Displays

Harmony, an Earth Explorer 10 candidate mission, consists of two receive-only Synthetic Aperture Radar (SAR) satellites using Sentinel-1D as the illuminator. The mission will switch between close formation phases and StereoSAR phases, dedicated to relative surface elevation and relative surface motion respectively. Interferometric observations of the ocean have, in the past, been hindered by the quick temporal decorrelation of the sea surface; a result of the along-track baseline that often comes with the cross-track baseline necessary for interferometry. Specialised SAR systems aiming to observe the oceans need to account for the decorrelation of the surface. SWOT overcomes the issue by fixing the two SAR antennas to physically eliminate their along-track separation. Due to the squinted, bistatic nature of the formation, Harmony can act as an altimeter, observing relative sea-surface heights (SSH) over unprecedented wide swaths. Hence, the mission promises to have highly coherent observations of the sea surface, leading to accurate surface elevation measurements. The wide swath will enable the recovery of mesoscale features of the ocean surface in a single pass. We will present the first results of the performance analysis of the mission's observations of elevation over the oceans. The effect of errors, namely the residual Doppler, baseline errors and sea-state bias, on the observations will also be discussed.

How to cite: Theodosiou, A., Lopez Dekker, P., Kleinherenbrink, M., and Mulder, G.: Harmony's ocean elevation measurements: potential and performance, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19069, https://doi.org/10.5194/egusphere-egu2020-19069, 2020.

The oceanic fine scales are highly energetic features (eddies, fronts, meanders, filaments) with relatively short lifetimes (days/weeks to months). Due to their associated strong gradients in physical and biogeochemical properties, they crucially affect ocean physics and ecology with potential impacts at the climate scale. The temporal scale associated with these horizontal and vertical fine scales is the same as many important ecological processes including phytoplankton growth and competition. This temporal resonance is one of the reasons behind the fine-scale variability appearing in the marine ecosystems structure and related domains, including biogeochemical cycles, trophic food-webs up to resources and biodiversity.

Over the past few decades, great progresses have been made in characterizing fine scales through modeling. Remote sensing is also improving rapidly in terms of resolution, with landmark missions like SWOT expected to be operational very soon (2022). However, in situ sampling remains challenging due to the difficulties of mapping a large domain covering the length of a filament or the diameter of an eddy (~100km) at high spatio-temporal frequency (~km and ~daily).

Here we present some sampling strategies we are developing for addressing this issue by combining remote sensing and in situ multi-platform high-resolution sampling of physical, biogeochemical and biological variables. 

In a series of campaigns in the Mediterranean Sea (OSCAHR 2015, PROTEVSMED-SWOT 2018, FUMSECK 2019), satellite-based adaptive and Lagrangian strategies proved to be successful to target and follow fine scale structures in situ. When paired with in situ biological measurements, like automated cytometry, these strategies highlight the important role of the fine scales in structuring the phytoplankton community by acting as fluid dynamical barriers and biodiversity hot-spots.

To extend these observations to other regions, we support an international coordinated experimental effort of the fine scale community at several sites all around the world to fully exploit the great opportunities offered by the launch of the satellite SWOT.

How to cite: Doglioli, A. M., Grégori, G., d'Ovidio, F., Petrenko, A. A., Barrillon, S., Fuda, J.-L., Thyssen, M., Tzortzis, R., Izard, L., Dumas, F., Garreau, P., Pascual, A., Marrec, P., Rousselet, L., Bhairy, N., Cyr, F., Tedetti, M., Berline, L., and Carlotti, F.: Combining remote sensing and in situ observations to study the physical-biological coupling at fine scale: recent Mediterranean campaigns and outlook., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7356, https://doi.org/10.5194/egusphere-egu2020-7356, 2020.

There has been a steady increase of interest in mining of deep-sea mineral in the Clarion-Clipperton Zone in the eastern Pacific Ocean during the last decade. This region is known as one of the most eddy-rich regions, typically at the mesoscale, which are mainly generated by the intense wind burst channelled through gaps in the Sierra Madre mountains in Central America. Here we use a combination of satellite and in situ observations to evaluate the relationship between deep-sea current variability at the region of potential future mining and Eddy Kinetic Energy (EKE) at the vicinity of gap winds.

A geometry-based eddy detection algorithm has been applied to altimetry sea surface height data for a period of 24 years from 1993 to 2016 in order to study the main characteristic parameters and the spatio-temporal variability of mesoscale eddies in the north-eastern tropical Pacific Ocean. Significant differences between the characteristics of eddies with different polarity (cyclonic vs. anti-cyclonic) were found. For eddies with lifetimes longer than 7 days, the total number of cyclonic eddies exceeds that of anticyclonic eddies by about 16%. However, anticyclonic eddies are larger in size and greater in vorticity, and survive longer in the ocean than cyclonic eddies (often 90 days or more). Besides the polarity of eddies, the location of eddy formation should be taken into consideration for investigating the variability of current velocity at deep ocean region as we found eddies originated by Tehuantepec (TT) gap wind lasting longer in the ocean and travel farther distances in different direction compare to eddies emanated from Papagayo gap wind. Long-lived anticyclonic eddies generated at the vicinity of the TT gap wind are observed to travel long distances up to 4500 km far offshore west of 110° W.

EKE anomalies observed in the surface of the interior ocean at a distance of ca. 2500 km from the coast correlate with the seasonal variability of EKE in the region of the TT gap winds with a time lag of 5-6 months. This is consistent with the required time for an anticyclone eddy with the average translation speed of 12 cm/s to reach the ocean interior. Significant seasonal variability of deep ocean current velocity recorded by ocean-bottom moorings at depth of 4100 m likely reflecting the energy transfer of surface EKE generated by the gap winds to the deep ocean is also found. On an interannual scale, a significant relationship between cyclonic eddy characteristics and El-Niño Southern Oscillation was found, whereas no robust correlation was detected for anticyclonic eddies.

How to cite: Purkiani, K., Paul, A., Vink, A., Walter, M., and Schulz, M.: New evidences of seasonal deep ocean current variability in the north-eastern tropical Pacific Ocean impacted by remote gap winds, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15821, https://doi.org/10.5194/egusphere-egu2020-15821, 2020.

Dynamical processes at the ocean surface are of high interest because they control the exchange processes between ocean and atmosphere. Furthermore, ocean surface drift determines the dispersion of heat, salt and material such as harmful substances or plastic litter. Still the measurement of ocean surface currents is a challenge because of wave and wave-breaking processes. Here we demonstrate the usefulness of TerraSAR-X/TanDEM-X data to determine ocean surface currents, wave and wind fields. Up to now there are no spatially resolved ocean surface currents measurements available, so that for the validation of surface currents a combined SAR and hydrodynamic modeling methodology is applied. Ocean surface currents are derived from SAR Along-track Interferometry, and the hydrodynamic model is a coupled wave sea ice-ocean model of the Baltic Sea. The model is driven by ERA-Interim atmospheric reanalysis data. Hydrodynamic model data are also used to support the geophysical interpretation of the multiparametrical information of the ocean surface provided by SAR.

How to cite: Lehmann, A. and Suchandt, S.: Ocean surface dynamics derived from TerraSAR-X/TanDEM-X and hydrodynamic modeling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9454, https://doi.org/10.5194/egusphere-egu2020-9454, 2020.

Hurricane Dorian was the first major hurricane of the 2019 Atlantic season. Dorian formed on August 24, from a tropical wave in the Central Atlantic. Over next several days it gradually strengthened and become a hurricane on August 28^th. Dorian rapidly intensified and reached Category 4 status on August 31st. Next day, Dorian reached Category 5 intensity, with maximum sustained winds of 185 mph while making landfall in Elbow Cay, Bahamas first and then another one on Grand Bahamas several hours later. Dorian stalled just north of Grand Bahamas for about a day. It was the strongest known tropical system to impact the Bahamas. A combination of cold water upwelling and an eyewall replacement cycle weakened Dorian to a Category 2 hurricane and it began to move slowly towards the north-northwest. In the early hours of September 6, Dorian weakened to Category 1 intensity as it picked up speed and turned northeast.

Hurricane Dorian was exceptionally well sampled by NOAA and Airforce hurricane hunter aircrafts. Large dataset of SFMR surface winds collected during the flights as well as dropsondes documented changes in Dorian’s wind field throughout its duration. AMSR-2 and SMAP radiometer, ASCAT and ScatSat-1 scatterometers as well as CYGNSS GNSS-R wind observations over Dorian were collected at NOAA for analysis. Understanding the differences between remote sending technologies utilized for wind observations is crucial for NOAA operational users. Large and rapid changes in Dorian’s wind field represent an interesting case study where strengths and weaknesses of different wind measurement technologies can be assessed and compared. SFMR wind measurements represent common surface ground truth that can be utilized to bring all these different measurements together.

While SMAP L-band radiometer sensitivity to high winds is not affected by rain its low measurement resolution smears wind field variations in the inner core depending on the storm size. AMSR-2 wind retrievals obtained from C-band measurements have somewhat larger measurement resolution and ability to provide more insight of the inner core wind variations although they can be enhanced by rain signature. These measurements are being utilized for 50 and 64kts wind radii. C-band scatterometer measurements such as ASCAT, even though attenuated by heaviest rain, provide both wind speed and direction information making then invaluable for early detection of tropical depression formation. As a matter of fact ASCAT measurements were used by NHC to initiate warnings for Dorian on August 24^th, 2019: “Two ASCAT passes between 1200-1300Z this morning indicated that the system had a closed circulation and surface winds of at least 30 kt, and that is the intensity set for this advisory.” Ku-band scatterometer ScatSat-1 is most impacted by rain and its winds can measurably differ from ASCAT in the areas of heavy rain. Understanding the thresholds when this occurs is imperative for successful utilization of ScatSat-1 winds in NOAA operations. Finally winds from CYGNSS GNSS-R measurements are assessed utilizing NOAA CYGNSS wind product. Examples of all measurements during Dorian duration will be presented and compared with aircraft observations.

How to cite: Jelenak, Z., Jelenak, Z., Chang, P., Sapp, J., Alsweiss, S., Soisuvarn, S., Said, F., Park, J., and Jelenak, Z.: Scatterometer, Radiometer and GNSS-R observations of Hurricane Dorian , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12198, https://doi.org/10.5194/egusphere-egu2020-12198, 2020.

