Analyzing predictability and communicating uncertainty: Lessons from the post-Groundhog Day 2009 storm and the March 2009 'megastorm'

Autor: Michael J. Bodner, Richard H. Grumm, Neil A. Stuart
Rok vydání: 2013
Předmět:
Zdroj: Journal of Operational Meteorology. 1:185-199
ISSN: 2325-6184
Popis: Forecasting winter storms in the northeastern United States during the 2008–2009 season was very challenging owing to large uncertainty in the numerical weather prediction guidance prior to each storm. Forecasts for the February 2009 post-Groundhog Day event and the March 2009 "megastorm" featured significant spatial and timing errors in storm track, precipitation type, and areal extent. Each storm’s impacts were communicated with considerable certainty, leading to confusion and misunderstanding of the actual uncertainty in each event. Both cases can serve as instructional examples for the forecast community to improve interpretation of levels of uncertainty, along with communication of uncertainty, during potentially high-impact events. Examples of spatial and temporal uncertainties associated with both storms are presented. These uncertainties are illustrated using ensemble data from the National Centers for Environmental Prediction Global Ensemble Forecast System, Short Range Ensemble Forecast, and the higher-resolution deterministic models such as the North American Mesoscale model, Global Forecast System, and European Center for Medium-Range Weather Forecasts. In addition to standard ensemble output, forecast anomalies are presented because highly anomalous situations frequently have large societal impacts. Techniques for analyzing ensemble probabilities for quantitative precipitation forecast (QPF) threshold values and ensemble plume QPF diagrams are demonstrated. In addition, various combinations of deterministic and ensemble means and spreads for mean sea level pressure and 500-hPa height will be presented to evaluate the predictability of surface low-pressure tracks. Several experimental techniques are presented to promote better understanding of predictability and better communication of uncertainty to users.
Databáze: OpenAIRE