Making Earth-System-Model (ESM) Data accessible is challenging due to the large amount of data that we are facing in this realm. The upload is time-consuming, expensive, technically complex, and every institution has their own procedures.

Non-ESM experts face a lot of problems and pure data portals are hardly usable for inter- and trans-disciplinary communication of ESM data and findings, as this level of accessibility often requires specialized web or computing services. 

With the Model Data Explorer, we want to simplify the generation of web services from ESM data, and we provide a framework that allows us to make the raw model data accessible to non-ESM experts.

Our decentralized framework implements the possibility for an efficient remote processing of distributed ESM data. Users interface with an intuitive map-based front-end to compute spatial or temporal aggregations, or select regions to download the data. The data generators (i.e. the scientist with access to the raw data) use a light-weight and secure python library based on the Data Analytics Software Framework (DASF, https://digital-earth.pages.geomar.de/dasf/dasf-messaging-python) to create a back-end module. This back-end module runs close to the data, e.g. on the HPC-resource where the data is stored. Upon request, the module generates and provides the required data for the users in the web front-end.

Our approach is intended for scientists and scientific usage! We aim for a framework where web-based communication of model-driven data science can be maintained by the scientific community. The Model Data Explorer ensures fair reward for the scientific work and adherence to the FAIR principles without too much overhead and loss in scientific accuracy. 

The Model Data Explorer is in the progress of development at the Helmholtz-Zentrum Hereon, together with multiple scientific and data management partners in other German research centers. The full list of contributors is constantly updated and can be accessed at https://model-data-explorer.readthedocs.io.

How to cite: Sommer, P. S., Baldewein, L., Takyar, H., Chaudhary, R., Hadizadeh, M., Dibeh, H., Böcke, M., Lorenz, C., Dinter, T., Pinkernell, S., Getzlaff, K., and Kleeberg, U.: ESM Data Exploration with the Model Data Explorer, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3624, https://doi.org/10.5194/egusphere-egu23-3624, 2023.

The European Plate Observing System (EPOS) is a European project about building a pan-European infrastructure for accessing solid Earth science data, governed now by EPOS ERIC (European Research Infrastructure Consortium). The EPOS-Norway project (EPOS-N; RCN-Infrastructure Programme - Project no. 245763) is a Norwegian project funded by National Research Council. The aim of the Norwegian EPOS e‑infrastructure is to integrate data from the seismological and geodetic networks, as well as the data from the geological and geophysical data repositories. Among the six EPOS-N project partners, four institutions are providing data – University of Bergen (UIB), - Norwegian Mapping Authority (NMA), Geological Survey of Norway (NGU) and NORSAR.

In this contribution, we present the EPOS-Norway Portal as an online, open access, interactive tool, allowing visual analysis of multidimensional data. It supports maps and 2D plots with linked visualizations. Currently access is provided to more than 300 datasets (18 web services, 288 map layers and 14 static datasets) from four subdomains of Earth science in Norway. New datasets are planned to be integrated in the future. EPOS-N Portal can access remote datasets via web services like FDSNWS for seismological data and OGC services for geological and geophysical data (e.g. WMS). Standalone datasets are available through preloaded data files. Users can also simply add another WMS server or upload their own dataset for visualization and comparison with other datasets. This portal provides unique way (first of its kind in Norway) for exploration of various geoscientific datasets in one common interface. One of the key aspects is quick simultaneous visual inspection of data from various disciplines and test of scientific or geohazard related hypothesis. One of such examples can be spatio-temporal correlation of earthquakes (1980 until now) with existing critical infrastructures (e.g. pipelines), geological structures, submarine landslides or unstable slopes. 

The EPOS-N Portal is implemented by adapting Enlighten-web, a server-client program developed by NORCE. Enlighten-web facilitates interactive visual analysis of large multidimensional data sets, and supports interactive mapping of millions of points. The Enlighten-web client runs inside a web browser. An important element in the Enlighten-web functionality is brushing and linking, which is useful for exploring complex data sets to discover correlations and interesting properties hidden in the data. The views are linked to each other, so that highlighting a subset in one view automatically leads to the corresponding subsets being highlighted in all other linked views.

