A one hour tour of wradlib


A guided tour of some \(\omega radlib\) notebooks.

(find all wradlib notebooks in the Tutorials & Examples.)

Some background, first

Development started in 2011…or more precisely:

October 26th, 2011

Key motivation

A community platform for collaborative development of algorithms

Your entry points

Start out from wradlib.org


Check out the online docs with tutorials and examples and a comprehensive library reference

User group

Get help and connect more than 120 users at the wradlib user group!

For developers

Fork us from https://github.com/wradlib/wradlib or raise an issue!


1. Install Anaconda or Miniconda

Get it Anaconda or Miniconda for Windows, Linux, or Mac.

2. Create environment, add conda-forge, install wradlib

$ conda config --add channels conda-forge
$ conda create --name newenv python=3.9
$ source activate newenv
(newenv) $ conda install wradlib

To run our tutorials…

  1. Get notebooks

  2. Get sample data

  3. Set environment variable WRADLIB_DATA

See also: https://docs.wradlib.org/en/stable/jupyter.html

Development paradigm

Keep the magic to a minimum

  • transparent

  • flexible, but lower level

Flat (or no) data model

  • pass data as numpy arrays,

  • and pass metadata as dictionaries.

Import wradlib

import wradlib
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
# check installed version

In the next cell, type wradlib. and hit Tab.

Inpect the available modules and functions.

[ ]:

Reading and viewing data

Zoo of file formats

This notebook shows you how to access various file formats.

Addressing observational errors and artefacts


In this example, we reconstruct path-integrated attenuation from single-pol data of the German Weather Service.

Clutter detection

wradlib provides several methods for clutter detection. Here, we look at an example that uses dual-pol moments and a simple fuzzy classification.

Partial beam blockage

In this example, wradlib attempts to quantify terrain-induced beam blockage from a DEM.

Integration with other geodata

Average precipitation over your river catchment

In this example, we compute zonal statistics over polygons imported in a shapefile.

Over and underlay of other geodata

Often, you need to present your radar data in context with other geodata (DEM, rivers, gauges, catchments, …).

Merging with other sensors

Adjusting radar-based rainfall estimates by rain gauges

In this example, we use synthetic radar and rain gauge observations and confront them with different adjustment techniques.