collected over the same area at regular intervals.

How to cite: Scarrott, R., Cawkwell, F., Jessopp, M., and Cusack, C.: The limitations of in situ data for validating satellite-derived spatio-temporal ocean products., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15999, https://doi.org/10.5194/egusphere-egu21-15999, 2021.

George Vanyushin and Tatiana Bulatova

The influence of fluctuating SST by satellite data in the Barents and Norwegian seas during periods of early ontogenesis NEA cod in 1998-2016 on its strength.


G.P. Vanyushin and T.V. Bulatova


Russian Federal Research Institute of Fisheries and Oceanography (VNIRO), Moscow, Russia

e-mail: ladimon@mail.ru



The paper presents preliminary results of the analysis of the influence of fluctuating seasonal sea surface temperature in the Barents and Norwegian seas during early ontogenesis of the Northeast Atlantic (NEA) cod in the period 1998-2016 on its future strength of generations at age 3+ accordingly in 2001-2019. The temperature data for control zones of these seas (May-October) to 1998-2016 were obtained from the analysis of daily infrared information by the NOAA series of satellites and quasisynchronous temperature data "in situ" from ships, buoys and coastal stations. Data about the strength of NEA cod generations at age 3+ to 2001-2019 was taken from ICES reports. Real comparative analysis was conducted for following three-zones: 1 - Murman-Novaya Zemlya zone (69-76N 30-54E), 2 – North Cape zone (71-76N 17-30E), 3 – West-Spitsbergen zone (69-76N 11-17E). Direct comparative analysis of these indicators revealed very low relationship between them, so R(less) 0,1 for every zone and the whole period. That is why we tried to use the data about distribution of monthly solar activity during solar cycles 23-24 in considering years. The border between these solar cycles is 2008-2009. New comparative analysis of the same indicators separated by cycle 23 (1998-2008 solar activity) and by cycle 24 (2009-2016 solar activity) revealed rather opposite results. In first case (cycle 23) R was received for zone 1 +0,72, zone 2 +0,62 and for zone 3 +0,50, but for cycle 24 R was accordingly equal for zone 1 -0,60, zone 2 -0,66 zone 3 -0,38. So, the influence of seasonal temperature conditions in the Barents and Norwegian seas during 1998-2016 on the strength of new NEA cod generations at age 3+ to 2001-2019 changed its sign on border between 23 and 24 cycles of solar activity for considering years. Perhaps, obtained dependence between these indicators is fairly only for this period of time. For all that ought to note intensification of different character influence of solar activity these cycles towards east.


Keywords: satellite monitoring, sea surface temperature (SST), the North-East Atlantic (NEA) cod generations, solar activity, comparative analysis.

How to cite: Vanyushin, G. and Bulatova, T.: The influence of fluctuating SST by satellite data in the Barents and Norwegian seas during periods of early ontogenesis NEA cod in 1998-2016 on its strength., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-347, https://doi.org/10.5194/egusphere-egu21-347, 2021.

Evangelos Moschos, Alexandre Stegner, Olivier Schwander, and Patrick Gallinari

Mesoscale eddies are oceanic vortices with radii of tens of kilometers, which live on for several months or even years. They carry large amounts of heat, salt, nutrients, and pollutants from their regions of formation to remote areas, making it important to detect and track them. Using satellite altimetric maps, mesoscale eddies have been detected via remote sensing with advancing performance over the last years [1]. However, the spatio-temporal interpolation between satellite track measurements, needed to produce these maps, induces a limit to the spatial resolution (1/12° in the Med Sea) and large amounts of uncertainty in non-measured areas.

Nevertheless, mesoscale oceanic eddies also have a visible signature on other satellite imagery such as Sea Surface Temperature (SST), portraying diverse patterns of coherent vortices, temperature gradients, and swirling filaments. Learning the regularities of such signatures defines a challenging pattern recognition task, due to their complex structure but also to the cloud coverage which can corrupt a large fraction of the image.

We introduce a novel Deep Learning approach to classify sea temperature eddy signatures [2]. We create a large dataset of SST patches from satellite imagery in the Mediterranean Sea, containing Anticyclonic, Cyclonic, or No Eddy signatures, based on altimetric eddy detections of the DYNED-Atlas [3]. Our trained Convolutional Neural Network (CNN) can differentiate between these signatures with an accuracy of more than 90%, robust to a high level of cloud coverage.

We furtherly evaluate the efficiency of our classifier on SST patches extracted from oceanographic numerical model outputs in the Mediterranean Sea. Our promising results suggest that the CNN could complement the detection, tracking, and prediction of the path of mesoscale oceanic eddies.

[1] Chelton, D. B., Schlax, M. G. and Samelson, R. M. (2011). Global observations of nonlinear mesoscale eddies. Progress in oceanography, 91(2),167-216.

[2] E. Moschos, A. Stegner, O. Schwander and P. Gallinari, "Classification of Eddy Sea Surface Temperature Signatures Under Cloud Coverage," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3437-3447, 2020, doi: 10.1109/JSTARS.2020.3001830.

[3] https://www.lmd.polytechnique.fr/dyned/

How to cite: Moschos, E., Stegner, A., Schwander, O., and Gallinari, P.: Deep Learning for Sea Temperature Eddy signature Classification, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7635, https://doi.org/10.5194/egusphere-egu21-7635, 2021.

Khassoum Correa, Eric Machu, Hervé Demarcq, and Daouda Diouf

Particularly interesting because of its socio-economic contribution, the Canary upwelling system encompasses a number of regions with very special characteristics. The wind that blow over this system induces a permanent upwelling off Mauritania and a seasonal upwelling in the south off Senegal, which boosts the development of phytoplankton. To refine the understanding of the phytoplankton in this region (its distribution, variability, response to physical forcings), we combine a number of tools and methods to arrive at a better estimate, and a better monitoring of the concentration of chlorophyll-a (Chl-a), an input parameter for primary production models. Remote sensing of ocean color has particularly interesting advantages, both in terms of global sampling and data acquisition frequency. This method is all the more interesting since ocean color algorithms can be adapted to reduce bias when standard methods have limitations. The regional ocean color algorithm called SOM-NV (Self-Organized Map-Neuro-variational) offers the advantage of making atmospheric correction in the presence of absorbent aerosols, especially desert dust, which sweeps this area permanently and which compels the standard algorithm to apply a mask when atmospheric optical thickness exceeds a threshold of 0.3. This contribution of SOM-NV in the process of atmospheric correction allowed us to 1 : obtain a better reflectance spectra, and as a consequence offer a better estimate of the Chl-a concentrations ; 2 : acquire a larger number of pixels by processing pixels with an optical thickness greater than 0.3 ; 3 : go beyond the general distribution towards the distribution of dominant groups according to the Physat spectral method. The synthesis of 16 years of data from the MODIS-Aqua sensor, allowed us to revisit the seasonality of Chl-a distribution and its cross-shore particularityand an extension towards the open sea which differs according to the season. The highest coastal values are measured in winter and spring, when upwelling intensifies, while the lowest values are measured in summer, when warm, nutrient-poor equatorial waters freplace upwelling waters along the Senegalese coast. This change in water masses impacts phytoplankton communities. According to the work of some authors, nanoplankton gradually replaces diatoms, known to be present during the upwelling season. This makes this region a particularly interesting zone for monitoring dominant groups of phytoplankton, knowing that the change in community impacts the upper levels of the marine food chain, with phytoplankton playing a leading role.

