Interannual Sea Level Variability in the North Pacific Ocean and Its Mechanisms

2010 ◽  
Vol 53 (1) ◽  
pp. 54-63 ◽  
Author(s):  
Yong-Chui ZHANG ◽  
Li-Feng ZHANG ◽  
Ye-Gui WANG
Keyword(s):  
Pacific Ocean ◽  
Sea Level ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Sea Level Variability ◽  
The North ◽  
The North Pacific

Machine Computations of Mass Transport in the North Pacific Ocean

1962 ◽  
Vol 19 (6) ◽  
pp. 1121-1141 ◽  
Author(s):  
N. P. Fofonoff
Keyword(s):  
Pacific Ocean ◽  
Mass Transport ◽  
Sea Level ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Theoretical Background ◽  
Mean Sea Level ◽  
The North ◽  
Geostrophic Velocities ◽  
The North Pacific

Recently, a series of computer programs was developed by the Fisheries Research Board of Canada Pacific Oceanographic Group to enable more intensive studies to be carried out of the effects of winds over the ocean surface on the circulation in the ocean. The programs are used to compute geostrophic velocities and mass transports from oceanographic station data, Ekman and total transports from mean sea-level atmospheric pressure.The theoretical background for the programs is developed and examples of the results obtained are given. More complete discussions of the results will be given as processed information is accumulated.

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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