Start
November 12, 2021 - 3:00 pm
End
November 12, 2021 - 4:00 pm
Categories
Convective Meteorology (Mesoscale Dynamics)Convective Meteorology (Mesoscale Dynamics)
Identification and Verification of Quasi-Linear Convective Systems in the Warn-on-Forecast System
Kelsey Britt
Friday, November 12
3:00 PM
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Quasi-linear convective systems (QLCSs) produce multiple hazards that pose a threat to life and property. These hazards include damaging straight-line winds, heavy precipitation that may lead to flash flooding, and the rapid development of tornadoes produced from shallow, embedded mesovortices. Therefore, accurately forecasting these systems and the associated hazards can be difficult. The Warn-on-Forecast System (WoFS) is a convection-allowing ensemble system that produces short-term, probabilistic forecasting guidance for severe convective events. WoFS has shown promise in accurately forecasting tornadic supercells, flash-flooding events, and even tropical cyclones. However, examination of the ensemble’s capability to predict the likelihood, timing, location, and spatial extent of QLCS hazards has not been assessed across a large dataset of cases.
A new storm mode object identification and classification algorithm has been developed and tuned to use composite reflectivity to better identify high-reflectivity, linear structures. This algorithm is applied to QLCS events occurring in the 2017–2021 WoFS forecasts and gridded observations from the Multi-Radar/Multi-Sensor System (MRMS) to identify QLCS convective line objects. The forecasted objects from all 18 ensemble members are matched to the observed objects to generate verification statistics. A centerline analysis is also used to examine the integrated differences between the forecasted and observed QLCS objects. For instance, these centerlines can be used to measure displacement in extent, timing, and even the location of bowing segments along the forecasted convective lines compared to observations. This verification analysis will ascertain the advantages and challenges to forecasting QLCS events with the current WoFS, which can then be used to determine components of the forecast system that may need to be improved.