Submitting Campus
Daytona Beach
Department
Applied Aviation Sciences
Document Type
Article
Publication/Presentation Date
4-2014
Abstract/Description
A precipitation climatology is compiled for warm-season events at Montreal, Québec, Canada, using 6-h precipitation data. A total of 1663 events are recorded and partitioned into three intensity categories (heavy, moderate, and light), based on percentile ranges. Heavy (top 10%) precipitation events (n = 166) are partitioned into four types, using a unique manual synoptic typing based on the divergence of Q-vector components. Type A is related to cyclones and strong synoptic-scale quasigeostrophic (QG) forcing for ascent, with high-θe air being advected into the Montreal region from the south. Types B and C are dominated by frontogenesis (mesoscale QG forcing for ascent). Specifically, type B events are warm frontal and feature a near-surface temperature inversion, while type C events are cold frontal and associated with the largest-amplitude synoptic-scale precursors of any type. Finally, type D events are associated with little synoptic or mesoscale QG forcing for ascent and, thus, are deemed to be convective events triggered by weak shortwave vorticity maxima moving through a long-wave ridge environment, in the presence of an anomalously warm, humid, and unstable air mass that is conducive to convection. In general, types A and B feature the strongest dynamical forcing for ascent, while types C and D feature the lowest atmospheric stability. Systematic higher precipitation amounts are not preferential to any event type, although a handful of the largest warm-season precipitation events appear to be slow-moving type C (stationary front) cases.
Publication Title
Weather and Forecasting
DOI
https://doi.org/10.1175/WAF-D-13-00030.1
Publisher
American Meteorological Society
Scholarly Commons Citation
Milrad, S. M., Atallah, E. H., Gyakum, J. R., & Dookhie, G. (2014). Synoptic Typing and Precursors of Heavy Warm-Season Precipitation Events at Montreal, Québec. Weather and Forecasting, 29(2). https://doi.org/10.1175/WAF-D-13-00030.1