EGU General Assembly 2020
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Do the rain microphysics provide information on the overlying ice cloud?

Kamil Mroz1, Alessandro Battaglia1, Stefan Kneifel2, and Jose Dias Neto2
Kamil Mroz et al.
  • 1National Centre for Earth Observation, University of Leicester, United Kingdom
  • 2Institute of Geophysics and Meteorology, University of Cologne, Cologne, Germany

This study investigates to what degree the information about the Drop Size Distribution (DSD) of rain can be used to narrow down uncertainty associated with complex ice microphysics. For this purpose, measurements from vertically pointing multi-frequency Doppler radar are thoroughly analysed. Linear Depolarization Ratio information is used to unambiguously separate hydrometeor phases. Within radar volumes where pure rain is identified multi-frequency Doppler spectra are utilised to retrieve a binned DSD with a high degree of confidence (Tridon et al. 2017). By assuming no breakup and negligible interaction between melting particles (Szyrmer and Zawadzki 1999, Olson et al. 2001, Matrosov 2008) the rain drop size distribution closest to the melting region is used to predict the particle size distribution (PSD) in the overlying snow. With these assumptions the resulting shape of the ice PSD depends solely on the hydrodynamical properties of snow that are dictated by its microphysics.  Several ice models are considered in the analysis, ranging from aggregates of columns, dendrites, needles and plates to different stages of rimed snow. Their scattering properties are simulated with Self-Similar-Rayleigh-Gans approximation (Leinonen et al. 2018) whereas falling velocities are modelled after Khvorostyanov and Curry (2005). Doppler spectra are simulated for the predicted ice PSD and compared to the measurements above the melting region. Results suggest that, if appropriate snow model used, the predicted reflectivity differs by less than 3 dB from the measured values as has been tentatively suggested by Fabry and Zawadzki (1995).

Tridon, F., A. Battaglia, E. Luke, P. Kollias, 2017. Rain retrieval from dual-frequency radar Doppler spectra: validation and potential for a midlatitude precipitating case study. Q. J. Roy. Meteorol. Soc. 143, 1364-1380. DOI: 10.1002/qj.3010

Szyrmer, W. and I. Zawadzki, 1999: Modeling of the Melting Layer. Part I: Dynamics and Microphysics. J. Atmos. Sci., 56, 3573–3592,<3573:MOTMLP>2.0.CO;2

S. Olson, P. Bauer, N. F. Viltard, D. E. Johnson, W-K. Tao, R. Meneghini, and L. Liao, “A melting layer model for passive/active microwave remote sensing applications—Part I: Model formulation and comparisons with observations,” J. Appl. Meteorol., vol. 40, no. 7, pp. 1145–1163, Jul. 2001

Y. Matrosov, "Assessment of Radar Signal Attenuation Caused by the Melting Hydrometeor Layer," in IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 4, pp. 1039-1047, April 2008. doi: 10.1109/TGRS.2008.915757

Fabry, F., and I. Zawadzki, 1995: Long-term radar observations of the melting layer of precipitation and their interpretation. J. Atmos. Sci., 52, 838–851.

Jussi, Leinonen, Kneifel, Stefan, Hogan, Robin J.. Evaluation of the Rayleigh–Gans approximation for microwave scattering by rimed snowflakes. Q J R Meteorol Soc 2018; 144 ( Suppl. 1): 77– 88.

How to cite: Mroz, K., Battaglia, A., Kneifel, S., and Neto, J. D.: Do the rain microphysics provide information on the overlying ice cloud?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16462,, 2020.

This abstract will not be presented.