Reconstructing tephra fall deposits via ensemble-based data assimilation techniques
Abstract. 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.
Leonardo Mingari et al.
Status: final response (author comments only)
- RC1: 'Comment on gmd-2022-246', Anonymous Referee #1, 20 Dec 2022
- RC2: 'Comment on gmd-2022-246', Anonymous Referee #2, 27 Dec 2022
- RC3: 'Comment on gmd-2022-246', Matthieu Plu, 30 Dec 2022
- AC1: 'Comment on gmd-2022-246', Leonardo Mingari, 16 Mar 2023
Leonardo Mingari et al.
Leonardo Mingari et al.
Viewed (geographical distribution)
The manuscript presents two assimilation methods for semi-positive-definite variables, specifically using the FALL3D model to study the deposition of tephra from the Calbuco eruption in Chile. These methods are proposed instead of more traditional methodologies (such as Kalman filter) as they are expected to work better in the case of positive-definite variables such as volcanic aerosols and tephra deposits.
The manuscript is well-written and organised. The two methods discussed here are the Gaussian with Nonnegative Constraints (GNC) and the Gamma, Inverse-Gamma (GIG) Gaussian Ensemble Kalman Filter are presented in detail and show promising results for the case study. Tephra transport and dispersion studies suffer large uncertainties due to the difficulties with sampling as well as poorly-defined initial conditions. As such, the use of data assimilation (especially using methodologies tailored for such application) represents an important way forward.
Overall, the work presented here represents an exciting step forward and I believe merits publication after a few revisions, as follows:
My biggest concern is that I feel that a comparison against a “control” Kalman filter methodology is necessary to highlight the strength of the new methods tested. As the main aim of the paper is to show that the two methods used are better-suited for tephra, I think that it is necessary to show that a “normal” Kalman filter methodology is problematic, or at least that it leads to worse results.
A smaller point is that I believe that the discussion section could be expanded a bit by including a paragraph that discusses uses of these methodologies beyond tephra, to highlight the strength of the methodologies in other settings.
A number of smaller comments can be found in the attached PDF.
One caveat of my review is that my experience with data assimilation techniques is limited. Judging from the presentation of the techniques, the arguments made by the authors and the results presented, both of the suggested methodologies seem to provide good candidates for such applications; however, the manuscript might also benefit from an additional review from a researcher who is more active in data assimilation techniques and modelling.
I hope that the authors find the points raised constructive and I would like to wish the writers the best of luck with the revisions (and a good time over the holidays).