Keywords: Phytoplankton, ocean color, upwelling, atmospheric correction, dust

How to cite: Correa, K., Machu, E., Demarcq, H., and Diouf, D.: Neural-variational algorithm adaptation from SeaWiFS to MODIS sensor for analysis of atmospheric and oceanic parameters, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15934, https://doi.org/10.5194/egusphere-egu21-15934, 2021.

Xiaotong Zhu and Jinhui Jeanne Huang

Remote sensing monitoring has the characteristics of wide monitoring range, celerity, low cost for long-term dynamic monitoring of water environment. With the flourish of artificial intelligence, machine learning has enabled remote sensing inversion of seawater quality to achieve higher prediction accuracy. However, due to the physicochemical property of the water quality parameters, the performance of algorithms differs a lot. In order to improve the predictive accuracy of seawater quality parameters, we proposed a technical framework to identify the optimal machine learning algorithms using Sentinel-2 satellite and in-situ seawater sample data. In the study, we select three algorithms, i.e. support vector regression (SVR), XGBoost and deep learning (DL), and four seawater quality parameters, i.e. dissolved oxygen (DO), total dissolved solids (TDS), turbidity(TUR) and chlorophyll-a (Chla). The results show that SVR is a more precise algorithm to inverse DO (R2 = 0.81). XGBoost has the best accuracy for Chla and Tur inversion (R2 = 0.75 and 0.78 respectively) while DL performs better in TDS (R2 =0.789). Overall, this research provides a theoretical support for high precision remote sensing inversion of offshore seawater quality parameters based on machine learning.

How to cite: Zhu, X. and Huang, J. J.: Remote sensing inversion of water quality in coastal sea area based on machine learning: a case study of Shenzhen bay, China, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1972, https://doi.org/10.5194/egusphere-egu21-1972, 2021.

Konstantin Klein, Hugues Lantuit, Birgit Heim, David Doxaran, Ingmar Nitze, and Bennet Juhls

The Arctic is directly impacted by climate change. The increase in air temperature drives the thawing of permafrost and an increase in coastal erosion and river discharge. This leads to a greater input of sediment and organic matter into coastal waters, which substantially impacts the ecosystems, the subsistence economy of the local population, and the climate because of the transformation of organic matter into greenhouse gases. Yet, the patterns of sediment dispersal in Arctic nearshore zones and their role in the Carbon cycle are not well known due to difficult accessibility and challenging weather conditions. In this study we present the first multi-sensor turbidtiy- reflectance relationship that was specifically calibrated for Arctic nearshore environments. Field data was collected during summer seasons 2018 and 2019 in the inner shelf waters of the Canadian Beaufort Sea close to Herschel Island Qikiqtaruk. The turbidity-reflectance relationship was calibrated to mid to high spatial resolution sensors which are used in ocean color remote sensing, including Landsat 8, Sentinel 2, and Sentinel 3, using the relative spectral response functions. The results for Landsat 8 and Sentinel 2 are very promising and showcase the possibility to resolve sediment accumulations, sediment pathways and filaments at higher detail than before. Both sensors are able to resolve high turbidity close to the coast with values comparable to our field measurements. Sentinel 3, on the other hand, is too coarse to resolve these features but provides great applicability due to its high temporal resolution. The transferability  of these relationships to nearshore environments outside the Canadian Beaufort Sea has to be tested in the future with the potential to map the sediment dispersal in nearshore environments at a circum- Arctic scale.

How to cite: Klein, K., Lantuit, H., Heim, B., Doxaran, D., Nitze, I., and Juhls, B.: High Resolution Turbidity Modelling in Arctic Nearshore Environments, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10073, https://doi.org/10.5194/egusphere-egu21-10073, 2021.

Aida Alvera-Azcárate, Dimitry Van der Zande, Alexander Barth, Samuel Martin, and Jean-Marie Beckers

The evolution of chlorophyll concentration (CHL) and suspended particle matter (SPM) in the North Sea over the period 1998-2017 is analysed. The domain covers 48 to 66 degrees North and -8 to 13 degrees East. Through the years between 76% and 87% of marine pixels are missing data due to cloud cover and satellite product quality control. A daily cloud-free dataset is produced with the help of DINEOF (Data Interpolating Empirical Orthogonal Functions). The gap-free dataset is used to investigate interannual variability and trends in the concentration of these variables in the North Sea, and their relation to long-term climatic signals such as the Atlantic Multidecadal Oscillation (AMO). The interannual variability of the initiation and length of the Spring bloom is studied, as well as its spatial dispersion. High latitudes (higher than 60°N) present large amounts of missing data due to the presence of clouds and low sun angles in winter, and therefore are more difficult to study using optical satellite data. The spatial and temporal variability of the CHL and SPM signals is assessed in these zones, like the occurrence and strength of the Spring bloom around the Faroe islands.

How to cite: Alvera-Azcárate, A., Van der Zande, D., Barth, A., Martin, S., and Beckers, J.-M.: Analysis of 20 years of daily cloud-free chlorophyll and suspended particulate matter in the North Sea, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10317, https://doi.org/10.5194/egusphere-egu21-10317, 2021.

Sven Gastauer, Jeffrey S. Ellen, and Mark D. Ohman

Zooglider is an autonomous buoyancy-driven ocean glider designed and built by the Instrument Development Group at Scripps. Zooglider includes a low power camera with a telecentric lens for shadowgraph imaging and two custom active acoustics echosounders (operated at 200/1000 kHz).  A passive acoustic hydrophone records vocalizations from marine mammals, fishes, and ambient noise.  The imaging system (Zoocam) quantifies zooplankton and ‘marine snow’ as they flow through a sampling tunnel within a well-defined sampling volume. Other sensors include a pumped Conductivity-Temperature-Depth probe and Chl-a fluorometer.  An acoustic altimeter permits autonomous navigation across regions of abrupt seafloor topography, including submarine canyons and seamounts.  Vertical sampling resolution is typically 5 cm, maximum operating depth is ~500 m, and mission duration up to 50 days.  Adaptive sampling is enabled by telemetry of measurements at each surfacing.  Our post-deployment processing methodology classifies the optical images using advanced Deep Learning methods that utilize context metadata.  Zooglider permits in situ measurements of mesozooplankton and marine snow - and their natural, three dimensional orientation - in relation to other biotic and physical properties of the ocean water column.  Zooglider resolves micro-scale patches, which are important for predator-prey interactions and biogeochemical cycling. 


