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Modelling atmospheric dispersal of volcanic ash and aerosols is becoming increasingly valuable for assessing the potential impacts of explosive volcanic eruptions on buildings, air quality, and aviation. Management of volcanic risk and reduction of aviation impacts can strongly benefit from quantitative forecasting of volcanic ash. However, an accurate prediction of volcanic aerosol concentrations using numerical modelling relies on proper estimations of multiple model parameters which are prone to errors. Uncertainties in key parameters such as eruption column height and physical properties of particles or meteorological fields represent a major source of error affecting the forecast quality. The availability of near-real-time geostationary satellite observations with high spatial and temporal resolutions provides the opportunity to improve forecasts in an operational context by incorporating observations into numerical models. Specifically, ensemble-based filters aim at converting a prior ensemble of system states into an analysis ensemble by assimilating a set of noisy observations. Previous studies dealing with volcanic ash transport have demonstrated that a significant improvement of forecast skill can be achieved by this approach. In this work, we present a new implementation of an ensemble-based data assimilation (DA) method coupling the FALL3D dispersal model and the Parallel Data Assimilation Framework (PDAF). The FALL3D+PDAF system runs in parallel, supports online-coupled DA, and can be efficiently integrated into operational workflows by exploiting high-performance computing (HPC) resources. Two numerical experiments are considered: (i) a twin experiment using an incomplete dataset of synthetic observations of volcanic ash and (ii) an experiment based on the 2019 Raikoke eruption using real observations of SO2 mass loading. An ensemble-based Kalman filtering technique based on the local ensemble transform Kalman filter (LETKF) is used to assimilate satel, We acknowledge the Partnership for Advanced Computing in Europe (PRACE) for awarding us access to the Joliot-Curie supercomputer at the CEA’s Very Large Computing Center (TGCC, France). Andrew T. Prata acknowledges funding from the European Commission, H2020 Marie Skłodowska-Curie Actions (STARS (grant no. 754433)). We thank the anonymous reviewers for their insightful comments and suggestions., Peer Reviewed, Postprint (published version)

Modelling atmospheric dispersal of volcanic ash and aerosols is becoming increasingly valuable for assessing the potential impacts of explosive volcanic eruptions on buildings, air quality, and aviation. Management of volcanic risk and reduction of aviation impacts can strongly benefit from quantitative forecasting of volcanic ash. However, an accurate prediction of volcanic aerosol concentrations using numerical modelling relies on proper estimations of multiple model parameters which are prone to errors. Uncertainties in key parameters such as eruption column height and physical properties of particles or meteorological fields represent a major source of error affecting the forecast quality. The availability of near-real-time geostationary satellite observations with high spatial and temporal resolutions provides the opportunity to improve forecasts in an operational context by incorporating observations into numerical models. Specifically, ensemble-based filters aim at converting a prior ensemble of system states into an analysis ensemble by assimilating a set of noisy observations. Previous studies dealing with volcanic ash transport have demonstrated that a significant improvement of forecast skill can be achieved by this approach. In this work, we present a new implementation of an ensemble-based data assimilation (DA) method coupling the FALL3D dispersal model and the Parallel Data Assimilation Framework (PDAF). The FALL3D+PDAF system runs in parallel, supports online-coupled DA, and can be efficiently integrated into operational workflows by exploiting high-performance computing (HPC) resources. Two numerical experiments are considered: (i) a twin experiment using an incomplete dataset of synthetic observations of volcanic ash and (ii) an experiment based on the 2019 Raikoke eruption using real observations of SO2 mass loading. An ensemble-based Kalman filtering technique based on the local ensemble transform Kalman filter (LETKF) is used to assimilate satel

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 sin

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 out, This work has been partially funded by the H2020 Center of Excellence for Exascale in Solid Earth (ChEESE) under the Grant Agreement 823 844 and the multi-year PRACE Project Access “Volcanic Ash Hazard and Forecast” (ID 2019 215 114)., Peer Reviewed, Postprint (published version)

