Wednesday, 1 September 2010
Alpine Ballroom B (Resort at Squaw Creek)
Cold air pooling over complex terrain is a well documented meteorological phenomenon, characterised by a temperature inversion (temperature increasing with height) forming soon after sunset during clear, calm, neutral/stable boundary layer conditions and decaying soon after sunrise. The temperature differences can be very large over relatively small scales. Associated with this phenomenon are a number of hazards, including valley fog, localised frost and pollution episodes (associated with the stagnation of air). Currently there are a number of uncertainties associated with the occurrence of these cold air pooling events and as such there are particular problems related to forecasting their occurrence, strength and location. The COLPEX field project is an extensive campaign conducted within the complex terrain setting of the Clun valley region in Shropshire, England. The COLPEX project is a collaboration between the University of Leeds, the Met Office and the University of Salford. The field campaign has recently concluded, taking place between June 2009 to April 2010 and has been a great success.
Some emphasis of this project will be on discovering whether cold air pool formation is dominated by one of two mechanisms, katabatic flows (cold drainage flows) or in-situ cooling, both of which are products of radiative cooling at the surface following sunset. The analysis of cold air pool breakup proposes some interesting questions also. The field observations will be used to understand the morning transition of cold air pool breakup, identifying how long after sunrise cold air pools can persist and to what extent the roles of solar heating, valley slope, shading and snow cover have on determining the length in time and spatial variability of cold air pool erosion. Over the same period and location investigations will be conducted using the Met Office Unified Model coupled with the field observations, with the aim of improving cold air pooling representation in models and increase predictive skill in forecasting surface temperatures and fog over complex terrain.
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