Sentence examples for mass eruption rate from inspiring English sources

Eruption scenarios reflect the typical eruption styles at a given volcano and are characterized by ranges of eruption source parameters (ESP) such as plume height, erupted mass, mass eruption rate (MER) and total grain size distribution (TGSD).

This approach combines time-dependant meteorological fields for the region, a spectrum of volcanological parameters (erupted mass, mass eruption rate (MER), column height and total grainsize distribution) and over 100 simulations of the ash dispersal model, FALL3D.

M thus provides a continuous measure of eruption magnitude based on estimates of erupted mass; more common is the use of the Volcanic Explosivity Index (VEI), which is discrete and assigned on the basis of either erupted volume or eruption intensity (average mass eruption rate).

Intensity is calculated using the mass eruption rate as defined by Pyle (2000): I = l o g 10 mass eruption rate k g / s + 3 (3).

The two reference scenarios analyzed correspond to a weak (mass eruption rate = 1.5 * 106 kg/s) and a strong volcanic plume (mass eruption rate = 1.5 * 109 kg/s) in absence of wind.

The height reached by the column increases with the mass eruption rate (e.g. Wilson and Walker 1987) and, for this reason, it is often used to estimate the mass eruption rate from column observations.

Numerical models of ash dispersion rely on estimates of initial plume height and mass eruption rates which remain, thus far, poorly constrained.

To verify the consistence of our model with the volcanological literature, our results were compared to independent estimates of the mass eruption rates during the Eyjafjallajökull eruption (see Table 5).

Ripepe infrasound 2.5 3.3 Ripepe et al. 2013 and Dürig et al. 2015 4) Mass eruption rates obtained by the pulse velocity derived model (PVDM) are compared with results from plume height models (PHM) and infrasound derived values.

Whilst numerical models can provide detailed forecasts of atmospheric ash dispersion patterns, they rely on accurate knowledge of key parameters such as initial plume height and mass eruption rates, which remain loosely constrained.

In recent years the use of acoustic infrasound technology has become commonplace at volcano observatories, in particular to detect eruptions in remote locations (e.g. Caplan-Auerbach and McNutt 2003; De Angelis et al. 2012; Petersen and McNutt 2006) and to measure parameters such as mass eruption rates and plume height (Caplan-Auerbach et al. 2010, Ripepe et al. 2013).