The objective of this work is to elucidate the salient challenges in forecasting extreme precipitation events in the Southeast U.S. for both numerical weather prediction (NWP) models and human forecasters. While human forecasters rely on NWP model guidance for many aspects of a weather forecast, it is the human recognition of local conditions, model error and bias, and past experience that is often most critical to successful forecasts of high-impact events. Therefore, improving both NWP guidance and forecaster awareness is key to improving the precipitation forecast.
The Southeast U.S. experiences extreme precipitation from a number of different phenomena, making quantitative precipitation forecasting (QPF) in this region especially challenging. As an initial step toward improving predictive capabilities, preliminary model-based experiments have been conducted on select heavy rainfall events in this region. Analysis of these experiments focuses on improved understanding of the forecast errors for events with the lowest skill, and also examines possible connections between specific forecast challenges and key environmental fields (e.g., CAPE, shear, precipitable water) and event characteristics (e.g., system size, duration, strong/weak moisture transport).
Simulations are generated in two ways. First, extreme event composite fields serve as initial conditions in order to examine a generalized extreme event environment. Second, select case studies are simulated and examined in more detail to diagnose operational forecast successes and challenges. Specifically, the flooding that affected the Atlanta, GA region in 2009 and the Nashville, TN region in 2010 will be highlighted, and key features and forecast challenges associated with each event will be contrasted.
The results of these experiments are intended to facilitate forecaster identification and understanding of particularly challenging forecast scenarios, and also to better understand existing NWP model challenges associated with such scenarios. The transition of this research to operations will be made through both standard, ongoing discussion and documentation, and also via more innovative R2O techniques such as realtime and/or retrospective forecaster experiments. Findings will also be useful toward improving and refining NWP numerical models in development.