| description abstract | A dataset of drylines within a region of the southern Great Plains was constructed to investigate the large-scale environments associated with the initiation of deep moist convection. Drylines were identified using NOAA/NWS Weather Prediction Center surface analyses for all April, May, and June days 2006–15. Doppler radar and visible and infrared satellite imagery were used to identify convective drylines, where deep, moist convection was deemed to have been associated with the dryline circulation. Approximately 60% of drylines were convective, with initiation most frequently occurring between 2000 and 2100 UTC. Composite synoptic analyses were created of 179 convective and 104 nonconvective dryline days. The composites featured an upper-level long-wave trough to the west of the Rockies and a ridge extending across the northern and eastern United States. At the surface, the composites featured a broad surface cyclone over western Texas and southerly flow over the south-central states. Convective drylines featured more amplified upper-level flow, associated with a deeper trough in the western United States and a stronger downstream ridge than nonconvective drylines up to 5 days preceding a dryline event. By the day of a dryline event, the convective composite features greater low-level specific humidity and higher CAPE than the nonconvective composite. These results demonstrate that synoptic-scale processes over several days help create conditions conducive to deep, moist convection along the dryline.The southern Great Plains dryline separates moist air from the Gulf of Mexico from drier air farther west. Drylines sometimes initiate convective storms, that is, storms that produce lightning, tornadoes, and other extreme weather. We wanted to know if we could tell the difference between days when such storms occur and days when they do not. We found that there were distinctive weather patterns in the middle and upper troposphere that distinguished these two sets of days. These differences were apparent 3–5 days ahead of time, suggesting an opportunity for more lead time in forecasting such storms. | |
