Assessing potential impacts on the air traffic routes due to an ash-producing eruption on Jan Mayen Island (Norway)

<p>Jan Mayen Island (Norway), located in the North Atlantic, is considered the world’s northernmost active subaerial volcano, with at least five eruptive periods recorded during the last 200 years. Explosive activity of the volcano may seriously affects the nearby important air traffic routes. However, no quantitative studies on the possible impact of a new explosive volcanic eruption on the air traffic have been conducted.  In this work, we statistically characterise the spatial and temporal distribution of airborne volcanic ash cloud and its persistence at different flight levels. Since current operational forecast products do not always meet the requirements of the aviation sector and related stakeholders (using coarse time and space scales, with outputs on a 40 km horizontal resolution grid and 6 hour time averages), and they neglect epistemic/aleatory uncertainties in quantitative forecasts on real time, we propose hourly high resolution hazard maps over a 3D-grid covering a 2 km-resolution spatial domain 2000 km x 2000 km wide. We present the use of high-performance computing (HPC) to overcome the computational limitations associated with unbiased long-term probabilistic volcanic hazard assessment (PVHA) .Considering a continuum of possible combinations of Eruptive Source Parameters (ESP) to assess and quantify the uncertainty, and the natural variability associated with wind fields over 20 years of data, from 1999 to 2019, we run thousands of analytical solutions (numerical simulations) using the most recent version of the FALL3D model. As a result, the first comprehensive long-term PVHA for Jan Mayen volcanic island is presented.</p>

Exploring forecast variability during volcanic ash cloud events

<p>Atmospheric transport and dispersion models are used by Volcanic Ash Advisory Centers (VAACs) to provide timely information on volcanic ash clouds to mitigate the risk of aircraft encounters. Inaccuracies in dispersion model forecasts can occur due to the uncertainties associated with source terms, meteorological data and model parametrizations. Real-time validation of model forecasts against observations is therefore essential to ensure their reliability. Forecasts can also benefit from comparison to model output from other groups; through understanding how different modelling approaches, variations in model setups, model physics, and driving meteorological data, impact the predicted extent and concentration of ash. The Met Office, the National Centre for Atmospheric Science (NCAS) and the British Geological Survey (BGS) are working together to consider how we might compare data (both qualitatively and quantitatively) from the atmospheric dispersion models NAME, FALL3D and HYSPLIT, using meteorological data from the Met Office Unified Model and the NOAA Global Forecast System (providing an effective multi-model ensemble). Results from the model inter-comparison will be used to provide advice to the London VAAC to aid forecasting decisions in near real time during a volcanic ash cloud event. In order to facilitate this comparison, we developed a Python package (ash-model-plotting) to read outputs from the different models into a consistent structure. Here we present our framework for generating comparable plots across the different partners, with a focus on total column mass loading products. These are directly comparable to satellite data retrievals and therefore important for model validation. We also present outcomes from a recent modelling exercise and discuss next steps for further improving our forecast validation.</p>

The Development of Volcanic Ash Cloud Layers over Hours to Days Due to Atmospheric Turbulence Layering

Volcanic ash clouds often become multilayered and thin with distance from the vent. We explore one mechanism for the development of this layered structure. We review data on the characteristics of turbulence layering in the free atmosphere, as well as examples of observations of layered clouds both near-vent and distally. We then explore dispersion models that explicitly use the observed layered structure of atmospheric turbulence. The results suggest that the alternation of turbulent and quiescent atmospheric layers provides one mechanism for the development of multilayered ash clouds by modulating vertical particle motion. The largest particles, generally μ>100 μm, are little affected by turbulence. For particles in which both settling and turbulent diffusion are important to vertical motion, mostly in the range of 10–100 μμm, the greater turbulence intensity and more rapid turbulent diffusion in some layers causes these particles to spend greater time in the more turbulent layers, leading to a layering of concentration. The results may have important implications for ash cloud forecasting and aviation safety.

Mount Semeru Eruption on December 1, 2020: A Volcanic Ash Cloud Assessment Using TIR Imagery with 8.5-12.0 µm Spectral Bands

Mount Semeru is one of the most active volcanoes in the Java Island. This article presents the results of observations and detections of volcanic ash cloud after Mt Semeru eruptions on 1 December 2020 at 01:23 AM. Volcanic ash cloud detection was conducted by analyzing thermal infrared (TIR) satellite images acquired by the NOAA-20 and SNPP with MODIS and VIIRS instruments. The TIR instruments have detected the presence of volcanic ash cloud. The results show increasing ash cloud brightness temperature (BT) from 240 to 270 Kelvin (K) several hours after eruptions. Increasing BT indicated the development of volcanic Cumulonimbus (Cb) at lower altitude. Northeast movements of 270 K BT clouds were observed at 06:12 AM. Presences of volcanic Cb and SO2 were confirmed using IR bands of 12.0-10.8 µm, 11.0-8.5µm and 11.0 µm. This Cb cloud was observed moving northeast directions. The data acquired from the TIR imagery resulted from this study is thought be used in future to support and complement ground-based observations and detections of active volcanoes mainly in Java Island.

