International Monitoring System and On-site Verification for the CTBT, disaster risk reduction and Earth sciences


International Monitoring System and On-site Verification for the CTBT, disaster risk reduction and Earth sciences
Co-organized by SM2
Convener: Martin Kalinowski | Co-conveners: Lars Ceranna, Yan Jia, Peter Nielsen, Ole Ross
vPICO presentations
| Thu, 29 Apr, 15:30–17:00 (CEST)
Public information:
The seismic, hydro-acoustic, infrasound and radionuclide data of the CTBT International Monitoring System may be used for scientific applications after signing a cost-free agreement. Please find more about this opportunity here: https://www.ctbto.org/specials/vdec/ and submit your request through a simple web form here: https://www.ctbto.org/specials/vdec/vdec-request-for-access/.

vPICO presentations: Thu, 29 Apr

Chairpersons: Peter Nielsen, Martin Kalinowski, Ole Ross
Session Introduction by Martin Kalinowski
Seismic/Chaired by Martin Kalinowski and Peter Nielsen
Michael Begnaud, Sanford Ballard, Andrea Conley, Patrick Hammond, and Christopher Young

Historically, location algorithms have relied on simple, one-dimensional (1D, with depth) velocity models for fast, seismic event locations. The speed of these 1D models made them the preferred type of velocity model for operational needs, mainly due to computational requirements. Higher-dimensional (2D-3D) seismic velocity models are becoming more readily available from the scientific community and can provide significantly more accurate event locations over 1D models. The computational requirements of these higher-dimensional models tend to make their operational use prohibitive. The benefit of a 1D model is that it is generally used as travel-time lookup tables, one for each seismic phase, with travel-time predictions pre-calculated for event distance and depth. This simple, lookup structure makes the travel-time computation extremely fast.

Comparing location accuracy for 2D and 3D seismic velocity models tends to be problematic because each model is usually determined using different inversion parameters and ray-tracing algorithms. Attempting to use a different ray-tracing algorithm than used to develop a model almost always results in poor travel-time prediction compared to the algorithm used when developing the model.

We will demonstrate that using an open-source framework (GeoTess, www.sandia.gov/geotess) that can easily store 3D travel-time data can overcome the ray-tracing algorithm hurdle. Travel-time lookup tables (one for each station and phase) can be generated using the exact ray-tracing algorithm that is preferred for a specified model. The lookup surfaces are generally applied as corrections to a simple 1D model and also include variations in event depth, as opposed to legacy source-specific station corrections (SSSCs), as well as estimates of path-specific travel-time uncertainty. Having a common travel-time framework used for a location algorithm allows individual 2D and 3D velocity models to be compared in a fair, consistent manner.

How to cite: Begnaud, M., Ballard, S., Conley, A., Hammond, P., and Young, C.: Comparing Higher-dimensional Velocity Models for Seismic Location Accuracy using a Consistent Travel Time Framework, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-926, https://doi.org/10.5194/egusphere-egu21-926, 2021.

Rayna Arora, Nimar Arora, and Ronan Le Bras

Deep Learning has shown a lot of promise in analyzing seismic waveforms for a number of tasks. However, most studies have focused on detections made at nearby stations that are roughly within 100km. These methods are not readily applicable for global-scale networks such as those maintained by the International Monitoring System (IMS). We look at the task of discriminating between earthquakes and explosions and attempt to apply a number of recent approaches for this task. In particular, we focus on events with magnitude between 3-4 mb that have been unambiguously classified by the International Seismological Center (ISC). We analyze the performance of methods that have been developed using a mix of Convolutional Neural Nets (CNNs) and Recursive Neural Nets (RNNs) as well as methods that use the so called GAN (Generative Adversarial Net)-“critic” approach of building features on seismic waveforms. We provide the guidance for the applicability of these methods for treaty monitoring purposes as well as building earthquake hazard maps using the IMS data.

