Comparison Between Continuous Integration and Targeted Initialization for Medium Range Prediction of Severe Convective Storms

Abstract

Abstract

Abstract This study investigates the skill of convection-allowing model (CAM) forecasts for severe convective storms (SCSs) using two different initialization approaches at lead days four and eight. Seven notable SCS events across the U.S. were simulated using the Weather Research and Forecasting model, initialized with reforecast data from the Global Ensemble Forecast System version 12. Continuous integration (CI) and targeted initialization (TI) methods were compared in an ensemble framework, employing two stochastic perturbation schemes to create three members. Forecast performance was evaluated through comparisons of accumulated precipitation, simulated radar reflectivity factor, and severe storm attributes against observed datasets, including the NCEP stage IV quantitative precipitation estimates and GridRad reflectivity composites. Fraction skill scores revealed limited statistically significant differences between the two initialization techniques, although targeted initialization exhibited slight improvements for certain metrics, particularly at lead day four. Furthermore, model simulations using targeted initialization substantially reduced computational wall-clock time, offering potential resource savings. These findings suggest that while both methods perform comparably, targeted initialization could be advantageous for operational CAM ensemble forecasting—particularly for medium- to extended-range lead times—where computational feasibility is often the biggest barrier.