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2006•Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIERequires access

Estimation of upper ocean heat content from remote sensing observations in the Arabian Sea

P. S. V. Jagadeesh, M. M. Ali

Open publisher page 5 citations

Abstract

In this paper, we attempted to estimate the upper ocean heat content from Sea Surface Height Anomaly (SSHA), Sea Surface Temperature (SST) and Wind Stress Curl (WSC) obtained from satellite observations through Artificial Neural Networks approach. For this purpose, we analyzed the monthly heat content derived from 3D-model, SSHA, SST and WSC during 2000-2003. Due to non availability of Argo profiles at all locations during all the seasons, estimation of heat content is not possible at different time scales. 3D-model derived heat content is validated with Argo heat content during 2003. We have developed a model for the Arabian Sea to estimate upper ocean heat content, with a standard deviation error of 0.05E+09 J/m2. In estimating the Heat Content (HC) we also studied the effect of each parameter through ANN model.

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What this paper is about

In this paper, we attempted to estimate the upper ocean heat content from Sea Surface Height Anomaly (SSHA), Sea Surface Temperature (SST) and Wind Stress Curl (WSC) obtained from satellite observations through Artificial Neural Networks approach. For this purpose, we analyzed the monthly heat content derived from 3D-model, SSHA, SST and WSC during 2000-2003. Due to non availability of Argo profiles at all locations during all the seasons, estimation of heat content is not possible at different time scales. 3D-model derived heat content is validated with Argo heat content during 2003. We have developed a model for the Arabian Sea to estimate upper ocean heat content, with a standard deviation error of 0.05E+09 J/m2. In estimating the Heat Content (HC) we also studied the effect of each parameter through ANN model.

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Available abstract

In this paper, we attempted to estimate the upper ocean heat content from Sea Surface Height Anomaly (SSHA), Sea Surface Temperature (SST) and Wind Stress Curl (WSC) obtained from satellite observations through Artificial Neural Networks approach. For this purpose, we analyzed the monthly heat content derived from 3D-model, SSHA, SST and WSC during 2000-2003. Due to non availability of Argo profiles at all locations during all the seasons, estimation of heat content is not possible at different time scales. 3D-model derived heat content is validated with Argo heat content during 2003. We have developed a model for the Arabian Sea to estimate upper ocean heat content, with a standard deviation error of 0.05E+09 J/m2. In estimating the Heat Content (HC) we also studied the effect of each parameter through ANN model.

Key concepts: Argo, Ocean heat content, Sea surface temperature, Sea-surface height, Environmental science, Satellite, Anomaly (physics), Content (measure theory)

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