How to cite: Arora, R., Arora, N., and Le Bras, R.: Analyzing Deep Learning Performance for Seismic Waveform Discrimination at Global Distances, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1831, https://doi.org/10.5194/egusphere-egu21-1831, 2021.

Christos Saragiotis

The number of aftershocks after a large main shock may increase the daily number of seismic events by an order of magnitude for a few days or even weeks. The large number of incoming arrivals reduces the effectiveness of automatic bulletin generation and significantly increases the work of the analysts. In the verification context such aftershocks may delay the production of the CTBTO Reviewed Event Bulletin, as well as mask clandestine nuclear tests. Consequently, the CTBTO has been investigating ways to improve the performance of the automatic processing during aftershock sequences.  

In line with this investigation, the PTS launched a project with the objective to evaluate three algorithms that could address this issue, namely the Empirical Matched Field developed at NORSAR, the SeisCorr developed at Sandia National Labs and XSEL developed at the IDC. In this abstract we present comparisons on the performance of the three methods on the aftershock sequences of four very strong earthquakes: the Tohoku earthquake in Japan (March 2011), the Gorkha earthquake in Nepal (April 2015), the  Illapel earthquake off the coast of Chile (September 2015) and the devastating earthquake in Papua New Guinea (February 2018).

How to cite: Saragiotis, C.: Efforts toward automatic aftershock sequences processing at the International Data Centre, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5062, https://doi.org/10.5194/egusphere-egu21-5062, 2021.

Andreas Köhler and Steffen Mæland

We combine the empirical matched field (EMF) method and machine learning using Convolutional Neural Networks (CNNs) for calving event detection at the IMS station SPITS and GSN station KBS on the Arctic Archipelago of Svalbard. EMF detection with seismic arrays seeks to identify all signals similar to a single template generated by seismic events in a confined target region. In contrast to master event cross-correlation detectors, the detection statistic is not the waveform similarity, but the array beam power obtained using empirical phase delays (steering parameters) between the array stations. Unlike common delay-and-sum beamforming, the steering parameters do not need to represent a plane wave and are directly computed from the template signal without assuming a particular apparent velocity and back-azimuth. As for all detectors, the false alarms rate depends strongly on the beam power threshold setting and therefore needs appropriate tuning or alternatively post-processing. Here, we combine the EMF detector using a low detection threshold with a post-detection classification step. The classifier uses spectrograms of single-station three-component records and state-of-the-art CNNs pre-trained for image recognition. Spectrograms of three-component seismic data are hereby combined as RGB images. We apply the methodology to detect calving events at tidewater glaciers in the Kongsfjord region in Northwestern Svalbard. The EMF detector uses data of the SPITS array, at about 100 km distance to the glaciers, while the CNN classifier processes data from the single three-component station KBS at 15 km distance using time windows where the event is expected according to the EMF detection. The EMF detector combines templates for the P and for the S wave onsets of a confirmed, large calving event. The CNN spectrogram classifier is trained using classes of confirmed calving signals from four different glaciers in the Kongsfjord region, seismic noise examples, and regional tectonic seismic events. By splitting the data into training and test data set, the CNN classifier yields a recognition rate of 89% on average. This is encouragingly high given the complex nature of calving signals and their visually similar waveforms. Subsequently, we process continuous data of 6 months in 2016 using the EMF-CNN method to produce a time series of glacier calving. About 90% of the confirmed calving signals used for the CNN training are detected by EMF processing, and around 80% are assigned to the correct glacier after CNN classification. Such calving time series allow us to estimate and monitor ice loss at tidewater glaciers which in turn can help to better understand the impact of climate change in Polar regions. Combining the superior detection capability of (less common) seismic arrays at a larger source distance with a powerful machine learning classifier at single three-component stations closer to the source, is a promising approach not only for environmental monitoring, but also for event detection and classification in a CTBTO verification context.

How to cite: Köhler, A. and Mæland, S.: Combing empirical matched field processing at IMS station SPITS and convolutional neural networks for calving event detection in Svalbard, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1994, https://doi.org/10.5194/egusphere-egu21-1994, 2021.

