wradlib Documentation - Bitbucket

wradlib Documentation, Release 0.1.1
>>> pl.plot(obs_coords, obs, ’o’, label="Gage observation", markersize=10.0, markerfacecolor="grey")
>>> pl.plot(radar_coords, adjusted, ’-’, color="green", label="Multiplicative adjustment", linewidth=
>>> pl.legend(prop={’size’:12})
>>> pl.show()
See Also:
Get more info in the library reference section Gage adjustment. There, you will also learn how to use the built-in
cross-validation in order to evaluate the performance of the adjustment approach.
2.1.10 Verification and quality control
Typically, radar-based precipitation estimation and the effectiveness of the underlying correction and adjustment methods
are verified by comparing the results against rain gauge observations on the ground. wradlib.verify provides procedures
not only to extract the radar values at specific gauge locations, but also a set of error metrics which are computed
from gage observations and the corresponding radar-based precipitation estimates (including standard metrics such
as RMSE, mean error, Nash-Sutcliffe Efficiency). In the following, we will illustrate the usage of error metrics by
comparing the “true” rainfall against the raw and adjusted radar rainfall estimates from the above example:
>>> raw_error = wradlib.verify.ErrorMetrics(truth, radar)
>>> adj_error = wradlib.verify.ErrorMetrics(truth, adjusted)
Error metrics can be reported e.g. as follows:
>>> raw_error.report()
>>> adj_error.report()
See Also:
Get more info in the library reference section Verification.
2.1.11 Visualisation and mapping
In the above sections Reading the data, Clutter removal, and Gridding you already saw examples of the wradlib’s
plotting capabilities.
See Also:
Get more info in the library reference section Visualisation.
2.1.12 Data export to other applications
Once you created a dataset which meets your requirements, you might want to export it to other applications or
archives. wradlib does not favour or spupport a specific output format. Basically, you have all the freedom of choice
offered by Python and its packages in order to export your data. Arrays can be stored as text or binary files by using
numpy functions. You can use the package NetCDF4 to write NetCDF files, and the packages h5py or PyTables
to write hdf5 files. At a later stage of development, wradlib might support a standardized data export by using the
OPERA’s BUFR or hdf5 data model (see Supported radar data formats). Of course, you can also export data as
images. See Visualisation for some options.
Export your data array as a text file:
>>> np.savetxt("mydata.txt", data)
Or as a gzip-compressed text file:
12 Chapter 2. Tutorials