Identification of eddies from sea surface temperature maps with neural networks

2006 ◽  
Vol 27 (8) ◽  
pp. 1601-1618 ◽  
Author(s):  
M. Castellani
Keyword(s):  
Neural Networks ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Temperature Maps

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.

Analysis of along track scanning radiometer-2 (ATSR-2) data for clouds, glint and sea surface temperature using neural networks

2005 ◽  
Vol 98 (2-3) ◽  
pp. 152-181 ◽  
Author(s):  
James J. Simpson ◽  
Yueh Lung (Ben) Tsou ◽  
Andrew Schmidt ◽  
Andrew Harris
Keyword(s):  
Neural Networks ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Along Track

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 Prediction of Sea Surface Temperature by Combining Numerical and Neural Techniques

2016 ◽  
Vol 33 (8) ◽  
pp. 1715-1726 ◽  
Author(s):  
Kalpesh Patil ◽  
M. C. Deo ◽  
M. Ravichandran
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Wavelet Neural Network ◽  
Sea Surface ◽  
Numerical Estimation ◽  
Error Statistics ◽  
Time Step

AbstractThe prediction of sea surface temperature (SST) in real-time or online mode has applications in planning marine operations and forecasting climate. This paper demonstrates how SST measurements can be combined with numerical estimations with the help of neural networks and how reliable site-specific forecasts can be made accordingly. Additionally, this work demonstrates the skill of a special wavelet neural network in this task. The study was conducted at six different locations in the Indian Ocean and over three time scales (daily, weekly, and monthly). At every time step, the difference between the numerical estimation and the SST measurement was evaluated, an error time series was formed, and errors over future time steps were forecasted. The time series forecasting was affected through neural networks. The predicted errors were added to the numerical estimation, and SST predictions were made over five time steps in the future. The performance of this procedure was assessed through various error statistics, which showed a highly satisfactory functioning of this scheme. The wavelet neural network based on the particular basic or mother wavelet called the “Meyer wavelet with discrete approximation” worked more satisfactorily than other wavelets.

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