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Modern digital scientific workflows - often implying Big Data challenges - require data infrastructures and innovative data science methods across disciplines and technologies. Diverse activities within and outside HGF deal with these challenges, on all levels. The series of Data Science Symposia fosters knowledge exchange and collaboration in the Earth and Environment research community. We invited contributions to the overarching topics of data management, data science and data infrastructures. The series of Data Science Symposia is a joint initiative by the three Helmholtz Centers HZG, AWI and GEOMAR Organization: Hela Mehrtens and Daniela Henkel (GEOMAR)

During the largest polar expedition in history starting in September 2019, the German research icebreaker Polarstern spends a whole year drifting with the ice through the Arctic Ocean. The MOSAiC expedition takes the closest look ever at the Arctic even throughout the polar winter to gain fundamental insights and most unique on-site data for a better understanding of global climate change. Hundreds of researchers from 20 countries are involved. Scientists will use the in situ gathered data instantaneously in near-real time modus as well as long afterwards all around the globe taking climate research to a completely new level. Hence, proper data management, sampling strategies beforehand, and monitoring actual data flow as well as processing, analysis and sharing of data during and long after the MOSAiC expedition are the most essential tools for scientific gain and progress.To prepare for that challenge we adapted and integrated the research data management framework O2A “Data flow from Observations to Archives” to the needs of the MOSAiC expedition on board Polarstern as well as on land for data storage and access at the Alfred Wegener Institute Computing and Data Center in Bremerhaven, Germany. Our O2A-framework assembles a modular research infrastructure comprising a collection of tools and services. These components allow researchers to register all necessary sensor metadata beforehand linked to automatized data ingestion and to ensure and monitor data flow as well as to process, analyze, and publish data to turn the most valuable and uniquely gained arctic data into scientific outcomes. The framework further allows for the integration of data obtained with discrete sampling devices into the data flow.These requirements have led us to adapt the generic and cost-effective framework O2A to enable, control, and access the flow of sensor observations to archives in a cloud-like infrastructure on board Polarstern and later on to land based repositories for international availability.Major roadblocks of the MOSAiC-O2A data flow framework are (i) the increasing number and complexity of research platforms, devices, and sensors, (ii) the heterogeneous interdisciplinary driven requirements towards, e. g., satellite data, sensor monitoring, in situ sample collection, quality assessment and control, processing, analysis and visualization, and (iii) the demand for near real time analyses on board as well as on land with limited satellite bandwidth.The key modules of O2A's digital research infrastructure established by AWI are implementing the FAIR principles:SENSORWeb, to register sensor applications and sampling devices and capture controlled meta data before and alongside any measurements in the field Data ingest, allowing researchers to feed data into storage systems and processing pipelines in a prepared and documented way, at best in controlled near real-time data streams Dashboards allowing researchers to find and access data and share and collaborate among partners Workspace enabling researchers to access and use data with research software utilizing a cloud-based virtualized infrastructure that allows researchers to analyze massive amounts of data on the spot Archiving and publishing data via repositories and Digital Object Identifiers (DOI)

The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) is a collaborative, international project to address pressing scientific questions in the central Arctic. The project’s success, and its ultimate impact on science and society, relies upon professional coordination and data sharing across the participants. A transparent Data Policy is essential to achieve MOSAiC science objectives, to facilitate collaboration, and to enable broad use and impact of the MOSAiC data legacy.

Ab September 2019 beginnt die vom AWI geleitete größte Arktisexpedition aller Zeiten: Bei der MOSAiC-Expedition erforschen Wissenschaftler*innen aus 17 Nationen die Arktis mit dem Forschungsschiff Polarstern, um den Einfluss der Arktis auf das globale Klima besser zu verstehen. Antonia Immerz ist als Data Scientist in MOSAiC tätig und bildet dort die Schnittstelle zur Wissenschaft. Daniela Ransby ist Datenkuratorin für die geowissenschaftliche und umweltwissenschaftliche Datenbank PANGAEA, in der die während der MOSAiC-Expedition erfassten Daten gespeichert werden. In ihrem Vortrag berichten sie über IT-Hintergründe dieser Expedition und wie die erhobenen Daten möglichst zeitnah zu den Klimaforscher*innen gelangen und der Wissenschaft langfristig zur Verfügung stehen. (Slides provided in English)

