Quantifying the effect of riming on snowfall using ground‐based observations

Abstract

Ground‐based observations of ice particle size distribution and ensemble mean density are used to quantify the effect of riming on snowfall. The rime mass fraction is derived from these measurements by following the approach that is used in a single ice‐phase category microphysical scheme proposed for the use in numerical weather prediction models. One of the characteristics of the proposed scheme is that the prefactor of a power law relation that links mass and size of ice particles is determined by the rime mass fraction, while the exponent does not change. To derive the rime mass fraction, a mass‐dimensional relation representative of unrimed snow is also determined. To check the validity of the proposed retrieval method, the derived rime mass fraction is converted to the effective liquid water path that is compared to microwave radiometer observations. Since dual‐polarization radar observations are often used to detect riming, the impact of riming on dual‐polarization radar variables is studied for differential reflectivity measurements. It is shown that the relation between rime mass fraction and differential reflectivity is ambiguous, other factors such as change in median volume diameter need also be considered. Given the current interest on sensitivity of precipitation to aerosol pollution, which could inhibit riming, the importance of riming for surface snow accumulation is investigated. It is found that riming is responsible for 5% to 40% of snowfall mass. The study is based on data collected at the University of Helsinki field station in Hyytiälä during U.S. Department of Energy Biogenic Aerosols Effects on Clouds and Climate (BAECC) field campaign and the winter 2014/2015. In total 22 winter storms were analyzed, and detailed analysis of two events is presented to illustrate the study.

How Does Riming Affect Dual‐Polarization Radar Observations and Snowflake Shape?

“…There are several ways to do such a fit, given that we are interested in comparing ground‐based and radar observations, the fitting is done by giving more weight to particles that contribute the most to the radar observations, as shown in . The comparison of the computed rime mass fraction values to the ones reported by Moisseev et al () shows a rather good agreement. Figure presents the comparison between equivalent LWP (ELWP) computed from FR and LWP measured by the MWR, as well as the cumulative distribution of LWP.…”

“…Moisseev et al () have followed this approach and have derived the mass‐size relation for unrimed snowflakes using the ensemble mean snow density retrieved by Tiira et al (). Using data collected during BAECC and winter 2014/2015, they have found that the relation …”

“…This expression is given in cgs units. The relation is derived from snow density‐median volume diameter observations by fitting data that corresponds to the lowest 5% of density values recorded during BAECC and winter 2014/2015 (Moisseev et al, ).…”

“…The FR retrieval method is similar to the one proposed by Moisseev et al (). The main difference between this FR retrieval method and the one proposed by Moisseev et al () is the used data. Von Lerber et al () have used hydrodynamic equations linking particle fall velocity and mass, as proposed by Böhm () and further extended in (Mitchell & Heymsfield, ).…”

McSnow: A Monte‐Carlo Particle Model for Riming and Aggregation of Ice Particles in a Multidimensional Microphysical Phase Space

“…Precipitation in midlatitudes originates mostly from mixed‐phase cloud systems and growth of ice particles by the collection of supercooled liquid drops. This process, known as riming, contributes significantly to surface precipitation, see e.g., Grazioli et al () or Moisseev et al () for some observational evidence based on state‐of‐the‐art measurement techniques. For example at the coastal areas of Japan, heavily rimed snow particles are in fact present in more than 70% of the snowfall events and contribute more than 50% to wintertime surface precipitation (Harimaya & Sato, ).…”