Sensitivity of simulated ensemble Kalman filter analyses and forecasts of low-level supercell rotation to environmental and physics parameterization errors

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Tuesday, 4 February 2014: 1:45 PM
Room C202 (The Georgia World Congress Center )
Corey K. Potvin, CIMMS/Univ. of Oklahoma, Norman, OK; and L. J. Wicker

Under the envisioned warn-on-forecast (WoF) paradigm, ensemble model guidance will play an increasingly critical role in the tornado warning process. While computational constraints will likely preclude explicit tornado prediction in initial WoF systems, real-time forecasts of low-level mesocyclone-scale rotation appear achievable within the next decade. Given that low-level mesocyclones are significantly more likely than higher-based mesocyclones to be tornadic, intensity and trajectory forecasts of low-level supercell rotation could provide valuable guidance to tornado warning and nowcasting operations. The quality of such forecasts is being explored within an observing system simulation experiment (OSSE) framework. In previous work (Potvin and Wicker 2013), highly idealized experiments were performed to obtain an upper-limit estimate of the accuracy of early WoF systems. The present OSSE study uses more realistic settings to obtain ensemble forecasts that likely better represent near-future WoF capability.

A set of supercell thunderstorms is simulated using the Advanced Research Weather Research and Forecasting (WRF-ARW) model with full physics and O(100 m) horizontal grid spacing. The simulation is initialized with a National Severe Storms Laboratory (NSSL) Mesoscale Ensemble (NME) analysis of the antecedent environment of a real tornado outbreak. Storms are initiated with thermal bubbles strategically positioned to achieve a range of storm environments. Multiple-Doppler velocity and reflectivity observations are synthesized from the simulation using a sophisticated radar emulator, then assimilated by the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter (EAKF) with O(1 km) horizontal grid spacing. The resulting ensemble analyses are used to initialize ~1 h ensemble forecasts from which probabilistic low-level rotation guidance is generated. Evaluation of the simulated forecasts focuses on the sensitivity of low-level circulation track and intensity to errors in the mesoscale analysis and physics paramaterization schemes. Implications of the experiments for using real-time storm-scale ensemble forecasts to extend tornado warning lead times are discussed.