A method for estimating sea surface nitrate concentrations from remotely sensed SST and chlorophyll a-a case study for the north Pacific Ocean using OCTS/ADEOS data

1999 ◽  
Vol 37 (3) ◽  
pp. 1633-1644 ◽  
Author(s):  
J.I. Goes ◽  
T. Saino ◽  
H. Oaku ◽  
D.L. Jiang
Keyword(s):  
Pacific Ocean ◽  
Chlorophyll A ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Remotely Sensed ◽  
Sea Surface ◽  
The North ◽  
Nitrate Concentrations ◽  
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.

scholarly journals Regional Dependence of Atmospheric Responses to Oceanic Eddies in the North Pacific Ocean

2020 ◽  
Vol 12 (7) ◽  
pp. 1161 ◽  
Author(s):  
Jinlin Ji ◽  
Jing Ma ◽  
Changming Dong ◽  
John Chiang ◽  
Dake Chen
Keyword(s):  
Wind Speed ◽  
Pacific Ocean ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
The North ◽  
Oceanic Eddies ◽  
The North Pacific

Based on sea surface height anomaly (SSHA) from satellite altimeter and microwave radiometer datasets, this study investigates atmospheric responses to oceanic eddies in four subdomains of the North Pacific Ocean with strongest eddy activity: Kuroshio Extension (KE), Subtropical Front (SF), California Coastal Current (CC) and Aleutian Islands (AI). Analyses show that anticyclonic eddies cause sea surface temperature, surface wind speed and precipitation rate to increase in all four subdomains, and vice versa. Through a further examination of the regional dependence of atmospheric responses to oceanic eddies, it is found that the strongest and the weakest surface wind speed responses (in winter and summer) are observed in the KE and AI region, respectively. For precipitation rate, seasonal variation of the atmospheric responses to oceanic eddies is strongest in winter and weakest in summer in the KE, CC and AI regions, but stronger in summer in the SF area. The reasons for such regional dependence and seasonality are the differences in the strength of SST anomalies, the vertical kinetic energy flux and atmospheric instability in the four subdomains.

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