부산대학교 도서관

Generation of spatial profiles of airborne volcanic ash that is spread at synoptic scales is a problem that directly impacts lives and properties. Robust algorithms are needed to model the distribution using sparse data sampled in the neighborhood of an erupting volcano. Existing Numerical Weather Prediction (NWP) algorithms model the dispersion at coarser spatial resolutions. In this study, we evaluate a geospatial interpolation technique called Kriging [1] to generate prediction and error surfaces. Location and temperature values of ash from 2010 Icelandic eruption were spatially autocorrelated using a stochastic kriging method, known as Empirical Bayesian Kriging (EBK) [7]. The EBK estimates were rigorously validated against NWP for regions with varying sample densities. Subsequently, a method to generate an accurate overlay map using EBK estimates to augment NWP outputs is proposed to aid in categorization and mapping of safety zones.