Using Radars to Study Snowfall

An international experiment provides new data on snowfall intensity from ground-based radars.

The Science

Quantitative estimates of snowfall intensity from radar measurements are useful both for providing information to the public during snowfall events and for evaluating weather and climate simulations of snowfall. However, radar-based quantitative precipitation estimation is a challenging task. To derive a relationship between the radar observations and precipitation rate requires knowledge of the particle size distribution, ice particle microphysical properties, and scattering properties of snow particles at the microwave frequencies measured by radars. During the ARM mobile facility campaign deployment in Finland, a snowfall intensive observation period was conducted in collaboration with ARM, the Finnish Meteorological Institute and the NASA Global Precipitation Measurement program. Researchers used data from multiple radars, disdrometers, and microwave radiometers during the intensive observational period to study relationships between radar reflectivity and snowfall intensity.

The Impact

Scientists find that relationships between radar reflectivity and snowfall intensity depend on the amount of riming present (freezing of liquid water droplets onto the surface of the snowflake). Lightly rimed snow events show a spectrally distinct signature from moderately or heavily rimed snow. Light-scattering calculations indicate that the approximation of snow as a soft spheroid particle can produce consistent results across multiple radar wavelengths, although the appropriate aspect ratio likely depends on the amount of riming.


Radar-based snowfall intensity retrieval is investigated at centimeter and millimeter wavelengths using co-located ground-based multi-frequency radar and video disdrometer observations. Using data from four snowfall events, recorded during the Biogenic Aerosols Effects on Clouds and Climate (BAECC) campaign in Finland, measurements of liquid-water-equivalent snowfall rate S are correlated to radar equivalent reflectivity factors Ze, measured by the Atmospheric Radiation Measurement (ARM) cloud radars operating at X, Ka and W frequency bands. From these combined observations, power-law Ze-S relationships are derived for all three frequencies considering the influence of riming. Using microwave radiometer observations of liquid water path, the measured precipitation is divided into lightly, moderately and heavily rimed snow. Interestingly lightly rimed snow events show a spectrally distinct signature of Ze-S with respect to moderately or heavily rimed snow cases. In order to understand the connection between snowflake microphysical and multi-frequency backscattering properties, numerical simulations are performed by using the particle size distribution provided by the in situ video disdrometer and retrieved ice particle masses. The latter are carried out by using both the T-matrix method (TMM) applied to soft-spheroid particle models with different aspect ratios and exploiting a pre-computed discrete dipole approximation (DDA) database for rimed aggregates. Based on the presented results, it is concluded that the soft-spheroid approximation can be adopted to explain the observed multifrequency Ze-S relations if a proper spheroid aspect ratio is selected. The latter may depend on the degree of riming in snowfall. A further analysis of the backscattering simulations reveals that TMM cross sections are higher than the DDA ones for small ice particles, but lower for larger particles. The differences of computed cross sections for larger and smaller particles are compensating for each other. This may explain why the soft-spheroid approximation is satisfactory for radar reflectivity simulations under study.

Principal Investigator(s)

Marta Tecla Falconi
Sapienza University of Rome, Italy


Marta Tecla Falconi was partly supported by the Center of Excellence CETEMPS, L’Aquila (Italy). The activity of Davide Ori was funded by the German Research Foundation (DFG) as part of the Emmy Noether Group OPTIMIce under grant KN 1112/2-1. Annakaisa von Lerber was supported by Horizon 2020 grant agreement no. 699221 (PNOWWA) and no. 700099 (ANYWHERE). The research of Dmitri Moisseev was supported by the Academy of Finland (grant 305175) and the Academy of Finland Finnish Centre of Excellence program (grant 3073314). Dmitri Moisseev also acknowledges the funding received from ERA-PLANET, trans-national project iCUPE (grant agreement no. 689443), funded under the EU Horizon 2020 Framework Programme.


Falconi, M.T., A. von Lerber, D. Ori, F.S. Marzano, and D. Moisseev. “Snowfall Retrieval at X, Ka and W Bands: Consistency of Backscattering and Microphysical Properties Using BAECC Ground-Based Measurements.” Atmospheric Measurement Techniques 11(5), 3059-3079 (2018). [DOI: