21.04.2018

The session "State of the Art in Earth Science Data Visualization" was organized by DKRZ and included four DKRZ-picos: Michael Böttinger talked about the topic „Visualization of uncertainty in climate projections imposed by volcanic activity”. He also co-authored the contribution on “Visual exploration of ensemble variability at the example of decadal climate predictions” by Christopher Kappe.

Florian Ziemen from the Max Planck Institute for Meteorology presented his project of the retrograde earth experiment (Visualization of a Retrograde Earth Experiment for Public Outreach). The visualizations for the project were developed in close collaboration with Niklas Röber.

Karin Meier-Fleischer gave a brief insight into the visualization services at DKRZ in the pico “Visualization of Climate Simulation Data – State of the Art“.

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More detailed information on that topic was given within the workshop “Visualization in Earth Science: best practices”, organized by Niklas Röber and Michael Böttinger, in which tools were shown, explained and demonstrated live, which are particularly suitable for the visualization of climate data. The DKRZ visualizers Niklas Röber and Karin Meier-Fleischer presented the software ParaView and NCL; and John Cline, a long-time cooperation partner, presented Vapor 3 live. More than hundred participants were interested in the workshop, so that not only the regular seating options of the room were used.

Presentation: Visualisation in Earth System Science (Download)

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Another workshop on „Git for science - or 'how to avoid the hunt for the last working version of ...'” was co-organized, among others, by Carsten Ehbrecht. Due to the numerous participants, the workshop, which was originally planned as a hands-on, could only be presented as a demo.

Further material for “Git for science”:
https://github.com/earthserver-eu/jupyter_notebooks/tree/master/EGU_2018/GIT%20for%20Science
https://github.com/cehbrecht/gitforscience-sandbox

In the pico session „YAC 1.3.0: An extendable coupling software for Earth system modelling“, René Redler from the Max Planck Institute for Meteorology presented the YAC coupler that he cojointly developed with Moritz Hanke.

Sofiane Bendoukha and Tobias Weigel gave a talk on the services developed within the framework of the EOSC hub project during the pico “ENES Climate Analytics Service (ECAS)”.

In the oral program, the session "High resolution weather and climate models on large supercomputers” offered among others scientists from the European projects PRIMAVERA, ESCAPE and ESiWACE as well as HighResMIP from CMIP6 the opportunity to meet and discuss with each other. Philipp Neumann used the opportunity to give a talk on “Performance Predictions for Storm-Resolving Simulations of the Climate System”, in which he presented current results of the ESiWACE project and thus results of high-resolution global climate simulations that used the ICON model.

The talk by Sandro Fiore “ECASLab: a user-friendly, integrated environment for scientific data analytics and visualization in the European Open Science Cloud landscape” was created in collaboration with Tobias Weigel and Sofiane Bendoukha.

During the EGU, participants were able to find out more about the projects, services and research results from more than 11,000 posters. The DKRZ employees Carsten Ehbrecht and Stephan Kindermann presented a poster „Web Processing Services for Copernicus Climate Change Service” on the subproject CP4CDS, short for Climate Projections for Climate Data Store. For this project, quality-tested CMIP5 data sets for the Copernicus data pool are provided via the ESGF network.

Fabian Wachsmann, Martin Schupfner and Stephanie Legutke presented the challenges for data preparation and archiving within the CMIP6 project with another poster on “Web-based generation of post-processing components for the CMIP6 data workflow”. For CMIP6 more than 2000 climate variables are requested from over 200 experiments. With its Data Request Web GUI, the DKRZ offers various web-based applications for the post-processing of model data, thus ensuring the CMIP data standards.