An observational and modeling study of a heavy orographic precipitation event over the Oregon Cascades

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An observational and modeling study of a heavy orographic precipitation event over the Oregon Cascades

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Title: An observational and modeling study of a heavy orographic precipitation event over the Oregon Cascades
Author: Garvert, Matthew F
Abstract: During 13--14 December 2001, a comprehensive set of observations over the central Oregon Cascades was collected as part of the IMPROVE-2 field project, permitting an unprecedented opportunity for the investigation of a heavy precipitation event over complex terrain. The collection of thermodynamic and kinematic observations concomitantly with microphysical measurements also provided a unique chance to isolate errors in a mesoscale scale model's microphysical parameterization which may be contributing to errors in quantitative precipitation forecasting. The Fifth Generation Penn State/NCAR Mesoscale Model (MM5) version 3.5 was run at various resolutions to simulate the 13--14 December 2001 system and verified against the vast observational dataset. The MM5's depiction of the synoptic features of the storm system was sufficiently accurate to permit further comparisons of mesoscale and microphysical data with the model.High-resolution model simulations and comprehensive airborne Doppler radar observations identified kinematic structures influencing the production and mesoscale distribution of precipitation and associated microphysical processes. Two distinct scales of mesoscale wave-like air motions were identified: (1) a vertically-propagating mountain wave anchored to the Cascade crest associated with strong mid-level zonal (i.e. cross barrier) flow, and (2) smaller-scale (< 20 km horizontal wavelength) undulations over the windward foothills triggered by interaction of the low-level along-barrier flow with multiple ridge-valley corrugations oriented perpendicular to the Cascade crest. Microphysical budgets and sensitivity analyses were also performed for the precipitation event and identified important parameters and assumptions in the model's microphysical parameterizations that should be rectified to improve quantitative precipitation forecasting.
Description: Thesis (Ph. D.)--University of Washington, 2005.
URI: http://hdl.handle.net/1773/10021

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