Radar signatures of snowflake riming: A modeling study

Abstract

The capability to detect the state of snowflake riming reliably from remote measurements would greatly expand the understanding of its global role in cloud‐precipitation processes. To investigate the ability of multifrequency radars to detect riming, a three‐dimensional model of snowflake growth was used to generate simulated aggregate and crystal snowflakes with various degrees of riming. Three different growth scenarios, representing different temporal relationships between aggregation and riming, were formulated. The discrete dipole approximation was then used to compute the radar backscattering properties of the snowflakes at frequencies of 9.7, 13.6, 35.6, and 94 GHz. In two of the three growth scenarios, the rimed snowflakes exhibit large differences between the backscattering cross sections of the detailed three‐dimensional models and the equivalent homogeneous spheroidal models, similarly to earlier results for unrimed snowflakes. When three frequencies are used simultaneously, riming appears to be detectable in a robust manner across all three scenarios. In spite of the differences in backscattering cross sections, the triple‐frequency signatures of heavily rimed particles resemble those of the homogeneous spheroids, thus explaining earlier observational results that were compatible with such spheroids.

Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures

“…In contrast, snowfall triple-frequency radar signatures that were modeled by Leinonen and Szyrmer (2015) based on detailed 3-D shape models of rimed snowflakes extend to higher values of DWR X/Ka and DWR Ku/Ka and roughly span the region between the W04 and N13 triple-frequency curves shown in Fig. 7 for small exponential slope parameters , depending on the amount of riming assigned to the snowflake 3-D shape models.…”

Fingerprints of a riming event on cloud radar Doppler spectra: observations and modeling

“…The second group of parameterizations introduced in the model are more suitable for larger more spherical particles; the parameterization developed for the accretional growth of raindrops (Beard and Grover, 1974), a parameterization derived for graupel (Cober and List, 1993), and the one proposed by Lohmann (2004) for aggregates based on the experimental results of Lew et al (1986) are included. Some examples of the dependence of the riming efficiency on the snow particle size calculated for different cloud droplet diameters are shown in the supporting information of Leinonen and Szyrmer (2015). Different approaches to describing the physics of riming result in different descriptions of the changes of the properties of particles undergoing riming growth.…”

Remote Sensing of Precipitation from Airborne and Spaceborne Radar

“…In these scattering models, for example, aggregates have much larger Ka-W DFR than pristine plates. Leinonen and Szyrmer (2015) also found that rimed particles tend to have a lower Ka-W DFR for a given Ku-Ka DFR than non-rimed aggregates.…”