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Training on High Performance Data Analytics and Visualisation in October 2020

This is the first 8-hour online training course on High Performance Data Analytics (HPDA) and visualisation organised by ESiWACE2.
Oct 06, 2020 02:00 PM to Nov 03, 2020 04:00 PM (Europe/Vienna / UTC200)
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This online training course aims to increase scientists’ expertise on data analysis and visualization applied to climate and weather domains, using high-performance data analytics and visualization tools available from the open source market (i.e., Ophidia and ParaView).

The training covers topics from simple analytics tasks to workflows and applications (e.g., Python-based) and provides best practices and guidelines on dealing with massive scientific datasets on HPC architectures. 

Examples of real applications of Ophidia for data analytics and ParaView for visualization in the climate and weather domain will be presented. Demos and hands-on concerning the tools will also be carried out during the training. 

This course is organized in the context of the “Centre of Excellence in Simulation of Weather and Climate in Europe” (ESiWACE) phase 2 project.

Ophidia snapshot



  • Introduction to scientific data management and analytics

  • Big Data in HPC: High-performance data management

  • Analytics workflows for eScience

  • Open-source High Performance Data Analytics (HPDA) tools

  • Data Processing using CDO

  • Introduction to data visualization using ParaView

  • Discussion of visualization workflows, from post to in-situ


The training targets an audience in the field of weather and climate research with different backgrounds, from computer to Earth system scientists. Basic knowledge of the following is expected to fully take advantage of the training: Python, Linux, general aspects concerning climate/weather data.


The training ran over four weeks in October and beginning of November, with 2 hours of work per week required from the participants. Training sessions combined presentations and hands-on and were scheduled as follows:

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