Estimation of ocean subsurface thermal structure from surface parameters: A neural network approach

Abstract

[1] Satellite remote sensing provides diverse and useful ocean surface observations. It is of interest to determine if such surface observations can be used to infer information about the vertical structure of the ocean's interior, like that of temperature profiles. Earlier studies used either sea surface temperature or dynamic height/sea surface height to infer the subsurface temperature profiles. In this study we have used neural network approach to estimate the temperature structure from sea surface temperature, sea surface height, wind stress, net radiation, and net heat flux, available from an Arabian Sea mooring from October 1994 to October 1995, deployed by the Woods Hole Oceanographic Institution. On the average, 50% of the estimations are within an error of ±0.5°C and 90% within ±1.0°C. The average RMS error between the estimated temperature profiles and in situ observations is 0.584°C with a depthwise average correlation coefficient of 0.92.

A Review of Ocean/Sea Subsurface Water Temperature Studies from Remote Sensing and Non-Remote Sensing Methods

“…Diverse and useful ocean subsurface observations have been provided by satellite remote sensing [41,50]. The interior dynamic parameters of oceans can be derived from subsurface and deeper ocean remote sensing, which can help us implicate climate change and determine the processes of subsurface and deeper oceans [40].…”

“…Researchers have used the relationship between sea surface and subsurface parameters to estimate the subsurface temperature structure [31,41,[50][51][52][53]. Specifically, they have used the relationships between salinity, height, and temperature, and have extended the methodology for assimilating the temperature of the subsurface of the water.…”

“…Their results showed that SSSA and SSWA, in addition to SSTA and SSHA, are useful parameters to estimate the subsurface thermal structure and improve the STA estimation accuracy. Ali et al [50] have estimated the sea subsurface water temperature from SST, SSH, wind stress, net radiation, and net heat flux by neural network approach. They illustrated a proper relationship between the estimated temperature profiles and in situ observations.…”

Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System

“…BP neutral networks are a popular neutral network architecture [49][50][51][52][53][54]. In this study, a BP neural-network model was established to estimate the RH_corrected PM2.5 concentrations.…”

Predicting cyclone tracks in the north Indian Ocean: An artificial neural network approach

“…Neural networks have been applied to a wide variety of areas like physical oceanography, biological oceanography, meteorology, acoustics, robotics and medical sciences [Nannariello and Fricke, 1998;Chen et al, 2000;Richaume et al, 2000;Pozzi et al, 2000;Schroeder et al, 2002;Bourras and Liu, 2003;Ali et al, 2004;Jain and Ali, 2006]. ANN model has been used to predict the hurricane intensity in the north Atlantic basin [Ramirez and Castro, 2006;Ramirez and Veneros, 2004].…”