Mesoscale predictability of an extreme warm-season precipitation event

During the period of June 29 through July 6, 2002, an extreme precipitation event occurred over Texas, resulting in catastrophic flooding. Operational forecasts performed poorly, neither predicting the copious amounts of rain nor its longevity. The Penn State University/NCAR Mesoscale Model version 5 (MM5) was used to conduct predictability experiments, which follow closely to the research conducted by Zhang et al. A control simulation initialized at 00Z 1 July is established over a 30-km grid. First, practical predictability experiments are performed by exploring the impacts due to different lead-times, resolution dependence, and different physics parameterizations. Second, intrinsic predictability is investigated by inducing a random temperature perturbation in the initial conditions, followed by numerous simulations with various perturbed initializations. Similar results to those found by Zhang et al. were discovered here: the prominent initial error growth is associated with moist processes leading to convection. Eventually these errors grow from the convective scale to sub-synoptic scale, essentially below 1000 kilometers. This indicates that as the forecast time extends further beyond initialization, the resulting errors will impact forecasts of larger-scale features such as differences in the positioning and intensity of positive PV anomalies and distribution of precipitation from the control simulation