Montgomery Flora;
Corey Potvin;
Louis Wicker
CIMMS/NSSL/OU
The 8th EnKF Data Assimilation Workshop
Practical Predictability of Supercells: Exploring ensemble forecast sensitivity to EnKF analysis spread
Talk:
Florad1.pptx
As convection-allowing ensembles are routinely used to forecast the evolution of severe thunderstorms, developing an understanding of storm-scale predictability is critical. Using a full-physics numerical weather prediction (NWP) framework, the sensitivity of ensemble forecasts of supercells to initial condition (IC) uncertainty is investigated. Three cases are used from the real-time NSSL Experimental WoF System for Ensembles (NEWS-e) from the 2016 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. NEWS-e has physics diversity and uses an ensemble adjustment Kalman filter (EAKF) to assimilate radar, satellite, mesonet (when available), and other conventional observations every 15 min onto a 3-km grid.
The forecast sensitivity to IC uncertainty is assessed by repeating the simulations with the initial ensemble perturbations reduced to 50 % and 25 % of their original magnitudes. The object-oriented analysis focuses on significant supercell features including the mid- and low-level mesocyclone, and rainfall. For a comprehensive analysis, supercell location and amplitude are evaluated separately. For all examined features and cases, forecast spread is greatly reduced by halving the IC spread, indicating that foreseeable improvements in ensemble analyses should considerably improve severe weather warnings. By reducing the IC spread from 50% to 25% of the original magnitude, forecast spread is still substantially reduced in two of the three cases. The practical predictability limit (PPL), or the lead-time beyond which the forecast spread exceeds some prechosen threshold, is case- and feature-dependent. Comparing to past studies reveals that practical predictability of supercells is substantially improved once storms are well established in the ensemble analysis.
As convection-allowing ensembles are routinely used to forecast the evolution of severe thunderstorms, developing an understanding of storm-scale predictability is critical. Using a full-physics numerical weather prediction (NWP) framework, the sensitivity of ensemble forecasts of supercells to initial condition (IC) uncertainty is investigated. Three cases are used from the real-time NSSL Experimental WoF System for Ensembles (NEWS-e) from the 2016 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. NEWS-e has physics diversity and uses an ensemble adjustment Kalman filter (EAKF) to assimilate radar, satellite, mesonet (when available), and other conventional observations every 15 min onto a 3-km grid.
The forecast sensitivity to IC uncertainty is assessed by repeating the simulations with the initial ensemble perturbations reduced to 50 % and 25 % of their original magnitudes. The object-oriented analysis focuses on significant supercell features including the mid- and low-level mesocyclone, and rainfall. For a comprehensive analysis, supercell location and amplitude are evaluated separately. For all examined features and cases, forecast spread is greatly reduced by halving the IC spread, indicating that foreseeable improvements in ensemble analyses should considerably improve severe weather warnings. By reducing the IC spread from 50% to 25% of the original magnitude, forecast spread is still substantially reduced in two of the three cases. The practical predictability limit (PPL), or the lead-time beyond which the forecast spread exceeds some prechosen threshold, is case- and feature-dependent. Comparing to past studies reveals that practical predictability of supercells is substantially improved once storms are well established in the ensemble analysis.
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