Investigation of sea surface temperature and wind fields in Joint US/Russia Internal Waves Remote Sensing Experiment

Author(s):  
V.S. Etkin ◽  
A.V. Kuzmin ◽  
M.N. Pospelov ◽  
A.I. Smirnov ◽  
Yu.G. Trokhimovsky
2021 ◽  
Author(s):  
Evangelos Moschos ◽  
Alexandre Stegner ◽  
Olivier Schwander ◽  
Patrick Gallinari

<p>Mesoscale eddies are oceanic vortices with radii of tens of kilometers, which live on for several months or even years. They carry large amounts of heat, salt, nutrients, and pollutants from their regions of formation to remote areas, making it important to detect and track them. Using satellite altimetric maps, mesoscale eddies have been detected via remote sensing with advancing performance over the last years <strong>[1]</strong>. However, the spatio-temporal interpolation between satellite track measurements, needed to produce these maps, induces a limit to the spatial resolution (1/12° in the Med Sea) and large amounts of uncertainty in non-measured areas.</p><p>Nevertheless, mesoscale oceanic eddies also have a visible signature on other satellite imagery such as Sea Surface Temperature (SST), portraying diverse patterns of coherent vortices, temperature gradients, and swirling filaments. Learning the regularities of such signatures defines a challenging pattern recognition task, due to their complex structure but also to the cloud coverage which can corrupt a large fraction of the image.</p><p>We introduce a novel Deep Learning approach to classify sea temperature eddy signatures <strong>[2]</strong>. We create a large dataset of SST patches from satellite imagery in the Mediterranean Sea, containing Anticyclonic, Cyclonic, or No Eddy signatures, based on altimetric eddy detections of the DYNED-Atlas <strong>[3]</strong>. Our trained Convolutional Neural Network (CNN) can differentiate between these signatures with an accuracy of more than 90%, robust to a high level of cloud coverage.</p><p>We furtherly evaluate the efficiency of our classifier on SST patches extracted from oceanographic numerical model outputs in the Mediterranean Sea. Our promising results suggest that the CNN could complement the detection, tracking, and prediction of the path of mesoscale oceanic eddies.</p><p><strong>[1]</strong> <em>Chelton, D. B., Schlax, M. G. and Samelson, R. M. (2011). Global observations of nonlinear mesoscale eddies. Progress in oceanography, 91(2),167-216.</em></p><p><strong>[2]</strong> <em>E. Moschos, A. Stegner, O. Schwander and P. Gallinari, "Classification of Eddy Sea Surface Temperature Signatures Under Cloud Coverage," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3437-3447, 2020, doi: 10.1109/JSTARS.2020.3001830.</em></p><p><strong>[3]</strong> <em>https://www.lmd.polytechnique.fr/dyned/</em></p>


2020 ◽  
Author(s):  
Olga Lavrova ◽  
Andrey Kostianoy

<p>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.</p><p>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.</p><p>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.</p><p>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.</p><p>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.</p>


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