A Simple Technique for Using Radar Data in the Dynamic Initialization of a Mesoscale Model

Abstract

A simple technique for using radar reflectivity to improve model initialization is presented. Unlike previous techniques, the scheme described here does not infer rain rates and heating profiles from assumed relationships between remotely sensed variables and precipitation rates. Rather, the radar data are only used to tell the model when and where deep moist convection is occurring. This information is then used to activate the model?s convective parameterization scheme in the grid elements where convection is observed. This approach has the advantage that the convective precipitation rates and heating profiles generated by the convective parameterization are compatible with the local (grid element) environment. The premise is that if convection is forced to develop when and where it is observed during a data assimilation period, convectively forced modifications to the environment will be in the correct locations at the model initial forecast time and the resulting forecast will be more accurate. Three experiments illustrating how the technique is applied in the simulation of deep convection in a warm-season environment are presented: a control run in which no radar data are assimilated, and two additional runs where radar data are assimilated for 12 h in one run and 24 h in the other. The results indicate that assimilating radar data can improve a model?s description of the mesoscale environment during the preforecast time period, thereby resulting in an improved forecast of precipitation and the mesoscale environment.