Reconstructing tephra fall deposits via ensemble-based data assimilation techniques

In recent years, there has been a growing interest in ensemble approaches for modelling the atmospheric transport of volcanic aerosol, ash, andlapilli (tephra). The development of such techniques enables the exploration of novel methods for incorporating real observations into tephradispersal models. However, traditional data assimilation algorithms, including ensemble Kalman filter (EnKF) methods, can yield suboptimal stateestimates for positive-definite variables such as those related to volcanic aerosols and tephra deposits. This study proposes two new ensemble-based dataassimilation techniques for semi-positive-definite variables with highly skewed uncertainty distributions, including aerosol concentrations andtephra deposit mass loading: the Gaussian with non-negative constraints (GNC) and gamma inverse-gamma (GIG) methods. The proposed methods are applied to reconstruct the tephra fallout deposit resulting from the2015 Calbuco eruption using an ensemble of 256 runs performed with the FALL3D dispersal model. An assessment of the methodologies is conductedconsidering two independent datasets of deposit thickness measurements: an assimilation dataset and a validation dataset. Different evaluationmetrics (e.g. RMSE, MBE, and SMAPE) are computed for the validation dataset, and the results are compared to two references: the ensemble prior mean andthe EnKF analysis. Results show that the assimilation leads to a significant improvement over the first-guess results obtained from the simpleensemble forecast. The evidence from this study suggests that the GNC method was the most skilful approach and represents a promising alternative forassimilation of volcanic fallout data. The spatial distributions of the tephra fallout deposit thickness and volume according to the GNC analysis arein good agreement with estimations based on field measurements and isopach maps reported in previous studies. On the other hand, although it is aninteresting approach, the GIG method failed to improve the EnKF analysis.