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. Using GNSS-R data collected by the UK TechDemoSat-1 (TDS-1) between 2014 and 2018, the National Oceanography Centre (NOC) has developed a GNSS-R wind speed retrieval algorithm called the Calibrated Bistatic Radar Equation (C-BRE), which now features updated data quality control mechanisms including flagging of radio frequency interference (RFI) and sea-ice detection based on the GNSS-R waveform. Here we present an assessment of the performance of the latest NOC GNSS-R ocean wind speed and sea-ice products (NOC C-BRE v1.0) using validation data from the ECMWF ERA-5 re-analysis model output. Results show the capability of spaceborne GNSS-R sensors for accurate wind speed retrieval and sea-ice detection. Additionally, ground-processed Galileo returns collected by TDS-1 are examined and the geophysical sensitivity of reflected Galileo data to surface parameters is investigated. Preliminary results demonstrate the feasibility of spaceborne GNSS-R instruments receiving a combination of GNSS signals transmitted by multiple navigation systems, which offers the opportunity for frequent, high-quality ocean wind and sea-ice retrievals at low relative cost. Other advancements in GNSS-R technology are represented by future mission concepts such as HydroGNSS, a proposed ESA Scout mission opportunity by SSTL offering support for enhanced retrieval capabilities exploiting dual polarisation, dual frequency, and coherent reflected signal reception.

How to cite: Hammond, M., Foti, G., Gommenginger, C., Srokosz, M., Unwin, M., and Rosello, J.: NOC GNSS-R Global Ocean Wind Speed and Sea-Ice Products, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18916, https://doi.org/10.5194/egusphere-egu2020-18916, 2020.

How to cite: Corgnati, L., Mantovani, C., Rubio, A., Mader, J., Reyes, E., Asensio Igoa, J. L., Novellino, A., Gorringe, P., and Griffa, A.: The European HF Radar Node: focal point to promote land-based remote sensing of coastal surface currents and its applications, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1537, https://doi.org/10.5194/egusphere-egu2020-1537, 2020.

Over the next decade, new satellite sensors are being developed or proposed to enhance our global observations of ocean surface parameters, many aiming at finer scale processes. These sensors include the new generation of satellite altimeters with finer resolution in alongtrack SAR mode or with swath SAR-interferometry; missions to observe total surface currents and wind-wave interactions; high resolution sea surface temperature and salinity; and ocean color, polarimetry and lidar missions for biogeochemistry, among others. Key observational challenges are to have finer-resolution across open ocean fronts, to observe the surface dynamical interactions over multiple scales, and to extend our satellite observing systems into the coastal and polar regions. Understanding smaller-scale variability will have benefits for climate, ocean operations and ocean health.

This presentation will give an overview of the OceanObs19 discussions on the opportunities and priorities for new satellite ocean sensors for the upcoming decade.

How to cite: Morrow, R. and the OceanObs19 Satellite Innovation team: Innovation in Satellite Sensors for Ocean Observations : An OceanObs19 overview, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7571, https://doi.org/10.5194/egusphere-egu2020-7571, 2020.

Sea Surface Salinity (SSS) is an Essential Climate Variable (ECV) that plays a fundamental role in the density-driven global ocean circulation, the water cycle, and climate. The satellite SSS observation from the Soil Moisture and Ocean Salinity (SMOS), Aquarius, and Soil Moisture Active Passive (SMAP) missions have provided an unprecedented opportunity to map SSS over the global ocean since 2010 at 40-150km scale with a revisit every 2 to 3 days. This observation capability has no historic precedent and has brought new findings concerning the monitoring of SSS variations related with climate variability such as El Niño-Southern Oscillation, Indian Ocean Dipole, and Madden-Julian Oscillation, and the linkages of the ocean with different elements of the water cycle such as evaporation and precipitation and continental runoff. It has enhanced the understanding of various ocean processes such as tropical instability waves, Rossby waves, mesoscale eddies and related salt transport, salinity fronts, hurricane haline wake, river plume variability, cross-shelf exchanges. There are also emerging use of satellite SSS to study ocean biogeochemistry, e.g. linked to air-sea CO₂ fluxes.

Following the success of the initial oceanographic studies implementing this new variable, the European Space Agency (ESA) Climate Change Initiative CCI+SSS project (2018-2020) aims at generating improved calibrated global SSS fields over 10 years period (2010-2019) from all available satellite L-band radiometer measurements, extended at regional scale to 2002-2019 from C-band radiometer measurements. It fully exploits the ESA/Earth explorer SMOS mission complemented with SMAP and AQUARIUS satellite missions. The project gathers teams involved in earth observation remote sensing, in the validation of satellite data and in climate variability study. In this presentation, we will present the first CCI+SSS product released to the scientific community (https://catalogue.ceda.ac.uk/uuid/9ef0ebf847564c2eabe62cac4899ec41). The comparisons with in situ ground truth indicate much better performances than the ones obtained with a single satellite data product, with global rmsd against in situ references of 0.16 pss. Large scale interannual variability is successfully reproduced and SSS variability in very variable regions like the Bay of Bengale and in river plumes in the Atlantic Ocean is very satisfactory, confirming the usefulness of these products for scientific studies. Nevertheless we also identify some caveats that will be discussed as well as the ways envisaged to resolve part of them in the next version of the product to be delivered publicly in Summer 2020.

The ESA CCI+SSS consortium gathers scientists and engineers from various European research institutes and companies (LOCEAN/IPSL, LOPS, University of Hamburg, NOC, ICM, ARGANS, ACRI-st, ODL) and is conducted in collaboration with US colleagues from NASA and Remote Sensing System.

How to cite: Boutin, J., Reul, N., Koehler, J., Martin, A., Catany, R., and Salinity Consortium, C. C. I.: Overview of the CCI+SSS project, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7513, https://doi.org/10.5194/egusphere-egu2020-7513, 2020.

Accurate satellite-based sea surface salinity (SSS) fields would address some gaps of knowledge and benefit the understanding of Baltic Sea salinity dynamics.  In particular, these fields can contribute to the monitoring of long-term salinity changes and to the detection of periods with anomalous salinity. These products can also be very useful as initial fields and validation data for improving the existing numerical models.

The Baltic Sea is one of the most challenging regions for the retrieval of SSS from L-band satellite measurements. Nowadays, 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 SMOS TB particularly over semi-enclosed seas, such as the high contamination by Radio-Frequency Interference (RFI) sources 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. Salinity and temperature values are very low in this basin, which implies that dielectric constant models are not fully tested in such conditions. In the recent years, the Barcelona Expert Center team has been working on the development of innovative algorithms for improving the quality of SMOS TB and SSS retrievals dealing with the main processing issues. 

In the context of the ESA Baltic+ Salinity Dynamics project (https://balticsalinity.argans.co.uk/), these methodologies have been adapted and consolidated towards the generation of the first  regional SMOS SSS product (2011-2020) that would suit to the needs of the Baltic research community. Very recently, the first version of the Baltic+ SSS product has been produced (3-year series) and is currently under validation against in-situ measurements. The quality assessment of the SSS product in the Baltic Sea is also an issue and its representativeness must be carefully assessed. The basin is strongly stratified and then, the differences between SMOS measurements (first centimeters) and in-situ observations (few meters depth) can be noticeable. Differences are more probable during ice melting and high runoff events in spring where there might be a freshwater layer at the top shallow surface. Feedback from the users will help identifying the limitations of the product. Additional technical developments will be addressed to meet the requirements of the communities working in the study of Baltic processes. 

We will present at the conference the Baltic+ SSS v1 product and its added-value with respect to other existing EO-based datasets. The potential scientific impact of this satellite SSS product in advancing on-going regional research initiatives like the Baltic Earth Working Group on Salinity dynamics will be discussed.

How to cite: Gonzalez-Gambau, V., Olmedo, E., Gonzalez-Haro, C., Turiel, A., Martinez, J., Gabarro, C., 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 and its oceanographic added-value, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16939, https://doi.org/10.5194/egusphere-egu2020-16939, 2020.

A method to reconstruct missing data in satellite data using a neural network is presented. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images. However, it is unclear how to handle missing data (or data with variable accuracy) in a neural network when using incomplete satellite data in the training phase. The present work shows a consistent approach which uses essentially the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The approach is motivated by the way models and observations are combined in the frame of data assimilation. The neural network is trained by maximizing the likelihood of the observed value. The corresponding error variances are estimated during training and do not need to be known a priori. The approach, called DINCAE (Data-Interpolating Convolutional Auto-Encoder) is applied to a relatively long time-series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data Interpolating Empirical Orthogonal Functions), a method to reconstruct missing data based on an EOF decomposition. The reconstruction error of both approaches is computed using cross-validation and in situ observations from the World Ocean Database. DINCAE results have lower error, while showing higher variability than the DINEOF reconstruction. The resulting error estimates are also validated using the cross-validation data and they follow closely the expected Gaussian distribution.

How to cite: Barth, A., Alvera Azcárate, A., Licer, M., and Beckers, J.-M.: A convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations (DINCAE), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9414, https://doi.org/10.5194/egusphere-egu2020-9414, 2020.

Starting from 2021, Surface  Water Ocean Topography (SWOT) satellite altimetry mission will provide an unprecedented amount of Sea Surface Height (SSH) measurements. In addition to allowing for a higher spatial resolution, SWOT will deliver two-dimensional horizontal SSH data thanks to its wide swath capacities, which is a remarkable leap compared to conventional current altimeters.

With the aim of extracting a clean SSH signal from the SWOT measurements, several challenges are expected to be encountered. In this work, we focus on filtering the footprints of Internal Gravity Waves (IGWs), this is of high interest for physical oceanographers who seek to better understand mesoscale and submesoscale ocean physics.

Thanks to recent developments in ocean numerical simulation, we can now have access to a considerable amount of simulation data with exceptional high spatial resolutions up to 1/60° and hourly temporal resolution. Here, we benefit from an advanced North Atlantic simulation of the ocean circulation (eNATL60) that models tidal motions, and design a supervised machine learning experiment that aims to test several techniques for filtering IGWs.

In particular, we show that deep convolutional neural networks are a relevant candidates for this task and presents promising results with regard to conventional linear filtering techniques. We also show how our method can be adapted to the context of the fast-sampling phase of SWOT, and can also take advantage from the presence of additional data such as Sea Surface Temperature.

How to cite: Lguensat, R., Fablet, R., Le Sommer, J., Metref, S., and Cosme, E.: Filtering tide-generated internal waves using Convolutional Neural Networks , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17404, https://doi.org/10.5194/egusphere-egu2020-17404, 2020.

