A one hour tour of wradlib

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

(find all wradlib notebooks in the `docs <https://docs.wradlib.org/en/latest/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…

  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

In [1]:
import wradlib
In [2]:
# check installed version
print(wradlib.__version__)
1.2.0-2-g502cc5a

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

Inpect the available modules and functions.

In [3]:

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.