How to cite: Gastauer, S., Ellen, J. S., and Ohman, M. D.: Ocean Zooglider:  an autonomous vehicle for optical and acoustic sensing of zooplankton and suspended particles, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8658, https://doi.org/10.5194/egusphere-egu21-8658, 2021.

Modulations in vertical stability trigger intraseasonal variations in phytoplankton bloom
Keerthi Madhavan Girijakumari, Marina Levy, and Olivier Aumont
Clément Haëck, Marina Levy, Laurent Bopp, and Roy El Hourany

Over large parts of the ocean, submesoscale fronts are known to enhance total phytoplankton abundance because they are the location of intense vertical transport of nutrients. Disparate in situ observations suggest that such frontal dynamics not only affects the total biomass of phytoplankton, but also significantly modifies its composition. Here we make use of a newly developed algorithm able to distinguish a set of phytoplankton-specific pigments to statistically explore the change in phytoplankton community composition over basin-wide regions. We use 15 years of SST and reflectance data from the MODIS sensor on the Aqua satellite, at 1km and daily resolutions and focus on the oligotrophic North Atlantic subtropical gyre and on the more productive gulf stream region. We locate submesoscale fronts by computing an index quantifying SST patchiness. Our results confirm that submesoscale fronts are collocated with elevated Chlorophyll-a concentration and show significant changes in phytoplankton composition. These results underline the influence of submesocale dynamics on phytoplankton diversity, and stress the need to better understand the underlying mechanisms.

How to cite: Haëck, C., Levy, M., Bopp, L., and El Hourany, R.: Modification of phytoplankton group diversity over submesoscale fronts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4178, https://doi.org/10.5194/egusphere-egu21-4178, 2021.

Shuang Long, Qing Dong, and Wanjiao Song

As one of the most significant physical processes in the ocean, mesoscale eddies play an import role in the local distributions of temperature, salinity, ocean current field and ecosystem through vertical mixing and horizontal advection. In addition, mass transport is importantly affected by the propagation of mesoscale eddies. Researches on the characteristics of mesoscale eddies and their effects on chlorophyll-a concentration in the Indo Pacific Warm Pool (20°S~20°N,60°E~170°W), the key area influencing the global climate change, help to further understand the bio-physical coupling processes in this domain. Using the remote sensing data from 1998 to 2018, combined with singular value decomposition, correlation analysis and other statistical methods, we have studied the distribution characteristics of mesoscale eddies with lifetime exceeding 4 weeks and the correlation with chlorophyll-a concentration in the Indo Pacific Warm Pool. The short-lived mesoscale eddies account for more than 70.0% and most of mesoscale eddies are nonlinear and propagate west. The seasonal numbers of mesoscale eddies vary insignificantly in the whole domain and plenty of the eddies generate in sea domains of 5°S~20°S and 5°N~20°N. The number of mesoscale eddies has little effect on chlorophyll-a concentration and the correlation between the kinetic energy of mesoscale eddies and chlorophyll-a concentration show both positive and negative.

How to cite: Long, S., Dong, Q., and Song, W.: Statistical analysis of mesoscale eddies and their effects on chlorophyll-a concentration in the Indo Pacific warm pool, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8149, https://doi.org/10.5194/egusphere-egu21-8149, 2021.

Astrid Bracher, Julia Oelker, Svetlana Losa, Mariana Altenburg Soppa, Andreas Richter, Alexei Rozanov, Vanda Brotas, Ana C. Brito, Mara Gomes, Maycira Costa, and Marie-Helene Rio

Hyperspectral satellite data are a source of the top of the atmosphere radiance signal which can be used for novel algorithms aimed for observations of marine ecosystems and the light-lit ocean. Atmospheric sensors such as SCIAMACHY, GOME-2 and OMI have proven in the past to yield valuable information on phytoplankton diversity, sun-induced marine fluorescence, and the underwater light field, however at low coverage and spatial resolution. Within the ESA Sentinel-5p+ Innovation themes, we explore TROPOMI's potential for deriving the diffuse attenuation coefficient and the quantification of different phytoplankton groups. As commonly used for the retrieval of atmospheric trace gases, we apply the differential optical absorption spectroscopy combined with radiative transfer modeling (RTM) to infer these oceanic parameters. We present results on a measure describing the diminishing of incoming radiation in the ocean with depth, the diffuse attenuation coefficient KD. KD is derived by the retrieval of the vibrational Raman scattering signal in backscattered radiances measured by TROPOMI in the UV and spectral range which then is further converted to the associated KD using RTM. The final TROMPOMI KD data sets resolved for three spectral regions (UV-B+short wave UV-A, UV-A and short blue) agree well with in situ data sampled during an expedition with RV Polarstern in 2018 in the Atlantic Ocean.  Further, KD-blue compared to wavelength-converted KD(490nm) products (OLCI-A and the merged OC-CCI) from common, multispectral, ocean color sensors, show that differences between the three data sets are within uncertainties given for the OC-CCI product. Our study shows for the first time KD products for the UV spectral range retrieved from space based data. TROPOMI KD-blue results have higher quality and much higher spatial coverage and resolution than previous ones from SCIAMACHY, GOME-2 and OMI.  Additionally, first results on TROPOMI’s potential for retrieving three phytoplankton groups will be shown and compared to similar multispectral phytoplankton group data for the same time period and ocean region as shown for TROPOMI KD.

How to cite: Bracher, A., Oelker, J., Losa, S., Altenburg Soppa, M., Richter, A., Rozanov, A., Brotas, V., Brito, A. C., Gomes, M., Costa, M., and Rio, M.-H.: The potential of Sentinel-5P’s high spectral resolution for ocean applications, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13009, https://doi.org/10.5194/egusphere-egu21-13009, 2021.

