Extreme convective events are increasing in northern and eastern Europe in frequency and intensity accounting for major economic damages related to natural disasters in several countries. Forecasting the land convection locally developed in a short time range is very challenging since the models are not able to resolve them and this is an issue for the air traffic management.
In recent years, attention was paid to the detection and monitoring of volcanic clouds as their impact on the European air traffic control system was unprecedented (e.g. Eyjafjallajökull 2010 eruption). Volcanic clouds are very dangerous for the aviation operations as they can cause damage of the aircraft systems and engines not only close to active volcanoes but also at large distance from the eruption.
The Convective and Volcanic Clouds (CVC) detection and estimation of their physical parameters is a highly multidisciplinary and challenging topic since the same techniques and instruments can be used for meteorology, volcanic monitoring, atmospheric physics and climate purposes. There is an urgent need to develop new techniques and instruments for monitoring, detecting, forecasting and modeling the CVC, to develop early warning systems and to support end-users (such as air traffic managers and pilots) and policy makers. Furthermore, there is a need for improved information exchange regarding the impact of the CVC on daily aviation operations. In this regard, we will draw on the work of the Single European Sky ATM Research (SESAR) Joint Undertaking, with special focus on the latest funded exploratory research projects dealing with these topics.
The objective of the session is to connect different communities in touch with the CVC and to promote discussions between scientists working in remote sensing, modelers, meteorologists, physicists, sensors engineers, engines manufacturers, pilots and aviation managers, allowing the researchers to understand the end-users’ needs and allowing the end-users to understand the research capabilities.
This session solicits the latest studies from the spectrum of:
- detection, monitoring and modeling of CVC with novel techniques and new sensors,
- forecasting and nowcasting extreme weather events,
- study of the CVC structure,
- understanding the impact of the CVC on air traffic management,
- proposal of new products, tools or services focused on the end-users prospective.
vPICO presentations: Mon, 26 Apr
The H2020 SINOPTICA Project (2020-2022) aims at exploiting the untapped potential of assimilating remote sensing (EO-derived and ground-based radar) as well as GNSS-derived datasets (including radio occultation data) and in-situ weather stations data. Those data will be used for very high-resolution, very short-range numerical weather forecasts to improve the prediction of extreme weather events to the benefit of Air Traffic Management (ATM) operations. This will be done by setting up a continuously updated database of remote sensing-derived, GNSS-derived and in-situ weather stations variables, in combination with an automated assimilation system to feed an NWP model. SINOPTICA weather forecast results will be integrated into ATM decision-support tools, visualizing weather information on the controller's display, and generating new 4D trajectories to avoid severe weather areas. This contribution presents the initial results of the assimilation of aforementioned observations into the WRF model, operated at cloud-resolving grid spacing, for two case studies: a hailstorm event occured on 11 May 2019 nearby Malpensa airport and a severe convection episode occurred near Punta Raisi airport (Palermo) on 15 July 2020.
How to cite: Parodi, A., Temme, M., Gluchshenko, O., Kerschbaum, M., Surian, N., Biondi, R., Realini, E., Gatti, A., Tagliaferro, G., Llasat, M. C., Rigo, T., Esbri, L., Milelli, M., Mazzarella, V., Lagasio, M., and Parodi, A.: H2020 SINOPTICA (Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM) project: initial results, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-891, https://doi.org/10.5194/egusphere-egu21-891, 2021.
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.
Convective weather represents a significant disruption to air traffic flow management (ATFM) operations. Thunderstorms are the cause for a substantial amount of delay in both the en-route and airport environment. Before the day of operations, poor prediction capability of convective weather prohibits traffic managers from considering weather mitigation strategies during the pre-tactical phase of ATFM planning. As a result, convective weather is mitigated tactically, possibly leading to excessive delays.
The skill of weather forecasting has greatly improved in recent years. Hi-resolution weather models can predict the future state of the atmosphere for some weather parameters. However, incorporating the output from these sophisticated weather products into an ATFM solution that provides easily interpreted information by the air traffic managers remains a challenge.
This paper combines data from high-resolution numerical weather predictions with actual storm observations from lightning detecting and satellite images. It applies supervised machine learning techniques such as binary classification, multiclass classification, and regression to train neural networks to predict the occurrence, severity, and altitude of thunderstorms. The model predictions are given up to 36hr in advance, within timeframes necessary for pre-tactical planning of ATFM, providing traffic managers with valuable information for developing weather mitigation plans.
