Investigating a fundamental component of a Warn-on-Forecast system in a collaborative real-time experiment

The Warn-on-Forecast paradigm (WoF) envisions probabilistic prediction of severe convective phenomena based on ensemble forecasts using high-resolution models. One of many scientific challenges facing Warn-on-Forecast is how to construct reliable probabilistic information regarding severe convective phenomena when these phenomena will not be explicitly resolvable for many years to come. One approach to address this issue is to identify “extreme” model-generated features that have strong correlations with observed extreme convective phenomena, and then use the former as surrogates for the extreme phenomena in question. This “surrogate-severe” (SS) approach is fundamentally different from traditional applications of NWP for severe weather because it is phenomenon based. In particular, it relies on identification of explicit convective phenomena rather than environmental conditions to predict the likelihood of severe thunderstorms. Sobash et al. (2009) established the viability of this approach using several different SS diagnostic quantities. Their work used a “neighborhood” approach based on the concepts in Theis et al. (2005) and Brooks et al. (1998) to produce severe-weather probability forecasts based on the locations of SS features in a deterministic model. In the current study, we extend the concepts developed by Sobash et al. (2009) to a 26-member storm-scale ensemble. This ensemble was produced by the Center for Analysis and Prediction of Storms (CAPS) during the 2010 NOAA HWT Spring Experiment. In the ensemble-based application it was found that interpretation of derived probabilistic forecasts depends strongly on the parameters used for post-processing. This presentation examines examples of various derived products, their potential utility for current Outlook-scale severe weather forecasts, and their possible application within the focused scales of WoF for severe weather.

References: Brooks, H. E., M. Kay, and J. A. Hart, 1998: Objective limits on forecasting skill of rare events. Preprints, 19th Conference on Severe Local Storms, Minneapolis, Minnesota, Amer. Meteor. Soc., 552-555. Sobash, R. A., J. S. Kain, D. R. Bright, A. R. Dean, M. C. Coniglio, S. J. Weiss, and J. J. Levit, 2009: Forecast guidance for severe thunderstorms based on identification of extreme phenomena in convection-allowing model forecasts. Preprints, 23rd Conference on Weather Analysis and Forecasting/19th Conference on Numerical Weather Prediction, Amer. Meteor. Soc., Omaha, NE. CD-ROM 4B.6 Theis, S. E., A. Hense, and U. Damrath, 2005: Probabilistic precipitation forecasts from a deterministic model: A pragmatic approach. Meteor. Appl., 12, 257–268.