Wildfires have become one of the biggest natural hazards for environments worldwide. The effects of wildfires are heterogeneous, meaning that the magnitude of their effects depends on many factors such as geographical region, climate and land cover/vegetation type. Yet, which areas are more affected by these events remains unclear. Here we present a novel application of the Generalized Synthetic Control (GSC) method that enables quantification and prediction of vegetation changes due to wildfires through a time-series analysis of in situ and satellite remote sensing data. We apply this method to medium to large wildfires (> 1000 acres) in California throughout a time-span of two decades (1996–2016). The method's ability for estimating counterfactual vegetation characteristics for burned regions is explored in order to quantify abrupt system changes. We find that the GSC method is better at predicting vegetation changes than the more traditional approach of using nearby regions to assess wildfire impacts. We evaluate the GSC method by comparing its predictions of spectral vegetation indices to observations during pre-wildfire periods and find improvements in correlation coefficient from R2 = 0.66 to R2 = 0.93 in Normalized Difference Vegetation Index (NDVI), from R2 = 0.48 to R2 = 0.81 for Normalized Burn Ratio (NBR), and from R2 = 0.49 to R2 = 0.85 for Normalized Difference Moisture Index (NDMI). Results show greater changes in NDVI, NBR, and NDMI post-fire on regions classified as having a lower Burning Index. We find that on average, wildfires cause a 25% initial decrease in the vegetation index (NDVI) and a larger than 80% drop in wetness indices (NBR and NDMI) after they occur. The GSC method also reveals that wildfire effects on vegetation can last for more than a decade post-wildfire, and in some cases never return to their previous vegetation cycles within our study period. We also find that the dynamical effects vary across regions and have an impact on seas, F. S.-B. would also like to thank the Barcelona Supercomputing Center for the Severo Ochoa Mobility Grant, and Delicado would like to thank the Spanish Ministerio de Ciencia e Innovación for the grant MTM2017-88142-P, and A. T. P. acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433., Peer Reviewed, Postprint (author's final draft)

This paper presents model validation results for the latest version release of the FALL3D atmospheric transport model. The code has been redesigned from scratch to incorporate different categories of species and to overcome legacy issues that precluded its preparation towards extreme-scale computing. The model validation is based on the new FALL3D-8.0 test suite, which comprises a set of four real case studies that encapsulate the major features of the model; namely, the simulation of long-range fine volcanic ash dispersal, volcanic SO2 dispersal, tephra fallout deposits and the dispersal and deposition of radionuclides. The first two test suite cases (i.e. the June 2011 Puyehue-Cordón Caulle ash cloud and the June 2019 Raikoke SO2 cloud) are validated against geostationary satellite retrievals and demonstrate the new FALL3D data insertion scheme. The metrics used to validate the volcanic ash and SO2 simulations are the structure, amplitude and location (SAL) metric and the figure of merit in space (FMS). The other two test suite cases (i.e. the February 2013 Mt. Etna ash cloud and associated tephra fallout deposit, and the dispersal of radionuclides resulting from the 1986 Chernobyl nuclear accident) are validated with scattered ground-based observations of deposit load and local particle grain size distributions and with measurements from the Radioactivity Environmental Monitoring database. For validation of tephra deposit loads and radionuclides, we use two variants of the normalised root-mean-square error metric. We find that FALL3D-8.0 simulations initialised with data insertion consistently improve agreement with satellite retrievals at all lead times up to 48 h for both volcanic ash and SO2 simulations. In general, SAL scores lower than 1.5 and FMS scores greater than 0.40 indicate acceptable agreement with satellite retrievals of volcanic ash and SO2. In addition, we show very good agreement, across several orders of magnitude, between the model and observat, This research has been supported by the European Commission, H2020 Excellence Science (ChEESE (grant no. 823844)), the European Commission, H2020 Marie Skłodowska-Curie Actions (STARS (grant no. 754433)), the European Commission, H2020 Research Infrastructures (EUROVOLC (grant no. 731070)) and the Ministero dell'Istruzione, dell'Università e della Ricerca (grant no. 805 FOE 2015)., Peer Reviewed, Postprint (published version)

