A modeling study of cloud microphysics: Part I: Effects of Hydrometeor Convergence on Precipitation Efficiency. C.-H. Sui and Xiaofan Li

Introduction Precipitation is one of the most important processes in earth hydrological cycles. Precipitation is generated by convective processes that are un-resolvable sub-grid scale eddies in current atmospheric global models, so the various cumulus parameterization schemes have been designed. The model-generated rainfall often contains large uncertainties. Explicit cloud microphysics parameterization schemes are employed in cloud-resolving mesoscale models, regional and even global models. In this study, we determine precipitation efficiency, especially the effect of hydrometeor convergence.

Model and experiment The Goddard Cumulus Ensemble (GCE) model is used for this study. The 2-D version of the model used by Sui et al. (1994, 1998) and further modified by Li et al. (1999) is used in this study. The cloud microphysics parameterization schemes are based on the schemes proposed by Rutledge and Hobbes (1983, 1984), Lin et al. (1983), Tao et al. (1989), Hsie et al. (1980), and Krueger et al. (1995). The corresponding equations are described in Li et al. (1999, 2002c).

Based on the 6-hourly TOGA COARE observations within the Intensive Flux Array (IFA) region. The model is integrated from 1992/12/19/0400 LST to 1993/01/09/0400 LST (21 days total). The horizontal domain is 768 km, the grid mesh of 1.5 km, the vertical grid resolution ranges from about 200 m near the surface to about 1 km about 100 mb and time step of 12 seconds.

Time evolution and horizontal distribution of surface rain rate simulated during 1992 /12/20/0000 LST to 1992/12/21/1200 LST.

q v : the mixing ratio of water vapor, [CONV qv ]: moisture convergence, : surface evaporation S qv = SI qv – SO qv : the source and sink in the water vapor budget. SI qv = [P CND ]+[P DEP ]+[P SDEP ]+[P GDEP ] SO qv = [P REVP ]+ [P MLTG ]+ [P MLTS ] C = q c + q r + q i + q s + q g : surface rain rate Vertically integrated budgets of water vapor and clouds

Collection efficiency: P RACW /P CND = 66% Precipitation efficiency: P S /(P CND +P DEP ) = 67%

Precipitation efficiency Cloud Microphysics Precipitation Efficiency (CMPE) Large-Scale Precipitation Efficiency (LSPE)

96 km48 km 24 km SI qv or –SO qv + Local Change of [qv] (mmh -1 ) RMS=1.3RMS=1.8 RMS=2.5 The correlation coefficients are about 0.89 for all the three cases. [P CND ]+[P DEP ]+[P SDEP ]+[P GDEP ] = SI qv Es+[CONV qv ] [SI qv ] ---- o –[SO qv ]+( )

Conclusions and discussions The precipitation efficiency > 100% occurs in light-rain conditions (< 5 mm hr -1 ) as a result of the additional hydrometeor converging into the atmospheric column. The dependence of CMPE on the rainfall intensity may be explained by the mesoscale-flow patterns in the convective and stratiform rain regions of the squall line. The suggested effect of horizontal hydrometeor advection on the precipitation generation and distribution is expected to be more important in severe weather events like hurricanes/typhoons that has spiral rainbands and very strong mesoscale circulation.

Convective   Stratiform : hydrometeor divergence for convective region; hydrometeor convergence for stratiform region.