Investigating Dependences of Ze-S-Relation on Microphysical Properties of Snow

This study investigates the microphysical properties of snow from the ground observations and links them to weather radar observations. The power-law (Z = aS^b) is widely used to convert radar observations to snowfall rate (e.g. Marshall and Gunn 1952, Sekhon and Srivastava 1970, Rasmussen et al. 2003, Huang et al. 2015), albeit coefficients of this relation vary. The factors a and b are dependent on parameters such as particle type, density, velocity and the particle size distribution, and these dependencies have been previously studied. e.g. by Rasmussen et al. 2003 and Bukovcic et al., 2015.

Our focus is on understanding microphysical processes and their evolution during winter storms. We have observed that snow microphysics can change within storms, and that the changes can happen on temporal scales of several minutes. To characterize the microphysics of winter precipitation we have implemented a procedure to retrieve mass-dimensional (m-D) properties of ice particles. For this the general hydrodynamic theory (Böhm, 1989, Mitchell and Heymsfield, 2005, Szyrmer and Zawadzki 2010, Huang et al. 2015) is applied to video-disdrometer Particle Imaging Package (PIP) measurements and errors associated with the observation geometry and the measured particle size distribution are addressed by devising a simple correction procedure (Wood et al. 2013). The value of the correction factor is determined by comparison of the retrieved precipitation accumulation to weighing gauge measurements. We show that retrieved m-D of ice particles corresponds to microphysical processes e.g. to aggregation and riming, and these can be linked to radar observations.

Based on the derived microphysical properties, event specific relations between the equivalent reflectivity factor and snowfall precipitation rate are fit to 5-minutes observations assuming Rayleigh-scattering approximation to be valid. For the studied events in winters 2014-2015, the prefactor of the case-specific Z_e-S -relation varied between 39 and 782 and the exponent was in the range of 1.1 – 2.1. Also a two-years-averaged relation is defined with prefactor value of 116 and exponent value 1.36. The factors are applied to operational C-band radar of Finnish Meteorological Institute. The radar-estimated snowfall rates have been compared to the observations of the operational gauges in the 30-70 km distance from the radar. Both total LWE accumulation and averaged 10-minute snowfall rate are studied and the accuracy of the estimates is good. Error limits of the radar-estimated snowfall rate are determined from the 5-minute values of the prefactor, a, and the exponent b, assuming the particle size distribution is exponential. The upper and lower error limits of a are calculated from the cumulative distribution at percentiles of 25% and 75%, the exponent b is kept constant at its mean value, and both parameters are subsequently inserted into the power-law.

The dependence of the factors a and b on m(D) -relation and PSD are investigated. The exponent b primarily depends on the exponent of the m(D) - relation, while dependence of the prefactor a is more complex. The prefactor can be defined as a function of the intercept parameter of the PSD, N₀, and prefactors of m(D)- and v(D) - relations. Changes in prefactor a for a given N₀ are shown to be linked to changes in liquid water path, which can be considered to be a proxy for degree of riming. The relative importance of LWP and N₀ are compared. It is shown that N₀ is the main contributor to the changes in a. The role of riming is smaller, but still noticeable. A parametrization expressing a as a function N₀ and LWP was also derived.

References:

Bukovcic, P., D. Zrnic, G. Zhang, and A. Ryzhkov, 2015: Snow variability in Oklahoma and Colorado characterized by 2D-video disdrometer and dual-polarization radar measurements.37th Conference on Radar Meteorology, American Meteorological Society, 14 - 18 September, Norman, Oklahoma, USA.

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Mitchell, D. L., and A. J. Heymsfield, 2005: Refinements in the treatment of ice particle terminal velocities, highlighting aggregates. Journal of Atmospheric Sciences, 62 (5), 1637 – 1644.

Huang, G.-J., V. N. Bringi, D. Moisseev, W. A. Petersen, L. Bliven, and D. Hudak, 2015: Use of 2D - video disdrometer to derive mean density - size and Ze - SR relations: Four snow cases from the Light Precipitation Validation EXperiment. Atmospheric Research, 153, 34–48.

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Rasmussen, R., M. Dixon, S. Vasiloff, F. Hage, S. Knight, J. Vivekanandan, and M. Xu, 2003: Snow nowcasting using a real-time correlation of radar reflectivity with snow gauge accumulation. Journal of Applied Meteorology, 42 (1), 20–36.

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Szyrmer, W., and I. Zawadzki, 2010: Snow studies. Part II: Average relationship between mass of snowflakes and their terminal fall velocity. Journal of the Atmospheric Sciences, 67, 3319 –3335.

Wood, N., T. L’Ecuyer, F. Bliven, and G. Stephens, 2013: Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity. Atmospheric Measurement Techniques, 6, 3635 – 3648.