Chairpersons: Christine Gommenginger, Tong Lee, Guoqi Han
Jacek Piskozub, Violetta Drozdowska, Iwona Wróbel-Niedźwiecka, Przemysław Makuch, Piotr Markuszewski, and Małgorzata Kitowska

The air-sea gas flux is proportional to the difference of partial pressure between the sea-water and the overlying atmosphere multiplied by gas transfer velocity k, a measure of the effectiveness of the gas exchange. Because wind is the source of turbulence making the gas exchange more effective, k is usually parameterized by wind speed. Unfortunately, measured values of gas transfer velocity at a given wind speed have a large spread in values. Surfactants have been long suspected as the main reason of this variability but few measurements of gas exchange and surfactants have been performed at open sea simultaneously and therefore their results were inconclusive. Only recently, it has been shown that surfactants may decrease the CO2 air-sea exchange by up to 50%. However the labour intensive methods used for surfactant study make it impossible to collect enough data to map the surfactant coverage or even create a gas transfer velocity parameterization involving a measure of surfactant activity. This is why we propose to use optical fluorescence as a proxy of surfactant activity.


Previous research done by our group showed that fluorescence parameters allow estimation the surfactant enrichment of the surface microlayer, as well as types and origin of fluorescent organic matter involved. We plan to measure, from a research ship, all the variables needed for calculation of gas transfer velocity k (namely CO2 partial pressure both in water and in air as well as vertical flux of this trace gas) and to use mathematical optimization methods to look for a parameterization involving wind speed and one of the fluorescence parameters which will minimize the residual k variability. Although our research will still involve water sampling and laboratory fluorescence measurements, the knowledge of which absorption and fluorescence emission bands are the best proxy for surfactant activity may allow to create remote sensing products (fluorescence lidars) allowing continuous measurements of surfactant activity at least from the ship board, if not from aircraft and satellites. The improved parameterization of the CO2 gas transfer velocity will allow better constraining of basin-wide and global air-sea fluxes, an important component of global carbon budget.


If an improved gas transfer velocity parametrization based on surfactant fluorescence spectrum in concert with a turbulence proxy (wind) were to be found, a tantalizing possibility arises of a remote sensing estimation of k. Namely a UV lidar can both excite and measure the fluorescence band identified as proxy of the surfactant effect on the gas transfer velocity. Depending on the wavelength bands needed to be utilized, the effect could be measured from a moving ship (already an improvements on methods needing sampling), an aircraft or possibly even a satellite. We intend to pursue this idea in cruises to both the Baltic and the North Atlantic, possibly in cooperation with other air-sea interaction groups (this presentation is in part an invitation to cooperation).

How to cite: Piskozub, J., Drozdowska, V., Wróbel-Niedźwiecka, I., Makuch, P., Markuszewski, P., and Kitowska, M.: Is remote sensing of the surfactant effect on gas transfer velocity possible?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1704, https://doi.org/10.5194/egusphere-egu21-1704, 2021.

Julius Harms and Thorsten A. Kern

Environmental and climate research is relying strongly on detailed measurement information especially from the ocean's surface. Recently the interest in small Drifter-Buoys is growing worldwide to increase the measurement resolution and monitoring capabilities. Manifold engineering challenges so far prevented a globally usable open-source platform. Commercially available drifters either only offer the possibility of position tracking or require high costs for the integration of special sensor technology. Custom developments are also expensive and require long development times. Considering this, the Institute of Mechatronics at TU-Hamburg is developing a self-sufficient modular multi sensor platform to collect different measurement data, considering a holistic design including energy-harvesting, cost- and performance optimized sensors, stability and drift-properties, and an electronic hardware architecture. The modular platform enables data acquisition and transmission for individually selected sensors and provides sufficient energy supply by energy harvesting methods. Inherent to the design, the presented concept targets for an open source platform enabling everyone interested to use the most important components for remote sensing, with an easy extension for individual needs.

The platform consists of a main board containing a GPS module, a MEMS-IMU, a temperature sensor, a satellite communication module and a power management circuit in addition to the processing unit. The motherboard alone enables a transmission of collected data according to user oriented settings. Onboard temperature sensor enables temperature monitoring of the device, GPS-module acquires an accurate position and the integrated IMU can measure the wave spectrum. Satellite communication is based on state of the art IOT-communication solutions. Additionally, an interface was developed to allow the extension of a sensor unit. According to a standardized protocol, any measurement data of the connected sensors can be processed. Special focus is put on the integration of e.g. water temperature and salinity sensors as a standard. To enable long time measurements, the self-sufficient module provides an energy supply for all components based on solar power and wave energy. The wave energy converter is a specially developed linear generator, gaining energy of the relative motion between buoy and drogue. The full hardware is designed based on low power electronics and stores the energy in non-toxic super capacitors.

A simple open source housing design provides a cost effective drifter solution, which can easily be manufactured by research groups all over the world. The modular system can be implemented in different stages of complexity to always have the best trade-off between cost and needs. This also allows a cost effective deployment of a high quantities to enable drifter measurements with high space resolution like for submesoscale analysis. The housing is targeted to be manufactured completely from bio compatible materials to avoid water pollution.

A first proof of concept prototype was tested in the Baltic sea off the coast of the German island of Fehmarn. Two prototypes and two CARTHE drifter were successfully deployed and compared. The development finished its concept definition and functional-sample chapter, and is now going into a first prototype phase following the implementation in a V-model development structure.

How to cite: Harms, J. and Kern, T. A.: Self-Sufficient Modular Multi Sensor Platform for Remote Long Time Ocean Observations in Large Quantities, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-93, https://doi.org/10.5194/egusphere-egu21-93, 2021.

Marcel Kleinherenbrink, Paco Lopez-Dekker, Bertrand Chapron, and Alexis Mouche

Tropical cyclones are commonly linked to devastation by hurricane-force winds, storm surges and rainfall. They are also responsible for large exchanges of heat in the upper ocean and the atmosphere, and the transport of large quantities of water from ocean to land. Due to the limited coverage of microwave observations from airplanes and the limited resolution of spaceborne scatterometers, the dynamics inside these extremes are poorly sampled and understood. Synthetic Aperture Radar (SAR) overcomes these limitations, but is only able to recover one-dimensional information, which limits the accuracy of estimated quantities like wind speed, total surface current and wave spectra. Waves radiating outward are, during their development, affected by wind and currents inside of the tropical cyclone and therefore contain information about the structure and dynamics of the system. Wave spectra in tropical cyclones can only partly be recovered, as the quickly changing sea surface limits the resolution of SAR in the azimuth direction. This presentation shows the benefit of having Harmony's bi-static receivers flying in a StereoSAR configuration with Sentinel-1D for the retrieval of wave spectra. Harmony's data allows for the retrieval of a larger fraction of the wave spectra. In the periphery of tropical cyclones Harmony will primarily enhance the recovery of medium-length (100-300 m) swell and wind waves, while Harmony also improves the recovery of long (swell) waves (>200 m) near the eye of the storm.

How to cite: Kleinherenbrink, M., Lopez-Dekker, P., Chapron, B., and Mouche, A.: Ocean wave spectra estimation with the Earth Explorer 10 candidate Harmony, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12591, https://doi.org/10.5194/egusphere-egu21-12591, 2021.

