AS1.27

Within the context of SINOPTICA (Satellite-borne and IN-situ Observations to Predict the Initiation of Convection for ATM, 2020-2022) project, the preliminary results of the radar analysis on different convective events affecting Italian airports are presented. Three cases of study have been selected for their relevant impact on the international airports of Milan-Malpensa, Marco Polo-Venice and Bergamo-Orio al Serio. Each one of the three cases has been characterised, identifying the best radar approach to obtain valuable information about weather hazard affecting air traffic management (ATM). This provides helpful information for forecasting and tracking convection around the airports.

The analysis is based on the mosaic radar images provided by the Italian Civil Protection, which included relevant data such as the top of the clouds, vertically integrated liquid (VIL), and VIL density products. Firstly, different zones around each affected airport were selected to monitor the different phases of the event. The proposed early warning system distinguishes four periods: non-storm alert, pre-alert, alert level 1, alert level 2. The proposed domain to be monitored would have a radius of 75 km from the airport.  The storm alert level 2 period would be considered when VIL radar echoes are above 1 mm within an area about 20 km from the airport, considering 1 km² spatial resolution and 5 min. temporal resolution (it is to say, maximum values are computed for each variable each 15 min.). The storm alert level 1 period would start two hours before the alert period, covering an area of 500 km² with a spatial resolution of 3 km² and temporal resolution of 15 min. The pre-alert period would correspond to the period between the first appearance of radar echoes on the Italian radar mosaic until the storm alert level 1 period starts. To monitor this period, the proposed spatial resolution is 5 km² and temporal resolution would be 30 min. for the whole radar mosaic.

This procedure would help to identify and track convective storm structures responsible for ATM difficulties. VIL density variable is considered the most suitable candidate to compare the different episodes since they can occur in different seasons. The application of the proposed methodology to the selected cases has shown good ability to efficiently quantify the severity of the thunderstorms. Additionally, various VIL density thresholds have been tested as severity indicators. Results show that in the three cases, storms developed at certain region past the Alps Mountain range that acts as a natural border north of Italy; then storms moved East and South-East. Maximum VIL density values in the affected region exceed 4 g/m³, however, on some occasions, they exceed 8 g/m³. VIL density showed a weak seasonal dependency with slightly higher values for summer events. A more detailed analysis comparing impacts and VIL density values is currently ongoing as part of the SINOPTICA project.

How to cite: Esbri, L., Rigo, T., Llasat, M. C., and Parodi, A.: Impact of severe weather in air traffic management. Radar analysis for three convective events affecting Italian international airports., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2932, https://doi.org/10.5194/egusphere-egu21-2932, 2021.

Bulgaria is a country with a high frequency of hail and thunderstorms from May to September. For the May–September 2010–2015 period, statistical regression analysis was applied to identify predictors/classification functions that contribute skills to thunderstorm forecasting in the Sofia plain. The functions are based on (1) instability indices computed from radiosonde data from Sofia station F1, and (2) combination of instability indices and Integrated Water Vapor (IWV), derived from the Global Navigation Satellite System (GNSS) station Sofia-Plana, F2. Analysis of the probability of detection and the false alarm ratio scores showed the superiority of the F2 classification function, with the best performance in May, followed by June and September. F1 and F2 scores were computed for independent data samples in the period 2017–2018 and confirmed the findings for the 2010–2015 period. Analysis of IWV and lightning flash rates for a multicell and supercell thunderstorm in June and July 2014 showed that the monthly IWV thresholds are reached 14.5 and 3.5 hours before the thunderstorm, respectively. The supercell IWV peak registered 40 min before the thunderstorm, followed by a local IWV minimum corresponding to a peak in the flash rate. In both cases, an increase of IWV during severe hail was registered, which is likely related to the hydrometeor contribution to GNSS path delay. The results of this study will be integrated into the Bulgarian Integrated NowCAsting tool for thunderstorm forecasting in the warm/convective season.

How to cite: Guerova, G., Dimitrova, T., and Georgiev, S.: Thunderstorm Classification Functions Based on Instability Indices and GNSS IWV for the Sofia Plain, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1819, https://doi.org/10.5194/egusphere-egu21-1819, 2021.

The Raikoke volcano emitted about 0.4-1.8 x 10⁹ kg of ash and 1-2 x 10⁹ kg of SO₂ up to 15 km into the atmosphere. However, the eruption was characterized by several puffs of different time periods and eruption heights. Here, we use the ICON-ART model in a model setup in which we resolve the phases of the Raikoke eruption. We calculated the eruption source parameters (ESPs) online by coupling ICON-ART to the 1-D plume model FPlume. The input heights for the different eruption phases needed for FPlume are geometrically derived from GEOS-17 satellite data. An empirical relationship is used to derive the amount of very fine ash (particles <32µm) which is relevant for long range transport in the atmosphere. In the first hours during and after the eruption, the modeled ash loading agrees very well with the observed ash loading from Himawari-8 due to the resolution of the eruption phase and the online calculation of the ESPs. In later hours, aerosol dynamical processes (nucleation, condensation, coagulation) explain the loss of ash in the atmosphere in agreement with the observations. However, a direct comparison is partly hampered by water and ice clouds overlapping the ash cloud in the observations. In case of SO₂, we compared 6-hourly means of model and Himawari data with respect to the structure, amplitude, and location (SAL-method). In the beginning, the structure and amplitude values differed largely because the dense ash cloud directly after the eruption leads to an underestimation of the SO₂ amount in the satellite data. On the second and third day, the SAL values are close to zero for all parameters indicating a good agreement of model and observations. We argue that representing the plume phases and ESPs in ICON-ART by FPlume enhances ash and SO₂ predictability in the first days after the eruption, especially in case of non-continuous volcanic eruptions like the Raikoke eruption 2019.

