Title: The Use of Ensemble-Based Sensitivity with Observations to Improve Predictability of Severe Convective Events

Author: Brian Ancell (Texas Tech University)
Aaron Hill (Texas Tech University)
Brock Burghardt (Texas Tech University)

Ensemble-based sensitivity analysis can reveal important weather features early in a forecast window relevant to the predictability of high-impact events later in time. Ensemble sensitivity has been shown on synoptic scales with simulated observations to be useful in identifying ensemble subsets that are more likely than the full ensemble mean, which may potentially add value to operational guidance of high-impact events. On convective scales, with highly nonlinear ensemble perturbation evolution and very non-Gaussian distributions of severe weather responses (e.g., simulated reflectivity above some threshold), it becomes more difficult to apply linear-based ensemble sensitivity to improve predictability of severe events. Here we test the ability of ensemble sensitivity to improve predictability of a severe convective event through identifying errors in sensitive regions of different members early in a forecast period using radar and surface-based observations. A number of convective cases are explored during the week of June 2, 2014 during which researchers from Texas Tech University participated in the Hazardous Weather Testbed Spring Experiment. Particular attention is given to those cases that appear to result from cold pools associated with prior convection, a situation often poorly handled in numerical weather prediction models. The integration of the proposed technique into an operational tool at the National Weather Service will be discussed.

GMAO Head: Steven Pawson Global Modeling and Assimilation Office NASA Goddard Space Flight Center

Curator: Nikki Privé Last Updated: Feb 9 2015