Rainfall Organization And Atmospheric Conditions Associated With Flash Flooding In The Northeastern United States
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Heavy precipitation and flash flooding have been extensively studied in the central U.S., but less so in the Northeast. This study examines 187 flash flood events identified in Storm Data to better understand the organization and structure of the precipitation systems that cause flash flooding in the Northeast. Based on the organization and movement of these features on radar, the events are classified into one of four categories - back-building, linear, scalar, and scattered - and then further classified into one of four sub-types for each category. Ten of these sub-types were not previously recognized in the literature. The back-building events were the most common, followed by the scattered, scalar, and linear types. The linear event types appear to produce flash flooding less commonly in the Northeast than in other regions. In general, the sub-types producing the highest precipitation estimates are those whose structure is most conducive to a long duration of sustained moderate to heavy rainfall. Composite maps were constructed to analyze the atmospheric conditions associated with each event type. Different event types were found to be associated with a variety of upper and lower tropospheric features: long-wave troughs, short-wave troughs, cutoff lows, zonal flow, and long wave ridges. There was no clear preference for a specific atmospheric configuration to produce the heaviest rainfall; any atmospheric configuration can produce heavy rainfall given the right ingredients. In general, the event types were found to be different from those in the central U.S. in that the events were more often found to be more disorganized in the Northeast. One event type in particular, back-building with merging features, while not more disorganized than the previously recognized event types, offers promise for improved forecasting because it makes the duration of sustained heavy precipitation potentially easier to predict.
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Walter, Michael Todd
Degaetano, Arthur T