Adaptive K–R relationships based on cloud phase classification using SEVIRI observations

Errors in the representation of the drop size distribution are a major source of uncertainty in rainfall estimation, since both radar reflectivity and microwave attenuation depend nonlinearly on precipitation microphysics. These uncertainties propagate directly into the specific attenuation–rain rate (k–R) relationship through the interaction between electromagnetic waves and hydrometeors, leading to systematic biases when globally fixed coefficients are used. In standard practice, the k–R relationship is expressed as a power law of the form k=aR^b, where the coefficients a and b are typically taken from the International Telecommunication Union (ITU) recommendations and assumed to be globally applicable. The use of the ITU coefficients implicitly assumes stationary rainfall microphysics, which is physically inconsistent under varying cloud and rain regimes. This highlights the need for stratified parameterizations in which the coefficients are optimized for different microphysical conditions. In this context, cloud phase information from geostationary satellites provides a physically meaningful basis for clustering the k–R relationship, as different cloud phases are associated with distinct precipitation formation processes and drop size distributions.

The objective of this study is to derive cloud phase dependent k–R parameterizations and to assess their performance across a large disdrometer network. A global disdrometer dataset (Ghiggi et al., 2021, DISDRODB) covering multiple climatic regions is used to simulate k–R relationships across a wide frequency range from 5 to 100 GHz using the T-matrix scattering method. SEVIRI MSG observations are used as input to the Cloud Physical Properties (CPP) product provided by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF), from which cloud phase is classified into water, supercooled water, mixed phase, deep convective, cirrus, and opaque ice categories. Frequency dependent k–R coefficients are derived separately for each cloud type. The framework is evaluated across more than 100 independent disdrometer sites, primarily concentrated in Europe.

Relative to the ITU recommended model (ITU-R P.838-3), the cloud phase adaptive parameterization substantially reduces root mean square error (RMSE), with the strongest improvements observed at 5 to 8 GHz. At these frequencies, more than 90 percent of sites show lower RMSE, with average reductions reaching up to 1.5 mm.h^-1. More moderate improvements are found at higher frequencies from 60 to 100 GHz, where around 60 percent of sites show RMSE reductions, with average improvements below 0.5 mm.h^-1.

These results show that cloud phase informed k–R parameterizations can significantly improve rainfall estimation from commercial microwave links and indicate potential applicability to radar systems.

Reference:

Ghiggi, G., Billault-Roux, A. C., Candolfi, K., Pillac-Mage, L., Unal, C., Schleiss, M., Uijlenhoet, R., Raupach, T., and Berne, A.: DISDRODB – A global disdrometer archive of raindrop size distribution observations, PrePEP 2025, Karlsruhe, Germany, 10–12 March 2025, https://indico.kit.edu/event/4015/contributions/18545/, 2025.

 

This work was supported by the Czech Science Foundation (GACR), Czech Republic, under Grant No. 24-13677L (MERGOSAT).