Reconstructing tephra fall deposits via ensemble-based data assimilation techniques

Abstract

In recent years, there has been a growing inter- est in ensemble approaches for modelling the atmospheric transport of volcanic aerosol, ash, and lapilli (tephra). The development of such techniques enables the exploration of novel methods for incorporating real observations into tephra dispersal models. However, traditional data assimilation al- gorithms, including ensemble Kalman filter (EnKF) meth- ods, can yield suboptimal state estimates for positive-definite variables such as those related to 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, in- cluding aerosol concentrations and tephra deposit mass load- ing: the Gaussian with non-negative constraints (GNC) and gamma inverse-gamma (GIG) methods. The proposed meth- ods are applied to reconstruct the tephra fallout deposit re- sulting from the 2015 Calbuco eruption using an ensemble of 256 runs performed with the FALL3D dispersal model. An assessment of the methodologies is conducted consider- ing two independent datasets of deposit thickness measure- ments: an assimilation dataset and a validation dataset. Dif- ferent evaluation metrics (e.g. RMSE, MBE, and SMAPE) are computed for the validation dataset, and the results are compared to two references: the ensemble prior mean and the EnKF analysis. Results show that the assimilation leads to a significant improvement over the first-guess results ob- tained from the simple ensemble forecast. The evidence from this study suggests that the GNC method was the most skilful approach and represents a promising alternative for assimila- tion of volcanic fallout data. The spatial distributions of the tephra fallout deposit thickness and volume according to the GNC analysis are in good agreement with estimations based on field measurements and isopach maps reported in previ- ous studies. On the other hand, although it is an interesting approach, the GIG method failed to improve the EnKF analysis.