Fabrice Ardhuin, Marine De Carlo, Matias Alday, Eleonore Stutzmann, Fabrice Collard, Maria Yurovskaya, Charles Peureux, and Craig Donlon

Microseisms in the dominant double-frequency band, around 5 s period and their ubiquitous presence makes them an interesting signal for exploring the solid Earth and associated natural hazards (e.g. Olivier et al. 2019).  These microseisms are generated by opposing ocean waves of equal frequencies (Hasselmann 1963) that generally arise within the locally generated sea state at high frequencies (class I, Ardhuin et al. 2011), due to coastal reflection (class II) or when swell from a distant storm collides with another wave system, which generally corresponds to the strongest microseism sources (class III). Improving on solid Earth knowledge and natural hazard monitoring can benefit from a better quantitative knowledge of these sources. Similar applications to the study of the stratosphere can use atmospheric infrasound that are generated by the same opposing ocean waves, the microbaroms (Brekhovskikh et al. 1973, De Carlo et al. 2020). So far, very few direct measurements of wave properties have been able to quantify the presence of waves in opposite directions, and the magnitude of microseism sources has relied on numerical simulations. Here we use sun glitter measurements from the Copernicus-Sentinel 2 satellites, as processed in the SARONG project (http://www.sarong.global/). We show that the presence of opposing waves gives a strong anomaly in the phase of co-spectra of optical images from Sentinel 2 (S2). When using only 2 time-lagged images this feature generally limits the possibility to measure surface currents from waves shorter than about 25 m, that always have a significant energy in opposing directions (class I microseism sources).

Caption: Processing from Sentinel 2 Level-1c images to phase speeds. Top: data from Copernicus Sentinel 2 on 29 April 2016 off California (See Figs. 3-9 in Kudryavtsev et al. 2017). Bottom: simulated S2 data based on in situ wave spectrum determined from directional moments using the Maximum Entropy Method. The phase speed anomalies, highlighted with the dashed magenta circle near the Nyquist wavelength L = 20 m, disappear when no energy propagates in opposing directions.

The same also happens for longer components when strong (class III) microseism sources are present. However this signature is also an opportunity to directly measure the sources of microseisms and quantify the energy in opposing directions using 2 or more different time lags (Ardhuin et al., in 2021). Given its coastal coverage, we find that S2 is particularly well suited for estimating reflection coefficients of waves off the coast, which is a major source of uncertainty for microseism and microbarom source modelling.


Ardhuin, F., Stutzmann, E., Schimmel, M., & Mangeney, A. (2011). Ocean wave sources of seismic noise. Journal of Geophysical Research, 116(C9). doi:10.1029/2011jc006952

Kudryavtsev, V., Yurovskaya, M., Chapron, B., Collard, F., & Donlon, C. (2017). Sun glitter imagery of ocean surface waves. Part 1: Directional spectrum retrieval and validation. Journal of Geophysical Research: Oceans, 122(2), 1369–1383. doi:10.1002/2016jc012425 

Olivier, G., Brenguier, F., Carey, R., Okubo, P., & Donaldson, C. (2019). Decrease in seismic velocity observed prior to the 2018 eruption of Kīlauea volcano with ambient seismic noise interferometry. Geophysical Research Letters. doi:10.1029/2018gl081609

How to cite: Ardhuin, F., De Carlo, M., Alday, M., Stutzmann, E., Collard, F., Yurovskaya, M., Peureux, C., and Donlon, C.: Remote sensing observations of ocean wave sources of microseisms and microbaroms, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14993, https://doi.org/10.5194/egusphere-egu21-14993, 2021.

Ben Timmermans, Andrew Shaw, and Chrsitine Gommenginger

Measurements of significant wave height from satellite altimeter missions are finding increasing application in investigations of wave climate, sea state variability and trends, in particular as the means to mitigate the general sparsity of in situ measurements. However, many questions remain over the suitability of altimeter data for the representation of extreme sea states and in particular applications that examine extremes in the coastal zone. In this paper, the limitations of altimeter data to estimate coastal Hs extremes (<10 km from shore) are investigated using the European Space Agency Sea State Climate Change Initiative (CCI) L2P altimeter data v1.1 product recently released. This Sea State CCI product provides near complete global coverage and a continuous record of 28 years. It is used here together with in situ data from moored wave buoys at a number of sites around the coast of the United States. The limitations of estimating extreme values based on satellite data are quantified and linked to several factors including the impact of data corruption nearshore, the influence of coastline morphology and local wave climate dynamics and the spatio-temporal sampling achieved by altimeters. The factors combine to lead to considerable underestimation of estimated Hs 10-yr return levels. Sensitivity to these factors is evaluated at specific sites, leading to recommendations about the use of satellite data to estimate extremes and their temporal evolution in coastal environments.

How to cite: Timmermans, B., Shaw, A., and Gommenginger, C.: Reliability of Extreme Significant Wave Height Estimation from Satellite Altimetry and In Situ Measurements in the Coastal Zone, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16359, https://doi.org/10.5194/egusphere-egu21-16359, 2021.

Houssam Yassin and Stephen Griffies

The baroclinic modes of quasigeostrophic theory are incomplete and the incompleteness manifests as a loss of information in the projection process. The incompleteness of the baroclinic modes is related to the presence of two previously unnoticed stationary step-wave solutions of the Rossby wave problem with flat boundaries. These step-waves are the limit of surface quasigeostrophic waves as boundary buoyancy gradients vanish. A complete normal mode basis for quasigeostrophic theory is obtained by considering the traditional Rossby wave problem with prescribed buoyancy gradients at the lower and upper boundaries. The presence of these boundary buoyancy gradients activates the previously inert boundary degrees of freedom. These Rossby waves have several novel properties such as the presence of multiple equivalent barotropic modes, a finite number of modes with negative norms, and their vertical structures form a basis capable of representing any quasigeostrophic state. Using this complete basis, we are able to obtain a series expansion to the potential vorticity of Bretherton (with Dirac delta contributions). We compare the convergence and differentiability properties of these complete modes with various other modes in the literature. We also examine the quasigeostrophic vertical velocity modes and derive a complete basis for such modes as well. In the process, we introduce the concept of the quasigeostrophic phase space which we define to be the space of all possible quasigeostrophic states. A natural application of these modes is the development of a weakly non-linear wave-interaction theory of geostrophic turbulence that takes prescribed boundary buoyancy gradients into account.

How to cite: Yassin, H. and Griffies, S.: Discrete Normal Mode Decompositions in Quasigeostrophic Theory, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-972, https://doi.org/10.5194/egusphere-egu21-972, 2021.

