Improving High-Impact Forecasts through Sensitivity-Based Ensemble Subsets: Demonstration and Initial Tests

Abstract

nsemble sensitivity can reveal features in the flow at early forecast times that are relevant to the predictability of chosen high-impact forecast aspects (e.g., heavy precipitation) later in time. In an operational environment, it thus might be possible to choose ensemble subsets with improved predictability over the full ensemble if members with the smallest errors in regions of large ensemble sensitivity can be identified. Since numerous observations become available hourly, such a technique is feasible and could be executed well before the next assimilation/extended forecast cycle, potentially adding valuable lead time to forecasts of high-impact weather events. Here, a sensitivity-based technique that chooses subsets of forecasts initialized from an 80-member ensemble Kalman filter (EnKF) is tested by ranking 6-h errors in sensitive regions toward improving 24-h forecasts of landfalling midlatitude cyclones on the west coast of North America. The technique is first tested within an idealized framework with one of the ensemble members serving as truth. Subsequent experiments are performed in more realistic scenarios with an independent truth run, observation error added, and sparser observations. Results show the technique can indeed produce ensemble subsets that are improved relative to the full ensemble for 24-h forecasts of landfalling cyclones. Forecast errors are found to be smallest when the greatest 70% of ensemble sensitivity magnitudes with subsets of size 5?30 members are used, as well as when only the cases of the largest forecast spread are considered. Finally, this study presents considerations for extending this technique into fully realistic situations with regard to additional high-impact events.