Evolution of Snow-size Spectra by the Growth Processes of vapor Deposition, Aggregation and Riming

A steady-state snow growth model (SGM) is formulated by microphysical growth processes of vapor deposition, aggregation and riming. SGM is capable of predicting the temporal and vertical evolution of ice particle size distribution (PSD) by using radar reflectivity (Z_w), supersaturation, temperature, liquid water content (LWC) and ice particle shape dependent mass-dimension power laws, and by solving the zeroth- and second- moment conservation equations with respect to mass.

It appears that the aggregation process is essential in characterizing the snowfall rates and the strong interaction between aggregation and riming leads to the snowfall rates significantly greater than those produced by the vapor deposition and riming alone. Moreover, alteration in cloud condensation nuclei (CCN), due to aerosols, can modify cloud droplet SD (size distribution) and therefore change the snowfall rate. So, snowfall rate is sensitive to the shape of cloud droplet SD.

Ice particle growth rates are uniquely formulated in the SGM in terms of ice particle mass-dimension (m-D) power laws (m = αD^β), and in this way the impact of ice particle shape on particle growth rates and fall speeds is accounted for. These growth rates appear qualitatively consistent with empirical growth rates, with slower (faster) growth rates predicted for higher (lower) β values.

It is well known that for a given ice particle habit, the m-D power law for the smallest ice particles differs considerably from the power law for the largest particles, with β being much larger for the smallest crystals. Our recent work quantitatively predicts β and α for cirrus clouds as a function of maximum dimension D where the m-D expression is a second-order polynomial in log-log space. By tailoring the m-D power law to the relevant PSD moments, the SGM ice particle growth rates and fall speeds are represented more accurate and realistic. It is speculated that by implementing this new m-D treatment in any cloud resolving model or climate model, the ice particle growth rates will become more accurate. The predicted size spectra by SGM are in good agreement with observed spectra from aircraft measurement during Lagrangian spiral descents through frontal clouds.

In addition to improving the modeling of cirrus clouds, this SGM can improve radar snowfall rate estimates. Since the lowest radar reflectivity over complex topography is often considerably above cloud base, radar quantitative precipitation estimates (QPE) often underestimate the precipitation at ground level. Our SGM is capable of being initialized with Z_w (e.g. at the lowest reliable radar echo) and consequently improves QPE at ground level.