How to cite: Bruckert, J., Hoshyaripour, G. A., Horváth, Á., Muser, L., Prata, F. J., Hoose, C., and Vogel, B.: Using multi-scale modeling and observations to link the eruption source parameters to the dispersion of volcanic clouds in case of the Raikoke eruption 2019, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9174, https://doi.org/10.5194/egusphere-egu21-9174, 2021.

Artificial neural networks (ANNs) have been successfully applied to various remote sensing problems. Here we use ANNs to detect and analyze volcanic ash clouds pixelwise in MSG-SEVIRI images. Therefore, radiative transfer calculations based on realistic ash properties and atmospheric profiles covering a wide range of possible atmospheric states are performed, and their results are used for the training of the ANNs.

With respect to the volcanic ash properties the role of the complex refractive index (RI) is highlighted: While it can vary strongly between different eruptions, some models use a limited set of RI measurements. Here we sketch a novel method to calculate the RI of volcanic ashes for wavelengths from 5 to 15 µm from measurements of their individual components (i.e. minerals, glasses, gas bubbles) based on generic petrological ash compositions. A comprehensive data set of RIs for volcanic glasses and bulk volcanic ashes of different chemical compositions is derived and used for the ANNs training data set.

The final ANNs with specific tasks (classification, retrieval of optical depth, cloud top height and particle effective radius) are validated against an unseen simulated test data set. This allows us to systematically investigate strengths and weaknesses of the retrievals with respect to cloud properties (e.g. optical thickness), geographic and meteorological conditions. To prove real-world applicability case studies for volcanic ash clouds produced by Eyjafjallajökull (2010) and Puyehue-Cordón Caulle (2011) are considered, and comparisons with lidar and in situ measurements show overall good agreement. As for the training only homogeneous single layer ash clouds were assumed, a sensitivity study was carried out to investigate the impact of the vertical mass profile, multiple layers and the geometrical extent of the clouds on the retrieval results.

Finally, a comparison with a precursor algorithm running operationally at the German weather service (DWD) since 2015 shows that in the case of the Eyjafjallajökull 2010 eruption the new algorithm detects more as well as higher concentrated volcanic ash clouds.

How to cite: Piontek, D., Bugliaro, L., Voigt, C., Hornby, A., Gasteiger, J., Schumann, U., Marenco, F., and Kar, J.: A New Algorithm for the Retrieval of Volcanic Ash Cloud Properties using MSG-SEVIRI and Artificial Neural Networks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-159, https://doi.org/10.5194/egusphere-egu21-159, 2021.

During volcanic eruptions Volcanic Ash Advisory Centers (VAAC) produce forecasts of ash location and concentration. However, these forecasts are deterministic and do not take into account the inherent uncertainty in the forecasts due to incomplete knowledge of the volcano’s eruption characteristics and imperfect representation of atmospheric processes in numerical models. This means flight operators have incomplete information regarding the risk of flying following an eruption, which could result in overly conservative decisions being made. There is a need for a new generation of volcanic ash hazard charts allowing end users to make fast and robust decisions using risk estimates based on  state-of-the-art probabilistic forecast methods .

In this presentation, a method for visualizing ash concentration matrix using a risk-matrix approach will be applied to two volcanic eruptions, Grimsvotn (2011) and Raikoke (2019). These risk-matrix graphics reduce the ensemble information into an easy-to-use decision-making tool. In this work the risk level is determined by combining the concentration of volcanic ash and the likelihood of that concentration occurring.

When applying this technique to the Grimsvotn eruption, the airspace containing volcanic ash concentrations deemed to be associated with the highest risk (high likelihood of exceeding a high concentration threshold) to aviation are reduced by over 85% compared to using an ensemble that gives an ash distribution similar to the VAAC issued deterministic forecast. The reduction during the Raikoke eruption can be as much as 40% at a forecast lead time of 48 hours. This has the potential to reduce the disruption to airline operations.  This tool could be extended to include other aviation hazards, such as desert dust, aircraft icing and clear air turbulence.

How to cite: Harvey, N., Dacre, H., and Capponi, A.: Calculating and communicating ensemble-based volcanic ash concentration risk for aviation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2718, https://doi.org/10.5194/egusphere-egu21-2718, 2021.