How to cite: Jardines, A., Soler, M., García-Heras, J., Ponzano, M., Raynaud, L., Rottner, L., Simarro, J., and Rey, F.: Predicting Convective Storm Characteristics using Machine Learning from Hi-Resolution NWP Forecasts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7516, https://doi.org/10.5194/egusphere-egu21-7516, 2021.
Sand and Dust Storms (SDS) are extreme meteorological phenomena that can be associated with high amounts of atmospheric mineral dust. SDS are an essential element of the Earth’s natural biogeochemical cycles but are also caused in part by human-induced drivers including climate change, unsustainable land management, and water use; in turn, SDS contribute to climate change and air pollution. Over the last few years, there has been an increasing need for SDS accurate information and predictions, particularly over desert regions as the Sahara and in the Middle East and regions affected by long-range dust transport as Europe, to support early warning systems, and preparedness and mitigation plans in addition to growing interest from diverse stakeholders in the aviation sector, including airlines, airports, engine manufacturers, as well as the military. SDS affect aviation operations mainly through reduced visibility and several types of mechanical effects that impact different parts of the aircraft (Clarkson and Simpson 2017); these have significant mid- to long-term implications for issues such as engine and aircraft maintenance, airport operations and resilience, and flight route planning and optimization.
In this contribution, we will present ongoing efforts on utilizing desert dust modelling products based on the MONARCH chemical weather prediction system and satellite observational constraint (Pérez et al, 2011; Di Tomaso et al., 2017) as the basis to understand the short- and long-term risks of operating in risky sand and dust environments. We will introduce two types of examples of the use of SDS information. First, a long-term assessment for Northern Africa, the Middle East and Europe of the SDS-threats surrounding visibility and aircraft/engine exposure to dust, based on a 10-year MONARCH dust reanalysis in the context of the EU ERA4CS DustClim project. We will subsequently revise the benefits of using daily dust forecasts based on MONARCH (the reference operational model of the WMO Barcelona Dust Forecast Center, https://dust.aemet.es/) for the early prediction of extreme events as the ones occurred in March 2018 in the Eastern Mediterranean and in February 2020 in the Canary Islands.
The authors acknowledge the DustClim project which is part of ERA4CS, an ERA-NET. COST Action inDust (CA16202) and the WMO SDS-WAS Regional Center are also acknowledged. We are thankful to T. Bolic for her suggestions and ideas regarding resilience of the aviation sector to SDS.
Clarkson, R., and Simpson, H., 2017: Maximising Airspace Use During Volcanic Eruptions: Matching Engine Durability against Ash Cloud Occurrence, NATO STO AVT-272 Specialists Meeting on “Impact of Volcanic Ash Clouds on Military Operations” Volume: 1.
Di Tomaso et al., (2017): Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0, Geosci. Model Dev., 10, 1107-1129, doi:10.5194/gmd-10-1107-2017.
Pérez et al.,: An online mineral dust aerosol model for meso to global scales: Model description, annual simulations and evaluation, Atmos. Chem. Phys., 11, 13001-13027, doi: 10.5194/acp-11-13001-2011, 2011.
Votsis et al., (2020), Operational risks of sand and dust storms in aviation and solar energy: the DustClim approach, FMI's Climate Bulletin: Research Letters 1/2020, DOI: 10.35614/ISSN-2341-6408-IK-2020-02-RL.
How to cite: Basart, S., Votsis, A., Rautio, T., Chouta, K., Barnaba, F., Di Tomaso, E., Mona, L., Mytilinaios, M., Formenti, P., Werner, E., and Pérez García-Pando, C.: Operating in risky sand and dust storm environments in Northern Africa, the Middle East and Europe: a portfolio of aviation climate services, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14490, https://doi.org/10.5194/egusphere-egu21-14490, 2021.
The Global Navigation Satellite Systems (GNSS) Radio Occultation (RO) technique allows the sounding of the atmosphere with a vertical resolution of about 100 m in the upper troposphere. It has already been demonstrated that the RO bending angle, by showing clear anomalies at the cloud top heights, is an efficient parameter to highlight the presence of dense clouds in the atmosphere. The objective of this work is to use the bending angle anomaly technique to systematically detect the presence of dense clouds in the atmosphere as well as their altitude and type. Several studies demonstrated the detection efficiency of the bending angle on tropical cyclones, severe convection and volcanic clouds altitude with high accuracy. However, the clouds type differentiation remains a challenge. One of the main issue on this regard, is the lack of volcanic cloud case studies, due to the low number of eruptions in comparisons to the extreme weather events, and to the large uncertainties on volcanic clouds detection techniques.