Sheila Peacock

Accurate seismic body-wave magnitudes (mb) are important in nuclear test-ban treaty verification.  Network mean magnitudes are known to be biased when the effect of noise obscuring signal at some stations in the monitoring network is ignored.  To overcome this bias a joint-maximum-likelihood method is used to invert bulletin amplitude and period measurements at a network of stations from a number of closely spaced sources, to estimate unbiased network mb values and station corrections. For each station a noise threshold is determined independently using the Kelly & Lacoss (1969) method, assuming that large samples of amplitudes reported in a bulletin (in this case from the International Seismological Centre, ISC) follow a Gutenberg-Richter distribution. Where stations report arrivals sufficiently frequently, the noise threshold can be estimated separately for different seasons, to highlight variations caused by, for instance, storms or freezing of nearby ocean.  The noise thresholds at some stations differ by up to 0.4 magnitude units between seasons.  Sensitivity of maximum-likelihood magnitude estimates of a group of announced explosions at the Nevada Test Site to variations in threshold at Canadian Arctic stations (compared with using the annual mean) is generally small (<∼0.01-0.02 units), and greatest for low-magnitude events in the “noisy” season, when the station magnitudes are below the seasonal threshold but above the annual average threshold.

UK Ministry of Defence © Crown copyright 2021/AWE

How to cite: Peacock, S.: Potential effect of seasonally varying station thresholds on joint-maximum-likelihood magnitude estimates from bulletin data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7650, https://doi.org/10.5194/egusphere-egu21-7650, 2021.

Felix M. Schneider, Petr Kolínský, and Götz Bokelmann

We study finite-frequency effects that arise in cavity detection. The task comes along with the Onsite-Inspection part for the Comprehensive Nuclear Test Ban Treaty (CTBT), where the remnants of a potential nuclear test need to be identified. In such nuclear tests, there is preexisting knowledge about the depths at which nuclear tests may take place, and also about sizes that such cavities can attain. The task of cavity detection has consistently been a difficult one in the past, which is surprising, since a cavity represents one of the strongest seismic anomalies one can ever have in the subsurface. A conclusion of this study is that considering finite-frequency effects are rather promising for cavity detection, and that it is worthwhile to take them into account. We utilize an analytical approach for the forward problem of the a seismic wave interacting with a underground cavity in order to develop an inversion routine that finds and detects an underground cavity utilizing the transmitted wave-field.


How to cite: Schneider, F. M., Kolínský, P., and Bokelmann, G.: Finite-frequency effects for imaging underground cavities, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12506, https://doi.org/10.5194/egusphere-egu21-12506, 2021.

Hydroacoustic/Chaired by Martin Kalinowski and Peter Nielsen
Peter Nielsen, Mario Zampolli, Ronan Le Bras, Georgios Haralabus, Kevin Heaney, Emanuel Coelho, Jeffry Stevens, and Jeffrey Hanson