Matthew Hammond, Giuseppe Foti, Christine Gommenginger, Meric Srokosz, and Nicolas Floury

Global Navigation Satellite System-Reflectometry (GNSS-R) is an innovative and rapidly developing approach to Earth Observation that makes use of signals of opportunity from Global Navigation Satellite Systems, which have been reflected off the Earth’s surface. CYGNSS is a constellation of 8 satellites launched in 2016 which use GNSS-R technology for the remote sensing of ocean wind speed. The ESA ECOLOGY project aims to evaluate CYGNSS data which has recently undergone a series of improvements in the calibration approach. Using CYGNSS collections above the ocean surface, an assessment of Level-1 calibration is presented, alongside a performance evaluation of Level-2 wind speed products. L1 data collected by the individual satellites are shown to be generally well inter-calibrated and remarkably stable over time, a significant improvement over previous versions. However, some geographical biases are found, which appear to be linked to a number of factors including the transmitter-receiver pair considered, viewing geometry, and surface elevation. These findings provide a basis for further improvement of CYGNSS products and have wider applicability to improving calibration of GNSS-R sensors for remote sensing of the Earth.

How to cite: Hammond, M., Foti, G., Gommenginger, C., Srokosz, M., and Floury, N.: An Assessment of CYGNSS Ocean Wind Speed Products, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3717, https://doi.org/10.5194/egusphere-egu21-3717, 2021.

Ru Wang, Yijun Hou, and Ze Liu

The locations and generation mechanisms of energy sources in the Kuroshio were analyzed. The slope of the one-dimensional spectral energy density varies between -5/3 and -3 in the wavenumber range of 0.03-0.1 cpkm (wavelengths of approximately 209 to 63 km, respectively), indicating an inverse energy cascade in the Kuroshio; according to the steady-state energy evolution, an energy source which occurs at scale smaller than Rhines scale must be present. By analyzing the wavenumber-frequency spectrum, the period of higher kinetic energy (KE) is about 89-209 days and spatial scale is less than 0.03 cpkm. The locations of energy sources were identified with using the spectral energy transfer calculated by altimetry and model data. At the sea surface, the KE sources are mainly within 23.2°-25.2°N and 28°-30°N at less than 0.03 cpkm and 23.2°-23.6°N and 26°-30°N at 0.03-0.1 cpkm. The available potential energy (APE) sources are mainly within 22.2°-28°N and 28.6°-30°N at less than 0.03 cpkm and 29.2°-30°N at 0.03-0.1 cpkm. Wind stress and density differences (including buoyancy flux, temperature flux and salinity flux) are primarily responsible for the KE and APE sources, respectively. Beneath the sea surface, the energy sources are mainly above 400 m depth, and buoyancy flux plays a major role in the generation of energy sources. The energy cycle process can be summarized as follows: once an energy source is formed, to maintain a steady state, energy cascades (mainly inverse cascades) will be engendered.


How to cite: Wang, R., Hou, Y., and Liu, Z.: Analysis of Energy Sources along the Kuroshio in the East of Taiwan Island and East China Sea, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3785, https://doi.org/10.5194/egusphere-egu21-3785, 2021.

Olga Shomina, Tatiana Tarasova, Olga Danilicheva, and Ivan Kapustin

Slick structures on the sea surface can mark processes occurring in upper ocean and atmosphere. Spiral shape of slicks observed in optical and radar images of water surface is traditionally interpreted through the manifestation of marine eddy which length scale is supposed to be equal to the scale of spiral. This assumption implies that wind has no effect on the kinematics of forming slick band, which, according to our estimation, is incorrect even at moderate wind velocities. This approach can cause misinterpretation of remote sensing data when estimating the characteristics of observed marine eddies. This study is devoted to the investigation of conditions necessary for the formation of slick spiral and to some peculiarities of its shape and scale.

The system of equations for the description of kinematics of Lagrangian particle (element of water surface covered with surface active substance) in the fields of axisymmetric eddy with non-zero radial velocity component and homogeneous wind was obtained. It is demonstrated that the spiral center is not collocated with the center of the eddy; the distance between them can achieve the eddy length scale. It is shown that the displacement of the spiral center and the direction of the main axis is quasi perpendicular to the wind direction when radial component of the eddy is small compared to the tangential component. The presence of the threshold wind velocity corresponding to the breakdown of the spiral structure is demonstrated analytically. The possibilities of correct retrieval of length scales and character velocities of observed sub mesoscale marine eddies are discussed.

The research was funded by the Russian Science Foundation (Project RSF 18-77-10066).

How to cite: Shomina, O., Tarasova, T., Danilicheva, O., and Kapustin, I.: Peculiarities of marine eddy manifestation in the structure of surfactant slick band, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7699, https://doi.org/10.5194/egusphere-egu21-7699, 2021.

Craig Donlon, Robert Cullen, Luisella Giulicchi, and Marco Fonari

The threat of sea level rise to coastal communities is an area of significant concern to the well-being and security of future generations. Environmental policy actions and decisions affecting coastal states are being made now.  Given the considerable range of applications, sustained altimetry satellite missions are required to address operational, science and societal needs. This article describes the Copernicus Sentinel-6 mission that is designed to address the needs of the European Copernicus programme for precision sea level, near-real-time measurements of sea surface height, significant wave height, and other products tailored to operational services in the climate, ocean, meteorology and hydrology domains. It is designed to provide enhanced continuity to the very stable time series of mean sea level measurements and ocean sea state started in 1992 by the TOPEX/Poseidon (T/P) mission and follow-on Jason-1, Jason-2 and Jason-3 satellite missions. The mission is implemented through a unique international partnership with contributions from NASA, NOAA, ESA, EUMETSAT, and the European Union (EU).  It includes two satellites that will fly sequentially (separated in time by 5 years). The first satellite, named Sentinel-6 Michael Freilich, launched from Vandenburg Air Force Base, USA on 21st November 2020. The main payload is the Poseidon-4 dual frequency (C/Ku-band) nadir-pointing radar altimeter providing synthetic aperture radar (SAR) processing in Ku-band to improve the signal through better along-track sampling and reduced measurement noise. The altimeter has an innovative interleaved mode enabling radar data processing on two parallel chains, one with the SAR enhancements and the other furnishing a "Low Resolution Mode" that is fully backward-compatible with the historical T/P and Jason measurements, so that complete inter-calibration between the state-of-the-art data and the historical record can be assured. A three-channel Advanced Microwave Radiometer for Climate (AMR-C) developed by NASA JPL provides measurements of atmospheric water vapour that would otherwise degrade the radar altimeter measurements. An experimental High Resolution Microwave Radiometer (HRMR) is also included in the AMR-C design to support improved performance in coastal areas. Additional sensors are included in the payload to provide Precise Orbit Determination, atmospheric sounding via GNSS-Radio Occultation and radiation monitoring around the spacecraft.

