HR: 1330h
AN: A53A-08    [Abstracts]
TI: Evaluating Microphysics in Cloud-Resolving Models using TRMM and Ground-based Precipitation Radar Observations
AU: Krueger, S K
EM: skrueger@met.utah.edu
AF: University of Utah, 135 S 1460 E, Rm 819, Salt Lake City, UT 84112 United States
AU: * Zulauf, M A
EM: mazulauf@met.utah.edu
AF: University of Utah, 135 S 1460 E, Rm 819, Salt Lake City, UT 84112 United States
AU: Li, Y
EM: yaping@met.utah.edu
AF: University of Utah, 135 S 1460 E, Rm 819, Salt Lake City, UT 84112 United States
AU: Zipser, E J
EM: ezipser@met.utah.edu
AF: University of Utah, 135 S 1460 E, Rm 819, Salt Lake City, UT 84112 United States
AB: Global satellite datasets such as those produced by ISCCP, ERBE, and CERES provide strong observational constraints on cloud radiative properties. Such observations have been widely used for model evaluation, tuning, and improvement. Cloud radiative properties depend primarily on small, non-precipitating cloud droplets and ice crystals, yet the dynamical, microphysical and radiative processes which produce these small particles often involve large, precipitating hydrometeors. There now exists a global dataset of tropical cloud system precipitation feature (PF) properties, collected by TRMM and produced by Steve Nesbitt, that provides additional observational constraints on cloud system properties. We are using the TRMM PF dataset to evaluate the precipitation microphysics of two simulations of deep, precipitating, convective cloud systems: one is a 29-day summertime, continental case (ARM Summer 1997 SCM IOP, at the Southern Great Plains site); the second is a tropical maritime case: the Kwajalein MCS of 11-12 August 1999 (part of a 52-day simulation). Both simulations employed the same bulk, three-ice category microphysical parameterization (Krueger et al. 1995). The ARM simulation was executed using the UCLA/Utah 2D CRM, while the KWAJEX simulation was produced using the 3D CSU CRM (SAM). The KWAJEX simulation described above is compared with both the actual radar data and the TRMM statistics. For the Kwajalein MCS of 11 to 12 August 1999, there are research radar data available for the lifetime of the system. This particular MCS was large in size and rained heavily, but it was weak to average in measures of convective intensity, against the 5-year TRMM sample of 108. For the Kwajalein MCS simulation, the 20 dBZ contour is at 15.7 km and the 40 dBZ contour at 14.5 km! Of all 108 MCSs observed by TRMM, the highest value for the 40 dBZ contour is 8 km. Clearly, the high reflectivity cores are off scale compared with observed cloud systems in this area. A similar conclusion can be reached by comparing the simulated microwave brightness temperatures with observed brightness temperatures at 85 GHz and 37 GHz. In each case, the simulations are more extreme than all observed MCSs in the region over the 5 year period. The situation is similar but less egregious for the southern Great Plains simulation. Inspection of the cloud microphysics output files reveals the source of the discrepancy between simulation and observations in the upper troposphere. The simulations have very large graupel concentrations between about 5-10 km, as high as 10 g/kg graupel mixing ratio. This guarantees that there are very high radar reflectivities extending into the upper troposphere, and unrealistically low microwave brightness temperatures. We also performed a set of short (6-h) numerical simulations of the life cycle of a single convection cell to examine the sensitivity of the simulated graupel fields to the intercept parameter and the density of the graupel. The control case used the same values as the ARM and KWAJEX simulations. Reducing the intercept parameter by a factor of 100 reduced the maximum graupel mixing ratios but increased the maximum dBZ values. This suggests that the discrepencies between the simulations and the observations must involve the graupel growth rates.
DE: 0320 Cloud physics and chemistry
SC: Atmospheric Sciences [A]
MN: 2005 Joint Assembly