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
A community platform for collaborative development of algorithms
Your entry points¶
Start out from wradlib.org¶
Get help and connect more than 120 users at the wradlib user group!
1. Install Anaconda or Miniconda¶
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…¶
Keep the magic to a minimum¶
flexible, but lower level
Flat (or no) data model¶
pass data as numpy arrays,
and pass metadata as dictionaries.
/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 print(wradlib.__version__)
In the next cell, type
wradlib. and hit
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.
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.