A one hour tour of wradlib¶

caption

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

(find all wradlib notebooks in the `docs <https://docs.wradlib.org/en/1.2.0/notebooks.html>`__.)

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

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!

Installation¶

1. Install Anaconda or Miniconda¶

Get it here for Windows, Linux, or Mac.

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

$ conda config --add channels conda-forge
$ conda create --name newenv python=3.6
$ source activate newenv
(newenv) $ conda install wradlib

To run our tutorials…¶

Get notebooks
Get sample data
Set environment variable WRADLIB_DATA

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¶

In [1]:

import wradlib

In [2]:

# check installed version
print(wradlib.__version__)

1.2.0

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

Inpect the available modules and functions.

In [3]:

Reading and viewing data¶

Read and quick-view

read and quick-view a radarscan

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.