Abstract:
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As technology progresses, the availability of massive environmental data with global spatial coverage has become quite common. An example of such data is Total Column Ozone (TCO), remotely sensed from a satellite. In their raw form, these data are often spatially (and temporally) dense, but irregular. The problem considered here is one of detecting large-scale spatial trend at a given time point (actually, in a given time interval). We propose a sequential aggregation method, producing different levels of coarser (spatial) resolution data and, at the same time, preserving both the local information content and the locations of the raw data. In estimating the large-scale trend, we consider different parameterizations of a smooth spatial trend on the sphere, all linear in the data and satisfying the topological constraints imposed by the sphere. The space-time residuals, obtained by subtracting the trend, can then be modeled using stochastic, dynamic, change-of-resolution models that respect the mass balance of TCO.
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