The Comprehensive Nuclear-Test-Ban Treaty’s (CTBT) International Monitoring System (IMS) is a world-wide network of stations and laboratories designed to detect nuclear explosions underground, in the oceans and in the atmosphere. The IMS incorporates four technologies: seismic, hydroacoustic and infrasound (collectively referred to as waveform technologies), and radionuclide (particulate and noble gas). The hydroacoustic component of the IMS consists of 6 hydroacoustic stations employing hydrophones suspended underwater near the axis of the Sound Fixing and Ranging (SOFAR) channel in the oceans and 5 near-shore seismic stations, called T-phase stations, located on islands or continental coastal regions. The T-phase stations are seismometers emplaced near the shore with the aim of detecting hydroacoustic signals that couple into the Earth’s crust near the coast. The main purpose of these hydroacoustic facilities is to detect nuclear test explosions in the oceans or near the surface of the oceans. Hydroacoustic signals propagate in the oceans very efficiently (little attenuation) and therefore the relatively small number of hydroacoustic stations suffice to cover most of the world’s oceans. However, interpretation of recorded signals even from known events can be difficult, since these signals propagate over very long distances. The ocean seismo-acoustic signals may on a global scale be affected by three-dimensional refraction, reflection and diffraction before arrival at a hydroacoustic station. In addition, ocean acoustic signals undergo a complex conversion to in-ground seismic signals when interacting with coastal regions that may modify signal features and evidence related to an explosion in the ocean before arrival at a T-station. The CTBTO has an ongoing effort to improve automatic detection, classification and localization of events, and to assist human analysts in interpreting these complex signals by incorporating knowledge obtained from high-fidelity seismo-acoustic modelling capabilities in the processing procedures. This presentation provides an overview of this project including justification of the choice of signal modelling approaches and validation of the models to fulfill accuracy criteria relevant for CTBTO. Examples of seismo-acoustic signal computations produced for inter-model comparisons and for assessing the relevance of such modelling capability to real operational scenarios are shown. Envisaged approaches for exploiting the complex modelling results and observations to improve the performance of the data processing are also presented.

How to cite: Nielsen, P., Zampolli, M., Le Bras, R., Haralabus, G., Heaney, K., Coelho, E., Stevens, J., and Hanson, J.: High-fidelity ocean seismo-acoustic propagation modelling for signal interpretation at the CTBT IMS hydroacoustic stations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7025, https://doi.org/10.5194/egusphere-egu21-7025, 2021.

Kevin Heaney, Emanuel Coelho, Peter Nielsen, Mario Zampolli, and Georgios Haralabus

The ocean is an excellent medium for the propagation of low frequency sound, so much so, that the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) can monitor all the world’s oceans for nuclear tests with a small number of hydroacoustic stations (with multiple underwater hydrophones for triangulation) distributed around remote regions of the earth.  The classification and localization system has been developed based upon 2-dimensional (2D) acoustic models, were the effects of horizontal refraction and diffraction have been ignored.  These effects have been shown to have a large impact on the energy received behind (and reflected from) islands and seamounts.   To demonstrate the maturity of modern 3-dimensional (3D) models, a set of test-cases were developed including: a benchmark (5°) wedge, a shallow water twin conical seamount case, a deep-water long-range island and seamount and the reconstruction of the acoustic propagation from the estimated source location of the hydroacoustic anomaly associated with the loss of the ARA San Juan off the coast of Argentina in 2017 to a receiving IMS hydroacoustic station.  The models compared include two 3D Parabolic equations and the Bellhop3D raytrace algorithms.   Comparisons show quantitative agreement between the models.  The expectation is that this validation will provide a way forward to incorporate various combinations of these models into the CTBTO detection, classification and localization processing algorithm.

How to cite: Heaney, K., Coelho, E., Nielsen, P., Zampolli, M., and Haralabus, G.: Validation of 3-dimensional ocean acoustic propagation models from benchmarks to global problems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16337, https://doi.org/10.5194/egusphere-egu21-16337, 2021.

Infrasound/Chaired by Ole Ross and Lars Ceranna
Christoph Pilger, Peter Gaebler, Patrick Hupe, Andre Kalia, Felix Schneider, Andreas Steinberg, Henriette Sudhaus, and Lars Ceranna

We report on a multi-technique analysis using publicly available data for investigating the huge, accidental explosion that struck the city of Beirut, Lebanon, on August 4, 2020. Its devastating shock wave led to thousands of injured with more than two hundred fatalities and caused immense damage to buildings and infrastructure. Our combined analysis of seismological, hydroacoustic, infrasonic and radar remote sensing data allows us to characterize the source as well as to estimate the explosive yield. The latter ranges between 0.8 and 1.1 kt TNT (kilotons of trinitrotoluene) equivalent and is plausible given the reported 2.75 kt of ammonium nitrate as explosive source. Data from the International Monitoring System of the CTBTO are used for infrasound array detections. Seismometer data from GEOFON and IRIS complement the source characterization based on seismic and acoustic signal recordings, which propagated in solid earth, water and air. Copernicus Sentinel data serve for radar remote sensing and damage estimation. As there are strict limitations for an on-site analysis of this catastrophic explosion, our presented approach based on openly accessible data from global station networks and satellite missions is of high scientific and social relevance that furthermore is transferable to other explosions.

