Advancing Situational Awareness of Forecast Scenarios through Ensemble Sensitivity Analysis

Meteorological forecasters constantly seek innovative approaches to improve weather predictions and provide timely, accurate information for decision-making. In this context, we present an ensemble sensitivity analysis framework aimed to enhance situational awareness in medium-range weather forecasting. Our methodology complements the existing ensemble clustering approach, originally developed at Stony Brook University and now implemented more operationally at the Weather Prediction Center.

This ensemble clustering technique proves effective at distilling an ensemble forecast down to its most prevalent forecast scenarios. Still, forecasters desire more information on which early features in the flow lead to these divergent forecast outcomes. Ensemble sensitivity analysis provides this by quantifying the influences of early forecast conditions on later forecast events, providing valuable insights into the origins of forecast uncertainty.

This combined methodology holds the potential to improve forecast accuracy, particularly in situations where subtle variations can lead to strongly divergent scenarios. Additionally, ensemble sensitivity analysis allows forecasters to fine-tune their messaging as an event unfolds, tailoring their confidence in a particular cluster based on the evolving atmospheric conditions.

To illustrate the practicality of this approach, we delve into a case study centered around a tropical cyclone scenario. This presentation dives deep into the ensemble sensitivity analysis tool, elaborating on its developmental journey, and offering a glimpse into our vision for its operational deployment. Through this work, we not only advocate for the approach's efficacy but also highlight its potential to become a staple in medium-range weather forecasting.