Coastlines globally are increasingly being illuminated with Artificial Light At Night (ALAN) from various urban infrastructures such as houses, offices, piers, roads, ports and dockyards. Artificial sky glow can now be detected above 22% of the world’s coasts nightly and will dramatically increase as coastal human populations more than double by the year 2060. One of the clearest demonstrations that we have entered another epoch, the urbanocene, is the prevalence of ALAN visible from space.

Photobiological life history adaptations to the moon and sun are near ubiquitous in the surface ocean (0-200m), such that cycles and gradients of light intensity and spectra are major structuring factors in marine ecosystems. The potential for ALAN to reshape the ecology of coastal habitats by interfering with natural light cycles and the biological processes they inform is increasingly recognized and is an emergent focus for research.

In this paper we derive a methodology to quantify and map the depths to which biologically relevant ALAN penetrates in the marine environment.  We use two satellite derived global datasets to achieve this: an artificial night sky brightness world atlas (Falchi et al., 2016) and an in-water Inherent Optical Property (Lee et al., 2002) dataset derived from ESA’s Ocean Colour Climate Change Initiative (OC-CCI https://www.oceancolour.org/).  These primary datasets are both used in conjunction with in-situ derived measurements and radiative transfer modelling in order to quantify the critical depth (Zc) to which biologically relevant ALAN penetrates throughout the global ocean’s estuarine, coastal and near shore regions, in particular the area defined by an individual country’s Exclusive Economic Zone. 

How to cite: Smyth, T., Davies, T., and McKee, D.: The global extent of artificial light pollution in the marine environment , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2415, https://doi.org/10.5194/egusphere-egu2020-2415, 2020.

Satellite altimetry is an important part of the Global Geodetic Observing System providing precise information on sea level on different spatial and temporal scales. Moreover, satellite altimetry-derived dynamic ocean topography heights enable the computation of ocean surface currents by applying the well-known geostrophic equations. However, in polar regions, altimetry observations are affected by seasonally changing sea-ice cover leading to a fragmentary data sampling.

In order to overcome this problem, an ocean model is used to fill in data gaps. The aim is to obtain a homogeneous ocean topography representation that enables consistent investigations of ocean surface current changes. For that purpose, the global Finite Element Sea-ice Ocean Model (FESOM) is used. It is based on an unstructured grid and provides daily water elevations with high spatial resolution.

The combination is done based on a Principal Component Analysis (PCA) after reducing both quantities by their constant and seasonal signals. In the main step, the most dominant spatial patterns of the modeled water heights as provided by the PCA are linked with the temporal variability of the estimated dynamic ocean topography elevations from altimetry. At the end, the seasonal signal as well as the absolute reference from altimetry is added back to the data set.

This contribution describes the combination process as well as the generated final product: a daily, more than 17 years covering dataset of geostrophic ocean currents. The combination is done for the marine regions Greenland Sea, Barents Sea and the Fram Strait and includes sea surface height observations of the ESA altimeter satellites ERS-2 and Envisat. In order to evaluate the combination results, independent surface drifter observations, corrected for a-geostrophic velocity components, are used.

How to cite: Müller, F. L., Dettmering, D., Wekerle, C., Schwatke, C., Passaro, M., Bosch, W., and Seitz, F.: Ocean Surface Currents in the northern Nordic Seas from a combination of multi-mission satellite altimetry and numerical modeling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4412, https://doi.org/10.5194/egusphere-egu2020-4412, 2020.

Temperature conditions of development juvenile NEA cod in the Barents sea for 1998-2015 on the basis of satellite data

Vanyushin G. P., Bulatova T. V.

Russian Federal Research Institute of Fisheries and Oceanography (VNIRO)

107140 17, V. Krasnoselskaya str., Moscow

tel: 8(499)264-01-33, fax: 8(499)264-91-87,

e-mail: ladimon@mail.ru

Abstract

The paper considers the real temperature conditions in the main spawning area of North-East Arctic cod in the Norwegian sea and the development of its juveniles in the Barents sea in the periods from March to October 1998-2015. Here was taken as a principle the analysis of materials Bank mean weekly maps of sea surface temperature (SST) built on complex process: infrared digital data from metrological satellites of the series "NOAA" and quasisynchronous temperature data "in situ" from ships, buoys and coastal stations. A continuous series of indicators on temperature variability in the surface layer of sea water in coastal zone of the Norwegian sea during spawning periods and later on during the early ontogenesis of juvenile cod in the Barents sea  allowed to establish the dynamics of interannual seasonal temperature trends on a mesoscale period of time (1998-2015). This made it possible to assess the indirect impact of temperature conditions on the prospect of survival and, accordingly, the number of juvenile cod in the first year of its life after spawning – the most important stage in the life cycle of a new generation of cod. The paper presents calculations of monthly and seasonal average values of SST and SST anomalies in the Norwegian and Barents seas, shows the interannual seasonal dynamics of these characteristics. Given for these years, the results of the comparative analysis between: seasonal values of temperature in the water surrounding the Lofoten Islands (March-April – time of the main spawning) and in the water of the Barents sea (May-October - time of the early onthogenesis of juvenile cod) and professional expert estimates the number of yearlings cod. The relationship between these statistical data was positive and about equal to R= + 0,67. Information on the number of generations of cod at different stages of its life cycle was taken from the annual reports of the Arctic Fisheries Working Group ICES.

Keywords: satellite monitoring, sea surface temperature (SST), the  Northeast Arctic cod, main spawning and habitat waters, yearlings of the cod.

How to cite: Vanyushin, G. and Bulatova, T.: Temperature conditions of development juvenile NEA cod in the Barents sea for 1998-2015 on the basis of satellite data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1350, https://doi.org/10.5194/egusphere-egu2020-1350, 2020.

In this study, Sea Surface Salinity (SSS) Level 3 (L3) daily product derived from Soil Moisture Active Passive (SMAP) during the year 2016, was validated and compared with SSS daily products derived from Soil Moisture and Ocean Salinity (SMOS) and in-situ measurements. Generally, the Root Mean Square Error (RMSE) of the daily SSS products is larger along the coastal areas and at high latitudes and is smaller in the tropical regions and open oceans. Comparisons between the two types of daily satellite SSS product revealed that the RMSE was higher in the daily SMOS product than in the SMAP, whereas the bias of the daily SMOS was observed to be less than that of the SMAP when compared with Argo floats data. In addition, the latitude-dependent bias and RMSE of the SMAP SSS were found to be primarily influenced by the precipitation and the Sea Surface Temperature (SST).Then, aregression analysis method which has adopted the precipitation and SST data was used to correct the larger bias of the daily SMAP product. It was confirmed that the corrected daily SMAP product could be used for assimilation in high-resolution forecast models, due to the fact that it was demonstrated to be unbiased and much closer to the in-situ measurements than the original uncorrected SMAP product.

How to cite: Qin, S.: Validation and correction of sea surface salinity retrieval from SMAP, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4389, https://doi.org/10.5194/egusphere-egu2020-4389, 2020.

Since 2010, the Soil Moisture and Ocean Salinity (SMOS) satellite mission monitors the earth emission at L-Band, providing the longest time series of Sea Surface Salinity (SSS) from space over the global ocean. However, retrieving SSS at high latitudes with a reasonable accuracy remains challenging, in particular due to the low sensitivity of L-Band radiometric measurements to SSS in cold waters and to the contamination of SMOS measurements by the vicinity of continents and sea ice as well as the presence of Radio Frequency Interferences. In this paper, we assess the quality of weekly SSS fields derived from swath-ordered instantaneous SMOS SSS (so called Level 2) distributed by the European Space Agency. These products are filtered according to new criteria. We use the pseudo-dielectric constant retrieved from SMOS brightness temperatures to filter SSS pixels polluted by sea ice. We identify that the dielectric constant model and the sea surface temperature auxiliary parameter used as prior information in the SMOS SSS retrieval are significant sources of uncertainty. We develop a novel correction methodology accordingly.

SSS Standard deviation of differences (STDD) between weekly SMOS SSS and in-situ near surface salinity significantly decrease after applying the SSS correction, from 1.46 pss to 1.26 pss. The correlation between new SMOS SSS and in-situ near surface salinity reaches 0.94. SMOS estimates better capture SSS variability in the Arctic Ocean in comparison to TOPAZ reanalysis (STDD = 1.86 pss), particularly in river plumes fresher by about 10 pss than surrounding waters. Furthermore, comparisons with in-situ measurements ranging from 1 to 11 m depths identify huge vertical stratification in fresh regions. This emphasizes the need to consider in-situ salinity as close as possible to the sea surface when validating L-band radiometric SSS which are representative of the first top centimeter.

How to cite: Supply, A., Boutin, J., Vergely, J.-L., Kolodziejczyk, N., Reverdin, G., Reul, N., and Tarasenko, A.: New insights into SMOS Sea Surface Salinity retrievals in the Arctic Ocean., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7472, https://doi.org/10.5194/egusphere-egu2020-7472, 2020.

The European Space Agency (ESA) Climate Change Initiative 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 first year has produced a 9-year dataset (2010-2018) 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 dataset is available at https://catalogue.ceda.ac.uk/uuid/9ef0ebf847564c2eabe62cac4899ec41.

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 0.16 pss and 0.10 pss in areas with low variability. 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 CCI SSS products uncertainty have been validated against references and show good agreement as long as the spatial representativeness is considered in presence of strong spatial gradients in salinity.

How to cite: Martin, A., Guimbard, S., Boutin, J., Reul, N., and Catany, R.: Global validation of the ESA CCI+SSS products, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11683, https://doi.org/10.5194/egusphere-egu2020-11683, 2020.

ESA SMOS satellite [1] has been providing first-ever Sea Surface Salinity (SSS) measurements from space for over a decade now. Until recently, inherent algorithm limitations or external interferences hampered a reliable provision of satellite SSS data in semi-enclosed basin such as the Mediterranean Sea. This has been however overcome through different strategies in the retrieval scheme and data filtering approach [2, 3]. This recent capability has been in turn used to infer the spatial and temporal distribution of Total Alkalinity (TA - a crucial parameter of the marine carbonate system) in the Mediterranean, exploiting basin-specific direct relationships existing between salinity and TA.

Preliminary results [4] focused on the differences existing in several parameterizations [e.g, 5] relating these two variables, and how they vary over a seasonal to interannual timescale.

Currently, to verify the consistency and accuracy of the derived products, these data are being validated against a proper ensemble of in-situ, climatology and model outputs within the Mediterranean basin. An error propagation exercise is also being planned to assess how uncertainties in the satellite data would translate into the final products accuracy.

