Constraining the Large-Scale Analysis of a Regional Rapid-Update-Cycle System for Short-Term Convective Precipitation Forecasting

This study examines the impact of a large-scale constraint (LSC) on the large-scale analysis and precipitation forecast of convective weather systems in a regional rapid-update-cycle system. The LSC is imposed by assimilating Global Forecast System forecast fields as bogus observations with a scale selection scheme. The scale selection is achieved by skipping data points of Global Forecast System forecast fields in the horizontal and vertical directions. It is shown that the LSC is able to modify the large-scale component of the analysis fields while leaving the small-scale component mostly intact compared with a control experiment without the constraint. The effects of the LSC on precipitation forecast are verified and analyzed using nine convective cases in the Rocky Mountain Front Range and its east plains. The results show that the LSC is effective in improving the precipitation forecast of different cases. However, the cases with weak large-scale forcing show greater improvements than those with strong large-scale forcing. Further analyses on the dynamic and thermodynamic variables indicate that the use of the LSC is able to construct a favorable environment for the initiation and development of convection in the case of weak large-scale forcing, which leads to significant improvement of convective precipitation forecasting when radar observations are assimilated.