How to cite: Michálek, J., Atakan, K., Ottemøller, L., Natvik, Ø., Langeland, T., Lampe, O. D., Fonnes, G., Cook, J., Christensen, J. M., Baadshaug, U., Kierulf, H. P., Grøtan, B. O., Dehls, J., Olesen, O., and Maupin, V.: EPOS-Norway Portal, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14349, https://doi.org/10.5194/egusphere-egu23-14349, 2023.

The MOSAiC Expedition 2019/20 (https://mosaic-expedition.org) brought together scientists from different research institutes around the globe for a year in the Central Arctic. They collected an incredible amount of data to expand the understanding of the Arctic, its distinct features, and the consequences of a changing climate. Since January 2023, the data collected during the MOSAiC Expedition is available as Open Source in the long-term archive Pangaea (https://pangaea.de) for anyone who would like to learn and study the Arctic Ocean and its features. The M-VRE webODV Project (https://mosaic-vre.org) aims to offer an interactive online exploration, visualization, and analysis of the MOSAiC data in a user-friendly environment. In the M-VRE webODV (https://mvre.webodv.cloud.awi.de), these data are presented as Data Collections that consist of similar datasets aggregated into singular collections and Interdisciplinary Collections, where complementary datasets are aggregated into collections. However, for the MOSAiC data to be explored, visualized, and analyzed with webODV, it has to be converted from the tab file format used in the Pangaea archive to an ODV readable format. Therefore, the data is converted through a six steps process: search, filtering, download of datasets, data aggregation, metadata preparation, and data conversion into the ODV format. Although several datasets after those steps are ready to be uploaded to the M-VRE webODV, other datasets need special and individualized conversions. As a result of the data conversion process and the special conversions, the Data Collections and Interdisciplinary Collections of MOSAiC Expedition data are uploaded to the M-VRE webODV and available for user exploration, visualization, and analysis. The M-VRE webODV is since January 2023 open to the global community, and the number of available Collections is increasing.

*MOSAiC – Multidisciplinary drifting Observatory for the Study of Arctic Climate

*M-VRE webODV – MOSAiC Virtual Research Environment web Ocean Data View

How to cite: Linck Rosenhaim, I., Mieruch-Schnülle, S., and Schlitzer, R.: Data preparation for the development of a user-friendly, free, online, and interactive platform for the visualization and analysis of interdisciplinary data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14352, https://doi.org/10.5194/egusphere-egu23-14352, 2023.

The O3as service is a tool designed to support the assessment of atmospheric ozone levels and trends. It was developed as one of the thematic services of the EOSC-Synergy project. It allows for the analysis of large datasets from chemistry-climate models and presents the information in a user-friendly format for a broad range of users, including scientists, pupils, and interested citizens. The service utilizes a unified approach to process the data, employs CF conventions for homogenization, and generates figures that can be published or downloaded as csv files. It was developed as part of the EOSC-Synergy project, and it runs on a cloud-based, containerized architecture orchestrated by Kubernetes and HPC resources, and uses the Large Scale Data Facility (LSDF) at the KIT for data storage. The service is developed with best software practices, including quality assurance, continuous integration and delivery, and compliance with the FAIR principles. 

This presentation will focus in particular on the architecture and functionality of the O3as service, with an example demonstration of its usage.

How to cite: Kerzenmacher, T., Kozlov, V., Esteban Sanchis, B., Cayoglu, U., Hardt, M., and Braesicke, P.: O3as: Ozone trend visualisations and return dates developed within within EOSC-synergy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15375, https://doi.org/10.5194/egusphere-egu23-15375, 2023.

