Application of a Snow Growth Model to Radar Remote Sensing Ehsan Erfani 1,2 and David L. Mitchell 1 1. Desert Research Institute, Reno, Nevada 2. University of Nevada, Reno, Nevada ehsan.erfani@dri.edu Microphysical growth processes of diffusion, aggregation and riming are incorporated analytically in a steady-state snow growth model (SGM) to solve the zeroth- and second- moment conservation equations with respect to mass. The SGM is initiated by radar reflectivity (Z w ), supersaturation, temperature, and liquid water content (LWC) and uses gamma size distribution (SD) to predict the vertical evolution of size spectra. Riming seems to play an important role in the evolution of snowfall rates, and the snowfall rates produced by aggregation, diffusion and riming are considerably greater than those produced by diffusion and aggregation alone. The impact of ice particle shape on particle growth rates and fall speeds is represented in the SGM in terms of ice particle mass-dimension (m-D) power laws (m = αD β ). These growth rates are qualitatively consistent with empirical growth rates, with slower (faster) growth rates predicted for higher (lower) β values. In most models, β is treated constant for a given ice particle habit, but it is well known that β is larger for the smaller crystals. Our recent work quantitatively calculates β and α as a function of D where the m-D expression is a second-order polynomial in log-log space. By adapting this method to the SGM, the ice particle growth rates and fall speeds are predicted more accurate and realistic. Moreover, the predicted size spectra by SGM are in good agreement with those from aircraft measurement during Lagrangian spiral descents through frontal clouds, indicating that the microphysical processes are modeled correctly. Since the lowest Z w over complex topography is often significantly above cloud base, the precipitation is often underestimated by radar quantitative precipitation estimates (QPE). Our SGM is capable of being initialized with Z w at the lowest reliable radar echo and consequently improves QPE at ground level. ABSTRACT Model Setup Basic Characteristics of the model: Basic Equations for the Evolution of Snow Size Spectra: Mitchell (1991, JAS) D (mm) n(D) (cm −4 ) ν < 0 ν > 0 ν=0 PSD Shape I: Gauss’ hypergeometric function Mitchell et al. (2014, JGR) 2 nd -order polynomial curve fit 101 103 105 107 2 2.5 3 3.5 4 4.5 5 5.5 6 0 0.05 0.1 0.15 0.2 0.25 Relative Humidity over Ice [%] Cloud Altitude [km] Liquid Water Content [g m -3 ] LWC RHi -23 °C 0 °C Vapor Diffusion Riming Aggregation References Erfani, E., and D. Mitchell, 2016: Developing and Bounding Ice Particle Mass- and Area-dimension Expressions for Use in Atmospheric Models and Remote Sensing, Atmos. Chem. Phys., 16, 4379-4400. Erfani, E., and D. Mitchell, 2016: Developing and Bounding Ice Particle Mass- and Area-dimension Expressions for Use in Atmospheric Models and Remote Sensing, Atmos. Chem. Phys., 16, 4379-4400, doi .org/10.5194/acp-16- 4379 -2016 Lo, K.K., Passarelli, R.E., 1982. The growth of snow in winter storms: an airborne observational study. J. Atmos. Sci. 39, 697–706. Mitchell, D. L., A. Huggins, and V. Grubisic, 2006: A new snow growth model with application to radar precipitation estimates. Atmos. Res., 82, 2–18 Erfani, E., 2016: A partial mechanistic understanding of North American Monsoon and microphysical properties of ice particles, University of Nevada-Reno, Ph.D. Dissertation, 229 pp. Erfani, E., 2016: A partial mechanistic understanding of North American Monsoon and microphysical properties of ice particles, University of Nevada-Reno, Ph.D. Dissertation, 229 pp, http ://doi.org/10.13140/RG.2.2.26526.79685 Pruppacher, H.R., Klett, J.D., 1997. Microphysics of Clouds and Precipitation. Springer, Boston, pp 976. From Mitchell (1991), JAS Observed spiral Lo and Passarelli (1982) Mitchell (1988) Lo (1983) Verification of Aggregation growth General Performance of SGM (negligible nucleation and updraft) Ice Particle Shape Evolution and Effect of Riming Snow Size Spectra Comparison of Model against Observation A New Approach for Treatment of Riming: R: riming factor (0<R<1)