Reconstruction of the Precipitation Field in a Training Line/Adjoining Stratiform Mesoscale Convective System in the Simulation Mode with the Weather Research and Forecasting (WRF) Model

摘要

Mesoscale convective systems (MCSs) are known for their ability to generate localized extreme precipitation. Therefore, it is important to understand how such storm systems will evolve in the context of a changing climate. This goal can be achieved by using a numerical weather model such as the weather research and forecasting (WRF) model, forced by a climate projection from a global climate model. Recent studies have shown that the WRF model can reconstruct relatively well MCSs in the forecasting mode, where data assimilation techniques are used to improve the model's performances. Nevertheless, no observation is available for the future. To this extent, it is important to evaluate the performances of numerical weather models in reproducing MCSs in the simulation mode, for which the model is only subject to the influence of the initial and boundary conditions. In this study, we used the WRF model to simulate at 5-km resolution a MCS which generated local extreme precipitation in the Northern United States on Aug 19th 2007. We tested two reanalysis datasets along with 150 combinations of the parameterization schemes. The model's performances were evaluated by the use of two statistics: the relative error for the total inner-domain averaged precipitation, and the percentage of overlapping between the simulated and observed fields associated with several precipitation thresholds. We show that under an appropriate choice of the model's options and boundary conditions, the WRF model provides satisfactory results in reproducing the location, intensity, and texture of the intense precipitation field in the MCS.