ESA GlobPermafrost (www.globpermafrost.info) provides a remote sensing data service for permafrost research and applications. This service comprises data product generation for various regions and spatial scales as well as specific infrastructures for visualisation and access to datasets. PerSys is the open access geospatial information system for dissemination and visualisation of remote sensing data derived within the ESA GlobPermafrost project. The data products are visualised in the PerSys WebGIS and are described and searchable in the PerSys Data Catalogue. The PerSys Data Catalogue is a core component of the Arctic Permafrost Geospatial Centre (APGC), set up within ERC PETA-CARB at AWI1. The visualization employs the AWI WebGIS infrastructure maps@awi (http://maps.awi.de), relying on OGC-standardized Web Mapping/Feature Services (WMS, WFS). The WebGIS supports the project specific visualisation of raster and vector data products such as land cover, Landsat multispectral index trends, InSAR-based land surface deformations, rock glacier velocities, and permafrost model outputs. The WebGIS projects are adapted to the products specific spatial scales, e.g. the Arctic WebGIS visualizes Circum-Artic products as well as other large-scale data products. Rock-glacier data products of higher spatial resolution are visualised on regional scale in the WebGIS projects Alps, Andes and Central Asia. PerSys is accessible via the GlobPermafrost project webpage.

The Indonesia Tsunami Early Warning System estimates tsunami impact by two methods: either matching scenarios from a pre-computed database or running realtime tsunami simulations. The database scenarios are based on the finite element model TsunAWI using a triangular mesh with resolution ranging from 20km in deep ocean to 300m in coastal areas and up to 50m in some highly-resolved areas. TsunAWI solves the nonlinear shallow water equations and contains an inundation scheme. The on-the-fly propagation model easyWave solves the linear shallow water equations on a regular finite-difference grid with a resolution of about 1km and utilizes several options to estimate coastal impact. This model is used for potential tsunami events in areas not covered by the database. Warning products like estimated wave height (EWH) and estimated time of arrival (ETA) along the coast are based on modeling results. Therefore comparisons of the forecasted warning levels for the two approaches are crucial. Resolutions and numerical settings of both models differ, therefore variations in the resulting outputs are to be expected; nevertheless, the extent of differences in warning levels should not be too large for identical sources. In the present study, we systematically investigate differences in warning products along forecast points facing the Sunda arc. TsunAWI determines warning products are directly in the coastal forecast points, easyWave offers several options for their approximation including projections from offshore grid points. Differences and potential reasons for variations of warning products like the role of bathymetry, resolution as well as the general approach for the assessment of EWH and ETA for different modeling frameworks are discussed.

The Indonesia Tsunami Early Warning System delivers simulated tsunami forecasts in two different ways: either matching scenario(s) from a pre-computed database or running on-the-fly tsunami simulation. Recently, the database has been extended considerably taking into account additional source regions not covered in earlier stages of the system. In this contribution, we present the current status of the data base coverage as well as a study investigating the warning products obtained by the two modeling approaches. The pre-computed tsunami scenarios are based on the finite element model TsunAWI that employs a triangular mesh with resolution ranging from 20km in deep ocean to 300m in coastal areas and to as much as 50m in some highly resolved areas. TsunAWI solves the nonlinear shallow water equations and contains a wetting-drying inundation scheme. The on-the-fly propagation model easyWave solves the linear shallow water equations on a regular finite-difference grid with a resolution of about 1 km and utilizes several simple options to estimate coastal impact. This model is used for forecasting after a tsunami has been generated in an area not covered by the database or after a moment tensor solution shows an earthquake focal mechanism not present in the database. Since warning products like estimated wave height (EWH) and estimated time of arrival (ETA) along the coast are based on modeling results, it is crucial to compare the resulting forecasted warning levels obtained by the two approaches. Resolutions and numerical settings of both models are quite different, therefore variations in the resulting outputs are to be expected; nevertheless, the extent of differences in warning levels should not be too large for identical sources. In the present study, we systematically investigate differences in resulting warning products along InaTEWS forecast points facing the Sunda arc. Whereas the finite-element mesh of TsunAWI covers the coast up to a terrain height of 50m and warning products have been pre-calculated directly in the forecast points, easyWave offers several options for their approximation including projections from offshore grid points or vertical wall. Differences and potential reasons for variations of warning products like the role of bathymetry resolution as well as the general approach for the assessment of EWH and ETA for different modeling frameworks are discussed.