How to cite: Pilger, C., Gaebler, P., Hupe, P., Kalia, A., Schneider, F., Steinberg, A., Sudhaus, H., and Ceranna, L.: Yield estimation of the 2020 Beirut explosion using open access waveform and remote sensing data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-89, https://doi.org/10.5194/egusphere-egu21-89, 2021.

Patrick Hupe, Christoph Pilger, Peter Gaebler, and Lars Ceranna

The infrasound technique is applied to monitor atmospheric explosions in the context of the Comprehensive Nuclear-Test-Ban Treaty and, among other purposes, to characterize large meteoroids entering Earth's atmosphere. Anyhow, for both types of sources, the exact location and time are initially unknown and sometimes difficult to precisely estimate. In contrast, rocket launches are well-defined ground-truth events generating strong infrasonic signatures. In this study, we analyse infrasound signatures of 1001 rocket launches for space missions recorded at stations of the International Monitoring System between 2009 and mid-2020. We include all surface- or ocean-based launches within this period with known launch time, location, rocket type, and mission name; whereas launches of sounding rockets and ballistic missiles for scientific and military purposes, respectively, are excluded from our study. We characterize the infrasonic signatures of over 70 different types of rockets launched at 27 different globally distributed spaceports and are able to identify infrasound signatures from up to 73% of the launches considered. We use this unique dataset to estimate the global detectability of such events and to characterize rocket infrasound. We provide the results as a DOI-assigned ground-truth reference dataset for supporting its further use in geophysical and atmospheric research.

How to cite: Hupe, P., Pilger, C., Gaebler, P., and Ceranna, L.: Ground-truth Reference Dataset of 1001 Rocket Launches for Space Missions and their Infrasonic Signatures, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8474, https://doi.org/10.5194/egusphere-egu21-8474, 2021.

Oleksandr Liashchuk, Yevheniy Kariahin, Yuriy Andrushchenko, Ivan Tolchonov, and Leonid Kolesnykov

The infrasound network in Ukraine is represented by three infrasound arrays in Kamenets-Podilsky, Malin, and Gorodok. Also, additional single sensors are installed near Odesa, Kharkiv, and Zhytomyr. A total of 6 infrasound arrays are expected to be deployed. Condenser-type microbarographs are installed everywhere, a wind noise reduction system is available for each. The main task of the network is to monitor technogenic and natural activity and emergencies. At the same time, such a dense enough network can be successfully used to study the characteristics of the atmosphere. All registered digital data IS sent to the server of the National Data Center, where it automatically processed using algorithms F-statistics. The results of processing are available to the analyst in the operational database, where he rejects signals according to the criteria of speed, duration, and period. Also, at this stage provided a comparison between acoustic signals and seismic events. If necessary, additional processing of infrasound data is carried out using the PMCC. For powerful events, data from IMS CTBTO stations are also taken into account. If it is possible to identify an event using additional information, this is done (for example, media monitoring, reports of mining enterprises). As a result, the final bulletin is formed. The overwhelming number of registered signals of an explosive origin due to the work of the mining industry, technogenic accidents, and military operations. A number of signals from fireballs were recorded. Refinements using atmospheric models had not been carried out before, this practice started only this year. The results obtained can be used for a preliminary assessment of the potential of the regional infrasound network.

How to cite: Liashchuk, O., Kariahin, Y., Andrushchenko, Y., Tolchonov, I., and Kolesnykov, L.: Regional infrasound monitoring in Ukraine, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6271, https://doi.org/10.5194/egusphere-egu21-6271, 2021.

