A one hour tour of wradlib¶
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
A community platform for collaborative development of algorithms
Your entry points¶
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…¶
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.
# check installed version print(wradlib.__version__)
In the next cell, type
wradlib. and hit
Inpect the available modules and functions.
Reading and viewing data¶
- read and quick-view a radarscan
- Read a polar data set from the German Weather Service
- Simple ways to plot this dataset
- Plotting just one sector
- Adding a crosshair to the PPI
- Placing the polar data in a projected Cartesian reference system
- Some side effects of georeferencing
- Simple Plot on Mercator-Map using cartopy
- More decorations and annotations
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.
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, …).