Within the German-Indonesian Tsunami Early Warning System project (GITEWS), a database containing approximately 4500 tsunami simulations for the Sunda arc, was created. Simulations were calculated using the finite element model TsunAWI implemented at AWI. The corresponding data products such as maximum wave height and estimated time of arrival at the coast are used operationally for tsunami early warning purposes. Visualization of tsunami simulation data in general and analyzing the information by non-modelers is nevertheless difficult, since accessing the data requires in depth knowledge on modeling with TsunAWI and specific programming libraries to be installed. However, it was intended to overcome this issue and provide an overview of the modeling efforts in Indonesia to a broader public. This was accomplished within the scope of the project Earth System Knowledge Platform (ESKP), initiated at institutions of the Helmholtz Association. Data products are visualized in the interactive Tsunami-WebGIS, a Geographical Information System (GIS) based web service, hosted on maps.awi.de, a GIS infrastructure implemented at AWI. The Tsunami-WebGIS provides an overview of the tsunami database and enables the user to trigger a tsunami at a certain epicenter and view the respective maximum wave heights as well as arrival time isochrones. This approach provides an overview of the simulation coverage and tsunami propagation in the the Sunda arc region to a non-expert audience. While it may be used for educational purposes, it has also proven to facilitate analysis by the modelers themselves. It is further planned to include extensions of the database in Indonesia as well as historical events in the region.

Das Alfred-Wegener-Institut macht eine interaktive Tsunami-Karte öffentlich zugänglich – für Medien und Fachkräfte vor Ort sowie Indonesien-Reisende., The Alfred Wegener Institute provides an interactive tsunami map for a public audience – for media, experts and travelers in Indonesia.

This contribution focuses on two applications of the FESOM model family. On the one hand, recent runs with the finite volume code FESOM2 on large global meshes with regional focus are presented. FESOM's shallow water branch TsunAWI is the subject of the second part. TsunAWI, still based on finite elements, is used as an operational model in the Indonesia Tsunami Early Warning System (InaTEWS). InaTEWS derives tsunami forecasts in two different ways: from scenarios in a pre-computed database or from an on-the-fly simulation. The pre-computed scenarios are based on TsunAWI simulations with inundation on a triangular mesh with a resolution ranging from 20km in the deep ocean to 300m - 50m in coastal areas. The on-the-fly propagation model EasyWave (Andrey Babeyko, GFZ) solves the linear shallow water equations on a regular finite-difference grid with a resolution of about 1 km and the coast line as a vertical wall. EasyWave is used after a tsunami has been generated in an area not covered by the database or after seismic measurements show an earthquake mechanism not present in the database. As the numerical settings of both models are quite different, variations in the outputs are to be expected; nevertheless, the differences in the warning levels should not be too large for identical sources. In the current study, we systematically compare the warning products like estimated wave height and estimated time of arrival by the two approaches.

The Indonesia Tsunami Early Warning System delivers simulated tsunami forecasts in two different ways: either matching scenario(s) from a pre-computed database or running on-the-fly tsunami simulation. The pre-computed tsunami scenarios are based on the finite element model TsunAWI that employs a triangular mesh with resolution ranging from 20km in deep ocean to 300m in coastal areas and to as much as 50m in some highly resolved areas. TsunAWI solves the nonlinear shallow water equations and contains a wetting-drying inundation scheme. The on-the-fly propagation model easyWave solves the linear shallow water equations on a regular finite-difference grid with a resolution of about 1 km and utilizes several simple options to estimate coastal impact. This model is used for forecasting after a tsunami has been generated in an area not covered by the database or after a moment tensor solution shows an earthquake focal mechanism not present in the database. Since warning products like estimated wave height (EWH) and estimated time of arrival (ETA) along the coast are based on modeling results, it is crucial to compare the resulting forecasted warning levels obtained by the two approaches. Resolutions and numerical settings of both models are quite different, therefore variations in the resulting outputs are to be expected; nevertheless, the extent of differences in warning levels should not be too large for identical sources. In the present study, we systematically investigate differences in resulting warning products along InaTEWS forecast points facing the Sunda arc. Whereas the finite-element mesh of TsunAWI covers the coast up to a terrain height of 50m and warning products have been pre-calculated directly in the forecast points, easyWave offers several options for their approximation including projections from offshore grid points or vertical wall. Differences and potential reasons for variations of warning products like the role of bathymetry resolution as well as the general approach for the assessment of EWH and ETA for different modeling frameworks are discussed in this contribution.