The Yellowstone to Yukon Conservation Corridor (Y2Y) is North America's largest nature corridor and connectivity project for wildlife. The 2,000-mile swath of land between Wyoming, USA and the Yukon Territory of Canada is one of the last remaining intact mountain ecosystems on Earth, and home to many endangered and at-risk species. The Y2Y is a mosaic of protected and unprotected land including Canadian and US national/state/provincial/territory parks, federally/state managed wildland and national forests, Indigenous territories, and privately managed conservation easements. We are developing a collaborative animal-movement archive for the Y2Y and research tools to study and communicate the effectiveness of protected areas, drivers of migration, and movement connectivity. These tools are applied by end users throughout the Y2Y to support decision making and land and wildlife management.

Our Movebank-based archive of in situ animal location observations provides a uniform data format and QA protocol for conducting large-scale, long-term, and multi-species analyses in support of wildlife management efforts in the region. These data will contribute to biodiversity assessments related to climate and other regional and global changes, and provide a baseline against which to detect early signals of local or large-scale ecosystem changes. We have developed an array of interactive tools for preparing and analyzing movement data using the MoveApps platform, a GUI-based App-development environment for data processing and analysis tools. These tools facilitate the integration of contextual environmental data from remote sensing and weather data products, and additional local environmental data layers. We have developed Apps to detect and quantify events of interest, particularly road crossings, parturition events and kill clusters, and are developing additional Apps to conduct resource and step-selection analyses using data from multiple studies at varying resolutions. To facilitate data exploration and data-based outreach and communication, we have developed ECODATA – a set of data preparation and visualization software packages in MATLAB and Python for building custom animated maps of animal movements along with contextual land management and environmental data layers.

MoveApps and ECODATA are general tools that can be applied to any animal movement dataset. Initial research questions and applications, catered to the decision-making needs of our end users in the Y2Y project, include: How are protected lands utilized by mammals throughout the Y2Y? How is connectivity between conservation areas influenced by current and predicted future environmental characteristics and anthropogenic disturbances (roads in particular)? Continuous joint development and application of tools with active collaboration with our end users guarantee that the research tools we develop answer the management and research needs of end users, while answering new and exciting questions about environmental drivers of movement in the Y2Y.

How to cite: Missik, J., Bohrer, G., Scyphers, M., Davidson, S., Kays, R., Chatterjee, N., Kelly, A., Lohr, A., Kölzsch, A., Wikelski, M., and Fieberg, J.: A new set of tools to explore, analyze, and communicate animal movements with environmental and anthropogenic context, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3652, https://doi.org/10.5194/egusphere-egu23-3652, 2023.

Visualizations are crucial for machine learning as they allow practitioners to understand, analyze, and communicate their models. They help interpret complex models by providing a graphical representation of both data and model performance. Visualizations can be used to understand the underlying patterns and trends in the data, identify biases and errors, and diagnose problems with the model. They also help in communicating the results of the model to a non-technical audience by providing an intuitive and interactive way to present the findings.

Tree-based machine learning methods, such as Classification and Regression Trees or Random Forest, are well-established and widely used in the Earth Sciences. However, visualization tools provided by common machine-learning environments in Python, R, or Matlab often provide graphical representations that could be more visually appealing or helpful in conveying a clear message.

Here we present FORESTER, a web-based and open-source software that produces visually appealing tree-based visualizations. Forester produces publication-ready plots that are, at the same time, interactive figures that can guide the user in interpreting the model. Visualizations can be streamlined to the user's requirements and offer a wide variety of insightful techniques. This makes Forester a promising alternative to currently used environments. Forester is open to collaborations, so we hope it will be extended within the Earth Science community and beyond, proving useful in other machine-learning-related fields.

How to cite: Strahl, D., Reinecke, R., and Wagener, T.: FORESTER – Interactive visualization of tree-based machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12762, https://doi.org/10.5194/egusphere-egu23-12762, 2023.

