Abstract
Defining spatial distribution of airborne volcanic ash in the neighbourhood of an erupting volcano is a synoptic scale problem, severely impacting lives and livelihoods. Robust algorithms are needed to model such complex phenomenon from sparse field data. This study investigated optimal modelling of the spatial dispersion of ash using Empirical Bayesian Kriging (EBK): a geostatistical, probabilistic algorithm. Both distance and ash temperature values of samples from the 2010 Icelandic eruption were spatially correlated using semivariograms to generate prediction and error surfaces. Results showed that block averages were 90% accurate as validated against NCEP NWP model data. The work supports the utility of EBK in datasets where spatial autocorrelation is not significant. Furthermore, the results could help generate risk maps to delineate safety zones for aircrafts.
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