Poster Session P2M.11 Disdrometer and Radar Observation-Based Microphysical Parameterization to Improve Weather Forecast

Tuesday, 25 October 2005
Alvarado F and Atria (Hotel Albuquerque at Old Town)
Guifu Zhang, Univ. of Oklahoma, Norman, OK; and J. Sun, E. A. Brandes, J. Dudhia, and W. Wang

Handout (1.9 MB)

Most model microphysical parameterization schemes were developed based on an assumption of a Marshall-Palmer (M-P) drop size distribution (DSD) model, an exponential distribution with a fixed intercept. Recent disdrometer observations indicate that the raindrop size distribution (DSD) can be represented by a constrained-gamma (C-G) distribution model. The model is used to retrieve DSDs from polarization radar measurements of reflectivity and differential reflectivity and to characterize rain microphysics and physical processes such as evaporation, accretion, and precipitation. The C-G model parameterization is simplified to a single parameter for applications in single-moment numerical weather models. This simplified C-G (S-C-G) parameterization is applied in the Weather Research Forecast (WRF) model to improve model forecasting.

The WRF single moment 6-class (WSM6) scheme is modified to accommodate the S-C-G model for rain water. Both an ideal case and a real case are studied with the S-C-G model, and results are compared to those for the M-P DSD model. The idealized case is a 2-dimensional squall line case. The S-C-G model parameterization gives different simulation results from the M-P model. The real forecasts are done for the May 2, 2005 case in central Oklahoma, where NCAR's disdrometer were deployed, and the KOUN polarization radar was in operation. The forecast results are compared with the disdrometer/radar observations. It is found that the S-C-G model parameterization better preserves the stratiform rain and produces weaker convective cores than the M-P model parameterization, which agrees with radar observations better. These results are consistent with those obtained with the Variational Doppler Radar Analysis System (VDRAS), in which radar data are assimilated for model initialization.

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