0. Introduction

0. Introduction#

In order to use the exercises, you need to be able to use Python.

Installation#

The Notebooks are provided via this git repository located at the University Bonn Gitlab instance.

Generally we can use the JupyterHub available at the Institute of Geosciences, Meteorology Section https://hub.meteo.uni-bonn.de:8000/. The lecture is also available directly via eCampus.

Using this resource all data is available from the intranet.

If you want to play with the notebooks from another location you would need to follow the actions below.

To run the notebooks you need Python and the necessary packages and dependencies.

Installing Dependencies on own hardware#

For linux and macOSX `bash`-shell is mandatory. For windows please refer to the available conda-documentation.

  • Install miniforge for your operating system.

  • Add conda-forge to your conda channels and add `strict` channel priority.

(base) $ conda config --add channels conda-forge
(base) $ conda config --set channel_priority strict

Cloning radarpolarimetry#

  • cd to the parent-folder, where you want to install the radarpolarimetry-notebooks. Then clone the radarpolarimetry-repo using the following git clone command:

(radarpol) $ git clone https://gitlab.uni-bonn.de/grp_meteo/radar/radarpolarimetry.git
(radarpol) $ cd radarpolarimetry
(radarpol) $ git checkout SS20XY

  • Create a new conda environment radarpol containing all needed packages

$ mamba env create -f environment.yml

  • Activate your new environment and install any other dependencies. Please note that wradlib is one main source of the used radar algorithms. wradlib itself has quite an extensive set documented Core Features and examples and tutorials.

$ conda activate radarpol
(radarpol) $ mamba install your_other_needed_package

Obtain Radar Data#

We use data available via IfGeo/Meteo intranet or on the IfGeo/Meteo FTP. No additional efforts needed. The data is cached locally inside the cloned repository filesystem.

Git-Tutorial#

Reading and visualizing radar data#

We start with a lessons on acquiring radar data:

Additional study material

The notebooks in this section will give you a glimpse of the Python language and important scientific packages, confront you with some Python exercises, and refer you to further online material. In addition, we will address some issues that are relevant to the lecture and exercise content.

As an introduction to scientific python, this section is necessarily incomplete. There are better and more comprehensive courses such as the SciPy Lecture Notes or the Dive into Python book.

Mostly, we will assume that you have some basic knowledge about programming, e.g. from other interpreted languages such as R or Matlab. Otherwise, a fundamental course such as Dive into Python might help you more.