A one hour tour of wradlib#

caption

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#

Documentation#

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#

Fork us from wradlib/wradlib or raise an issue!

Installation#

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/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.

Import wradlib#

[1]:

import wradlib

[2]:

# check installed version
print(wradlib.__version__)

1.20.0.dev10+g9dfc030

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#

Attenuation#

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.