Community radar data analysis in Python wradlib: an open source weather radar library. Maik Heistermann and Kai Muehlbauer

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1 Community radar data analysis in Python wradlib: an open source weather radar library Maik Heistermann and Kai Muehlbauer Potsdam and Bonn Last update: May 2015

In brief wradlib is a Python package started in 2011 facilitates collaborative development addresses the major steps of a weather radar processing chain allows

2 In brief wradlib is a Python package started in 2011 facilitates collaborative development addresses the major steps of a weather radar processing chain allows interactive data analysis has more than 60 registered users supports Linux, Mac, and Windows Example sector PPI created by wradlib Last update: May 2015 Page 2

3 Development Team In alphabetical order Maik Heistermann (University of Potsdam) Kai Muehlbauer (University of Bonn) Thomas Pfaff (retired from radar formerly University of Stuttgart) And with contributions from Irene Crisologo (University of the Philippines, Diliman, Quezon City) Edouard Goudenhooft (Royal Meteorological Institute, Brussels) Stephan Jacobi (University of Potsdam) Scott Sinclair (University of Kwazulu-Natal) Last update: May 2015 Page 3

4 Code hosting on Bitbucket Raise an issue Community tools Contribute your code via pull requests Fork the repository Commit and push your changes (Hg) Submit a Pull Request and engage into an interactive review of your code Report bugs, request enhancements, make a proposal... here Q & A: the user forum Register to the mailing list wradlib-users. Stay tuned about recent developments, get help (often quickly), provide help. Last update: May 2015 Page 4

5 Installation Cross-Platform Support Linux Mac Windows Get it from PyPI $ pip install wradlib Use a Python distribution to satisfy dependencies Python(x,y) satisfies all dependencies on Windows Anaconda recommended for Linux/Mac Last update: May 2015 Page 5

6 Documentation Documentation is wradlib's backbone make algorithms transparent to users and developers keeping code and docs in sync (continuous integration to build the docs) library reference + tutorials + recipes Last update: May 2015 Page 6

7 Development paradigm Keep the magic to a minimum transparent, flexible, but rather low level Flat (or no) data model data is represented by numpy arrays, metadata is represented by dictionaries. Last update: May 2015 Page 7

Selected modules Package overview io supports ODIM_H5, NetCDF, HDF5, Rainbow, DX, RADOLAN, GeoTIFF, ESRI ASCII georef to georeference and project polar and gridded data dp to reconstruct

composites from multiple radars ipol for spatial interpolation (gap filling, gridding, ) vpr to construct 3D Cartesian volumes adjust to adjust radar rainfall by rain gauge observations vis

8 Selected modules Package overview io supports ODIM_H5, NetCDF, HDF5, Rainbow, DX, RADOLAN, GeoTIFF, ESRI ASCII georef to georeference and project polar and gridded data dp to reconstruct differential propagation phase zr to convert reflectivity to rainfall clutter to detect non-meteorological echoes atten to retrieve path-integrated attenuation comp to build (weighted) composites from multiple radars ipol for spatial interpolation (gap filling, gridding, ) vpr to construct 3D Cartesian volumes adjust to adjust radar rainfall by rain gauge observations vis to plot polar data as PPIs or RHIs verify to verify radar-based QPE by rain gauge observations Reconstruction of heavy rainfall around Metro Manila (Source: Potsdam University) Last update: May 2015 Page 8

9 Quick start: Read and inspect data Python on win32 >>> import wradlib >>> data, meta = wradlib.io.readdx( "raa00-dx_ fbg---bin") >>> data.shape (360, 128) >>> print meta["producttype"] 'DX' >>> print meta["datetime"] datetime.datetime(2008, 6, 2, 17, 5) >>> wradlib.vis.plot_ppi(data) Last update: May 2015 Page 9

Quick start: Read and inspect data Python 2.7.5 on win32 >>> import wradlib >>> data, meta = wradlib.io.readdx( "raa00-dx_10908-0806021705-fbg---bin") >>> data.

10 Quick start: Read and inspect data Python on win32 >>> import wradlib >>> data, meta = wradlib.io.readdx( "raa00-dx_ fbg---bin") >>> data.shape (360, 128) >>> print meta["producttype"] 'DX' >>> print meta["datetime"] datetime.datetime(2008, 6, 2, 17, 5) >>> wradlib.vis.plot_ppi(data) Last update: May 2015 Page 10

11 Examples showcase Annual rainfall accumulation from DWD's RADOLAN RW product with shapefile overlay Last update: May 2015 Page 11

12 Examples showcase Reconstruction of differential propagation phase from Philippine C-band data Last update: May 2015 Page 12

13 Examples showcase 3D Cartesian volume created from a polar volume (based on an KNMI ODIM_H5 sample) Last update: May 2015 Page 13

14 Examples showcase Decorated sector PPI plotted on a curvilinear grid (based on DWD polar data from Tuerkheim) Last update: May 2015 Page 14

15 Examples showcase Beam blockage for Bonn radar computed from SRTM DEM v4 (PR will be merged soon) Last update: May 2015 Page 15

16 Examples showcase Demonstration of projection & over-/underlay of raster-/shape files (PR will be merged soon) Last update: May 2015 Page 16

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