In this work we collected all the RO collocate in a short time range with tropical cyclones and volcanic clouds, and we collocate them with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) backscatter. The bending angle anomaly profile is given in input to a machine learning algorithm to retrieve the presence of the cloud and its height. The CALIOP backscatter has 30-meter vertical resolution in the troposphere and 60-meter in the upper troposphere/lower stratosphere. We manually constrain the cloud edges, compute the cloud top height from each cloud and use this value as target for the algorithm output. To get a balanced training of the algorithm, we add to the dataset an equal number of clear sky samples.
The algorithm aims at quickly providing the cloud top height to be used for aviation and nowcast issues and to be included in early warning systems.
How to cite: Biondi, R., Tournigand, P.-Y., and Hammouti, M.: Machine learning cloud top height detection based on GNSS radio occultations: a step ahead towards an operational use, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8789, https://doi.org/10.5194/egusphere-egu21-8789, 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, 2020.
Strong explosive eruptions of volcanoes are the most dangerous for aircraft because they can produce in a few hours or days to the atmosphere and the stratosphere till several cubic kilometers of volcanic ash and aerosols. Ash plumes and the clouds, depending on the power of the eruption, the strength and wind speed, can travel thousands of kilometers from the volcano for several days, remaining hazardous to aircraft, as the melting temperature of small particles of ash below the operating temperature of jet engines.
There are 30 active volcanoes in the Kamchatka, and several of them are continuously active. Scientists of KVERT monitor Kamchatkan volcanoes since 1993. In 2020, four of these volcanoes (Sheveluch, Klyuchevskoy, Bezymianny, and Karymsky) had strong and moderate explosive eruptions.
The eruptive activity of Sheveluch volcano began since 1980 (growth of the lava dome) and it is continuing at present. In 2020, strong explosions sent ash up to 7-10 km a.s.l. on 08 April, and 22 and 29 December. Ash from explosions rose up to 5-6 km a.s.l. on 13 June, and 24 December. Ash plumes extended more 625 km mainly to the south-east of the volcano. A form of resuspended ash was observed on 20 April, 28 June, 24 August, and 07-10 October: ash plumes extended for 310 km to the northeast and southeast of the volcano. Activity of Sheveluch was dangerous to international and local aviation.
Two moderate explosive-effusive eruptions of Klyuchevskoy volcano occurred in 2020: first from 01 November 2019 till 03 July 2020, and second from 30 September, it is continuing in 2021. Explosions sent ash up to 7 km a.s.l., gas-steam plumes containing some amount of ash extended for 465 km to the different directions of the volcano. The lava flows moved along Apakhonchichsky and Kozyrevsky chutes. Activity of the volcano was dangerous to local aviation.
The strong explosive eruption of Bezymianny volcano occurred on 21 October: explosions sent ash up to 11 km a.s.l., the large ash cloud was located over Klyuchevskoy group of volcanoes long time and later drifted up to1200 km to the southeast of the volcano. Activity of the volcano was dangerous to international and local aviation.
Eruptive activity of Karymsky volcano was uneven in 2020: ash explosions were observed from one (June) to seven (October) days a month, for five months the volcano was quiet. Explosions rose ash up to 8 km a.s.l. (08 November), ash plumes and clouds drifted for 380 km to the different directions of the volcano. The eruptive volcanic activity was observed in April, May, June, July, October, November, and December. Activity of Karymsky was dangerous to international and local aviation.
How to cite: Girina, O., Melnikov, D., Manevich, A., Nuzhdaev, A., Romanova, I., Loupian, E., and Sorokin, A.: The 2020 Activity of Kamchatkan Volcanoes and Danger to Aviation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1448, https://doi.org/10.5194/egusphere-egu21-1448, 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 (LUTp) 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 LUTp 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.
Volcanic emission is a major risk for air traffic. Flying through a volcanic cloud can have a strong impact on engines (damage caused by ash and/or sulphur dioxide – SO2) and persons. The knowledge of the height of the volcanic plume is indeed essential for pilots, airlines and passengers.
In this presentation, we study recent volcanic emissions to illustrate the difficulty for obtaining information about the height of the SO2 plume in a form relevant to aviation. Our study uses satellite data products. We consider SO2 layer height from TROPOMI (UV-vis hyperspectral sensor on board S5P, a polar orbiting platform), as shown by SACS (Support to Aviation Control Service), combined with cloud top observations (from the same sensors or from geostationary broadband imagers) to determine the minimum SO2-cloud height. This is a validation which is of interest to aviation.