The resulting preliminary estimates of Alkalinity in the Mediterranean Sea will be linked to the overall carbonate system in the broader context of Ocean Acidification assessment and marine carbon cycle.

References:

[1] J. Font et al., "SMOS: The Challenging Sea Surface Salinity Measurement From Space," in Proceedings of the IEEE, vol. 98, no. 5, pp. 649-665, May 2010. doi: 10.1109/JPROC.2009.2033096

[2] Olmedo, E., J. Martinez, A. Turiel, J. Ballabrera-Poy, and M. Portabella,  “Debiased non-Bayesian retrieval: A novel approach to SMOS Sea Surface Salinity”. Remote Sensing of Environment 193, 103-126 (2017).

[3] Alvera-Azcárate, A., A. Barth, G. Parard, J.-M. Beckers, Analysis of SMOS sea surface salinity data using DINEOF, In Remote Sensing of Environment, Volume 180, 2016, Pages 137-145, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2016.02.044.

[4] Sabia, R., E. Olmedo, G. Cossarini, A. Turiel, A. Alvera-Azcárate, J. Martinez, D. Fernández-Prieto, Satellite-driven preliminary estimates of Total Alkalinity in the Mediterranean basin, Geophysical Research Abstracts, Vol. 21, EGU2019-17605, EGU General Assembly 2019, Vienna, Austria, April 7-12, 2019.

[5] Cossarini, G., Lazzari, P., and Solidoro, C.: Spatiotemporal variability of alkalinity in the Mediterranean Sea, Biogeosciences, 12, 1647-1658, https://doi.org/10.5194/bg-12-1647-2015, 2015.

How to cite: Sabia, R., Olmedo, E., Cossarini, G., Alvera-Azcárate, A., Gonzalez-Gambau, V., and Fernández-Prieto, D.: SMOS-based estimation and validation of Total Alkalinity in the Mediterranean basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20753, https://doi.org/10.5194/egusphere-egu2020-20753, 2020.

Since 2010, space missions dedicated to Sea Surface Salinity (SSS) have been providing observations with almost complete coverage of the global ocean and a resolution of about 45 km every 3 days. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission was the first orbiting radiometer to collect regular SSS observations from space. The Aquarius and SMAP (Soil Moisture Active-Passive) missions of the National Aeronautics and Space Administration (NASA) then reinforced the SSS observing system between mid-2011 and mid-2015 and since mid-2015, respectively.

Using the most recent SSS Climate Change Initiative project dataset merging data from the 3 missions, this study investigates the SSS signal associated with mesoscale eddies in the Southern Ocean. Eddies location and characteristics are obtained from the daily v3 mesoscale eddy trajectory atlas produced by CLS. SSS anomalies along the eddies journey are computed and compared to Sea Surface Temperature (SST) anomalies (v4 Remote Sensing Systems) as well as the SubAntarctic Front (SAF) position (CTOH, LEGOS). The vertical structure of the eddies is further investigated using profiles from colocated Argo autonomous floats. 

This study highlights a robust signal in SSS depending on both the eddies rotation (cyclone/anticyclone) and latitudinal position with respect to the SAF. Moreover, this dependence is not found in SST. These observations reveal oceanic the interaction of eddies with the larger scale ocean water masses. SSS and SST anomalies composites indeed show different patterns either bi-poles linked with horizontal stirring of fronts, mono-poles from trapping water or vertical mixing changes, or a mix of the two.

This analysis gives strong hints for the erosion of subsurface waters, such as mode waters, induced by enhanced mixing caused by the deep-reaching eddies of the southern ocean.

How to cite: Hasson, A., Pegliasco, C., Boutin, J., and Morrow, R.: Colder and smaller : 10 years of observations of surface salinity by SMOS, Aquarius and SMAP to study mesoscale eddies in the Southern Ocean, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21449, https://doi.org/10.5194/egusphere-egu2020-21449, 2020.

The Centre Aval de Traitement des Données SMOS (CATDS), developped by the CNES in collaboration with the CESBIO and IFREMER, produces and continuously improves SMOS sea surface salinity (SSS) products.

The aim of this poster is to present the last version of CATDS L3 products developed by the LOCEAN CATDS Expertise Center (CEC-LOCEAN debiased v4, https://www.catds.fr/Products/Available-products-from-CEC-OS/CEC-Locean-L3-Debiased-v4), and to highlight its main improvements with respect to previous version 3.

The L3 products are available for 9-day and 18-day Gaussian averaging. Both versions 3 and 4 contain a bias correction based on internal consistency of SMOS SSS retrieved in various locations across swath, and on seasonal variability of salinity. The main evolutions of version 4 consist in refining the absolute correction methodology, limiting wind speed to 16m/s, add a refined filtering for sea ice and radio frequency contamination based on SMOS retrieved pseudo dielectric constant, the so-called ACARD (Waldteufel et al. 2004) and an improved sea surface temperature (SST) correction in cold waters based on Dinnat et al. (2019) observed dependency.

Improvements with respect to version 3 are assessed through systematic validation that consists in two main stages: (1) Comparison with respect to in-situ measurements (repetitive ship transects across Atlantic and Arctic regions, and Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) moorings); (2) Comparison with the In-Situ Analysis System (ISAS) monthly fields (Kolodziejczyk, 2017), in terms of both mean spatial maps and time series of key statistics parameters. The key statistics parameters are computed both over the global ocean and for individual areas of interest. Thus, both the mean spatial patterns and temporal variability in various regions are evaluated.

Comparisons between the two last versions exposed in this poster are based on relevant examples from this systematic validation: main improvements are observed in high latitudes (over 45° latitude).In the Southern Ocean modification of wind speed filtering and SST correction lead to a decrease in the mean difference between SMOS  and ISAS SSS south of 45S from 0.16+/-0.07 to 0.02+/-0.05pss. Std of the differences and r2 are also improved over global ocean. Statistics obtained with this new version are close to the ones obtained with SMAP RemSS v4 SSS.

Dinnat, E.P.; Le Vine, D.M.; Boutin, J.; Meissner, T.; Lagerloef, G. Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters. Remote Sens. 2019, 11, 750.

Kolodziejczyk Nicolas, Prigent-Mazella Annaig, Gaillard Fabienne (2017). ISAS-15 temperature and salinity gridded fields. SEANOE. https://doi.org/10.17882/52367

Waldteufel, P., J. L. Vergely, and C. Cot, A modified cardioid model for Processing multiangular radiometric observations, IEEE Transactions on Geoscience and Remote Sensing, vol.42, issue.5, pp.1059-1063, 2004. DOI : 10.1109/TGRS.2003.821698.

How to cite: Thouvenin-Masson, C., Boutin, J., Vergely, J.-L., Khvorostyanov, D., and Tarot, S.: CATDS CEC-LOCEAN debiased version 4 Sea Surface Salinity, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7545, https://doi.org/10.5194/egusphere-egu2020-7545, 2020.

Poorly treated or completely untreated sewage water discharges are common problem which might have major consequences in coastal water regions, smaller water basins and semi-enclosed seas. Although satellite remote sensing has a great potential for coastal water quality monitoring such outfalls are difficult for detection due to the small scale of the events and the complex effects on the physical and biogeochemical parameters. In search for an appropriate technique for detection of  sewage discharges through satellite remote sensing, we examine areas with similar optical water properties, such as small river plumes flowing into the sea. They are expected to be visible in a similar manner as they have high turbidity levels, higher nutrients concentration and are fresh compared to the salty sea water.

In the current study we examine small river inflows in the Black Sea as they have optical and radar properties comparable with poorly or completely untreated sewage discharges in the region. Additionally, the Black Sea is an intriguing study area because of the unique ecosystem with challenging optical properties and water characteristics.

The temporal and spatial variability of the inherent optical properties and sea surface roughness are studied in the area of river plumes and are compared with open sea values. The impact of atmospheric conditions given by wind speed, wind direction and precipitation on the river plume detectability is observed in the regions of interest. Long time series of images for three years are analysed in order to reveal the seasonal and annual variability of the events. The satellite data is taken from the Sentinel missions and the atmospheric variables are from the ERA5 reanalysis.

The outcome of the study gives a solid base for estimation of the potential of satellite remote sensing for monitoring of poorly treated or completely untreated sewage outfalls or other land sources flowing into the sea.

How to cite: Gancheva, I., Campbell, G., and Peneva, E.: Satellite remote sensing techniques for detection of riverine and other sources flows in the coastal part of the Black Sea, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8336, https://doi.org/10.5194/egusphere-egu2020-8336, 2020.

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

How to cite: Mir Calafat, F., Banks, C., Snaith, H., Gommenginger, C., Shaw, A., Cipollini, P., Dayoub, N., Bouffard, J., and Meloni, M.: Evaluation and scientific exploitation of CryoSat ocean products for oceanographic studies, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11812, https://doi.org/10.5194/egusphere-egu2020-11812, 2020.

Numerous new satellites and sensors have arised during the past decade. This satellite constellation has never been so dense and diverse. It provides a wide range of view angles to the ocean surface from the coast to the open ocean, at various scales and from physical to biological processes. Sentinel 1-2-3 program covers various sensors such as SAR, Optical, radiometer or altimeter with a repeat subcycle of only a few days, yet the repeat frequency for each sensor alone is not enough to monitor meso to submeso scales.

In the other hand, in-situ data are sparse in space but offers a high sample frequency and therefore complementary to remote sensing
observations. Handling consistently these huge heterogeneous datasets in a simple, fast and convenient way is now possible using the free and open Ocean Virtual Laboratory online portal or its standalone version. These tools are starting to be widely used by the scientific community to better discover, understand and monitor oceanic processes. We will demonstrate the potential and functionalities of these tools using various test cases:

Collocating Sentinel 1-2-3 for wave current interaction analysis
Creating synoptic charts of fronts and eddies, highlighting strong and energetic ocean currents
Campaign at sea planning and real time analysis of in-situ / remote sensing data. 
Validation and comparison of currents (derived from satellite and models) with a Lagrangian approach using SEAScope stand alone interactive tool. 

Online tool is available at https://ovl.oceandatalab.com and standalone version at https://seascope.oceandatalab.com. A splinter-meeting will
be organised at the conference to provide hands-on demonstration. 

How to cite: Gaultier, L., Collard, F., El Khoury Hanna, Z., Guitton, G., Herlédan, S., and Le Séach, G.: Taking advantage of Multisensor Synergy: New discovery and analysis tools, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22600, https://doi.org/10.5194/egusphere-egu2020-22600, 2020.

