Reconstructing tephra fall deposits via ensemble-based data assimilation techniques
<p>In recent years, there has been a growing interest 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 dispe...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2023-06-01
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Series: | Geoscientific Model Development |
Online Access: | https://gmd.copernicus.org/articles/16/3459/2023/gmd-16-3459-2023.pdf |
Summary: | <p>In recent years, there has been a growing interest 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 algorithms, including ensemble Kalman filter (EnKF) methods, 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, including aerosol concentrations and
tephra 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 the
2015 Calbuco eruption using an ensemble of 256 runs performed with the FALL3D dispersal model. An assessment of the methodologies is conducted
considering two independent datasets of deposit thickness measurements: an assimilation dataset and a validation dataset. Different 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 obtained 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
assimilation 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 previous studies. On the other hand, although it is an
interesting approach, the GIG method failed to improve the EnKF analysis.</p> |
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ISSN: | 1991-959X 1991-9603 |