PREDICTABILITY AND DYNAMICS OF THE 9-10 JUN 2003 SQUALL LINE AND BOW ECHO EVENT DURING BAMEX
Open Access
- Author:
- Melhauser, Christopher Lee
- Graduate Program:
- Meteorology
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- April 02, 2010
- Committee Members:
- Fuqing Zhang, Thesis Advisor/Co-Advisor
Fuqing Zhang, Thesis Advisor/Co-Advisor - Keywords:
- squall line
ensemble forecasts
predictability
bow echo - Abstract:
- An ensemble of cloud-resolving Weather Research and Forecasting Model (WRF) forecasts initialized with perturbations from an ensemble Kalman filter (EnKF) analysis is used to explore the predictability of a bow echo event during the Bow Echo and MCV Experiment (BAMEX) on 9-10 June 2003. The success of multiple WRF deterministic forecasts during the BAMEX campaign suggested that deterministic numerical weather prediction of convective-scale processes had come to fruition. Large variability in evolution and mode of convection and the environment of ensemble members prove the contrary, highlighting the limit of practical predictability given realistic initial condition uncertainties provided by the EnKF analysis. Even though most members forecast severe convective weather over a broad area, some of the members produce bow echoes very similar to that observed while others perform poorly, either producing the wrong convective mode or wrong intensity. Given strong spatial and temporal variability in the environment, it is found that commonly used severe storm predictors based on single soundings (e.g., CAPE, CIN, low-level shear) have very limited capability in forecasting this bow echo event. Nevertheless, averaging of 10 good members versus 10 poor members provides a clear difference in storm evolution, with environments of good members having stronger CAPE and shear along with an upper-level shortwave and the development of a squall line, while poor members having weaker CAPE and shear, along with a weaker upper-level shortwave forming back-building MCSs. Two distinct storm modes within the ensemble indicate a bifurcation point between two regimes that is associated with upscale error growth due to moist convection. To further explore the bifurcation and this event’s intrinsic predictability, a perfect model assumption is made in conjunction with a simulated reduction in initial condition spread in temperature, meridional and zonal winds, mixing ratio, and pressure to locate the divergence point between forecasts. In essence, the ensemble forecast and additional sensitivity experiments demonstrated that: (1) this storms predictability has a practical limit given realistic initial condition spread and its predictability can be improved with more accurate initial conditions; (2) if the storms initial conditions are near bifurcation points, there may be an intrinsic limit. The limit of both practical and intrinsic predictability highlights the demands of probabilistic and ensemble forecasts for cloud-resolving severe weather prediction.