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.
Compared to potential temperature （θ） in the dry atmosphere and equivalent potential temperature （θc） in the saturated atmosphere, generalized potential tem- perature （θ＂） has already proven a better thermodynamic parameter in describing the non-uniformly saturated real atmosphere. To add otherwise absent graphic explanations, this paper first presents the physical definition of θ through a tephigram. Then, the utility of the measurement in identifying and forecasting the locations of precipita- tion maxima and heat wave areas with diagnostic com- parison studies and traditionally used thermodynamic parameters is shown.
To compare the initial perturbation techniques using breeding vectors and ensemble transform vectors, three ensemble prediction systems using both initial perturbation methods but with different ensemble member sizes based on the spectral model T213/L31 are constructed at the National Meteorological Center, China Meteorological Administration （NMC/CMA）. A series of ensemble verification scores such as forecast skill of the ensemble mean, ensemble resolution, and ensemble reliability are introduced to identify the most important attributes of ensemble forecast systems. The results indicate that the ensemble transform tech- nique is superior to the breeding vector method in light of the evaluation of anomaly correlation coefficient （ACC）, which is a deterministic character of the ensemble mean, the root-mean-square error （RMSE） and spread, which are of probabilistic attributes, and the continuous ranked probability score （CRPS） and its decomposition. The advantage of the ensemble transform approach is attributed to its orthogonality among ensemble perturbations as well as its consistence with the data assimilation system. Therefore, this study may serve as a reference for configuration of the best ensemble prediction system to be used in operation.
The surface rainfall processes associated with the torrential rainfall event over Hubei,China,during July 2007 were investigated using a two-dimensional cloud-resolving model.The model integrated the large-scale vertical velocity and zonal wind data from National Centers for Environmental Prediction（NCEP）/Global Data Assimilation System（GDAS） for 5 days.The time and model domain mean surface rain rate was used to identify the onset,mature,and decay periods of rainfall.During the onset period,the descending motion data imposed in the lower troposphere led to a large contribution of stratiform rainfall to the model domain mean surface rainfall.The local atmospheric drying and transport of rain from convective regions mainly contributes to the stratiform rainfall.During the mature periods,the ascending motion data integrated into the model was so strong that water vapor convergence was the dominant process for both convective and stratiform rainfall.Both convective and stratiform rainfalls made important contributions to the model domain mean surface rainfall.During the decay period,descending motion data input into the model prevailed,making stratiform rainfall dominant.Stratiform rainfall was mainly caused by the water vapor convergence over raining stratiform regions.
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.
