A one hour tour of wradlib#


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

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

Openradar discourse#

Get help and connect with weather radar enthusiasts from all over the world at openradar-discourse!

For developers#

See our contributing guide.


See Installation-Section.

To run our tutorials#

  1. Get notebooks

  2. Get sample data

  3. Set environment variable WRADLIB_DATA

See also: https://docs.wradlib.org/en/latest/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.

Labelled multi-dimensional arrays#

  • combine data, coordinates and attributes in flexible structures with xarray and xradar

  • transparent multiprocessing using dask

Import wradlib#

import wradlib
/home/runner/micromamba/envs/wradlib-tests/lib/python3.11/site-packages/h5py/__init__.py:36: UserWarning: h5py is running against HDF5 1.14.3 when it was built against 1.14.2, this may cause problems
  _warn(("h5py is running against HDF5 {0} when it was built against {1}, "
# check installed version

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

Inpect the available modules and functions.

[ ]:

Reading and viewing data#

Read and quick-view#

Let’s see how we can read and quick-view a radar scan.

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.