EUMETSAT provides user support and training for all users of the Copernicus Marine Data Stream (CMDS). The CMDS refers to all the level 1 and level 2 marine data from sensors on the Sentinel-3 and Jason-3 satellites, including ocean colour, sea surface temperature, and surface topography data. Details on the products and processing methodologies are available through handbooks, product notices, and a number of services including a help desk, and online forum. The training service aims to support all users wishing to explore potential applications of the CMDS. The service is primarily based around the delivery of two week, blended courses with both an online and classroom component.  The online component is hosted on a Moodle platform and uses a variety of prepared resources including short articles, videos, software installation, and basic software tutorials; supported by discussion forums, to prepare participants for the classroom phase. The classroom phase is focused on practical work, with no lectures given. Participants are led through examples of workflows using SNAP and Jupyter Notebooks/Python, and are then given one-on-one/small group trainer support to work for 3 days on personal projects that they defined during the online phase. These projects yield a diverse range of synergistic use cases of ocean remote sensing data for societally relevant applications The training service has also run a variety of collaborative courses with community led initiatives, and proposes to develop online courses and resources in response to community needs. Feedback and requests from the ocean remote sensing community are welcomed.

How to cite: Evers-King, H., Traeger-Chatterjee, C., Wannop, S., Biermann, L., and Clements, O.: EUMETSAT Copernicus Marine Training and User Support , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17583, https://doi.org/10.5194/egusphere-egu2020-17583, 2020.

The Chinese Ocean Colour and Temperature Scanner (COCTS), Coastal Zone Imager (CZI) and the novel Ultra-Violet Imager (UVI) which on-board the Chinese Ocean Satellite  HY-1C was launched in September 2018. The atmospheric correction of ocean color sensors was a critical step for accurate retrieval of the remote sensing reflectance, and the look-up-tables (LUTs), for the Rayleigh scattering, the aerosol scattering, and the diffuse transmittance, which were built bases on a Successive Order Scattering Vector Radiative Transfer Solver, played an important role in the processing step. Preliminary evaluation has been performed using the SeaWiFS LUTs and the MODIS data, it showed that COCTS can provide accurate ocean color products.

How to cite: Du, K., Chen, S., Ding, J., and Lee, Z.: Preliminary Evaluation of the Atmospheric Correction Look-Up-Tables (LUTs) of the COCTS-HY1C, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21206, https://doi.org/10.5194/egusphere-egu2020-21206, 2020.

Ocean color remote sensing proved to be a good alternative to traditional methods for total suspended solids concentration (TSS) monitoring purposes: numerous sensors have been developed for ocean color applications and different algorithms to retrieve TSS from remotely sensed data already exist.

Nevertheless, their application is generally limited by site-specific factors, and presently there is no uniform remote sensing model to estimate TSS.

The present study is focused in the development, evaluation and validation of different algorithms to estimate total suspended solids concentration based on laboratory reflectance data.

At this aim, a laboratory experiment was designed to collect the spectral reflectance of water containing fixed suspended particulate matter in terms of its concentration.

During the experiment, a total of 10 silty clay loam sediment samples were introduced into a tank filled with clear water up to a depth of 22 cm, illuminated by two 45 W lamps focused on center of water surface. After sieving, sediments were weighed so that TSS concentration ranging from 150 up to 2000 mg/L were obtained in the tank, being soil sediments suspension guaranteed by means of a mechanical pump-driven device.

Optical data were collected few minutes after each sediment introduction, using an Ocean Optics Jaz spectroradiometer mounted on a platform above the tank.

In accordance with previous studies, collected reflectance spectra of water containing sediments showed that, whatever is sediment concentration in water, reflectance in the red region is larger than that in the NIR region. Furthermore, reflectance spectra generally present two peaks: one between 550 nm and 750 nm, and the other between 750 nm and 850 nm, being the second peak insignificant for samples with very small TSS (e.g., SSC=150 mg/L), due to strong absorption of water.

After collection, laboratory reflectance spectra were integrated over the bandpass of different sensors’ selected bands, modulated by their relative response functions (RSR).

The basic principle of using a specific band, or band ratios to estimate a water parameter is to select spectral bands representative of its scattering/absorption features.

Band selection was achieved testing some previously formulated ocean color algorithms for the estimation of water quality parameters.

After band selection, linear regression model was applied to estimate the relationship between sensors’ reflectance at these bands and suspended solids concentration.

Results showed high correlation between selected sensors’ spectral red band and total suspended solids concentration higher than 500 mg/L up to 1360 mg/L, while less accuracy was observed for TSS concentrations higher than 1360 mg/L. Furthermore, the ratio between spectral red and green bands better estimates TSS in waters where total suspended concentration is not higher than 500 mg/L.

How to cite: Carlino, M. and Di Francesco, S.: Spectral reflectance of suspended solid sediments: a laboratory experiment., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10760, https://doi.org/10.5194/egusphere-egu2020-10760, 2020.

Abstract

Meghna Estuary is a very complex and dynamic estuarine system because of its irregular shape, wide seasonal variation and the changing role of tides. Every year, a major portion of the flow laden with high amount of sediments from the Ganges-Brahmaputra-Meghna river system are transferred through this estuary to the Bay of Bengal (BoB). The purpose of this analysis is to extract the dominant patterns of satellite Chlorophyll-a (CHL), Total Suspended Matter (TSM) and Sea Surface Temperature (SST) variability off the Meghna Estuary. CHL and TSM data were downloaded from GlobColour Project (http://globcolour.info/) and SST data were downloaded from Ocean Colour Project (https://oceancolor.gsfc.nasa.gov) using the monthly, 4km resolution of the global earth domain over eleven years (April 2002-April 2012). A temporal Empirical Orthogonal Function (EOF) analysis was performed to all the variables. The results revealed that the mean spatial distribution of the selected environmental variables in the Meghna estuary and its adjacent area during the study period showed clear latitudinal patterns in case of CHL and TSM, with higher values around the northern coast and a gradual offshore decreasing gradient. The SST map showed the same spatial pattern but with lower temperature values around the northern coastal fringe and higher values offshore. Only the first EOFs mode was found to be relevant, representing 25.55%, 15% and 89.43% of the CHL, TSM and SST variance, respectively, and depicted that the seasonal variability is the dominant pattern in this complex estuarine environment.

How to cite: Chowdhury, M., Laiz Alonso, I., and Caballero de Frutos, I.: Spatial and temporal scales of variability in the Meghna estuary from ocean remote sensing, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8655, https://doi.org/10.5194/egusphere-egu2020-8655, 2020.

High-quality satellite-based ocean colour products can provide valuable support and insights in the management and monitoring of coastal ecosystems. Today’s availability of Earth Observation (EO) data is unprecedented including medium resolution ocean colour systems (e.g. Sentinel-3/OLCI), high resolution land sensors (e.g. Sentinel-2/MSI) and geostationary satellites (e.g. MSG/SEVIRI). Each of these sensors offers specific advantages in terms of spatial, temporal or radiometric characteristics. In the Multi-Sync project, we developed advanced ocean colour products (i.e. remote sensing reflectance, turbidity, and chlorophyll a concentration) through the synergetic use of these multi-scale EO data taking advantage of spectral characteristics of traditional medium resolution sensors, the high spatial resolution of some land sensors and the high temporal resolution of geostationary sensors.

To achieve this goal a multi-scale DINEOF (Data Interpolating Empirical Orthogonal Functions) approach was developed to reconstruct missing data using empirical orthogonal functions (EOF), reduce noise and exploit spatio-temporal coherency by joining several spatial and temporal resolutions. Here we present the capacity of DINEOF to extract multi-scale information through the integration of Sentinel-3, Sentinel-2 and SEVIRI datasets.

The functionality of the advanced multi-scale products will be demonstrated in a case study for the Belgian Coastal Zone (BCZ) highly relevant to the user community: sediment transport modelling near the harbour of Zeebrugge in support of dredging operations. As stated in the OSPAR treaty (1992), Belgium is obliged to monitor and evaluate the effects of all human activities on the marine ecosystem. Dredging activities in and near Belgian harbors fall under this treaty and are performed daily to ensure accessibility of the port by ships. Optimization of these dredging activities requires monitoring data which is typically acquired through in situ observations or modelling data. In this case study we take advantage of Sentinel-3, Sentinel-2 and SEVIRI data characteristics to provide a satellite product that meets the end user requirements in terms of product quality and temporal/spatial resolution.

How to cite: Van der Zande, D., Alvera-Azcárate, A., Troupin, C., Cardoso Dos Santos, J., and Van den Eynde, D.: Multi-scale ocean colour synergy products for coastal water quality monitoring, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19123, https://doi.org/10.5194/egusphere-egu2020-19123, 2020.

Hourly sea surface height (SSH) from a coastal ocean model off eastern Newfoundland is used to generate simulated surface water and ocean topography (SWOT) data by a SWOT simulator. The simulated SWOT data are then used to reconstruct SSH by applying optimal interpolation (OI) in time and space. The reconstructed SSH is further used to calculate geostrophic currents associated with the inshore Labrador Current. The simulated SWOT data are also assimilated into the coastal ocean model by applying a deterministic ensemble Kalman filter (DEnKF). It is found that the inshore Labrador Current is fairly well reconstructed at the weekly scale, while the DEnKF assimilation reproduces well the  inshore Labrador Current at not only weekly but also daily scales.

How to cite: Han, G. and Ma, Z.: Reconstruction of the Inshore Labrador Current using SWOT: from OI to DENKF, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3626, https://doi.org/10.5194/egusphere-egu2020-3626, 2020.

For 20 years, ocean surface topography maps, essential for understanding the ocean circulation, have been built by statistically interpolating sea surface height (SSH) data provided by along-track nadir altimeters.  The space-time distribution of observed data limits the resolution of the maps to approximately 150 km and 20 days in wavelength. The launch of the next-generation SWOT altimetry mission in 2021 opens the way to high resolution maps thanks to an unprecedented kilometric resolution over a swath wide of 120 km. However, new advanced mapping techniques that involve information on ocean dynamics should be explored to take advantage of the high spatial resolution of SWOT. In this study, a data assimilation algorithm, the back and forth nudging (BFN), is implemented with a one and half layer quasi geostrophic model (QG) to dynamically interpolate the altimetric data.  We test the QG/BFN system to dynamically map SSH with synthetic but realistically distributed altimetric observations in the framework of Observing System Simulation Experiments (OSSE). The study focuses on two regions of the North-Atlantic ocean, presenting different dynamics and characterized by different temporal samplings of SWOT.