Analysis of properties of the 19 February 2018 volcanic eruption of Mount Sinabung in S5P/TROPOMI and Himawari-8 satellite data

This study presents an analysis of TROPOMI cloud heights as a proxy for volcanic plume heights in the presence of absorbing aerosols and sulfur dioxide for the 19 February 2018 eruption plume of the Sinabung volcano on Sumatra, Indonesia. Comparison with CALIPSO satellite data shows that all three TROPOMI cloud height data products based on oxygen absorption which are considered here (FRESCO, ROCINN, O22CLD) provide volcanic ash cloud heights comparable to heights measured by CALIPSO for optically thick volcanic ash clouds. FRESCO and ROCINN heights are very similar, with the only differences for FRESCO cloud top heights above 14 km altitude. O22CLD cloud top heights unsurprisingly fall below those of FRESCO and ROCINN, as the O22CLD retrieval is less sensitive to cloud top heights above 10 km altitude. For optically thin volcanic ash clouds, i.e., when Earth's surface or clouds at lower altitudes shine through the volcanic ash cloud, retrieved heights fall below the volcanic ash cloud heights derived from CALIPSO data. Evaluation of corresponding Himawari-8 geostationary infrared (IR) brightness temperature differences (ΔBTs) – a signature for detection of volcanic ash clouds in geostationary satellite data and widely used as input for quantitative volcanic ash cloud retrievals – reveals that for this particular eruption the ΔBT volcanic ash signature changes to a ΔBT ice crystal signature for the part of the ash plume reaching the upper troposphere beyond 10 km altitude several hours after the start of the eruption and which TROPOMI clearly characterizes as volcanic (SO2 > 1 DU – Dobson units – and AAI > 4 – absorbing aerosol index – or, more conservatively, SO2 > 10). The presence of ice in volcanic ash clouds is known to prevent the detection of volcanic ash clouds based on broadband geostationary satellite data. TROPOMI does not suffer from this effect and can provide valuable and accurate information about volcanic ash clouds and ash top heights in cases where commonly used geostationary IR measurements of volcanic ash clouds fail.

Atmospheric Dispersion Modelling at the London VAAC: A Review of Developments since the 2010 Eyjafjallajökull Volcano Ash Cloud

It has been 10 years since the ash cloud from the eruption of Eyjafjallajökull caused unprecedented disruption to air traffic across Europe. During this event, the London Volcanic Ash Advisory Centre (VAAC) provided advice and guidance on the expected location of volcanic ash in the atmosphere using observations and the atmospheric dispersion model NAME (Numerical Atmospheric-Dispersion Modelling Environment). Rapid changes in regulatory response and procedures during the eruption introduced the requirement to also provide forecasts of ash concentrations, representing a step-change in the level of interrogation of the dispersion model output. Although disruptive, the longevity of the event afforded the scientific community the opportunity to observe and extensively study the transport and dispersion of a volcanic ash cloud. We present the development of the NAME atmospheric dispersion model and modifications to its application in the London VAAC forecasting system since 2010, based on the lessons learned. Our ability to represent both the vertical and horizontal transport of ash in the atmosphere and its removal have been improved through the introduction of new schemes to represent the sedimentation and wet deposition of volcanic ash, and updated schemes to represent deep moist atmospheric convection and parametrizations for plume spread due to unresolved mesoscale motions. A good simulation of the transport and dispersion of a volcanic ash cloud requires an accurate representation of the source and we have introduced more sophisticated approaches to representing the eruption source parameters, and their uncertainties, used to initialize NAME. Finally, upper air wind field data used by the dispersion model is now more accurate than it was in 2010. These developments have resulted in a more robust modelling system at the London VAAC, ready to provide forecasts and guidance during the next volcanic ash event.

Dispersion Modelling at the London VAAC 10 Years after the Eyjafjallajökull Ash Cloud

<p>It has been 10 years since the ash cloud from the eruption of Eyjafjallajökull caused chaos to air traffic across Europe. Although disruptive, the longevity of the event afforded the scientific community the opportunity to observe and extensively study the transport and dispersion of a volcanic ash cloud. Here we present the development of the NAME atmospheric dispersion model and modifications to its application in the London VAAC forecasting system since 2010, based on the lessons learned.</p><p>Our ability to represent both the vertical and horizontal transport of ash in the atmosphere and its removal have been improved through the introduction of new schemes to represent the sedimentation and wet deposition of volcanic ash, and updated schemes to represent deep atmospheric convection and parameterizations for plume spread due to unresolved mesoscale motions. A good simulation of the transport and dispersion of a volcanic ash cloud requires an accurate representation of the source and we have introduced more sophisticated approaches to representing the eruption source parameters, and their uncertainties, used to initialize NAME. Further, atmospheric dispersion models are driven by 3-dimensional meteorological data from Numerical Weather Prediction (NWP) models and the Met Office’s upper air wind field data is now more accurate than it was in 2010. These developments have resulted in a more robust modelling system at the London VAAC, ready to provide forecasts and guidance during the next volcanic ash event affecting their region.</p>

Volcanic ash forecast using ensemble-based data assimilation: an ensemble transform Kalman filter coupled with the FALL3D-7.2 model (ETKF–FALL3D version 1.0)

Quantitative volcanic ash cloud forecasts are prone to uncertainties coming from the source term quantification (e.g., the eruption strength or vertical distribution of the emitted particles), with consequent implications for an operational ash impact assessment. We present an ensemble-based data assimilation and forecast system for volcanic ash dispersal and deposition aimed at reducing uncertainties related to eruption source parameters. The FALL3D atmospheric dispersal model is coupled with the ensemble transform Kalman filter (ETKF) data assimilation technique by combining ash mass loading observations with ash dispersal simulations in order to obtain a better joint estimation of the 3-D ash concentration and source parameters. The ETKF–FALL3D data assimilation system is evaluated by performing observing system simulation experiments (OSSEs) in which synthetic observations of fine ash mass loadings are assimilated. The evaluation of the ETKF–FALL3D system, considering reference states of steady and time-varying eruption source parameters, shows that the assimilation process gives both better estimations of ash concentration and time-dependent optimized values of eruption source parameters. The joint estimation of concentrations and source parameters leads to a better analysis and forecast of the 3-D ash concentrations. The results show the potential of the methodology to improve volcanic ash cloud forecasts in operational contexts.