This paper presents model validation results for the latest version release of the FALL3D atmospheric transport model. The code has been redesigned from scratch to incorporate different categories of species and to overcome legacy issues that precluded its preparation towards extreme-scale computing. The model validation is based on the new FALL3D-8.0 test suite, which comprises a set of four real case studies that encapsulate the major features of the model; namely, the simulation of long-range fine volcanic ash dispersal, volcanic SO2 dispersal, tephra fallout deposits and the dispersal and deposition of radionuclides. The first two test suite cases (i.e. the June 2011 Puyehue-Cordón Caulle ash cloud and the June 2019 Raikoke SO2 cloud) are validated against geostationary satellite retrievals and demonstrate the new FALL3D data insertion scheme. The metrics used to validate the volcanic ash and SO2 simulations are the structure, amplitude and location (SAL) metric and the figure of merit in space (FMS). The other two test suite cases (i.e. the February 2013 Mt. Etna ash cloud and associated tephra fallout deposit, and the dispersal of radionuclides resulting from the 1986 Chernobyl nuclear accident) are validated with scattered ground-based observations of deposit load and local particle grain size distributions and with measurements from the Radioactivity Environmental Monitoring database. For validation of tephra deposit loads and radionuclides, we use two variants of the normalised root-mean-square error metric. We find that FALL3D-8.0 simulations initialised with data insertion consistently improve agreement with satellite retrievals at all lead times up to 48¿h for both volcanic ash and SO2 simulations. In general, SAL scores lower than 1.5 and FMS scores greater than 0.40 indicate acceptable agreement with satellite retrievals of volcanic ash and SO2. In addition, we show very good agreement, across several orders of magnitude, between the model and observat

Volcanic activity occurring in tropical moist atmospheres can promote deep convection and trigger volcanic thunderstorms. These phenomena, however, are rarely observed to last continuously for more than a day and so insights into the dynamics, microphysics and electrification processes are limited. Here we present a multidisciplinary study on an extreme case, where volcanically-triggered deep convection lasted for six days. We show that this unprecedented event was caused and sustained by phreatomagmatic activity at Anak Krakatau volcano, Indonesia during 22–28 December 2018. Our modelling suggests an ice mass flow rate of ~5 × 106 kg/s for the initial explosive eruption associated with a flank collapse. Following the flank collapse, a deep convective cloud column formed over the volcano and acted as a ‘volcanic freezer’ containing ~3 × 109 kg of ice on average with maxima reaching ~1010 kg. Our satellite analyses reveal that the convective anvil cloud, reaching 16–18 km above sea level, was ice-rich and ash-poor. Cloud-top temperatures hovered around −80 °C and ice particles produced in the anvil were notably small (effective radii ~20 µm). Our analyses indicate that vigorous updrafts (>50 m/s) and prodigious ice production explain the impressive number of lightning flashes (~100,000) recorded near the volcano from 22 to 28 December 2018. Our results, together with the unique dataset we have compiled, show that lightning flash rates were strongly correlated (R = 0.77) with satellite-derived plume heights for this event., We thank Dr. Leonardo Mingari of the Barcelona Supercomputing Center for extracting and processing the ERA5 profiles used in our analysis. We thank Dr. Lieven Clarisse from the Free University of Brussels for providing SO2 columns and heights from IASI-A&B. A.T.P. acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433. A.F. and A.C. have been partially funded by the H2020 Center of Excellence for Exascale in Solid Earth (ChEESE) under the Grant Agreement 823844. A.C. acknowledges the LMU Center of Advanced Studies fellowship “Magma to Tephra: Ash in the Earth System”, the European project EUROVOLC (grant agreement number 731070), and the MIUR project Premiale Ash-RESILIENCE. C.C. acknowledges the support of Deutsche Forschungsgemeinschaft project CI 254/2-1. R.B. acknowledges funding from the VESUVIO project within the Supporting Talent in ReSearch (STARS) grant at Università degli Studi di Padova, IT. This paper contains modified Copernicus Sentinel data [2019] processed by A.T.P., Peer Reviewed, Postprint (published version)