A systematic comparison with the traditional objective mapping technique demonstrates that the QG/BFN brings a significant improvement of the quality of the maps using only conventional nadir altimeter data. This outperformance of the QG/BFN is further increased when SWOT is added in the altimetric dataset. Our method is particularly effective for reconstructing non-linear surface dynamics at small mesoscales (40-100 km) that are smoothed out by conventional methods based on optimal mapping.

How to cite: Le Guillou, F., Metref, S., Ballarotta, M., Ubelmann, C., Cosme, E., Le Sommer, J., and Verron, J.: Mapping altimetry in the forthcoming SWOT era by back-and-forth nudging the quasi-geostrophic dynamics, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19273, https://doi.org/10.5194/egusphere-egu2020-19273, 2020.

The north Atlantic Ocean is regularly traversed by extratropical cyclones and winter low pressure systems originated in the Western part of the basin that can potentially generate dangerous extreme sea states. The region where these extreme sea states occur is linked to the tracks of the low-pressure systems in the north Atlantic basin.

Extreme sea states are usually generated by storms that can traverse whole ocean basins and generate high-energy swells that can propagate for thousands of kilometers. Additionally, rogue waves are a recognized source of extreme waves that needs to be considered when designing for operation at sea.

This study aims at the spatial distribution of the mean and extreme wave significant wave height inside the extratropical cyclones. We studied the significant wave height distribution of extratropical cyclones using merged satellite altimetry data to produce composite maps of this sea state variable. Although there are large variations among individual cyclones, the compositing method allows obtaining general features. We find that the higher waves are in the south-eastern quadrant of the cyclone, due to the extended fetch mechanism. The highest wave heights are found during the 48h period when the cyclone’s strength is maximum.

How to cite: Ponce de León, S. and Bettencourt, J.: Composite distribution of North Atlantic extreme wind waves inferred from satellite altimetry data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5909, https://doi.org/10.5194/egusphere-egu2020-5909, 2020.

Altimeters can provide global long-duration observations of oceanic wind speed and wave height. However, altimeters face the undersampling problem in estimating wind and wave climate because of their sparse sampling pattern and the changing number of in-orbit satellites. In this study, the undersampling error of altimeters was studied by sampling the ERA5 oceanic wind speed and wave height data using the track information of multiplatform altimeters. Comparisons were made between the statistics (mean, 90^th and 99^th percentiles, and long-term trends of them) of the original ERA5 data and the gridded along-track sampling of the ERA5 data. The results show a large discrepancy with respect to the extreme values (90^th and 99^th percentiles). The undersampling of altimeters can lead to significant underestimations of monthly extreme values of oceanic wind speed and wave height. Meanwhile, this underestimation is alleviated with the increase of the number of in-orbit altimeters, leading to very large overestimations of long-term trends of these extreme values over the period 1985-2018. In contrast, the annual extreme values of oceanic wind speed and wave height and their long-term trends are more reliable, although slight aforementioned biases of extreme values still exist and the data from GEOSAT are not suitable for computing annual statistics. For altimeter data, the annual values are a better option to compute long-term trends than the monthly data. This study also presents a correction scheme of using model data to compensate for the wind and wave events missed by altimeter tracks. After the correction, the global trends in oceanic wind speed and wave height over 1992-2017 are recomputed using annual statistics. The results show a clear discrepancy between the trends of wind speed and wave height during this period: the wind speed increased, while the wave height decreased. However, the reason for this discrepancy is unknown at this stage.

How to cite: Jiang, H.: Evaluation of altimeter undersampling in estimating global wind and wave climate using virtual observation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2191, https://doi.org/10.5194/egusphere-egu2020-2191, 2020.

The Doppler Centroid Anomaly (DCA) registered by microwave Synthetic Aperture Radar (SAR) contains information about ocean surface motion in the radar line-of-sight direction. The recorded signal is associated with the motion induced by the total wavefield (i.e., both wind waves and swell) and underlying ocean surface currents. Hence, accurate estimates of the wave-induced contribution to the observed DCA is required in order to obtain reliable information about underlying ocean surface current. In this study, we develop an empirical geophysical model function for the estimation of the wave-induced DCA. The study is based on two months of Sentinel-1 SAR Wave mode (WV) DCA observations collocated with wind field at 10m height from the ECMWF model and sea state information from the WAVEWATCH III model.

Analysis of two months of observations acquired over land showed that thanks to the novel Sentinel-1 DCA calibration, the uncertainty in the data does not exceed 3Hz (corresponding to a radial velocity of 0.21/014 m/s in the near/far range. The relationship between the DCA and the near-surface wind is in agreement with previously reported findings under the assumption of fully developed seas; the DCA is about 24% of the range wind speed at 23° incidence angle and decreasing (up to 50%) with increasing incidence angle from 23° to 36°. However, the difference between upwind (i.e., the wind blows towards antenna) and downwind (i.e., wind blows away from the antenna) configurations is inconsistent from study to study. Reliable information about the wave field indeed helps to describe the spread in the DCA, especially at low and moderate wind speeds, and when the ocean surface is dominated by the remotely generated swell.

The CDOP model is used as a baseline for estimating the wind-wave-induced Doppler shift. Retraining of the CDOP model for the Sentinel-1 SAR observations (CDOP-S) yielded a significantly better fit. Then, we extended the GMF with parameters of the wavefield (significant wave height, mean wave period and direction) in the moment of SAR acquisition. Combining information about near-surface wind and ocean surface wave fields also considerably improves the accuracy of the wave-induced Doppler shift estimates. In turn,  the accuracy of the ocean surface current retrievals are improved as demonstrated by the promising agreement with the near-surface ocean surface current climatology based on multiyear drifter observations.

How to cite: Moiseev, A., Johnsen, H., and Johannessen, J.: Estimation of wave-induced contribution to Sentinel-1 Doppler shift observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4870, https://doi.org/10.5194/egusphere-egu2020-4870, 2020.

Aviso (Archiving, Validation and Interpretation of Satellite Oceanographic data) is a service set up by CNES to process, archive and distribute data and products from altimetry satellite missions. Its portal AVISO+ (www.aviso.altimetry.fr) is the entry point to freely access more than 40 products from CNES (Centre National d'Etudes Spatiales) and CTOH (Center for Topographic studies of the Ocean and Hydrosphere) not only for ocean-oriented applications but also for hydrology, coastal, ice applications. In addition, the website proposes information (handbooks, use case, outreach material, …) to discover the products and their use. New products are regularly added to the catalogue, whether they are operational or demonstration products. For example, in 2019, the catalogue has been enriched with L2P level products from the Copernicus Sentinel-3A & 3B missions, the new version of the Mean Dynamic Topography CNES/CLS 2018, the mesoscale Eddy Trajectory Atlas in Near Real Time, several datasets of along-track and gridded SSALTO/DUACS experimental products, and some others. In 2020, some products of the CFOSAT (China-France Oceanography SATellite) mission devoted to the monitoring of ocean surface winds and waves, as well as CTOH ice and snow products will be available via Aviso+.

This presentation gives an overview of available products and services. We also present here all the recent novelties, and those to come in 2020.

How to cite: Soudarin, L., Mertz, F., Rosmorduc, V., Schgounn, C., Guinle, T., Birol, F., and Niño, F.: Aviso+: what’s new on the reference portal in satellite altimetry?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19669, https://doi.org/10.5194/egusphere-egu2020-19669, 2020.

The SWIM (Surface Waves Investigation and Monitoring) carried by the CFOSAT (Chinese-French Oceanography Satellite) is designed to obtain the nadir wave height and directional spectrum. This work first introduces the efficiency of deep learning technique to improve wave height and spectrum observation from CFOSAT. A set of deep learning neural networks (DNN) are established and trained to improve the accuracy of SWIM nadir wave height. According to the assessment based on independent buoy observations, the DNN reduces the root mean square error (RMSE) of significant wave height by 32.2% (from 0.26 m to 0.17 m), and the scatter index by 25.7% (from 14 % to 10 %). The result shows that the bias is significantly decreased from 0.11 m to -0.02 m. To correct the SWIM wave spectra, 6 months of NDBC frequency spectra have been used to obtain 19 sets of DNN, each of them is corresponding to one effective frequency of SWIM accordingly. Each set of DNN contains 14 hidden layers with the input as the energy from the different beams 6°, 8° and 10°. Then, the DNN forms a new combined spectrum based on wave spectra of all beams of the SWIM instrument. The independent assessment shows that the wave spectra which come from the 19 sets of DNN significantly reduced the relative error (RE) by 10% to 46% in comparison with beam 10°, which has the best accuracy performance among all the beams. The deep learning technique is also used as a quality control procedure before the assimilation of SWIM wave data. The Siamese convolution neural network (CNN) connected with a deep learning neural network (DNN) is applied to perform such comparison and verification for the SWIM directional spectra. The SWIM directional wave spectra are considered as the 2-dimensional “energy-pictures” with a matrix dimension of 17 frequencies and 9 directions. The Siamese CNN network is made up of 2 pairs of convolution layer and pooling layer, in which the 32 groups of convolution kernels are used to generate one-dimensional features from the directional wave spectra. Both wave spectra of SWIM instrument and wave model are inputted into the same Siamese CNN network, being transformed into 2 sets of features accordingly. Then the features would go to the DNN to generate the index of the similarity. This gives a quantitative description of how different between the SWIM directional spectra and the ones from wave model. Through the training of 26014 pairs of directional wave spectra, the Siamese CNN and DNN have showed a consistency of 78% to 86% with the “partition distance” method in the independent testing data, but with a much faster computation speed. We revealed that the Siamese technique increases the number of wave spectra passing through the verification than the “partition distance” method. This will ensure larger impact of the assimilation of SWIM data in the analysis and forecast period. The case study by running the wave model MFWAM using Siamese network and “partition distance” in assimilation is investigated.

How to cite: Wang, J., Aouf, L., Dalphinet, A., and Li, B.: On the use of deep learning for the improvement of swh and wave spectra from CFOSAT, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10051, https://doi.org/10.5194/egusphere-egu2020-10051, 2020.

Remote sensing of ocean near-surface currents based on measurements of the wave spectrum is an attractive means of mapping currents over a large area simultaneously. The most common wave measurement method involves marine X-band radar (Lund et al. 2015), with optical video measurements using drones more recently being used as an alternative (Streßer, Carrasco & Horstmann, 2017). In both cases, analysis of the wave dispersion within a subset window of the spatial domain is performed to determine the spatially varying near-surface current. An improved method for determining the depth-dependence of sub-surface currents from measured wave spectra was recently developed by our group (Smeltzer et al 2019).

