Quantifying the relative skill of observation-based and NWP-based probablistic forecasts of convection

A number of research studies indicate that the recent history of convection contains a great deal of information on its future state out to 6 hours and beyond (e.g., Golding 2000). The scale of the convective feature often determines the length of time that it will persist and thus be predictable through extrapolation alone (Wilson et al. 1998). Additional work has been done in developing heuristic models of convection that can be used to predict the Lagrangian evolution (growth and decay) of convection (Hand 1996; Pierce 2000; Megenhardt 2005). The National Convective Weather Forecast (NCWF-2) combines extrapolation and a heuristic approach to produce 0-2 hr probabilistic forecasts of convection that are available in real-time for use by the aviation community. This system has been extended out to 6 hours in a research-only mode. While these observation-based techniques have demonstrable skill in the very short term (e.g., 0-3 hr), their skill decreases rapidly with lead time. On the other hand, the skill of numerical weather prediction models is poor initially due to model spin-up issues, but increases with lead time. Weygandt and Benjamin (2004) mitigate the poor skill of NWP models at short lead times by developing a probabilistic approach to forecasting convection by using an ensemble of time-lagged Rapid Update Cycle (RUC) model forecasts. In this study we quantify the relative merits of the NCWF and RUC probabilistic forecasts by performing standard statistical analyses on “operational runs” of the two systems. The goal of this study is to quantify the relative skill of each technique for the entire 2005 summer season for the purpose of developing a new operational forecast product that optimally blends the two methods. The new system will be tested using a set of unique cases from the summer 2005 season. The predictability of different types of convective organization will be assessed using the three forecast techniques.