Comment on gmd-2022-246

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> In recent years, there has been a growing interest in ensemble approaches for modelling volcanic plumes. The development of such techniques enables the exploration of novel methods for incorporating real observations into tephra dispersal models. However, traditional data assimilation algorithms, including ensemble Kalman filter methods, can yield suboptimal state estimates for positive-definite variables such as volcanic aerosols and tephra deposits. This study proposes two new ensemble-based data assimilation techniques for semi-positive-definite variables with highly skewed uncertainty distributions, including aerosol concentrations and tephra deposit mass loading. The proposed methods are applied to reconstruct the tephra fallout deposit resulting from the 2015 Calbuco eruption using an ensemble of 256 runs performed with the FALL3D dispersal model. Two datasets of deposit thickness measurements are considered: an assimilation dataset including 161 observations, and a validation dataset for an independent assessment of the methods. Results show that the assimilation leads to a significant improvement over the first-guess results, obtained from the simple ensemble forecast. The spatial distribution of the tephra fallout deposit thickness and the ashfall volume according to the analyses are in good agreement with estimations based on field measurements and isopach maps reported in previous studies. Both assimilation methods show a similar performance in terms of evaluation metrics and spatial distribution of the deposit. Finally, the potential application of the methodologies for the improvement of ash-cloud forecasts produced for operational models is also discussed.