PrecipType: Dual-pol C-band radars for analyzing the precipitation type on the ground

Belgium has complete coverage with dual polarization Doppler radars. The horizontal and vertical polarizations allow not only the estimation of precipitation amounts, but also gathering of additional information about the detected hydrometeors, e.g. their size and shape. Horizontal reflectivity in combination with radar variables using signals from both polarizations, such as differential reflectivity, correlation coefficient and specific differential phase, are further analyzed to estimate the hydrometeor class at the height of the radar beam. Hydrometeor classification algorithms (HCAs) suggest the most probable hydrometeor class for each pixel of the radar image. Then the ground transition applies the method of Steinert et al. (2021) [1] to estimate the precipitation type on the ground.

Currently, four different HCAs are being tested at the Royal Meteorological Institute of Belgium (RMI) and Belgian C-band radars: (i) an algorithm adapted from the Australian Bureau of Meteorology Research Center (BMRC) [2], (ii) the Dolan algorithm [3], (iii) the Besic algorithm [4] and (iv) the HCA based on Zrnic et al. (2001) [5] as implemented in the Python library Wradlib [6].

Our poster briefly introduces the HCAs and then compares their results for cases of winter weather and convective storms in 2025. Reports from the RMI weather app from the population are used to verify the results.

 

References:

[1] Steinert, J., P. Tracksdorf, and D. Heizenreder, 2021: Hymec: Surface Precipitation Type Estimation at the German Weather Service. Wea. Forecasting, 36, 1611–1627, https://doi.org/10.1175/WAF-D-20-0232.1.

[2] based on Keenan, T., 2003: Hydrometeor classification with a C-band polarimetric radar, Aust. Met. Mag. Hydrometeor 52 (2003) 23-31.

[3] Dolan, B., S. A. Rutledge, S. Lim, V. Chandrasekar, and M. Thurai, 2013: A Robust C-Band Hydrometeor Identification Algorithm and Application to a Long-Term Polarimetric Radar Dataset. J. Appl. Meteor. Climatol., 52, 2162–2186, https://doi.org/10.1175/JAMC-D-12-0275.1.

[4] Besic, N., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A., 2016: Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach, Atmos. Meas. Tech., 9, 4425–4445, https://doi.org/10.5194/amt-9-4425-2016.

[5] Zrnić, D. S., A. Ryzhkov, J. Straka, Y. Liu, and J. Vivekanandan, 2001: Testing a Procedure for Automatic Classification of Hydrometeor Types. J. Atmos. Oceanic Technol., 18, 892–913, https://doi.org/10.1175/1520-0426(2001)018<0892:TAPFAC>2.0.CO;2.

[6] https://docs.wradlib.org/en/latest/notebooks/classify/2d_hmc.html, last access 2025/05/22