scholarly journals Correction to: Geographical variation in spawning histories of age-1 Pacific saury Cololabis saira in the North Pacific Ocean during June and July

2020 ◽  
Vol 86 (3) ◽  
pp. 597-597
Author(s):  
Satoshi Suyama ◽  
Hitomi Ozawa ◽  
Yasutoki Shibata ◽  
Taiki Fuji ◽  
Masayasu Nakagami ◽  
...  
Keyword(s):  
Open Access ◽  
Pacific Ocean ◽  
North Pacific ◽  
Geographical Variation ◽  
North Pacific Ocean ◽  
Pacific Saury ◽  
Internet Portal ◽  
The North ◽  
Cololabis Saira ◽  
The North Pacific

The article "Geographical variation in spawning histories of age 1 Pacific saury Cololabis saira in the North Pacific Ocean during June and July", written by Satoshi Suyama, Hitomi Ozawa, Yasutoki Shibata, Taiki Fuji, Masayasu Nakagami and Akio Shimizu was originally published electronically on the publisher's internet portal on 27 March 2019 without open access.

scholarly journals Geographical variation in spawning histories of age-1 Pacific saury Cololabis saira in the North Pacific Ocean duringJune and July

2020 ◽  
Vol 86 (4) ◽  
pp. 252-252
Author(s):  
SATOSHI SUYAMA ◽  
HITOMI OZAWA ◽  
YASUTOKI SHIBATA ◽  
TAIKI FUJI ◽  
MASAYASU NAKAGAMI ◽  
...  
Keyword(s):  
Pacific Ocean ◽  
North Pacific ◽  
Geographical Variation ◽  
North Pacific Ocean ◽  
Pacific Saury ◽  
The North ◽  
Cololabis Saira ◽  
The North Pacific

Geographic variation in feeding of Pacific saury Cololabis saira in June and July in the North Pacific Ocean

2020 ◽  
Vol 29 (6) ◽  
pp. 558-571
Author(s):  
Hiroomi Miyamoto ◽  
Dharmamony Vijai ◽  
Hideaki Kidokoro ◽  
Kazuaki Tadokoro ◽  
Tsuyoshi Watanabe ◽  
...  
Keyword(s):  
Pacific Ocean ◽  
Geographic Variation ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Pacific Saury ◽  
The North ◽  
Cololabis Saira ◽  
The North Pacific

scholarly journals Neural Network Approach to Retrieving Ocean Subsurface Temperatures from Surface Parameters Observed by Satellites

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 388
Author(s):  
Hao Cheng ◽  
Liang Sun ◽  
Jiagen Li
Keyword(s):  
Neural Network ◽  
Pacific Ocean ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Sea Surface ◽  
Satellite Measurements ◽  
Surface Parameters ◽  
The North ◽  
The North Pacific ◽  
Bpnn Model

The extraction of physical information about the subsurface ocean from surface information obtained from satellite measurements is both important and challenging. We introduce a back-propagation neural network (BPNN) method to determine the subsurface temperature of the North Pacific Ocean by selecting the optimum input combination of sea surface parameters obtained from satellite measurements. In addition to sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS) and sea surface wind (SSW), we also included the sea surface velocity (SSV) as a new component in our study. This allowed us to partially resolve the non-linear subsurface dynamics associated with advection, which improved the estimated results, especially in regions with strong currents. The accuracy of the estimated results was verified with reprocessed observational datasets. Our results show that the BPNN model can accurately estimate the subsurface (upper 1000 m) temperature of the North Pacific Ocean. The corresponding mean square errors were 0.868 and 0.802 using four (SSH, SST, SSS and SSW) and five (SSH, SST, SSS, SSW and SSV) input parameters and the average coefficients of determination were 0.952 and 0.967, respectively. The input of the SSV in addition to the SSH, SST, SSS and SSW therefore has a positive impact on the BPNN model and helps to improve the accuracy of the estimation. This study provides important technical support for retrieving thermal information about the ocean interior from surface satellite remote sensing observations, which will help to expand the scope of satellite measurements of the ocean.

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