The effect of wind and plume height reconstruction methods on the accuracy of simple plume models — a second look at the 2010 Eyjafjallajökull eruption

Abstract Real-time monitoring of volcanic ash plumes with the aim to estimate the mass eruption rate is crucial for predicting atmospheric ash concentration. Mass eruption rates are usually assessed by 0D and 1D plume models, which are fast and require only a few observational input parameters, ofte...
Ausführliche Beschreibung

Abstract Real-time monitoring of volcanic ash plumes with the aim to estimate the mass eruption rate is crucial for predicting atmospheric ash concentration. Mass eruption rates are usually assessed by 0D and 1D plume models, which are fast and require only a few observational input parameters, often only the plume height. A model’s output, however, depends also on the plume height data handling strategy (sampling rate, gap reconstruction methods and statistical treatment), especially in long-term eruptions with incomplete plume height records. To represent such an eruption, we used Eyjafjallajökull 2010 to test the sensitivity of six simple and two explicitly wind-affected plume models against 22 data handling strategies. Based on photogrammetric measurements, the wind deflection of the plume was determined and used to recalibrate radar-derived height data. The resulting data was then subjected to different data handling strategies, before being used as input for the plume models. The model results were compared to the erupted mass measured on the ground, allowing us to assess the prediction accuracy of each combination of data handling strategy and model. Combinations that provide highest prediction accuracies vary, depending on data coverage, eruption intensity and fragmentation mechanism. However, for this type of moderate-to-weak eruption (VEI 3 in terms of maximum intensity), the most important factor was found to be the prevailing wind speed. When wind speeds exceed 20 m/s, most combinations of strategies and models provide predictions that underestimate the erupted mass by more than 40%. Under such conditions, the optimal choice of data handling strategy and plume model is of particular importance. Ausführliche Beschreibung