When an eruption event occurs it is necessary to accurately and rapidly determine the position and evolution during time of the volcanic cloud and its parameters (such as Aerosol Optical Depth-AOD, effective radius-Re and mass-Ma of the ash particles), in order to ensure the aviation security and the prompt management of the emergencies.

Here we present different procedures for volcanic ash cloud detection and retrieval using S3 SLSTR (Sentinel-3 Sea and Land Surface Temperature Radiometer) data collected the 22 June at 00:07 UTC by the Sentinel-3A platform during the Raikoke (Kuril Islands) 2019 eruption.

The volcanic ash detection is realized by applying an innovative machine learning based algorithm, which uses a MultiLayer Perceptron Neural Network (NN) to classify a SLSTR image in eight different surfaces/objects, distinguishing volcanic and weather clouds, and the underlying surfaces. The results obtained with the NN procedure have been compared with two consolidated approaches based on an RGB channels combination in the visible (VIS) spectral range and the Brightness Temperature Difference (BTD) procedure that exploits the thermal infrared (TIR) channels centred at 11 and 12 microns (S8 and S9 SLSTR channels respectively). The ash volcanic cloud is correctly identified by all the models and the results indicate a good agreement between the NN classification approach, the VIS-RGB and BTD procedures.

The ash retrieval parameters (AOD, Re and Ma) are obtained by applying three different algorithms, all exploiting the volcanic cloud “mask” obtained from the NN detection approach. The first method is the Look Up Table (LUT_p) procedure, which uses a Radiative Transfer Model (RTM) to simulate the Top Of Atmosphere (TOA) radiances in the SLSTR thermal infrared channels (S8, S9), by varying the aerosol optical depth and the effective radius. The second algorithm is the Volcanic Plume Retrieval (VPR), based on a linearization of the radiative transfer equation capable to retrieve, from multispectral satellite images, the abovementioned parameters. The third approach is a NN model, which is built on a training set composed by the inputs-outputs pairs TOA radiances vs. ash parameters. The results of the three retrieval methods have been compared, considering as reference the LUT_p procedure, since that it is the most consolidated approach. The comparison shown promising agreement between the different methods, leading to the development of an integrated approach for the monitoring of volcanic ash clouds using SLSTR.

The results presented in this work have been obtained in the sphere of the VISTA (Volcanic monItoring using SenTinel sensors by an integrated Approach) project, funded by ESA and developed within the EO Science for Society framework [https://eo4society.esa.int/projects/vista/].

How to cite: Petracca, I., De Santis, D., Corradini, S., Guerrieri, L., Picchiani, M., Merucci, L., Stelitano, D., Del Frate, F., Prata, A., and Schiavon, G.: Volcanic ash detection and retrievals using SLSTR. Test case: 2019 Raikoke  eruption, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7363, https://doi.org/10.5194/egusphere-egu21-7363, 2021.

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.

How to cite: Beckett, F., Burton, R., Dioguardi, F., Witham, C., Stevenson, J., and Valters, D.: Exploring forecast variability during volcanic ash cloud events, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8620, https://doi.org/10.5194/egusphere-egu21-8620, 2021.

Explosive volcanic eruptions release high amounts of ash into the atmosphere. Accurate tracking and forecasting of ash dispersal into the atmosphere and quantification of its uncertainty is of fundamental importance for volcanic hazard mitigation. Numerical models represent a powerful tool to monitor ash clouds in real-time, but limits and uncertainties affect numerical results. A way to improve numerical forecasts is by assimilating satellite observations of ash clouds through Data Assimilation algorithms, such as Ensemble-based Kalman Filters. In this study, we present the implementation of the so-called Local Ensemble Transform Kalman Filters inside a numerical procedure which simulates the release and transport of volcanic ash during explosive eruptions. The numerical procedure consists of the eruptive column model PLUME-MoM coupled with the tephra transport and dispersal model HYSPLIT. When satellite observations are available, ash maps supplied by PLUME-MoM/HYSPLIT are sequentially corrected/modified using ash column loading as retrieved from space. The new volcanic ash state represents the optimal solution with minimized uncertainties with respect to numerical estimates and observations. To test the Data Assimilation procedure, we used satellite observations of the volcanic cloud released during the explosive eruption that occurred at Mt. Etna (Italy) on 24 December 2018. Satellite observations have been carried out by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument, on board the Meteosat Second Generation (MSG) geostationary satellite. Results show that the assimilation procedure significantly improves the current ash state and the forecast. In addition, numerical tests show that the use of sequential Kalman Filters does not require a precise initialization of the numerical model, being able to improve the forecasts as the assimilation cycles are performed.

How to cite: Pardini, F., Corradini, S., Costa, A., Guerrieri, L., Esposti Ongaro, T., Merucci, L., Neri, A., Stelitano, D., and de' Michieli Vitturi, M.: Ensemble-Based Data Assimilation of volcanic ash clouds from satellite observations: application to the 24 December 2018 Mt.Etna explosive eruption., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14235, https://doi.org/10.5194/egusphere-egu21-14235, 2021.