The NOAA Warn-on-Forecast program (Stensrud et al. 2013) aims to develop convection—allowing ensemble numerical weather prediction systems that will enable longer warning lead times for tornadoes, flash floods, damaging wind and hail, and other thunderstorm hazards. The optimal design of these ensembles is largely determined by the intrinsic and practical predictability limits of convective storms, for two reasons. First, knowledge of the primary sensitivities of modeled storm evolution is needed to prioritize improvements to ensemble prediction systems. For example, if sub-kilometer model grid spacing greatly improves our ability to distinguish between storms that will or will not become tornadic, this suggests that computational resources be preferentially allocated toward finer model grids versus, e.g., larger ensembles. Second, knowledge of the predictability limits of storms will help prevent wasteful resource allocation in ensemble systems. For example, if current observational and model limitations render storm track forecasts highly uncertain after X hours, this suggests that forecast periods >X hours be sacrificed in favor of, e.g., more frequent forecast updates. Thorough understanding of the predictability of storms is also needed to objectively calibrate and subjectively interpret ensemble forecast output.
Comprehensive investigation of storm predictability requires idealized experiments (e.g., Potvin and Wicker 2013; Potvin and Flora 2015), which allow individual sources of forecast error to be explored; real case studies, which better represent contemporary observational and modeling limitations; and hybrid approaches that combine the strengths of the idealized and real-data frameworks (e.g., Potvin et al. 2017; Flora et al. 2018). Research proposals are invited on any aspect of the predictability of organized convection within any of these experimental frameworks.
Flora, M. L., C. K. Potvin, and L. J. Wicker, 2018: Practical predictability of supercells: Exploring ensemble forecast sensitivity to initial condition spread. Mon. Wea. Rev., 46, 2361-2379.
Potvin, C. K., E. M. Murillo, M. L. Flora, and D. M. Wheatley, 2017: Sensitivity of supercell simulations to initial-condition resolution. J. Atmos. Sci., 74, 5-26.
Potvin, C. K., and M. L. Flora, 2015: Sensitivity of idealized supercell simulations to horizontal grid spacing: Implications for Warn-On-Forecast. Mon. Wea. Rev., 143, 2998-3024.
Potvin, C. K., and L. J. Wicker, 2013: Assessing ensemble forecasts of low-level supercell rotation within an OSSE framework. Wea. Forecasting, 28, 940–960, https://doi.org/10.1175/WAF-D-12-00122.1.
Stensrud, D. J., L. J. Wicker, M. Xue, D. T. Dawson II, N. Yussouf, D. M. Wheatley, T. E. Thompson, N. A. Snook, T. M. Smith, A. D. Schenkman, C. K. Potvin, E. R. Mansell, T. Lei, K. M. Kuhlman, Y. Jung, T. A. Jones, J. Gao, M. C. Coniglio, H. E. Brooks, and K. A. Brewster, 2013: Progress and challenges with Warn-on-Forecast. Atmos. Res., 123, 2-16.
Predictability; Prediction; Convection; Assimilation; Ensemble; Tornado; Warning; Storm; Forecast;