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Dynamical analysis of extreme precipitation in the US northeast based on large-scale meteorological patterns

Abstract : Previous work has identified six large-scale meteorological patterns (LSMPs) of dynamic tropopause height associated with extreme precipitation over the Northeast US, with extreme precipitation defined as the top 1% of daily station precipitation. Here, we examine the three-dimensional structure of the tropopause LSMPs in terms of circulation and factors relevant to precipitation, including moisture, stability, and synoptic mechanisms associated with lifting. Within each pattern, the link between the different factors and extreme precipitation is further investigated by comparing the relative strength of the factors between days with and without the occurrence of extreme precipitation. The six tropopause LSMPs include two ridge patterns, two eastern US troughs, and two troughs centered over the Ohio Valley, with a strong seasonality associated with each pattern. Extreme precipitation in the ridge patterns is associated with both convective mechanisms (instability combined with moisture transport from the Great Lakes and Western Atlantic) and synoptic forcing related to Great Lakes storm tracks and embedded shortwaves. Extreme precipitation associated with eastern US troughs involves intense southerly moisture transport and strong quasi-geostrophic forcing of vertical velocity. Ohio Valley troughs are associated with warm fronts and intense warm conveyor belts that deliver large amounts of moisture ahead of storms, but little direct quasi-geostrophic forcing. Factors that show the largest difference between days with and without extreme precipitation include integrated moisture transport, low-level moisture convergence, warm conveyor belts, and quasi-geostrophic forcing, with the relative importance varying between patterns.
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Submitted on : Tuesday, July 19, 2022 - 8:27:30 AM
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Laurie Agel, Mathew Barlow, Frank Colby, Hanin Binder, Jennifer L. Catto, et al.. Dynamical analysis of extreme precipitation in the US northeast based on large-scale meteorological patterns. Climate Dynamics, Springer Verlag, 2019, 52, pp.1739-1760. ⟨10.1007/s00382-018-4223-2⟩. ⟨insu-03727025⟩



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