Sensitivity of satellite multichannel sea surface temperature retrievals to the air-sea temperature difference

1993 ◽  
Vol 98 (C7) ◽  
pp. 12567 ◽  
Author(s):  
Douglas A. May ◽  
Ronald J. Holyer
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temperature Difference ◽  
Sea Surface ◽  
Sea Temperature

Deep Learning for Sea Temperature Eddy signature Classification

2021 ◽  
Author(s):  
Evangelos Moschos ◽  
Alexandre Stegner ◽  
Olivier Schwander ◽  
Patrick Gallinari
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mediterranean Sea ◽  
Satellite Imagery ◽  
Mesoscale Eddies ◽  
Sea Surface ◽  
Sea Temperature ◽  
Cloud Coverage

<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>

scholarly journals On changes of sea temperature in the English Channel

1960 ◽  
Vol 39 (3) ◽  
pp. 449-458 ◽  
Author(s):  
A. J. Southward
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Air Temperature ◽  
Sea Surface ◽  
English Channel ◽  
Sea Temperature ◽  
Celtic Sea

In a series of papers dealing with changes in the distribution of intertidal animals around Britain, and especially along the English Channel, attention was drawn to some local aspects of the general warming-up of sea and air that has taken place in the last 50 years (Southward & Crisp, 1954, 1956; Crisp and Southward, 1958). In the first of these papers smoothed values of annual mean air temperature at Plymouth Hoe, and sea surface temperature of the southern Celtic Sea were given, the latter values being based on Smed (1952). The graphs showed a rise of about 1° C in the air temperature and 0·5° C in sea temperature during 50 years.

scholarly journals KAJIAN SEA SURFACE TEMPERATURE (SST), SOUTHERN OSCILLATION INDEX (SOI) DAN DIPOLE MODE PADA KEGIATAN PENERAPAN TEKNOLOGI MODIFIKASI CUACA DI PROPINSI RIAU DAN SUMATERA BARAT JULI – AGUSTUS 2009

2010 ◽  
Vol 11 (1) ◽  
pp. 1 ◽  
Author(s):  
M Djazim Syaifullah
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temperature Anomaly ◽  
Southern Oscillation ◽  
Sea Surface ◽  
Dipole Mode ◽  
Atmospheric Conditions ◽  
Cloud Seeding ◽  
Sea Temperature ◽  
Global Condition

Kajian suhu muka laut, SOI dan Dipole Mode Index (DMI) telah dilakukan untuk melihatpengaruh global terhadap kondisi pertumbuhan awan di daerah DAS Kotapanjang danSingkarak pada pelaksanaan Teknologi Modifikasi Cuaca (TMC) Juli – Agustus 2009.Data yang dipakai dalam penelitian ini adalah data Sea Surface Temperature (SST)yang diambil dari University Corporation for Athmospheric Research (UCAR). suhumuka laut yang dianalisis adalah daerah Nino dan daerah Sumatera bagian barat. Darihasil analisis terlihat bahwa selama kegiatan TMC nilai anomali SST untuk keempatdaerah Nino (Nino12, Nino3, Nino34 dan Nino4) adalah positif, hal ini menunjukkanbahwa selama kegiatan TMC kondisi global sudah memasuki fase ElNino meskipunbelum begitu kuat. Sedangkan di wilayah Sumatera bagian barat secara umum sejakawal bulan April 2009 nilai suhu muka laut berada di atas rerata dari normalnya (anomali positif). Dilihat dari nilai SOI secara umum berada pada kisaran normal. Hasil analisis menunjukkan bahwa selama kegiatan TMC kondisi atmosfer kedua DAS cukup kering dan sangat sulit untuk mendapatkan awan-awan yang potensial untuk disemai. Study of sea surface temperature, SOI and dipole mode indices (DMI), was held to seeglobal influence conditions of cloud growth in Kotapanjang and Singkarak catchment on the cloud seeding project from July to August 2009. The data used in this study was sea surface temperature (SST), taken from University Corporation Athmospheric research(UCAR). The sea surface temperature was analysed in Nino12 regions and Westernregion of Sumatra. Based on the analysis shows that during cloud seeding period thesea surface temperature anomaly for the four regions of Niño (Niño2 Niño3, Niño34and Niño4) is positive, while in the western of Sumatra in general since the beginning ofApril 2009 the sea temperature was higher than normal. This indicates that during cloudseeding period global condition has entered a stage of Elnino, although not so strong.The soi is generally in the range of normal. The analysis showed that during the cloudseeding period either watershed atmospheric conditions dry enough and very difficult toget a potential cloud for sowing.

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