A Stochastic Approach for Representing Ice Cloud Microphysical Processes in Models
Poster PDF
Authors
Joseph Finlon — University of Illinois at Urbana-Champaign
Greg McFarquhar — University of Oklahoma
Junshik Um — University of Oklahoma
Wei Wu — NOAA National Ocean Service
Hugh Clifton Morrison — University Corporation for Atmospheric Research
Steve Nesbitt — University of Illinois at Urbana-Champaign
Category
Microphysics (cloud, aerosol and/or precipitation)
Description
The representation of ice microphysical processes in numerical models is challenged by the wide range of particle shapes, sizes, and densities observed among various environmental conditions. These properties are typically specified using empirical parameters (e.g., mass (m)-dimension (D), projected area (A)-D, perimeter (P)-D, and fall velocity (V)-D relations, and single-scattering properties) derived from in-situ observations, and held constant within models. While some studies have investigated the sensitivity of simulated bulk variables to the choice of coefficients, few studies have shown the impact of natural parameter variability. A stochastic approach is proposed here for representing variability in parameterizations. This uses a probabilistic representation of parameters, rather than fixed values, is used to account for their uncertainty (e.g., measurement error), natural variability, and co-variability.
This approach is illustrated for m-D, A-D, and P-D relations. Data from a variety of environments sampled during the Mid-latitude Continental Convective Clouds Experiment (MC3E) are used to establish a probability density function for a and b coefficients describing the m-D relation as m=aD^b using a technique that minimizes the chi-squared difference between the total water content (TWC) and radar reflectivity (Z) derived from size distributions measured by cloud probes on the University of North Dakota (UND) Citation and that directly measured by a TWC probe and radar. Further, direct measurements of particle projected area, perimeter, and size from cloud probes are used to derive best fit parameters for A-D and P-D relations. Each of these relations are examined as functions of temperature, TWC, and vertical velocity to understand how environmental parameters influence the natural parameter variability. It is shown that fixed coefficients in numerical modeling schemes do not adequately represent the variability of cloud conditions. Future efforts to determine how these coefficient surfaces vary as a function of spatiotemporal scale and how they will be implemented in microphysical parameterization schemes are discussed.
