Analyses of Green Island wake caused by Kuroshio from satellite imagery

Author(s):  
Kai-Ho Cheng ◽  
Chung-Ru Ho ◽  
Po-Chun Hsu
2015 ◽  
Vol 315 ◽  
pp. 1141-1150 ◽  
Author(s):  
Tai-Wen Hsu ◽  
Dong-Jiing Doong ◽  
Kai-Jiun Hsieh ◽  
Shin-Jye Liang

Author(s):  
Shin-Jye Liang ◽  
Nan-Jung Kuo ◽  
Dong-Jiing Doong ◽  
Tai-Wen Hsu

2015 ◽  
Vol 12 (6) ◽  
pp. 3199-3233 ◽  
Author(s):  
T.-W. Hsu ◽  
M.-H. Chou ◽  
T.-H. Hou ◽  
S.-J. Liang

Abstract. Green Island located in the typhoon active eastern Taiwan coastal water is the potential Kuroshio power plant site. In this study, a high resolution (250–2250 m) shallow-water equations (SWEs) model is used to investigate the effect of typhoon on the hydrodynamics of Kuroshio and Green Island wake. Two wind induced flows, typhoon Soulik and Holland's wind field model, are studied. Simulation results of the typhoon Soulik indicate that salient characteristics of Kuroshio and downstream island wake seems less affected by the typhoon Soulik because typhoon Soulik is 250 km away Green Island and the wind speed near Green Island is small. Moreover, Kuroshio currents increase when flow is in the same direction as the counterclockwise rotation of typhoon, and vice versa. This finding is in favorable agreements with the TOROS observed data. The SWEs model, forced by the Kuroshio and Holland's wind field model, successfully reproduces the downstream recirculation and meandering vortex street. Numerical results unveil that the slow moving typhoon has a more significant impact on the Kuroshio and downstream Green Island wake than the fast moving typhoon does. Due to the counterclockwise rotation of typhoon, Kuroshio currents increase (decrease) in the right (left) of the moving typhoon's track. This rightward bias phenomenon is evident, especially when typhoon moves in the same direction as the Kuroshio mainstream.


Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3039
Author(s):  
Tien-Hung Hou ◽  
Jen-Yi Chang ◽  
Chia-Cheng Tsai ◽  
Tai-Wen Hsu

The aim of the present study is to apply the three-dimensional Princeton Ocean Model to study the wind effects on Kuroshio-induced island wake in the lee of Green Island, Taiwan. Numerical results indicate that the effect of NE winds squeezes the Kuroshio-induced island vortex street close to the coast and the SW winds tend to push the island vortex street farther away from the coast. The simulated vortex streets are analyzed by the dimensionless spatial lengths to quantify the prescribed feature. By comparing the three-dimensional results with different wind conditions, the Ekman transports are observed and the influence depths of wind effects are studied. Additionally, some cold eddies are found in temperature fields resulting from numerical simulations. These results are in qualitative agreement with field measurements and satellite images.


2014 ◽  
Vol 35 (11-12) ◽  
pp. 4484-4495 ◽  
Author(s):  
Shih-Jen Huang ◽  
Chung-Ru Ho ◽  
Sheng-Lin Lin ◽  
Shin-Jye Liang

2020 ◽  
Vol 2020 (8) ◽  
pp. 114-1-114-7
Author(s):  
Bryan Blakeslee ◽  
Andreas Savakis

Change detection in image pairs has traditionally been a binary process, reporting either “Change” or “No Change.” In this paper, we present LambdaNet, a novel deep architecture for performing pixel-level directional change detection based on a four class classification scheme. LambdaNet successfully incorporates the notion of “directional change” and identifies differences between two images as “Additive Change” when a new object appears, “Subtractive Change” when an object is removed, “Exchange” when different objects are present in the same location, and “No Change.” To obtain pixel annotated change maps for training, we generated directional change class labels for the Change Detection 2014 dataset. Our tests illustrate that LambdaNet would be suitable for situations where the type of change is unstructured, such as change detection scenarios in satellite imagery.


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