The effects of vertical wind shear, radiation and ice microphysics on precipitation efficiency （PE） were investigated through analysis of modeling data of a torrential rainfall event over Jinan, China during July 2007. Vertical wind shear affected PE by changing the kinetic energy conversion between the mean and perturbation circulations. Clou^radiation interaction impacted upon PE, but the relationship related to cloud radiative effects on PE was not statistically significant. The reduction in deposition processes as- sociated with the removal of ice microphysics suppressed efficiency. The relationships related to effects of vertical wind shear, radiation and ice clouds on PEs defined in cloud and surface rainfall budgets were more statistically significant than that defined in the rain microphysical budget.
The effects of water and ice clouds on the cloud microphysical budget associated with rainfall are investigated through the analysis of grid-scale data from a series of two-dimensional cloud-resolving model equilibrium sensitivity simulations. The model is imposed without large-scale vertical velocity. In the control experiment, the contribution from rainfall （cM） associated with net evaporation and hydrometeor loss/convergence is about 29% of that from the rainfall （Cm） associated with net condensation and hydrometeor gain/divergence and about 39% of that from the rainfall （CM） associated with net condensation and hydrometeor loss/convergence. The exclusion of ice clouds enhances rainfall contribution of CM, whereas it reduces rainfall contributions of Cm and cM. The removal of radiative effects of water clouds increases rainfall contribution of CM, barely changes rainfall contribution of Cm and reduces the rainfall contribution of cM in the presence of the radiative effects of ice clouds. Elimination of the radiative effects of water clouds reduces the rainfall contributions of CM and Cm, whereas it increases the rainfall contribution of cM in the absence of the radiative effects of ice clouds.
The effects of precipitation on the moist potential vorticity substance(MPVS) are investigated by analyzing the MPVS with precipitation mass forcing and its impermeability in daily 1°× 1° data of the National Centers for Environmental Prediction/National Center for Atmospheric Research(NCEP/NCAR) over the Yangtze River Basin from 21 June to 2 July 1999. The results show that the positive MPVS anomalies appear mainly along the Mei-yu front, where the maximum MPVS collocates with the maximum surface rainfall. Rain case diagnoses indicate that the MPVS anomaly may be used as a dynamical signal to detect the location and shift of the rain band when its impermeability is considered.
Atmospheric variability is driven not only by internal dynamics, but also by external forcing, such as soil states, SST, snow, sea-ice cover, and so on. To investigate the forecast uncertainties and effects of land surface processes on numerical weather prediction, we added modules to perturb soil moisture and soil temperature into NCEP＇s Global Ensemble Forecast System （GEFS）, and compared the results of a set of experiments involving different configurations of land surface and atmospheric perturbation. It was found that uncertainties in different soil layers varied due to the multiple timescales of interactions between land surface and atmospheric processes. Perturbations of the soil moisture and soil temperature at the land surface changed sensible and latent heat flux obviously, as compared to the less or indirect land surface perturbation experiment from the day-to-day forecasts. Soil state perturbations led to greater variation in surface heat fluxes that transferred to the upper troposphere, thus reflecting interactions and the response to atmospheric external forcing. Various verification scores were calculated in this study. The results indicated that taking the uncertainties of land surface processes into account in GEFS could contribute a slight improvement in forecast skill in terms of resolution and reliability, a noticeable reduction in forecast error, as well as an increase in ensemble spread in an under-dispersive system. This paper provides a preliminary evaluation of the effects of land surface processes on predictability. Further research using more complex and suitable methods is needed to fully explore our understanding in this area.