The ultimate goal of establishing InaTEWS is to reduce as much as possible the victims due to tsunami. Since 2008, InaTEWS has been operated by BMKG and has shown its performance, proven by its ability to reliably detect and analyze earthquakes within 5 minutes, produce early warnings, and disseminate the products promptly. One of the important elements in the tsunami early warning is the availability of a pre-calculated tsunami database. The tsunami database provides the information about estimated tsunami arrival times and heights in the affected area. In InaTEWS, the pre-calculated tsunami database is integrated into a decision support system that helps operators to take decisions in issuing tsunami warnings. In 2012, this system has been equipped with a database of tsunami simulations covering the Sunda Zone. Obviously this coverage is not sufficient to account for the Eastern part of Indonesia that is also prone to tsunami. As a result that stage of the tsunami result needs to be extended. A project involving BMKG, Alfred Wegener Institute (AWI) and DMInnovation started in May 2015 to strengthen the decision support system of InaTEWS. Several workshops at BMKG were carried out to expand the scenario database in eastern Indonesia. The basis for tsunami scenario computations is the unstructured mesh finite-element numerical model TsunAWI developed by AWI. It is based on non-linier shallow water theory. The discretization scheme is based on finite elements with the mesh generation (covering the whole Indonesian region) that allows for an accurate local resolution along the coast while keeping coarse resolution in the deep ocean. The discretization of faults as a source for tsunami model was developed by Geoscience Australia / DMInnovation. The tsunami scenarios have been calculated for 13 additional source zones, on total 11949 scenarios in eastern Indonesia. The data products of all these scenarios are integrated to the decision support system TOAST developed by gempa GmbH via the Scenario Database Interface TsunDaBI. The sensitivity of the scenarios database with respect to the epicenter density and magnitude variation needed to be assessed, to test as to whether the current tsunami database is already sufficient or additional simulations are needed. In total the scenario repository was extended from 4580 to more than 16000 simulations. The extended tsunami database is expected to improve the tsunami warning bulletin in East Indonesia considerably.

Nach dem verheerenden Tsunami im Indischen Ozean 2004 wurde das internationale Kooperationsprojekt ''German-Indonesian Tsunami Early Warning System'' ins Leben gerufen und das Frühwarnzentrum am Amt für Meteorologie, Klimatologie und Geophysik in Jakarta aufgebaut. Auf deutscher Seite wurde das Projekt vom Helholtz-Zentrum Potsdam, Deutsches Geoforschungszentrum geleitet. Die Warnung nach einem starken Erdbeben basiert auf einer Datenbank möglicher Tsunamiszenarien, so dass schnell die Gefährdung der Küsten abgeschätzt werden kann. Im Vorfeld dienen detailiierte Überflutungsrechnungen als Basis für Evakuierungspläne. Der Vortrag stellt den Aufbau des Warnsystems mit einem Schwerpunkt auf der Rolle der Tsunami-Simulation vor. Insbesondere werden die physikalischen und numerischen Grundlagen des Simulationsmodells TsunAWI beleuchtet und am Beispiel einiger Modellrechnungen Möglichkeiten und Grenzen der Simulation aufgezeigt.

The talk covers the basics of tsunami modelling with a focus on sources of uncertainty in the early warning process. The tsunami scenario database for Indonesia is briefly introduced and Hovmöller diagramms along virtual SMART cables are compared for some scenarios (varying epicenter and magnitude).

The numerical simulation code TsunAWI was developed in the framework of the German-Indonesian Tsunami Early Warning System (GITEWS). The Numerical simulation of prototypic tsunami scenarios plays a decisive role in the a priori risk assessment for coastal regions and in the early warning process itself. TsunAWI is suited for both tasks. It is based on a finite element discretisation, employs unstructured grids with high resolution along the coast, and includes inundation. This contribution presents two fields of applications. In the Indonesian tsunami early warning system, the existing TsunAWI scenario database covers the Sunda subduction zone from Sumatra to the Lesser Sunda Islands with 715 epicenters and 4500 scenarios. In a collaboration with Geoscience Australia, we support the scientific staff at the Indonesian warning center to extend the data base to the remaining tectonic zones in the Indonesian Archipelago. The extentension started for North Sulawesi, West and East Maluku Islands. For the Hydrographic and Oceanographic Service of the Chilean Navy (SHOA), we calculated a small scenario database of 100 scenarios (sources by Universidad de Chile) for a lightweight decision support system prototype (built by DLR). The earthquake and tsunami events on 1 April 2014 and 16 November 2016 showed the practical use of this approach in comparison to hind casts of these events.