Our long-term goal is to develop methods whereby the best possible representation of the three-dimensional sub-surface current can be obtained from remote measurement of waves. Methods based on current retrieval from wave spectra must assume that horizontal current variations are slow compared to a typical wavelength, but this is not always so. To resolve horizontal space, retrieved images must be subdivided into windows and the velocity vector at the midpoint is determined from the 3D spectrum of the waves within the window only.

In this work we examine the dependence of the spatial window size on the results of the current reconstruction. When the window size is decreased, greater spatial resolution is achieved being able to capture currents that vary on a faster horizontal length scale, at the expense of lower resolution in wavevector spectral space which may decrease the accuracy of the reconstructed currents, especially when information as the depth-dependence of the flow is desired. When the window size is larger, the reconstructed current may not be representative of the average current within the window. We present experiments conducted in a laboratory where spatially varying currents and waves of can be well-controlled and measured in situ, a valuable test-bed setup compared to field measurements. We investigate the factors involved which determine the optimal choice of window size.

References

Lund, B., et al. A new technique for the retrieval of near-surface vertical current shear from marine X-band radar images. J. Geophys. Res.: Oceans (2015) 120 8466-8496.

Smeltzer, B.K., Æsøy, E., Ådnøy, A. and Ellingsen S.Å., An improved method for determining near-surface currents from wave dispersion measurements. J. Geophys. Res.: Oceans. (2019) 124, https://doi.org/10.1029/2019JC015202.

Streßer, M., Carrasco, R. and Horstmann, J., Video-based estimation of surface currents using a low-cost quadcopter, IEEE Geosci. Remote Sens. Lett. (2017) 14 2027-2031.

How to cite: Smeltzer, B. K., Gundersen, I. S., and Ellingsen, S. Å.: Remote measurement of near-surface currents via wave spectra: currents varying vertically and horizontally, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20388, https://doi.org/10.5194/egusphere-egu2020-20388, 2020.

As many oceanic observations can only reflect the sea surface, such as satellite data, the retrieval of the ocean interior structure from sea surface information is of great importance for us to understand the three-dimensional ocean. In order to retrieve the three-dimensional salinity and density structure from surface data, two new method are proposed as follows. One proposed method is called generalized regression neural network with the fruit fly optimization algorithm (FOAGRNN), which is a nonlinear method and used to estimate subsurface salinity profiles from sea surface parameters. Compared with linear methodology, the estimated salinity profiles from the FOAGRNN method are in better agreement with the measured profiles at the halocline.The results suggest a potential new approach for salinity profile estimation derived from sea surface data. The other proposed method is called SQG-mEOF-R, which estimate the interior density from the sea surface density (SSD) and sea surface height (SSH) by combining the dynamical surface trapped mode derived from the Surface Quasi-Geostrophic (SQG) function with the statistical mode calculated from multivariate EOF reconstruction (mEOF-R) method and. This method is applied to the eddy-resolving OFES (Ocean General Circulation Model For the Earth simulator) simulation and compared with the conventional SQG or isQG (interior plus SQG) and mEOF-R methods. The results manifest that, no matter in the NorthWest Pacific (NWP) region dominated by surface-intensified eddies or the SouthEast Pacific (SEP) region characterized by subsurface-intensified eddies, SQG-mEOF-R perform a robust work in mesoscale density reconstruction.

How to cite: Wang, H., Zhang, R., Bao, S., Yan, H., Zhang, W., and Zhang, X.: Retrieving the Ocean Interior Structure from Surface Data:FOAGRNN and SQG-mEOF-R, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5286, https://doi.org/10.5194/egusphere-egu2020-5286, 2020.

Internal waves (IWs) are an intrinsic feature of all density stratified water bodies: oceans, seas, lakes and reservoirs. IWs occur due to various causes. Among them are tides and inertial motions, variations in atmospheric pressure and wind, underwater earthquakes, water flows over bottom topography, anthropogenic factors, etc. In coastal areas of oceans and tidal seas,  IWs induced by tidal currents over shelf edge predominate. Such IWs are well-studied in multiple field, laboratory and numerical experiments. However, the data on IWs in non-tidal seas, such as the Black, Baltic and Caspian Seas, are scarce. Meanwhile, our multi-year satellite observations prove IWs to be quite a characteristic hydrophysical phenomenon of the Caspian Sea. The sea is considered non-tidal because tide height does not exceed 12 cm at the coastline. And yet surface manifestations of IWs are regularly observed in satellite data, both radar and visible. The goal of our study was to reveal spatial, seasonal and interannual variability of IW surface manifestations in the Caspian Sea in the periods of 1999-2012 and 2018-2019 from the analysis of satellite data. All available satellite radar and visible data were used, that is data from ERS1/2 SAR; Envisat ASAR; Sentinel-1A,1B SAR-C; Landsat-4,5 TM; Landsat-7 ETM+; Landsat-8 OLI; Sentinel-2A,2B MSI sensors. During the year, IWs were observed from the beginning of May to mid-September. In certain years, depending on hydrometeorological conditions, such as water heating, wind field, etc., no IWs could be seen in May or September. IWs regularly occur in the east of Middle Caspian and in the northeast of South Caspian. In North Caspian, due to its shallowness and absence of pronounced stratification, IWs are not generated, at least their surface signatures cannot be found in satellite data. In the west of the sea, IWs are scarcely observed, primarily at the beginning of the summer season. IW trains propagate toward the coast, their generation sites are mainly over the depths of 50-200 m.

According to the available data for the studied periods, the time of the first appearance of IW signatures differs significantly from year to year. For example, in 1999 and 2000 it happened only in July.

Since no in situ measurements were conducted in the sites of regular IW manifestations, an attempt  was made to establish the dependence of IW occurrence frequency  on seasonal and interannual variations of sea surface temperature, an indirect indicator of the depth of the diurnal or seasonal thermocline, that is where IW were generated. Sea surface temperature was also estimated from satellite data.

Another issue addressed in the work was the differentiation between the sea surface signatures of IWs in the atmosphere and the sea. The Caspian Sea is known for their close similarity in spatial characteristics.

The work was carried out with financial support of the Russian Science Foundation grant #19-77-20060.  Processing of satellite data was carried out by Center for Collective Use “IKI-Monitoring” with the use of “See The Sea” system, that was implemented in frame of Theme “Monitoring”, State register No. 01.20.0.2.00164.

How to cite: Lavrova, O. and Kostianoy, A.: Spatio-temporal variability of internal waves in the Caspian Sea, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10011, https://doi.org/10.5194/egusphere-egu2020-10011, 2020.

Internal Solitary Waves (ISW) are particularly large amplitude internal waves which may propagate in the ocean over tens of kilometres while preserving their shape via a balance between non-linearity and non-hydrostatics effects. These waves may have wide impacts on the ocean dynamics (mixing or inducing vertical currents) and on human activities (fisheries, underwater acoustic or offshore activities).

ISW can be detected on satellite scenes. For instance, they may induce surface currents and thus enhance or damp the capillary waves at the sea surface which signed on the Synthetic Aperture Radar (SAR) scenes. On SAR images, ISW appear as successions of bright and dark bands over a grey background. From these images, the amplitude of the ISW and the depth of the pycnocline may be inferred using the Korteweg-DeVries (KdV) theoretical framework. Several SAR images interpretation methods have been developed based on curve fitting or Peak-to-Peak methods (Zheng et al., 2001) or parametric autoregressive techniques (Le Caillec, 2006). The KdV theory relies on the weakly nonlinear approximation and a Two-Layers Ocean Model (TLOM).

In Gibraltar Strait, the tidal dynamic leads to strong periodic currents. The exchanges between the Mediterranean sea and the Atlantic ocean occurred according a two layer scheme that maintains large density gradient located at the interface between Atlantic and Mediterranean Waters.  At some tidal outflow, an internal hydraulic jump is formed above Camarinal sill, when the tidal ouflow slackens, it is released and leads to the formation of eastward propagating internal solitary waves. The site is thus considered as an ISW “hot-spot”. Part of the energy carried by these waves propagates eastward into the Alborean Sea, although the stratification may differ from the TLOM.

If the stratification differs from TLOM, a given surface signature of ISW could match to several configurations of the pycnocline geometry and ISW amplitude, depending on the associated stratification.

In order to assess the impact of the stratification on the surface signature of the ISW, we implemented an idealized 2DV (one vertical and one longitudinal directions) configuration with the Coastal and Regional Ocean modelling COmmunity model (CROCO) using its non-Boussinesq (pseudo compressible) capability. The bathymetry and the density profile are inspired from oceanic observations. The tidal forcing is simplified to a pure monochromatic M2 tide.

First, simulations are initialized with a two-layer density profile and different pycnocline depths. Then, we added continuous stratification in each of the two (surface/bottom) layers. We tested also several tidal regimes in order to represent the various strengths between the neap and spring tide. SAR images interpretation techniques are then tested in each configurations. Pycnocline depths and ISW amplitudes computed from SAR methods are then compared with the ones initially simulated by the CROCO model.

Le Caillec, J.-M., 2006. Study of the SAR signature of internal waves by nonlinear parametric autoregressive models. IEEE Trans. Geosci. Remote Sens. 44, 148–158. https://doi.org/10.1109/TGRS.2005.859954

Zheng, Q., Yuan, Y., Klemas, V., Yan, X.-H., 2001. Theoretical expression for an ocean internal soliton synthetic aperture radar image and determination of the soliton characteristic half width. J. Geophys. Res. Oceans 106, 31415–31423. https://doi.org/10.1029/2000JC000726

How to cite: Dessert, M., Carton, X., Le Caillec, J.-M., Messager, C., Bordois, L., Honnorat, M., and La, T. V.: Korteweg-deVriès limitations for the interpretation of SAR images in the Strait of Gibraltar: impact of different stratifications on ISW surface signature. , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21497, https://doi.org/10.5194/egusphere-egu2020-21497, 2020.