Early in-orbit performance data are presented.

How to cite: Donlon, C., Cullen, R., Giulicchi, L., and Fonari, M.: First results from the e Copernicus Sentinel-6 satellite mission, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3165, https://doi.org/10.5194/egusphere-egu21-3165, 2021.

Daniele Ciani, Marie-Hélène Rio, Bruno Buongiorno Nardelli, Stéphanie Guinehut, Elodie Charles, Hélène Etienne, and Rosalia Santoleri

Measuring the ocean surface currents at high spatio-temporal resolutions is crucial for scientific and socio-economic applications. Since the early 1990s, the synoptic and global-scale monitoring of the ocean surface currents has been provided by constellations of Radar Altimeters. The Altimeter observations enable to derive the geostrophic component of the surface currents with effective spatial-temporal resolutions O(100 km) and O(10 days), respectively. Therefore, only the largest mesoscale oceanic features can be accurately resolved. In order to enhance the altimeter system capabilities, we propose a synergistic use of high resolution, satellite-derived Sea Surface Temperature (SST), Chlorophyll concentrations (Chl) and Altimeter-derived currents. Our approach is tested in both global-scale and regional contexts.
At global scale, relying on past numerical studies, we perform a sensitivity experiment based on several gap-free SST datasets, emphasizing strengths and weaknesses in ocean currents applications. Overall, the comparison with in-situ measured currents shows that our synergistic method can improve the altimeter estimates up to 30% locally.
Then, our method is also implemented with Chl data in the  Mediterranean Sea, where the most energetic variable signals are found at spatio-temporal scales up to 10 km and few days. We test the method feasibility in an Observing System Simulation Experiment relying on model outputs of the European Copernicus Marine Service. Statistical analyses based on the 2017 daily data show that our approach can improve the altimeter-derived currents accuracy up to 50% at the basin scale, also enhancing the effective spatial-temporal resolutions up to 30 km and less than 10 days, respectively. The method efficiency decreases when the surface Chl patterns are dominated by the biological activity rather than the currents advection, which mostly occurs in the mid-February to mid-March time window. Preliminary tests on the method applicability to satellite-derived data are also presented and discussed.

How to cite: Ciani, D., Rio, M.-H., Buongiorno Nardelli, B., Guinehut, S., Charles, E., Etienne, H., and Santoleri, R.: Ocean Circulation from the synergy of altimeter-derived and oceanic tracers observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14355, https://doi.org/10.5194/egusphere-egu21-14355, 2021.

Kaveh Purkiani, Maren Walter, Matthias Haeckel, Katja Schmidt, André Paul, Annemiek Vink, and Michael Schulz

During RV Sonne expedition SO268 to the northeast tropical Pacific Ocean between March and May 2019, the impact of a mesoscale eddy on the seawater properties was investigated by conducting a multiple of observations. A subsequent analysis of an altimeter data revealed the formation of an anticyclonic mesoscale eddy in the Tehuantepec gulf between 15 and 20 June 2018 with a mean radius of 185 km and an average speed of 13 cm/s. This extremely long-lived eddy carried sea-water characteristics from near coastal Mexican waters westward far into the open ocean. The water mass stayed largely isolated during the 11 months of travel time due to high rotational speed.

The eddy exhibited a conical-shape vertical structure with concurrent deepening of the main thermocline. The water in the eddy core showed an extreme positive temperature anomaly of 8C, a negative salinity anomaly of -0.5 psu and a positive dissolved oxygen concentration anomaly of +160 μmol/kg centered at 80 m depth. The sub-surface impact of the eddy is clearly evident in the temperature and salinity profiles at a depth of 1500 m. For dissolved oxygen the eddy-induced anomaly reached even deeper to the seafloor.

This study provides new insights to the offshore transport of heat and salt driven by the long-lived anticyclonic eddy in the northeast tropical Pacific Ocean. Considering the water column trapped within the eddy, a positive heat transport anomaly of 1-3 ×1011 W and a negative salt transport anomaly of -8×103 kg/s were estimated. However, due to the rare occurrence of long-lived anticyclone eddies in this region, they likely do not play a significant role in affecting the heat and salt balance of the northeastern tropical Pacific Ocean.

How to cite: Purkiani, K., Walter, M., Haeckel, M., Schmidt, K., Paul, A., Vink, A., and Schulz, M.: Anomalous transport of heat and salt by a long-lived anticyclonic eddy  in the northeast tropical Pacific Ocean, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13346, https://doi.org/10.5194/egusphere-egu21-13346, 2021.

Cécile Pujol, Aida Alvera-Azcárate, Charles Troupin, Alexander Barth, and Hugo Romanelli

In April 2019, a large anticyclonic Eddy has formed in Western Mediterranean Sea between Sardinia and Balearic Islands. This anticyclone was observable with Sentinel-3 SST satellite data for 7 months and its diameter was estimated to 150 km. Although mesoscale anticyclones are quite common in this part of the Mediterranean Sea, such large and long-live eddies remain exceptional and repercussions for ocean-atmospheric exchanges and for biodiversity might be consequent. However, due to the increase of temperatures during summer, the satellite SST track of the eddy has been lost during a few weeks in August and September. Indeed, the SST signature of the eddy was not distinguishable from surrounding waters anymore. In order to track the eddy during its entire life and have a better understanding of its characteristics, sea level anomaly derived from altimetric data will be analysed in this study with the Py Eddy Tracker toolbox to investigate the variation of its position, its altimetry and its size. The distribution of other remarkable eddies in this zone and period will also be considered. Moreover, a high-resolution SST field will be reconstructed with DINEOF method so the comparison between eddy’s SST and altimetric characteristics will be assured.

How to cite: Pujol, C., Alvera-Azcárate, A., Troupin, C., Barth, A., and Romanelli, H.: Characterizing an anticyclone in Western Mediterranean Sea using altimetric data and an eddy tracker, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5955, https://doi.org/10.5194/egusphere-egu21-5955, 2021.

