Ensemble-Based Forecast of Volcanic Clouds Using FALL3D-8.1
Operational forecasting of volcanic ash and SO2 clouds is challenging due to the large uncertainties that typically exist on the eruption source term and the mass removal mechanisms occurring downwind. Current operational forecast systems build on single-run deterministic scenarios that do not accou...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-01-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2021.741841/full |
| _version_ | 1818975500883722240 |
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| author | Arnau Folch Arnau Folch Leonardo Mingari Andrew T. Prata Andrew T. Prata |
| author_facet | Arnau Folch Arnau Folch Leonardo Mingari Andrew T. Prata Andrew T. Prata |
| author_sort | Arnau Folch |
| collection | DOAJ |
| description | Operational forecasting of volcanic ash and SO2 clouds is challenging due to the large uncertainties that typically exist on the eruption source term and the mass removal mechanisms occurring downwind. Current operational forecast systems build on single-run deterministic scenarios that do not account for model input uncertainties and their propagation in time during transport. An ensemble-based forecast strategy has been implemented in the FALL3D-8.1 atmospheric dispersal model to configure, execute, and post-process an arbitrary number of ensemble members in a parallel workflow. In addition to intra-member model domain decomposition, a set of inter-member communicators defines a higher level of code parallelism to enable future incorporation of model data assimilation cycles. Two types of standard products are automatically generated by the ensemble post-process task. On one hand, deterministic forecast products result from some combination of the ensemble members (e.g., ensemble mean, ensemble median, etc.) with an associated quantification of forecast uncertainty given by the ensemble spread. On the other hand, probabilistic products can also be built based on the percentage of members that verify a certain threshold condition. The novel aspect of FALL3D-8.1 is the automatisation of the ensemble-based workflow, including an eventual model validation. To this purpose, novel categorical forecast diagnostic metrics, originally defined in deterministic forecast contexts, are generalised here to probabilistic forecasts in order to have a unique set of skill scores valid to both deterministic and probabilistic forecast contexts. Ensemble-based deterministic and probabilistic approaches are compared using different types of observation datasets (satellite cloud detection and retrieval and deposit thickness observations) for the July 2018 Ambae eruption in the Vanuatu archipelago and the April 2015 Calbuco eruption in Chile. Both ensemble-based approaches outperform single-run simulations in all categorical metrics but no clear conclusion can be extracted on which is the best option between these two. |
| first_indexed | 2024-12-20T15:56:57Z |
| format | Article |
| id | doaj.art-e4199baf892740fb8d80283dd60b2cec |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-12-20T15:56:57Z |
| publishDate | 2022-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-e4199baf892740fb8d80283dd60b2cec2022-12-21T19:34:26ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632022-01-01910.3389/feart.2021.741841741841Ensemble-Based Forecast of Volcanic Clouds Using FALL3D-8.1Arnau Folch0Arnau Folch1Leonardo Mingari2Andrew T. Prata3Andrew T. Prata4Geociencias Barcelona (GEO3BCN-CSIC), Barcelona, SpainBarcelona Supercomputing Center (BSC), Barcelona, SpainBarcelona Supercomputing Center (BSC), Barcelona, SpainBarcelona Supercomputing Center (BSC), Barcelona, SpainSub-Department of Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, United KingdomOperational forecasting of volcanic ash and SO2 clouds is challenging due to the large uncertainties that typically exist on the eruption source term and the mass removal mechanisms occurring downwind. Current operational forecast systems build on single-run deterministic scenarios that do not account for model input uncertainties and their propagation in time during transport. An ensemble-based forecast strategy has been implemented in the FALL3D-8.1 atmospheric dispersal model to configure, execute, and post-process an arbitrary number of ensemble members in a parallel workflow. In addition to intra-member model domain decomposition, a set of inter-member communicators defines a higher level of code parallelism to enable future incorporation of model data assimilation cycles. Two types of standard products are automatically generated by the ensemble post-process task. On one hand, deterministic forecast products result from some combination of the ensemble members (e.g., ensemble mean, ensemble median, etc.) with an associated quantification of forecast uncertainty given by the ensemble spread. On the other hand, probabilistic products can also be built based on the percentage of members that verify a certain threshold condition. The novel aspect of FALL3D-8.1 is the automatisation of the ensemble-based workflow, including an eventual model validation. To this purpose, novel categorical forecast diagnostic metrics, originally defined in deterministic forecast contexts, are generalised here to probabilistic forecasts in order to have a unique set of skill scores valid to both deterministic and probabilistic forecast contexts. Ensemble-based deterministic and probabilistic approaches are compared using different types of observation datasets (satellite cloud detection and retrieval and deposit thickness observations) for the July 2018 Ambae eruption in the Vanuatu archipelago and the April 2015 Calbuco eruption in Chile. Both ensemble-based approaches outperform single-run simulations in all categorical metrics but no clear conclusion can be extracted on which is the best option between these two.https://www.frontiersin.org/articles/10.3389/feart.2021.741841/fullensemble forecastvolcanic cloudsFALL3D modelcategorical metricsAmbae eruptionCalbuco eruption |
| spellingShingle | Arnau Folch Arnau Folch Leonardo Mingari Andrew T. Prata Andrew T. Prata Ensemble-Based Forecast of Volcanic Clouds Using FALL3D-8.1 Frontiers in Earth Science ensemble forecast volcanic clouds FALL3D model categorical metrics Ambae eruption Calbuco eruption |
| title | Ensemble-Based Forecast of Volcanic Clouds Using FALL3D-8.1 |
| title_full | Ensemble-Based Forecast of Volcanic Clouds Using FALL3D-8.1 |
| title_fullStr | Ensemble-Based Forecast of Volcanic Clouds Using FALL3D-8.1 |
| title_full_unstemmed | Ensemble-Based Forecast of Volcanic Clouds Using FALL3D-8.1 |
| title_short | Ensemble-Based Forecast of Volcanic Clouds Using FALL3D-8.1 |
| title_sort | ensemble based forecast of volcanic clouds using fall3d 8 1 |
| topic | ensemble forecast volcanic clouds FALL3D model categorical metrics Ambae eruption Calbuco eruption |
| url | https://www.frontiersin.org/articles/10.3389/feart.2021.741841/full |
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