The sea surface Doppler spectrum centroid is a principal parameter for the sea surface current retrieval from Doppler radar measurements. Satellite Doppler scatterometers are proposed to operate in the Ka-band (SKIM, DopplerScatt/WaCM, SEASTAR) in order to achieve sufficient measurement accuracy. Todays documentation of the Ka-band sea surface backscattering parameters is poor, thus this work is aimed at presenting a model for the sea surface Doppler spectrum centroid (DC) deducted from field data collected from the Black Sea research platform. The model relies on the well-known two-scale surface separation approach. Within this framework, the small-scale waves are the scatterers moving at their inherent speed (Bragg wave phase velocity or specular point velocity), which, in turn, are advected by the large-scale wave orbital velocities. These modulations lead to correlated variations of local scatterer cross-section and speed. The inherent scatterer velocity is computed theoretically, while the modulation term is described by the empirical modulation transfer function (MTF) which naturally involves both tilt and hydrodynamics components as a function of look geometry and sea state. The proposed semi-empirical DC model is in good agreement with measurements if in situ wave gauge directional spectrum is used as a wave input. Based on this finding, we extrapolate the semi-empirical DC model on the arbitrary surface described by the physical model of the wind wave spectrum. The resulting DC model is compared to the published empirical models and measurements (SAXON-FPN, DopplerScatt, AirSWOT, Wavemill field campaigns, and CDOP Envisat ASAR model). The model DC dependencies on incidence angle and wind speed are consistent with Ku-band
SAXON-FPN, Ka-band AirSWOT, and DopplerScatt datasets, but differs from C-band CDOP model and X-band Wavemill dataset, which generally have higher DC magnitude (besides longer operating radar wavelength, the difference can be attributed to swell dominated sea observed in the CDOP and Wavemill cases). The model predicts that the DC rises with wind speed at small incidence angles, 20–30^o, but the DC level is almost independent of wind at larger incidence angles, 50–55^o. Such behavior is explained by the balance between opposing wind dependencies of the MTF magnitude and the magnitude of modulating wave orbital velocities.

The work is supported by the Russian Science Foundation under grant No. 17-77-30019.

How to cite: Yurovsky, Y., Kudryavtsev, V., Grodsky, S., and Chapron, B.: Semi-Empirical Model for the Ka-band Sea Surface Doppler Centroid, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8634, https://doi.org/10.5194/egusphere-egu2020-8634, 2020.

Seawater temperature and salinity are the two key parameters related to the regional sea level variability. In this study, the spatial-temporal variabilities of the thermal and halo steric height over the South China Sea (SCS) are investigated using multi-senor satellite remote sensing products, in-situ measurements and reanalysis. The sea surface temperature and salinity products are used to reconstruct the upper layer sea level components, and the relative contribution of these two components are quantified. It is revealed that the thermal and halo components vary in an out-of-phase pattern, and dominant different regions within SCS. Variabilities of the sea level components on different timescale are further analyzed, and the linkage with large scale processes, such as the indo-pacific warm pool, will be presented.

How to cite: Sha, J. and Li, X.: Variabilities of sea level components over the South China Sea based on multi-sensor satellite observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6198, https://doi.org/10.5194/egusphere-egu2020-6198, 2020.

• Sargassum horneri is one of the major components of the floating sargassum that is distributed widely along the coast, including Korea, China and Japan. Sargassum horneri has an air pocket called an ‘air sec’ on its body which leads to a floating life, and drifts by ocean currents and winds. Due to these characteristics, blooms of Sargassum horneri have occurred April-June in the East China Sea. If these blooms flow into Jeju Island in South Korea, the blooms can cause enormous damage to fishing activities and the marine tourism industry. In order to minimize the damage caused by these blooms, we have been studied using remote sensing and field measurements.

  This study investigates environmental factors associated to the inflow of Sargassum horneri into Korean Peninsula. We used floating algae detection algorithm developed by a Geostationary Ocean Color Imager (GOCI). Since GOCI provides data of the seas surrounding the Korea eight times a day (00 to 07 UTC), it is suitable to monitor the blooms. The algorithm was made using the Red-edge effect which has a sharply rising reflectivity at around 700 nm but a low reflectivity in the red area (660-680 nm). And it was considered that the reflectivity of background seawater which varies from place to place is eliminated. Based on the results of the algorithm for detecting floating algae, Sargassum horneri’s inflow into the Korean Peninsula was analyzed January to June for six years (2014 to 2019). Also, the environment factors affecting to the inflow path were investigated each months and years.

How to cite: Kim, J. and Park, Y.: Investigation of the environmental factors associated with the inflow of Sargassum horneri into the Korean Peninsula using GOCI, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22278, https://doi.org/10.5194/egusphere-egu2020-22278, 2020.

Beach litters such as plastic bottles and styrofoam destroys coast ecosystems and creates aesthetic discomfort that lowers the value of the beach. Also, these beach litters are consumed by marine creatures, causing secondary ecosystem destruction. In order to solve this beach litters problem, it is necessary to study the generation and distribution pattern of waste and the cause of the inflow. However, the data for the study were only sample data collected in some areas of the beach. Also, most of the data covers only the total amount of beach litters. The total amount obtained from the sampling method was difficult to represent the total amount of beach litters.

UAV(Unmanned Aerial Vehicle) and Deep Neural Network can be effectively used to detect and monitor beach litter. Using UAV, it is possible to easily survey the entire beach. Recently, Object Detection technologies based on the Convolutional Neural Network have produced remarkable results in the general object recognition field. The Deep Neural Network can also identify the type of coastal litters. Therefore, using UAV and Deep Neural Network, it is possible to acquire spatial information by type of beach litters.

This paper proposes a Beach litter detection algorithm based on UAV and Deep Neural Network and a Beach litter monitoring process using it. It also offers optimal shooting altitude and image duplication to detect small beach litter such as plastic bottles and styrofoam pieces found on the beach. We are also suggest to effectively methods for training on imbalanced data.

In this study, DJI Mavic 2 Pro was used. The camera on the UAV is a 1-inch CMOS with a resolution of 20MP. The images obtained through UAV are produced as orthoimages and input into a pre-trained neural network algorithm.

How to cite: Bak, S., Jeong, M., Kim, N., and Yoon, H.: Beach Litter Detection and Monitoring Using UAV Image and Deep Neural Network under Imbalanced Data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22318, https://doi.org/10.5194/egusphere-egu2020-22318, 2020.

Recently, marine debris, which is emerging most in the marine environment, becomes a global problem and becomes a factor of marine environmental pollution. In particular, the United Nation General Assembly adopted the Sustainable Development Goals(SDGs), the largest joint goal of the United Nations and the international community, which is to be implemented from 2016 to 2030, including the reduction of marine waste by 2025 in the detailed marine sector. Marine debris, which is emerging as an international environmental issue, is emphasized mainly by coastal countries and can be seen in domestic coasts. Accordingly, coastal debris monitoring is carried out to investigate debris on designated coasts in Korea. However, the current monitoring methods have limitations in identifying the amount and type of coastal debris by region. And when comparing the amount of inflow and collection of marine debris in Korea, the collection amount is less than half compared to the inflow amount. Therefore, it is necessary to understand the movement pattern characteristics by tracking the behavior of marine debris. In this study uses the location tracking buoys which can identify the movement characteristics of marine debris in accordance with the floating and direct influence in real-time. Therefore, GPS terminals built into objects that are shaped like marine debris, suggesting that they can track what movement patterns are characteristic of current and wind effects.

How to cite: Jeong, M., Bak, S., Kim, N., and Yoon, H.: Characteristics of Marine Debris Movement Pattern Using Location Tracking Buoys, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22321, https://doi.org/10.5194/egusphere-egu2020-22321, 2020.

Ambient noise measurements have been widely used to estimate environmental information such as water column sound speed, pH, seabed properties, and wind speed. In this study, 30 days of ambient noise data recorded on two vertically oriented hydrophones deployed near Alvin canyon on the New England shelf break were used to estimate the ocean mixed layer depth (MLD). The vertical noise coherence was computed and compared to a wave-number integral noise model comprised of a two-segment piecewise linear summer sound speed profile in a shallow water waveguide. Measurements of noise and sound speed profiles, together with a wavenumber integral ambient noise model were used to calculate the mixed layer thickness. Noise model results showed variations in the first zero-crossing frequency, which was in accordance with the semi-diurnal variability of the MLD. MLD was determined by matching the zero-crossing frequency of the real part of measured coherence with the model results for the entire one-month period. The comparison of the estimated MLD using ambient noise showed good agreement with the measured MLD from the temperature sensors.

How to cite: Shajahan, N. and Barclay, D.: Remote sensing mixed layer depth using ocean ambient noise, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9671, https://doi.org/10.5194/egusphere-egu2020-9671, 2020.

A sea fog is a fog caused by the cooling of the air near the ocean-atmosphere boundary layer when the warm sea surface air moves to a cold sea level. Sea fog affects a variety of aspects, including maritime and coastal transportation, military activities and fishing activities. In particular, it is important to detect sea fog as they can lead to ship accidents due to reduced visibility. Due to the wide range of sea fog events and the lack of constant occurrence, it is generally detected through satellite remote sensing. Because sea fog travels in a short period of time, it uses geostationary satellites with higher time resolution than polar satellites to detect fog. A method for detecting fog by using the difference between 11 μm channel and 3.7 μm channel was widely used when detecting fog by satellite remote sensing, but this is difficult to distinguish between lower clouds and fog. Traditional algorithms are difficult to find accurate thresholds for fog and cloud. However, machine learning algorithms can be used as a useful tool to determine this. In this study, based on geostationary satellite imaging data, a comparative analysis of sea fog detection accuracy was conducted through various methods of machine learning, such as Random Forest, Multi-Layer Perceptron, and Convolutional Neural Networks.

How to cite: Kim, N., Bak, S., Jeong, M., and Yoon, H.: Evaluation of Sea fog Detection Accuracy Based on Geostationary Satellite Image Using Machine Learning, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22319, https://doi.org/10.5194/egusphere-egu2020-22319, 2020.

The representation of the Ushant front in Meteo-France numerical models is not accurate. The aim of this study is to evaluate the impact of a better representation of this front derived from SST satellite observation data on the weather forecast in Brittany.

The study consisted in finding and selecting cases from 2016 to 2018 where the Ushant front was present in satellite SST analysis (high spatial and temporal resolution ) with differences in weather pattern between North and South Brittany. Then compare this to the operational Arome model (Meteo France non hydrostatic model).

Situations of disagreement between the model and the observations were selected. Some weather forecast simulations with Mesonh model (very close to Arome) were performed on these cases with a better definition of the Ushant front. We present some results.

How to cite: Orain, F., Bouin, M.-N., Redelsperger, J.-L., and Garnier, V.: Effect of the Ushant SST front on regional weather forecasting in the event of anticyclonic events, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22412, https://doi.org/10.5194/egusphere-egu2020-22412, 2020.