Abstract:
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The Argo data is a modern oceanography dataset that provides unprecedented global coverage of temperature and salinity measurements in the upper 2,000 meters in depth of the ocean and has just begun to see research in the statistics community. We consider an analysis of the Argo data from the perspective of functional data analysis (FDA). We develop spatio-temporal FDA methodology for mean and covariance estimation to predict temperature and salinity at a fixed location as a continuous function of depth. Our approach provides advantages over existing methodologies that consider pointwise estimation at fixed depths. First, our approach naturally leverages the irregularly-sampled data in space, time, and depth, and we use data from the entire upper 2,000m of the ocean simultaneously. Second, we estimate the dependence between measurements within the same profile and provide predictions continuously in depth. Lastly, we show how properties of the functional estimates, including derivatives and integrals, can address scientific problems such as heat content or the depth of the mixed layer, which we introduce as a product of our estimation approach.
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