Raw radar data and KDP processed data to analyse a variety of publicly available KDP estimation methods.

The data has been processed using Python.

The folder "radar" includes several subfolders: "yyyy/mm/dd/iris/raw/VAN". The "VAN" subfolder includes the .raw radar with PPI's observed by Vantaa radar at an elevation angle of 0.7 for a specific time: "yyyymmddHHMM_VAN.PPI3_B.raw". This data can be further read with PyArt (https://arm-doe.github.io/pyart/)

The folder "KDP_data" includes 5 .hdf5 files storing tables containing information about date (in pandas numerical value. It requires transformation to datetime object), Z (in dBZ), Zdr (in dB), attenuated gate (as boolean), theoretical or self-consistency KDP (in deg / km) and computed KDP (in deg / km) from a given method for different settings. The method is indicated in the name of the file as kdp_{method}_scatter.hdf5, where method can be:  - 'iris_sch', referring to table containing KDP from iris software (used in the Finnish Meteorological Institute) and KDP computed from PyArt's implementation of Schneebeli et al. (2014). These two methods were computed together because only one KDP output was retrieved. They do not feature any user-configurable parameters to test. - 'mae', referring to table containing KDP computed from PyArt's implementation of Maesaka et al. (2012). The columns correspond to KDP computed by varying parameter 'Clpf'. - 'vulpiani', referring to table containing KDP computed from PyArt's implementation of Vulpiani et al. (2012). The columns correspond to KDP computed by varying parameters 'windsize'  and 'n_iter'. - 'pplp', referring to table containing KDP computed from PyArt's implementation of Giangrande et al. (2013). The columns correspond to KDP computed by varying parameter 'windowlen'. - 'wradlib', referring to table containing KDP computed from Wradlib's implementation of Vulpiani et al. (2012). The columns correspond to KDP computed by varying parameters  'winlen' and 'dr'.

References:

• Maesaka, T., K. Iwanami, and M. Maki, 2012: Non-negative KDP estimation by monotone increasing PhiDP assumption below melting layer. Seventh European Conf. on Radar in Meteorology and Hydrology, Toulouse, France, ERAD, http://www.meteo.fr/cic/meetings/2012/ERAD/extended_abs/QPE_233_ext_abs.pdf.
• Vulpiani, G., M. Montopoli, L. D. Passeri, A. G. Gioia, P. Giordano, and F. S. Marzano, 2012: On the use of dual-polarized C-band radar for operational rainfall retrieval in mountainous areas. J. Appl. Meteor. Climatol., 51, 405–425, https://doi.org/10.1175/JAMC-D-10-05024.1.
• Schneebeli, M., J. Grazioli, and A. Berne, 2014: Improved estimation of the specific differential phase shift using a compilation of Kalman filter ensembles. IEEE Trans. Geosci. Remote Sens., 52, 5137–5149, https://doi.org/10.1109/TGRS.2013.2287017.
• Giangrande, S. E., R. McGraw, and L. Lei, 2013: An application of linear programming to polarimetric radar differential phase processing. J. Atmos. Oceanic Technol., 30, 1716–1729, https://doi.org/10.1175/JTECH-D-12-00147.1
• Wang, Y., and Chandrasekar, V., 2009: Algorithm for estimation of the specific differential phase. Journal of Atmospheric and Oceanic Technology, 26(12), 2565–2578. https://doi.org/10.1175/2009jtecha1358.1