Lars Ceranna, Thomas Bruns, Christian Koch, Dominique Rodrigues, Stephen Robinson, Jacob Holm Winther, Franck Larsonnier, and Richard Barham

Infra-AUV is a new EU project that will establish primary measurements standards for low frequency phenomena across the fields of airborne and underwater acoustics and vibration (seismology). Combining expertise from the national measurement institutes and geophysical monitoring station operators, it will develop both high-precision laboratory-based methods of calibration and methods suitable for field use. Infra-AUV will also address requirements for reference sensors that link laboratory calibration capabilities to field requirements for measurement traceability.

To establish standards in the three technical areas, a variety of calibration principles will be employed, including extension of existing techniques such as reciprocity and optical interferometry, and development of new methods. There will also be an investigation of the potential for in-situ calibration methods, including use of both artificially generated and naturally occurring stimuli such as microseisms and microbaroms. The influence of calibration uncertainties on the determination of the measurands required by the monitoring networks will also be studied.

The project was strongly motivated by the CTBTO strategy to drive new metrology capability to underpin IMS data. The intention is to maintain interaction with stakeholders, not only in connection with the IMS, but with the broad range of users of low frequency acoustic and vibration data. 

How to cite: Ceranna, L., Bruns, T., Koch, C., Rodrigues, D., Robinson, S., Winther, J. H., Larsonnier, F., and Barham, R.: Infra-AUV project: Metrology for low-frequency sound and vibration, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16476, https://doi.org/10.5194/egusphere-egu21-16476, 2021.

Radionuclide/Chaired Ole Ross and Lars Ceranna
Martin Kalinowski and Boxue Liu

For the International Monitoring System (IMS) to be effective, it is vital that nuclear explosion signals can be distinguished from natural and man-made radioactivity in the atmosphere. The International Data Centre (IDC) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) applies standard event screening criteria, with the objective of characterizing, highlighting, and thereby screening out, events considered to be consistent with natural phenomena or non-nuclear explosive, man-made phenomena. The objective of this study is to apply the kernel density (KD) approach to generate and investigate probability distributions of isotopic ratios for radioxenon releases from certain types of sources. The goal is to create probability density functions that could be applied e.g. with a Bayesian method to determine the probability whether an IMS observation can be explained by known sources or could possibly be caused by a nuclear explosion. KD equations for nuclear facility releases are derived from the data set of the radioxenon emission inventory of all nuclear power plants and all nuclear research reactors, as well as selected medical isotope production facilities in the calendar year 2014. For all types of sources, KD equations will be linked with isotopic ratio calculations that connect the sources and IMS stations as receiver.

How to cite: Kalinowski, M. and Liu, B.: Data-based kernel density equations for probability distributions of releases of CTBT-relevant radioxenon isotopes from nuclear facilities and when arriving at IMS stations after atmospheric transport, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12100, https://doi.org/10.5194/egusphere-egu21-12100, 2021.

Giuseppe Ottaviano, Antonietta Rizzo, Chiara Telloli, Alberto Ubaldini, Barbara Ferrucci, and Franca Padoani
In June 2020, the Swedish station SEP63 of the International Monitoring System (IMS) of the Comprehensive Nuclear Test Ban Treaty Organization (CTBTO) recorded anomalous values of a mixture of some fission products and neutron activation products not present in the natural background of the station itself. Some concentration activity values above the statistical range of the station were measured. An online search for any relevant news reports was carried out and atmospheric transport modelling (ATM) conducted to identify the possible source of the emissions and to assess the related source-term. The aim of this work is to sketch out a preliminary forensic approach to characterize the event.

How to cite: Ottaviano, G., Rizzo, A., Telloli, C., Ubaldini, A., Ferrucci, B., and Padoani, F.: Anomalous measurements at SEP63 IMS station in June 2020: a preliminary forensic approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9390, https://doi.org/10.5194/egusphere-egu21-9390, 2021.