Probabilistic flood forecasts often concentrate on streamflow, but water depth and extent might convey more tangible flood information for some people. Water depths and extent can also be used more directly than streamflow as part of an impact-based forecasting set-up. However, within a probabilistic or ensemble approach, the uncertainty inherent to water extent and depth applies to all three spatial dimensions: the depth itself is uncertain, and so is the extent in terms of latitude and longitude. The notion of forecast uncertainty is generally well accepted by users, and on the one hand, the addition of new information (flood extent, depth, velocity, etc.) has the potential to be useful for decision makers. On the other hand, it also has the potential to be overwhelming and confusing. Therefore, visualising probabilistic flood forecast maps and communicating the information to the general public and to decision-makers poses multiple challenges. In this presentation we will synthesise the results from a large-scale survey of forecast users, including 28 government representatives, 52 municipalities, 9 organisations, as well as 37 citizens and farmers. Those different groups have different roles, realities, and perspectives. They also have different needs and preferences in terms of hydrological forecasts. The survey consisted of individual and group interviews. The participants were asked a variety of open questions regarding their needs and preferences for hydrological forecasts and also for the visualisation and the communication of those forecasts. One key element of the interviews was the presentation of four alternative visualisation prototypes for probabilistic forecasts of flood depth and extent. The participants were asked to compare those prototypes, to express their preferences in terms of colour maps, wording and the representation of uncertainty. They also provided useful comments on potential modifications to those prototypes and sometimes suggested ideas for entirely new prototypes. Our results highlight that most participants, regardless of their role or background, had the same overall preference in terms of the proposed prototypes, with prototype number 2 the overall favorite (all prototypes will be shown and explained during the presentation). Nevertheless, we also found several specificities among the respective preferences of different user groups. Our results also highlight specific issues related to the understanding of probabilities in the context of flood forecast maps.  The results of this research are currently being used to inform the design of the new forecast communication and visualisation platform in the province of Quebec, Canada.

How to cite: Boucher, M.-A., Jean, V., Frini, A., and Roussel, D.: Visualizing and communicating probabilistic flood forecasts maps for decision-making, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2379, https://doi.org/10.5194/egusphere-egu23-2379, 2023.

Climate research in Northern Germany provides important information to enable adaption to climate change. However, the increasing complexity and the amount of data that needs to be processed makes the information inaccessible for external parties outside of the climate modeling community. Since 2007 the Coastal and Climate Office for Northern Germany at Helmholtz-Zentrum Hereon has maintained a long term stakeholder dialogue. In this context, we make knowledge on coastal climate research available to the public, and to decision-makers. Our range of stakeholders consists of adjacent scientific research groups, interested individuals, governmental bodies, non-governmental organizations, media, education and more.

Web applications, such as the Climate Monitor for Northern Germany, play a central role in our efforts to transfer scientific knowledge to our stakeholders. Originally released in 2014, the monitor comprehends data derived from freely available climate datasets of the last few decades, such as CoastDat, eOBS, CRU TS and more. We provide derived climate information for the most-requested parameters, namely temperature, precipitation, humidity, wind, cloudiness, and vegetation but also analyze indices on extremes such as heat, severe rain fall and storms. We answer the questions of our regional stakeholders, e.g. “How does a changing climate affect our interests?”, by visualizing spatial averages (municipality to state-level scale), as well as comprehensive, interactive and comparable time-series and a descriptive interpretation of both. This tool has been proven to be a valuable asset in stakeholder communication and allows everyone to access crucial climate information for their region of interest.

In our latest release we take user needs into account and redesign the front-end using a mixture of open-source libraries and OGC services provided by ESRI. With the re-design we introduce interactive webmaps and apps, intended to simplify navigability through this complex theme and its far-reaching visualization collection. We aim to increase user engagement through a familiar user interface, consistent with similar web applications. Our data processing pipelines have been streamlined to make the results conform to the FAIR principles. Besides the visual representation of the results, we provide download options for the raw data, and the computational methods are published open-source in the form of Jupyter notebooks. We focus on ease of maintenance, accessibility and on instantaneous publication of the latest results. In this presentation we highlight the workflows and experiences behind creating this user centric web tool, and discuss where we see the benefits of integrating web tools in knowledge transfer.

How to cite: Benninghoff, M., Sommer, P. S., Baldewein, L., and Meinke, I.: Making complex climate information available for a stakeholder dialogue: the Climate Monitor for Northern Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11191, https://doi.org/10.5194/egusphere-egu23-11191, 2023.