A13P-04:
Use of S-Band Profiling Radar and in Situ Size Distribution Measurements to Probabilistically Constrain Ice Sticking Efficiencies
Monday, 15 December 2014: 2:25 PM
Marcus van Lier-Walqui1,2, Ann M Fridlind2, Andrew S Ackerman2, Christopher R Williams3, Jingyu Wang4, Xiquan Dong4, Wei Wu5, Greg M McFarquhar6, Alice Grandin7, Fabien Dezitter7, J Walter Strapp8 and Alexei Korolev9, (1)Columbia University of New York, Palisades, NY, United States, (2)NASA GISS, New York, NY, United States, (3)University of Colorado Boulder, Boulder, CO, United States, (4)University of North Dakota, Grand Forks, ND, United States, (5)University of Illinois at Urbana Champaign, Urbana, United States, (6)Univ Illinois, Urbana, IL, United States, (7)Airbus, Toulouse, France, (8)Met Analytics Inc., Aurora, ON, Canada, (9)Environment Canada Toronto, Toronto, ON, Canada
Abstract:
The efficiencies of aggregation between colliding ice particles (sticking efficiencies) are highly uncertain. Within model schemes, temperature and size dependence may or may not be included and electrostatic forces are generally neglected. Sticking efficiencies across ice types (e.g., graupel, snow or cloud ice) may differ by an order of magnitude or more from one scheme to another. Here we assess the degree to which a combination of S-band profiler and in situ ice size distribution measurements can provide constraints on sticking efficiencies within quasi-steady-state stratiform rain columns. We select two cases of extended stratiform rain, one observed over Oklahoma and a second observed over Darwin, Australia, which appear remarkably similar in mean reflectivity and Doppler velocity profiles. Both cases exhibit an extended period over which mid-tropospheric mean Doppler velocities are uncorrelated with radar reflectivity, consistent with a stability of normalized mass size distribution shape observed in situ. Using these observations and best estimates of associated measurement uncertainties, a Bayesian Markov chain Monte Carlo framework is used to probabilistically estimate sticking efficiency parameters. This method provides estimates of non-linear model sensitivity to multivariate parameter perturbation as well as non-Gaussian uncertainty in the estimated parameter values.
