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Abstract:
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Snow density estimates below the surface, used with airplane-acquired ice-penetrating radar measurements, give a site-specific history of snow water accumulation. Because it is infeasible to drill snow cores across all of Antarctica to measure snow density and because it is critical to understand how climatic changes are affecting the world's largest freshwater reservoir, we develop methods that enable snow density estimation with uncertainty in regions where snow cores have not been drilled.
We present a novel class of integrated spatial process models that allow the interpolation of monotonically increasing snow density curves. For computational feasibility, we construct the space-depth process through kernel convolutions of log-Gaussian spatial processes. We discuss model comparison, model fitting, and prediction. Using this model, we extend estimates of snow density beyond the depth of the original core and estimate snow density curves where snow cores have not been drilled. We use interpolated snow density curves to estimate recent water accumulation and find predominantly decreasing water accumulation over recent decades.
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