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Title: Evaluating the Effective Inflow Layer of Simulated Supercell Updrafts
Abstract Proper prediction of the inflow layer of deep convective storms is critical for understanding their potential updraft properties and likelihood of producing severe weather. In this study, an existing forecast metric known as the effective inflow layer (EIL) is evaluated with an emphasis on its performance for supercell thunderstorms, where both buoyancy and dynamic pressure accelerations are common. A total of 15 idealized simulations with a range of realistic base states are performed. Using an array of passive fluid tracers initialized at various vertical levels, the proportion of simulated updraft core air originating from the EIL is determined. Results suggest that the EIL metric performs well in forecasting peak updraft origin height, particularly for supercell updrafts. Moreover, the EIL metric displays consistent skill across a range of updraft core definitions. The EIL has a tendency to perform better as convective available potential energy, deep-layer shear, and EIL depth are increased in the near-storm environment. Modifications to further constrain the EIL based on the most-unstable parcel height or storm-relative flow may lead to marginal improvements for the most stringent updraft core definitions. Finally, effects of the near-storm environment on low-level and peak updraft forcing and intensity are discussed.  more » « less
Award ID(s):
1928319
NSF-PAR ID:
10188819
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Monthly Weather Review
Volume:
148
Issue:
8
ISSN:
0027-0644
Page Range / eLocation ID:
3507 to 3532
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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