ERAD 2022 Open Radar Science Shortcourse

nightly-build Binder

This tutorial covers how to get started with the Open Radar Science stack!


The course will take place on 28 August 2022, the day before before the 2022 ERAD Radar Conference. We will introduce the participants to community software packages designed for radar data processing, including (but not limited to) BALTRAD, LROSE, Py-ART, and wradlib. Following a welcome, there will be an introduction to Open Science concepts with the Open Radar context.

The common ground for most of those tools is Python, so we’ll feature a quick intro to the Python programming language, and endow participants with the basics of how to contribute to community software.

List of Instructors

  • Scott Collis (Argonne National Laboratory, USA)

  • Bobby Jackson (Argonne National Laboratory, USA)

  • Maxwell Grover (Argonne National Laboratory, USA)

  • Daniel Michelson (Environment and Climate Change Canada)

  • Jordi Figueras i Ventura (Météo-France, France)

  • Daniel Wolfensberger (MeteoSwiss, Switzerland)

  • Mike Dixon (National Center for Atmospheric Research, USA)

  • Kai Mühlbauer (University of Bonn, Germany)

  • Velibor Pejčić (University of Bonn, Germany)


Course program

  • 09:00 - 09:15 Welcome and getting started

  • 09:15 - 09:45 Community weather radar software and Open Science

  • 09:45 - 10:30 Overview of the open source radar processing packages

  • 10:30 - 11:00 Coffee break

  • 11:00 - 11:45 Hands on Py-ART

  • 11:45 - 12:30 Hands on wradlib

  • 12:30 - 13:30 Lunch break

  • 13:30 - 14:15 Hands on BALTRAD BALTRAD

  • 14:15 - 15:00 Hands on LROSE

  • 15:00 - 15:30 Coffee break

  • 15:30 - 16:00 Combining multiple packages

  • 16:00 - 16:30 Becoming a developer in an open source project, best practices

  • 16:30 - 17:00 Open slot, discussion, evaluation


Tool Foundations

Content relevant to each of the Open Radar packages (ex. Py-ART, wradlib, LROSE, BALTRAD).

Example Workflows

Workflows utilizing the various packages and open radar data.

Things You Need to Prepare

Participants need to bring their own 64-bit notebook (Linux, Windows, Mac). The exercices will take place on a cloud server. On Windows, the use of a ssh-client such as Putty or MobaXterm will be necessary.