2020
DOI: 10.3390/atmos11040359
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Ensemble-Based Data Assimilation of Volcanic Ash Clouds from Satellite Observations: Application to the 24 December 2018 Mt. Etna Explosive Eruption

Abstract: Accurate tracking and forecasting of ash dispersal in the atmosphere and quantification of its uncertainty are of fundamental importance for volcanic risk mitigation. Numerical models and satellite sensors offer two complementary ways to monitor ash clouds in real time, but limits and uncertainties affect both techniques. Numerical forecasts of volcanic clouds can be improved by assimilating satellite observations of atmospheric ash mass load. In this paper, we present a data assimilation procedure aimed at im… Show more

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Cited by 30 publications
(20 citation statements)
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References 87 publications
(73 reference statements)
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“…Our microphysical model, used to parameterise a volcanic ash cloud in the radiative transfer calculations, assumes that ash particles are spherical, composed of andesite and conform to a lognormal size distribution with a spread equal to 0.5 (geometric standard deviation σ g = 1.65), similar to existing operational volcanic ash retrieval algorithms (e.g. Francis et al, 2012;Pavolonis et al, 2013). The retrieval scheme relies on interpolating precomputed LuTs generated by conducting radiative transfer calculations made for varying values of r e , τ , T s and T c .…”
Section: Discussionmentioning
confidence: 99%
“…Our microphysical model, used to parameterise a volcanic ash cloud in the radiative transfer calculations, assumes that ash particles are spherical, composed of andesite and conform to a lognormal size distribution with a spread equal to 0.5 (geometric standard deviation σ g = 1.65), similar to existing operational volcanic ash retrieval algorithms (e.g. Francis et al, 2012;Pavolonis et al, 2013). The retrieval scheme relies on interpolating precomputed LuTs generated by conducting radiative transfer calculations made for varying values of r e , τ , T s and T c .…”
Section: Discussionmentioning
confidence: 99%
“…At each observation point, this results on a 2 × 2 model-observations "contingency table" (true positives, true negatives, false positives, false negatives), from which a series of "geometric-based" or "contour-based" categorical metrics can be constructed, e.g., the probability of detection, the false alarm rate, etc. (Marti and Folch, 2018;Pardini et al, 2020). In this section, several classical categorical metrics widely used in deterministic forecast contexts are generalised to probabilistic forecasts with the objective of having a same set of forecast skill scores usable in both contexts.…”
Section: Generalised Categorical Metricsmentioning
confidence: 99%
“…Added to these, ensembles can also be used as multiple trial simulations, e.g., in optimal source term inversions by calculating correlations between the different members and observations (e.g., Zidikheri et al, 2017;Zidikheri et al, 2018;Harvey et al, 2020) or to make more robust in flight-planning decisions (Prata et al, 2019). Finally, ensembles are also the backbone of most modern data assimilation techniques, which require estimates of forecast uncertainty to merge a priori forecasts with observations during data assimilation cycles (e.g., Fu et al, 2015;Fu et al, 2017;Osores et al, 2020;Pardini et al, 2020).…”
Section: Introductionmentioning
confidence: 99%
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“…A recent overview of methods and issues in coupled DA is provided by Penny et al (2017). By now the weakly coupled assimilation is the common choice for assimilation into L. Nerger et al: Building a coupled data assimilation system coupled models and recent studies assess the effect of this assimilation approach.…”
Section: Introductionmentioning
confidence: 99%