Ida Margrethe Ringgaard, Jacob L. Høyer, Kristine S. Madsen, Adili Abulaitijiang, and Ole B. Andersen

The rise and fall of the sea surface in the coastal region is observed closely by two different sources: tide gauges measure the relative sea level anomaly at the coast at high temporal resolution (minutes or hours) and satellite altimeters measure the absolute sea surface height of the open ocean along tracks multiple times a day. However, these daily tracks are scattered across the Baltic Sea with each track being repeated at a lower temporal resolution (days). Due to the inverse relationship between spatial and temporal coverage of the satellite altimetry data, gridded satellite altimetry products often prioritize spatial coverage over temporal resolution, thus filtering out the high sea level variability. In other words, the satellite data, and especially averaged products, often miss the daily sea level variability, such as storm surges, which is most important for all societies in the coastal region. To compensate for the sparse spatial coverage from satellite altimetry, we here present an experimental product developed as part of the ESA project Baltic+SEAL:  on a 3-day scale, the DMI Optimal Interpolation (DMI-OI) method is combined with error statistics from a storm surge model as well as 3-day averages from both tide gauge observations and satellite altimetry tracks to generate a gridded sea level anomaly product for the Baltic Sea for year 2017. The product captures the overall temporal evolution of the sea level changes well for most areas with an average RMSE wrt. tide gauge observations of 17.2 cm and a maximum of 34.2 cm. Thus, the 3-day mean gridded product shows potential as an alternative to monthly altimetry products, although further work is needed.

How to cite: Ringgaard, I. M., Høyer, J. L., Madsen, K. S., Abulaitijiang, A., and Andersen, O. B.: Sea level variations in the coastal region from altimetry – Using optimal interpolation in the Baltic Sea for 3-day mean sea level from altimetry, tide gauge and model data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10733, https://doi.org/10.5194/egusphere-egu21-10733, 2021.

Fabio Mangini, Antonio Bonaduce, Léon Chafik, and Laurent Bertino

Satellite altimetry measurements, complemented by in-situ records, have made a fundamental contribution to the understanding of global sea level variability for almost 30 years. Due to land contamination, it performs best over the open ocean. However, over the years, there has been a significant effort to improve the altimetry products in coastal regions. Indeed, altimetry observations could be fruitfully used in the coastal zone to complement the existing tide gauge network which, despite its relevance, does not represent the entire coast. Given the important role of coastal altimetry in oceanography, we have recently decided to check the quality of a new coastal altimetry dataset, ALES, along the coast of Norway. The Norwegian coast is well covered by tide gauges and, therefore, particularly suitable to validate a coastal altimetry dataset. Preliminary results show a good agreement between in-situ and remote sensing sea-level signals in terms of linear trend, seasonal cycle and inter-annual variability. For example, the linear correlation coefficient between the inter-annual sea level variability from altimetry and tide gauges exceeds 0.8. Likewise, the root mean square difference between the two is less than 2 cm at most tide gauge locations. A comparison with Breili et al. (2017) shows that ALES performs better than the standard satellite altimetry products at estimating sea level trends along the coast of Norway. Notably, in the Lofoten region, the difference between the sea level trends computed using ALES and the tide gauges range between 0.0 to 0.7 mm/year, compared to circa 1 to 3 mm/year found by Breili et al. (2017). These preliminary results go in the direction of obtaining an accurate characterization of coastal sea-level at the high latitudes based on coastal altimetry records, which can represent a valuable source of information to reconstruct coastal sea-level signals in areas where in-situ data are missing or inaccurate.

How to cite: Mangini, F., Bonaduce, A., Chafik, L., and Bertino, L.: Validation of the ALES Coastal Altimetry Dataset against the Norwegian Tide Gauges, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14596, https://doi.org/10.5194/egusphere-egu21-14596, 2021.

Manuella Fagundes and Felipe Geremia-Nievinski

Contemporary sea level rise renders tide gauging essential in support of adaptation and mitigation strategies and to minimize its economic and societal impacts. Global Navigation Satellite System Reflectometry (GNSS-R) has been widely demonstrated for coastal sea level monitoring. One particular configuration of GNSS-R, called GNSS multipath reflectometry (GNSS-MR), is based on the combined tracking of direct and reflected radio waves against a single signal replica. The most common observable in GNSS-MR is the signal-to-noise ratio (SNR), which records the constructive/destructive interference pattern arising from the superposition of the two coherent ray paths. Recently we reported the development of a complete hardware and software system for SNR-based GNSS-R. We made it freely available as open-source based on low-cost commercial off-the-shelf components. We have deployed multiple working units of the sensor in the field, where they have operated uninterruptedly 24/7 for years, having resisted severe weather conditions. Initial validation was done by a lake (30.0277° S, 51.2287° W) for 317 days by comparison to a co-located radar-type tide gauge. Statistics confirmed that the sensor can retrieve water level with a very high correlation (0.989) and centimeter-level RMSE (2.9 cm). Here we report further coastal validation results of our GNSS-R sensor. The experiment was setup in a port (28.232019° S, 48.651064° W) with several co-located tide gauges within 100-m distance, including a radar sensor with 5-minute update interval and millimeter numerical resolution. We analyzed the time series of one week (June 19-25, 2019), and found a correlation of 0.885 and RMSE of 8.0 cm. We should emphasize this is the instantaneous sea level results and results for daily mean sea level would be improved. Although the location is sheltered from breaking waves, wind-driven waves are much greater, compared to the initial lake experiment. The increased surface roughness affects the coherence of radio wave reflections, which may eventually hamper the interferometric superposition principle, essential in GNSS-MR. This is part of ongoing validation efforts to quantity how correlation and RMSE in sea level altimetry are degraded due to the above error sources.

How to cite: Fagundes, M. and Geremia-Nievinski, F.: Further validation of an open-source low-cost GNSS-R remote sensor for coastal sea level altimetry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8064, https://doi.org/10.5194/egusphere-egu21-8064, 2021.

Ole Baltazar Andersen, Adil Abulaitijiang, Shengjun Zhang, and Stine Kildegaard Rose

A new Mean Sea Surface (DTU21MSS) for referencing sea level anomalies from satellite altimetry is presented. The major new advance leading up to the release of this MSS the use of 5 years of Sentinel-3A and an improved 10 years Cryosat-2 LRM+SAR+SARin record including retracked altimetry in Polar regions using the SAMOSA+ physical retracker via the ESA GPOD facility.

A new processing chain with updated editing and data filtering has been implemented. The filtering implies, that the 20Hz sea surface height data are filtered using the Parks-McClellan filter to derive 1Hz. This has a clear advantage over the 1 Hz boxcar filter in not introducing sidelobes degrading the MSS in the 10-40 km wavelength band. Similarly, the use of consistent ocean tide model for the Mean sea surface improves the usage of sun-syncronous satellites in high latitudes.

The presentation will also focus on the difficult issues to consolidating Cryosat-2 and Sentinel-3 onto a past 20 year mean sea surface. This is implemented using simultaneous estimation of the mean, sea level trend and annual and semi-annual variations in sea level.  

How to cite: Andersen, O. B., Abulaitijiang, A., Zhang, S., and Rose, S. K.: A new high resolution Mean Sea Surface (DTU21MSS) for improved sea level monitoring, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16084, https://doi.org/10.5194/egusphere-egu21-16084, 2021.