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Authors: Scollo, S.*
Prestifilippo, M.*
Coltelli, M.*
Peterson, R. A.*
Spata, G.*
Title: A Statistical Approach to Evaluate the Tephra Deposit and Ash Concentration from Puff Model Forecasts
Title of journal: Journal of Volcanolgy and Geothermal Research
Issue Date: 2-Dec-2010
DOI: 10.1016/j.jvolgeores.2010.12.004
Keywords: ash dispersal
PUFF model;
Abstract: In this paper we present a new statistical approach able to provide tephra deposit load and ash concentration using PUFF, a lagrangian model widely used to forecast volcanic ash dispersal during volcanic crisis. We perform a parametric study in order to analyze the influence of each input parameter on model outputs. For this test, we simulate two eruptive scenarios like to the 2001 (Scenario 1) and 1998 (Scenario 2) Etna eruptions using high resolution weather data and a domain of 170 x 170 km. Results show that for both scenarios, we are able to calculate the tephra deposit load and ash concentration but the use of millions of particles is required. Specifically, up to 33 and 220 millions of particles were necessary to accurately predict the tephra deposit and ash concentration in air, respectively. This is approximately two orders of magnitude larger than values typically considered running PUFF. The parametric study shows that the horizontal diffusion coefficient, the time step of the simulations, the topography and the standard deviation of the particle distribution greatly affect the model outputs. We also validate the model by best fit procedures. Results show a good comparison between field data of the 2001 Etna eruption and PUFF simulations, being inside 5 and 1/5 times the observed data, comparable with results of Eulerian models. This work will allow to reliably outlining the areas of contaminated airspace using PUFF or any other lagrangian model in order to define the No Fly Zone and ensure the safety to aviation operations as required after the Eyjafjallaj√∂kull eruption.
Appears in Collections:01.02.03. Forecasts
Papers Published / Papers in press

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