This poster shows preliminary results from an Arctic and an Antarctic research expedition on board RV Polarstern in the framework of the Iceflux projects of IMARES and AWI. Community composition at different depth strata of the under-ice water column were investigated to assess its structure and highlight species that define the under-ice community.

The numerical simulation code TsunAWI was developed in the framework of the German-Indonesian Tsunami Early Warning System (GITEWS). The numerical simulation of prototypical tsunami scenarios plays a decisive role in the a priory risk assessment for coastal regions and in the early warning process itself. TsunAWI is based on a finite element discretization, employs unstructured grids with high resolution along the coast, and includes inundation. This contribution gives an overview of the model itself and presents two applications. For GITEWS, the existing scenario database covering 528 epicenters / 3450 scenarios from Sumatra to Bali was extended by 187 epicenters / 1100 scenarios in the Eastern Sunda Arc. Furthermore, about 1100 scenarios for the Western Sunda Arc were recomputed on the new model domain covering the whole Indonesian Seas. These computations would not have been feasible in the beginning of the project. The unstructured computational grid contains 7 million nodes and resolves all coastal regions with 150m, some project regions and the surrounding of tide gauges with 50m, and the deep ocean with 12km edge length. While in the Western Sunda Arc, the large islands of Sumatra and Java shield the Northern Indonesian Archipelago, tsunamis in the Eastern Sunda Arc can propagate to the North. The unstructured grid approach allows TsunAWI to easily simulate the complex propagation patterns with the self-interactions and the reflections at the coastal regions of myriads of islands. For the Hydrographic and Oceanographic Service of the Chilean Navy (SHOA), we calculated a small scenario database of 100 scenarios (sources by Universidad de Chile) to provide data for a lightweight decision support system prototype (built by DLR). This work is part of the initiation project "Multi hazard information and early warning system in cooperation with Chile" and aims at sharing our experience from GITEWS with the Chilean partners.

Die drei Poster beschreiben *) Die Grundlagen der Tsunami-Modelierung: Modellphysik, numerische Umsetzung mit dem am AWI entwickelten Programm TsunAWI. *) Das indonesische Tsunami-Frühwarnsystem am Amt für Meteorologie, Klimatologie und Geophysik in Jakarta: Schematischer Aufbau, Modellierungskomponente. Das Warnsystem wurde von 2005-2011 im großen Verbundprojekt ''German-Indonesian Tsunami Early Warning System'' entwickelt und installiert. Im Folgeprojekt PROTECTS 2011-2014 wurden das System konsolidiert und die indonesischen Mitarbeiter geschult. *) Den Beitrag der "alten" FS Sonne zu GITEWS: Bathymetriemessungen.

Nach dem verheerenden Tsunami im Indischen Ozean 2004 wurde das internationale Kooperationsprojekt ''German-Indonesian Tsunami Early Warning System'' ins Leben gerufen und das Frühwarnzentrum am Amt für Meteorologie, Klimatologie und Geophysik in Jakarta aufgebaut. Auf deutscher Seite wurde das Projekt vom Helholtz-Zentrum Potsdam, Deutsches Geoforschungszentrum geleitet. Die Warnung nach einem starken Erdbeben basiert auf einer Datenbank möglicher Tsunamiszenarien, so dass schnell die Gefährdung der Küsten abgeschätzt werden kann. Im Vorfeld dienen detailiierte Überflutungsrechnungen als Basis für Evakuierungspläne. Der Vortrag stellt den Aufbau des Warnsystems mit einem Schwerpunkt auf der Rolle der Tsunami-Simulation vor. Neben den physikalischen und numerischen Grundlagen des Simulationsmodells TsunAWI wird die Einbettung der Tsunami-Szenariendatenbank beleuchtet. Wie kommt man von der Simulation zur konkreten Warnung, wie werden Szenarien zu vorhandenen Messdaten ausgewählt? Außerdem wird die Erarbeitung von Evakuierungsplänen am Beispiel von Bali kurz vorgestellt.