The flight level, not the km, is the measure, the unit for expressing height during cruise flight used on board by the pilots to ensure safe vertical separation between aircraft, despite natural local variations in atmospheric air pressure and temperature. Thus, it is critical to provide the corresponding SO2 contamination expressed as flight levels. Our study will focus on this conversion that is one item currently being developed in the frame of ALARM H2020 project (https://alarm-project.eu) and SACS early warning system (https://sacs.aeronomie.be) in the creation of NetCDF alert products.
How to cite: Sievers, K., Brenot, H., Theys, N., and Kessinger, C.: Investigation on flight level contamination using volcanic SO2 plume and cloud top height satellite products, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10309, https://doi.org/10.5194/egusphere-egu21-10309, 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.
Volcanic eruptions are a natural disaster with significant impact on human activities. The unprecedented European Volcanic Ash Crisis in 2010 demonstrated the vulnerability of the infrastructure and the need for new approaches to enable stakeholders in the aviation sector to obtain fast and accurate information. Currently, there are many data sources available and cutting-edge technology to provide the means to detect and monitor high impact eruptions. However, the information from multiple data sources is not yet efficiently integrated and aviation-specific products incorporating multi-platform datasets is not in place. To this end, the integration of tailored ground-based, satellite, and model data as well as information from volcanic observatories in Europe is essential. The Pilot EO4D_ash – Earth observation data for detection, discrimination & distribution (4D) of volcanic ash – of the e-shape project aims to strengthen the Earth Observation and in-situ data exploitation and multi-source (satellite, remotely sensed, and ground-based network) data integration to derive innovation for ash discrimination and monitoring; to enhance the capability of 4D forecasting volcanic ash dispersal and to foster innovation in the decision making processes and mitigate ash related impact and hazard resilience. The overall pilot structure, tailored products, aerosol lidar profile assimilation and study cases will be presented at the conference.
Acknowledgements: This work has been conducted within the framework of the H2020 e-shape (Grant Agreement n. 820852) project.
How to cite: Papagiannopoulos, N., Mona, L., Dema, C., Amiridis, V., Gialitaki, A., Kampouri, A., Uppstu, A., Scollo, S., Merucci, L., Boichu, M., Goloub, P., Barsotti, S., and Parks, M.: EO4D_ash – Earth observation data for detection, discrimination & distribution (4D) of volcanic ash, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14552, https://doi.org/10.5194/egusphere-egu21-14552, 2021.
The Icelandic Meteorological Office (IMO) maintains a network of web cameras for monitoring the environment and identifying possible hazards, including reduced atmospheric visibility, changing river flow conditions and snow accumulation. Recently, the network has been expanded to improve the volcano monitoring capacity, with the specific aim of observing eruption onset and estimating volcanic plume heights. Here, we present how sites for cameras are chosen, the environmental constraints that inform the two camera designs currently in use, how the data is transmitted to the institute, stored, and pushed through the data processing system, and the different techniques used to calibrate the cameras and calculate the orientations of plumes such that measurements can be made from the images they produce. Camera calibration is a particular challenge for such a diverse range of cameras and environments, with some cameras already installed and inaccessible, and here we show how we use laboratory calibration, feature matching, horizon matching and star matching to find the internal camera geometry and camera orientation in different scenarios. Once calibrated, geometric measurements can be extracted from the images by either providing constraints from Numerical Weather Prediction (NWP) models on the likely orientation of the plume, or by using two images with different views, which provide enough information to pin down a point in three dimensions. In the latter case we show how ray projection can be used to locate a point. These plume calculation tools and final images are made available to the forecasters and natural hazard specialists on-duty using an interactive webpage. The plume height time series are easily saved for ingestion into the Volcanic Eruptive Source Parameter Assessment (VESPA) inversion system designed to assess eruption intensity and to provide calculated eruption source parameters in input to the tephra dispersion forecasting model.
How to cite: Barnie, T., Hjörvar, T., Sigurðsson, E. M., Pfeffer, M. A., Arason, Þ., and Barsotti, S.: A calibrated visual web camera network for measuring volcanic plume heights: technical aspects and implementation for operational use, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15235, https://doi.org/10.5194/egusphere-egu21-15235, 2021.
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