scholarly journals Abnormal events detection using deep neural networks: application to extreme sea surface temperature detection in the Red Sea

2019 ◽  
Vol 28 (02) ◽  
pp. 1 ◽  
Author(s):  
Mohamad Mazen Hittawe ◽  
Shehzad Afzal ◽  
Tahira Jamil ◽  
Hichem Snoussi ◽  
Ibrahim Hoteit ◽  
...  
Keyword(s):  
Neural Networks ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Red Sea ◽  
Deep Neural Networks ◽  
Sea Surface ◽  
Temperature Detection ◽  
Events Detection ◽  
Abnormal Events

scholarly journals Basin-Scale Prediction of Sea Surface Temperature with Artificial Neural Networks

2018 ◽  
Vol 35 (7) ◽  
pp. 1441-1455 ◽  
Author(s):  
Kalpesh Patil ◽  
M. C. Deo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Prediction Skill ◽  
Basin Scale ◽  
Ann Models ◽  
Artificial Neural

AbstractThe prediction of sea surface temperature (SST) on the basis of artificial neural networks (ANNs) can be viewed as complementary to numerical SST predictions, and it has fairly sustained in the recent past. However, one of its limitations is that such ANNs are site specific and do not provide simultaneous spatial information similar to the numerical schemes. In this work we have addressed this issue by presenting basin-scale SST predictions based on the operation of a very large number of individual ANNs simultaneously. The study area belongs to the basin of the tropical Indian Ocean (TIO) having coordinates of 30°N–30°S, 30°–120°E. The network training and testing are done on the basis of HadISST data of the past 140 yr. Monthly SST anomalies are predicted at 3813 nodes in the basin and over nine time steps into the future with more than 20 million ANN models. The network testing indicated that the prediction skill of ANNs is attractive up to certain lead times depending on the subbasin. The ANN models performed well over both the western Indian Ocean (WIO) and eastern Indian Ocean (EIO) regions up to 5 and 4 months lead time, respectively, as judged by the error statistics of the correlation coefficient and the normalized root-mean-square error. The prediction skill of the ANN models for the TIO region is found to be better than the physics-based coupled atmosphere–ocean models. It is also observed that the ANNs are capable of providing an advanced warning of the Indian Ocean dipole as well as abnormal basin warming.

scholarly journals Comparative Analysis of Sea Surface Temperature Pattern in the Eastern and Western Gulfs of Arabian Sea and the Red Sea in Recent Past Using Satellite Data

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Neha Nandkeolyar ◽  
Mini Raman ◽  
G. Sandhya Kiran ◽  
Ajai
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Red Sea ◽  
Arabian Sea ◽  
Sea Surface ◽  
Temperature Pattern ◽  
The Indian Ocean ◽  
Sst Anomaly ◽  
The Persian Gulf ◽  
Very High

With unprecedented rate of development in the countries surrounding the gulfs of the Arabian Sea, there has been a rapid warming of these gulfs. In this regard, using Advanced Very High Resolution Radiometer (AVHRR) data from 1985 to 2009, a climatological study of Sea Surface Temperature (SST) and its inter annual variability in the Persian Gulf (PG), Gulf of Oman (GO), Gulf of Aden (GA), Gulf of Kutch (KTCH), Gulf of Khambhat (KMBT), and Red Sea (RS) was carried out using the normalized SST anomaly index. KTCH, KMBT, and GA pursued the typical Arabian Sea basin bimodal SST pattern, whereas PG, GO, and RS followed unimodal SST curve. In the western gulfs and RS, from 1985 to 1991-1992, cooling was observed followed by rapid warming phase from 1993 onwards, whereas in the eastern gulfs, the phase of sharp rise of SST was observed from 1995 onwards. Strong influence of the El Niño and La Niña and the Indian Ocean Dipole on interannual variability of SST of gulfs was observed. Annual and seasonal increase of SST was lower in the eastern gulfs than the western gulfs. RS showed the highest annual increase of normalized SST anomaly (+0.64/decade) followed by PG (+0.4/decade).

scholarly journals Atmospheric Forcing of the High and Low Extremes in the Sea Surface Temperature over the Red Sea and Associated Chlorophyll-a Concentration

2020 ◽  
Vol 12 (14) ◽  
pp. 2227
Author(s):  
Kamal A. Alawad ◽  
Abdullah M. Al-Subhi ◽  
Mohammed A. Alsaafani ◽  
Turki M. Alraddadi
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Red Sea ◽  
Wind Stress ◽  
Sea Surface ◽  
Boreal Summer ◽  
Southern Europe ◽  
Atmospheric Forcing ◽  
Chl A ◽  
Northern Basin

Taking advantage of 37-year-long (1982–2018) of high-quality satellite datasets, we examined the role of direct atmospheric forcing on the high and low sea surface temperature (SST) extremes over the Red Sea (RS). Considering the importance of SST in regulating ocean physics and biology, the associated impacts on chlorophyll (Chl-a) concentration were also explored, since a small change in SST can cause a significant impact in the ocean. After describing the climate features, we classified the top 5% of SST values (≥31.5 °C) as extreme high events (EHEs) during the boreal summer period and the lowest SST values (≤22.8 °C) as extreme low events (ELEs) during the boreal winter period. The spatiotemporal analysis showed that the EHEs (ELEs) were observed over the southern (northern) basin, with a significant warming trend of 0.027 (0.021) °C year−1, respectively. The EHEs were observed when there was widespread less than average sea level pressure (SLP) over southern Europe, northeast Africa, and Middle East, including in the RS, leading to the cold wind stress from Europe being relatively less than usual and the intrusion of stronger than usual relatively warm air mass from central Sudan throughout the Tokar Gap. Conversely, EHEs were observed when above average SLP prevailed over southern Europe and the Mediterranean Sea as a result of the Azores high and westward extension of the Siberian anticyclone, which led to above average transfer of cold and dry wind stress from higher latitudes. At the same time, notably less wind stress due to southerlies that transfer warm and humid air masses northward was observed. Furthermore, physical and biological responses related to extreme stress showed distinct ocean patterns associated with each event. It was found that the Chl-a concentration anomalies over the northern basin caused by vertical nutrient transport through deep upwelling processes are the manifestation of the superimposition of ELEs. The situation was the opposite for EHEs due to the stably stratified ocean boundary layer, which is a well-known consequence of global warming.

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