Spatio-temporal variability of internal waves in the Caspian Sea

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>

Author(s):  
M. A. Syariz ◽  
L. M. Jaelani ◽  
L. Subehi ◽  
A. Pamungkas ◽  
E. S. Koenhardono ◽  
...  

The Sea Surface Temperature (SST) retrieval from satellites data Thus, it could provide SST data for a long time. Since, the algorithms of SST estimation by using Landsat 8 Thermal Band are sitedependence, we need to develop an applicable algorithm in Indonesian water. The aim of this research was to develop SST algorithms in the North Java Island Water. The data used are in-situ data measured on April 22, 2015 and also estimated brightness temperature data from Landsat 8 Thermal Band Image (band 10 and band 11). The algorithm was established using 45 data by assessing the relation of measured in-situ data and estimated brightness temperature. Then, the algorithm was validated by using another 40 points. The results showed that the good performance of the sea surface temperature algorithm with coefficient of determination (<i>R</i><sup>2</sup>) and Root Mean Square Error (<i>RMSE</i>) of 0.912 and 0.028, respectively.


2016 ◽  
Vol 37 (6) ◽  
pp. 2831-2849 ◽  
Author(s):  
Blandine L'Hévéder ◽  
Sabrina Speich ◽  
Olivier Ragueneau ◽  
Francis Gohin ◽  
Philippe Bryère

2020 ◽  
Author(s):  
George Vanyushin ◽  
Tatyana Bulatova

&lt;p&gt;&lt;strong&gt;Temperature conditions of development juvenile NEA cod in the Barents sea for 1998-2015 on the basis of satellite data&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Vanyushin G. P., Bulatova T. V.&lt;/p&gt;&lt;p&gt;Russian Federal Research Institute of Fisheries and Oceanography (VNIRO)&lt;/p&gt;&lt;p&gt;107140 17, V. Krasnoselskaya str., Moscow&lt;/p&gt;&lt;p&gt;tel: 8(499)264-01-33, fax: 8(499)264-91-87,&lt;/p&gt;&lt;p&gt;e-mail: [email protected]&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Abstract&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;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 &quot;NOAA&quot; and quasisynchronous temperature data &quot;in situ&quot; 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 &amp;#160;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 &amp;#8211; 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 &amp;#8211; 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.&lt;/p&gt;&lt;p&gt;Keywords: satellite monitoring, sea surface temperature (SST), the&amp;#160; Northeast Arctic cod, main spawning and habitat waters, yearlings of the cod.&lt;/p&gt;


Author(s):  
F. Bayat ◽  
M. Hasanlou

Sea surface temperature (SST) is one of the critical parameters in marine meteorology and oceanography. The SST datasets are incorporated as conditions for ocean and atmosphere models. The SST needs to be investigated for various scientific phenomenon such as salinity, potential fishing zone, sea level rise, upwelling, eddies, cyclone predictions. On the other hands, high spatial resolution SST maps can illustrate eddies and sea surface currents. Also, near real time producing of SST map is suitable for weather forecasting and fishery applications. Therefore satellite remote sensing with wide coverage of data acquisition capability can use as real time tools for producing SST dataset. Satellite sensor such as AVHRR, MODIS and SeaWIFS are capable of extracting brightness values at different thermal spectral bands. These brightness temperatures are the sole input for the SST retrieval algorithms. Recently, Landsat-8 successfully launched and accessible with two instruments on-board: (1) the Operational Land Imager (OLI) with nine spectral bands in the visual, near infrared, and the shortwave infrared spectral regions; and (2) the Thermal Infrared Sensor (TIRS) with two spectral bands in the long wavelength infrared. The two TIRS bands were selected to enable the atmospheric correction of the thermal data using a split window algorithm (SWA). The TIRS instrument is one of the major payloads aboard this satellite which can observe the sea surface by using the split-window thermal infrared channels (CH10: 10.6 μm to 11.2 μm; CH11: 11.5 μm to 12.5 μm) at a resolution of 30 m. The TIRS sensors have three main advantages comparing with other previous sensors. First, the TIRS has two thermal bands in the atmospheric window that provide a new SST retrieval opportunity using the widely used split-window (SW) algorithm rather than the single channel method. Second, the spectral filters of TIRS two bands present narrower bandwidth than that of the thermal band on board on previous Landsat sensors. Third, TIRS is one of the best space born and high spatial resolution with 30&thinsp;m. in this regards, Landsat-8 can use the Split-Window (SW) algorithm for retrieving SST dataset. Although several SWs have been developed to use with other sensors, some adaptations are required in order to implement them for the TIRS spectral bands. Therefore, the objective of this paper is to develop a SW, adapted for use with Landsat-8 TIRS data, along with its accuracy assessment. In this research, that has been done for modelling SST using thermal Landsat 8-imagery of the Persian Gulf. Therefore, by incorporating contemporary in situ data and SST map estimated from other sensors like MODIS, we examine our proposed method with coefficient of determination (R2) and root mean square error (RMSE) on check point to model SST retrieval for Landsat-8 imagery. Extracted results for implementing different SW's clearly shows superiority of utilized method by R<sup>2</sup>&thinsp;=&thinsp;0.95 and RMSE&thinsp;=&thinsp;0.24.


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