Characterizing the Atmospheric Conditions Leading to Large Error Growth in Volcanic Ash Cloud Forecasts

Abstract

Volcanic ash poses an ongoing risk to safety in the airspace worldwide. The accuracy with which volcanic ash dispersion can be forecast depends on the conditions of the atmosphere into which it is emitted. In this study, meteorological ensemble forecasts are used to drive a volcanic ash transport and dispersion model for the 2010 Eyjafjallajökull eruption in Iceland. From analysis of these simulations, the authors determine why the skill of deterministic-meteorological forecasts decreases with increasing ash residence time and identify the atmospheric conditions in which this drop in skill occurs most rapidly. Large forecast errors are more likely when ash particles encounter regions of large horizontal flow separation in the atmosphere. Nearby ash particle trajectories can rapidly diverge, leading to a reduction in the forecast accuracy of deterministic forecasts that do not represent variability in wind fields at the synoptic scale. The flow-separation diagnostic identifies where and why large ensemble spread may occur. This diagnostic can be used to alert forecasters to situations in which the ensemble mean is not representative of the individual ensemble-member volcanic ash distributions. Knowledge of potential ensemble outliers can be used to assess confidence in the forecast and to avoid potentially dangerous situations in which forecasts fail to predict harmful levels of volcanic ash.

The 2019 Raikoke volcanic eruption: Part 1 Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide

“…A study by Dacre and Harvey (2018) shows that the impact of the atmospheric conditions on the NAME simulations can be large, especially in conditions of large horizontal flow separation in the atmosphere. The specific atmospheric conditions for this particular eruption (i.e.…”

Transport of Canadian forest fire smoke over the UK as observed by lidar

“…Once deposited in the free troposphere or stratosphere, forest fire smoke can travel great distances, e.g. from North America to Europe (Forster et al, 2001;Wandinger et al, 2002), from Siberia to Europe (Müller et al, 2005;Sitnov and Mokhov, 2017), from Siberia to Japan (Murayama et al, 2004) or even around the globe (Damoah et al, 2004). Long-range smoke transport has also been observed at lower latitudes, from Africa to South America (Ansmann et al, 2009).…”

The Use of Gaussian Mixture Models with Atmospheric Lagrangian Particle Dispersion Models for Density Estimation and Feature Identification

“…Over time, this gradual vertical separation due to gravitational settling can turn into large differences in the location of different particle sizes mainly because the movement to different heights causes particles to encounter different regions of the flow [29][30][31]. The amount of time for significant separation to occur is expected to vary significantly for different eruptions because it depends on the details of the atmospheric flow.…”

“…[30] tracks the temporal evolution of the fractional skill score, FSS, for a volcanic ash simulation to identify when the model forecast diverges significantly from observations. Measures of separation of pairs of particle trajectories to indicate when different simulations start to diverge could also be used as in [31]. A comprehensive comparison of the method presented here with other methods is beyond the scope of this paper, but we offer a few thoughts on how this method fits in.…”