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Thermocline Model for Estimating Argo Sea Surface Temperature
Argo has become an important constituent of the global ocean observation system. However, due to the lack of sea surface measurements from most Argo profiles, the application of Argo data is still limited. In this study, a thermocline model was constructed based on three key thermocline parameters, i.e, thermocline upper depth, the thermocline bottom depth, and thermocline temperature gradient. Following the model, we estimated the sea surface temperature of Argo profiles by providing the relationship between sea surface and subsurface temperature. We tested the effectiveness of our proposed model using statistical analysis and by comparing the sea surface temperature with the results obtained from traditional methods and in situ observations in the Pacific Ocean. The root mean square errors of results obtained from thermocline model were found to be significantly reduced compared to the extrapolation results and satellite retrieved temperature results. The correlation coefficient between the estimation result and in situ observation was 0.967. Argo surface temperature, estimated by the thermocline model, has been theoretically proved to be reliable. Thus, our model generates theoretically feasible data present the mesoscale phenomenon in more detail. Overall, this study compensates for the lack surface observation of Argo, and provides a new tool to establish complete Argo data sets.
Thermocline Model for Estimating Argo Sea Surface Temperature
Argo has become an important constituent of the global ocean observation system. However, due to the lack of sea surface measurements from most Argo profiles, the application of Argo data is still limited. In this study, a thermocline model was constructed based on three key thermocline parameters, i.e, thermocline upper depth, the thermocline bottom depth, and thermocline temperature gradient. Following the model, we estimated the sea surface temperature of Argo profiles by providing the relationship between sea surface and subsurface temperature. We tested the effectiveness of our proposed model using statistical analysis and by comparing the sea surface temperature with the results obtained from traditional methods and in situ observations in the Pacific Ocean. The root mean square errors of results obtained from thermocline model were found to be significantly reduced compared to the extrapolation results and satellite retrieved temperature results. The correlation coefficient between the estimation result and in situ observation was 0.967. Argo surface temperature, estimated by the thermocline model, has been theoretically proved to be reliable. Thus, our model generates theoretically feasible data present the mesoscale phenomenon in more detail. Overall, this study compensates for the lack surface observation of Argo, and provides a new tool to establish complete Argo data sets.
Thermocline Model for Estimating Argo Sea Surface Temperature
Zhang ChunLing (author) / Zhang Meng-Li (author) / Wang Zhen-Feng (author) / Hu Song (author) / Wang Dan-Yang (author) / Yang Sheng-Long (author)
2022
Article (Journal)
Electronic Resource
Unknown
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