Ole Ross, Nicolai Gesternann, Peter Gaebler, Lars Ceranna, Antonietta Rizzo, and Giuseppe Ottaviano

National Data Centre (NDC) Preparedness Exercises (NPE) base on partially simulated scenarios of CTBT relevant events distributed to all NDC. They provide an opportunity to practice the verification procedures for the detection of nuclear explosions in the framework of CTBT monitoring. The NPE 2019 scenario was developed in close cooperation between the Italian NDC-RN (ENEA) and the German NDC (BGR). The fictitious state RAETIA announced a reactor incident with release of unspecified radionuclides into the atmosphere. Simulated concentrations of particulate and noble gas isotopes at IMS stations were given to the participants. The task was to check the consistency with the announcement and to search for waveform events in the potential source region of the radioisotopes.
During NPE2019 an Exercise Expert Technical Analysis was requested from the IDC for the first time. A fictitious state party provided within the scenario (simulated) national measurements of radionuclides and asked for assisistance in analysing the additional samples. Especially backward ATM and the search for seismic events in the possible source region was requested. In addition the overall consistency to potential emissions of the reactor incident declared by the ficititious state RAETIA was questioned. In the third and last stage of the exercise, national regional seismic data were distributed among the particpants which contained an (synthetically manipulated) anomaly pointing on a explosive event.

How to cite: Ross, O., Gesternann, N., Gaebler, P., Ceranna, L., Rizzo, A., and Ottaviano, G.: Scenario design and analysis tasks of the National Data Centre Preparedness Exercise (NPE) 2019, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15064, https://doi.org/10.5194/egusphere-egu21-15064, 2021.

Jolanta Kusmierczyk-Michulec, Anne Tipka, and Martin Kalinowski

For every atmospheric radionuclide sample taken by the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty Organisation (CTBTO), the CTBTO makes use of operational Atmospheric Transport Modelling (ATM) to assist States Signatories in localization of possible source regions of any measured substance. Currently, ATM is accomplished by using the Lagrangian particle dispersion model (LPDM) FLEXPART driven by global meteorological fields with a spatial resolution of 0.5 degrees and 1 hourly temporal resolution. Meteorological fields are provided by the European Centre for Medium-Range Weather Forecasts (ECMWF ) and the National Centers for Environmental Prediction (NCEP).  

Recent studies to increase the accuracy in the CTBTO’s localization process to be applied for specific detection events, utilizes High-Resolution Atmospheric Transport Modelling (HRATM) by using the Weather Research and Forecasting model (WRF) to generate high-resolution meteorological input data for the LPDM version Flexpart-WRF.   

This presentation uses measurements from the International Monitoring System (IMS) station DEX33 in Germany of seven episodes of elevated Xe-133 concentrations in 2014 in combination with with the stack emission data of the medical isotope production facility IRE in Fleurus, Belgium. Each episode consists of 6 to 11 subsequent 24-hour samples. Backward simulations for each sample are conducted and the sensitivity to the stack emission data are analysed. All samples determined to represent a detection of IRE releases are selected to be used for an evaluation study. 

Evaluating the CTBTO’s utilization of HRATM requires to investigate the ability to localize the source region as well as the accuracy of the match and the computational performance to accomplish these results. The evaluation of HRATM results is done by using statistical metrics established during former ATM challenges. Concerning the computational performance and to account for uncertainties, sensitivity studies with varying spatial resolutions, physical parameterization variations and different regional domain setups for WRF were accomplished. This comprises a reference comparison to the operational ATM FLEXPART model with an increased spatial resolution to 0.1 degrees.   

How to cite: Kusmierczyk-Michulec, J., Tipka, A., and Kalinowski, M.: Evaluation of High-Resolution Atmospheric Transport Modelling within the framework of the CTBT with Xe-133 observations in Germany and stack emission data from medical isotope production, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10866, https://doi.org/10.5194/egusphere-egu21-10866, 2021.