scholarly journals Measurements of ozone concentrations on a commercial vessel in the marine boundary layer over the northern North Pacific Ocean

2005 ◽  
Vol 110 (D11) ◽  
Author(s):  
Koichi Watanabe
Keyword(s):  
Boundary Layer ◽  
Pacific Ocean ◽  
North Pacific ◽  
Marine Boundary Layer ◽  
North Pacific Ocean ◽  
Ozone Concentrations

Particulate sulfate and nitrate in the boundary layer over the North Pacific Ocean

1985 ◽  
Vol 90 (D6) ◽  
pp. 10586-10596 ◽  
Author(s):  
J. M. Prospero ◽  
D. L. Savoie ◽  
R. T. Nees ◽  
R. A. Duce ◽  
J. Merrill
Keyword(s):  
Boundary Layer ◽  
Pacific Ocean ◽  
North Pacific ◽  
North Pacific Ocean ◽  
The North ◽  
The North Pacific

Free tropospheric and boundary layer measurements of NO over the central and eastern North Pacific Ocean

1987 ◽  
Vol 92 (D2) ◽  
pp. 2049 ◽  
Author(s):  
D. D. Davis ◽  
J. D. Bradshaw ◽  
M. O. Rodgers ◽  
S. T. Sandholm ◽  
S. KeSheng
Keyword(s):  
Boundary Layer ◽  
Pacific Ocean ◽  
North Pacific ◽  
North Pacific Ocean

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.

scholarly journals Nursery origin and population connectivity of swordfish Xiphias gladius in the North Pacific Ocean

2021 ◽  
Author(s):  
R. J. David Wells ◽  
Veronica A. Quesnell ◽  
Robert L. Humphreys ◽  
Heidi Dewar ◽  
Jay R. Rooker ◽  
...  
Keyword(s):  
Pacific Ocean ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Population Connectivity ◽  
The North ◽  
Xiphias Gladius ◽  
The North Pacific
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