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Rainfall is a critical component of the Earth's water cycle, influencing global economic stability, access to food and freshwater, and daily life. Rain is also frequently used as a calibration target for various remote-sensing instruments. As such, timely and accurate observations of rainfall are vital for meteorological applications. The microphysical properties of rain are commonly characterized by the drop-size distribution (DSD), which determines the water content, intensity of precipitation, and kinetic energy of the rain. Conventional methods for measuring DSD include in situ instruments such as optical disdrometers and polarimetric weather radars. Disdrometers measure the size and velocity of raindrops within a narrow laser beam, providing data only at the surface level and having uncertainties due to the limited sampling area. Polarimetric weather radars, on the other hand, can observe rain profiles over larger areas, but typically only capture higher moments of the DSD, which then require specialized retrieval methods to derive DSD properties. Such retrievals are typically based on known size-shape-velocity relations for raindrops and a scattering model. Polarimetric variables are of an especial value because they allow to decouple the contribution of shape, size, and concentration of raindrops to the observations. In addition, the polarimetric variables can be accurately calibrated. The results of retrieval based on the moments are, however, prone to uncertainties related to measurement errors and limited information content of the DSD moments. Polarimetric Doppler cloud radars, operating at millimeter wavelengths, offer an alternative to traditional methods of the DSD estimation. They can measure the same set of parameters as weather radars but spectrally resolved, i.e. the cloud radar can separately measure droplets coexisting in the same volume but moving with different velocities relative to the radar. Since velocity of droplets is a proxy of their size, spectrally resolved measurements contain much more information about the underlying DSD. This study explores the potential of polarimetric cloud radars to retrieve DSD profiles. We highlight the advantages of this approach, including the ability of the non-parametric estimation of DSD profiles. We also examine existing challenges, such as the impact of resonance effects on observations due to the comparable wavelength of cloud radars and droplet sizes. These effects require accurate representation in scattering models and size-shape-velocity relationships. Current literature lacks explanations for some observations, indicating a need for further research and development of retrieval methods based on spectral polarimetric cloud radar data.
Myagkov et al. (Fri,) studied this question.