Abstract

Katabatic flows are high density air flows traversing down a slope under the action of a gravitational force. These flows can be exploited for wind energy and are gaining importance in predicting scalar transport (e.g. pollutants, water vapor, CO2 drainage) within mountainous regions and in forming large cold air pools in valleys and basins. Summertime measurements over a steep slope in a narrow alpine valley (Val Ferret, Switzerland) were collected so as to explore the components of the mean longitudinal momentum balance leading to the formation of the katabatic jet. During clear-sky nights with weak synoptic forcing, observations show a strong, near-surface temperature gradient and a subsequent, weak katabatic jet with a peak velocity at less than 1 m from the surface. Two distinct log-linear layers, both in mean velocity and in temperature, characterize the katabatic jet layer (up to ~6 m) where fluxes of heat and momentum vary with height. This departure from the so-called constant-stress region typifies difficulties in modeling and predicting flows over steep topography. To circumvent some of these difficulties, a one-dimensional model for the vertical flux gradient that couples momentum and thermal balances was used to predict the mean velocity and turbulent flux profiles for katabatic flows over steep, vegetated slopes. The model predicts realistic profiles of heat and momentum fluxes in comparison with the field measurements. For example, in the case of the modeled momentum flux, the sign of the flux changes at the height of peak velocity and the higher gradient near the surface is well reproduced. It is conjectured that unsteadiness in synoptic scale conditions can be partly accommodated via a dynamic mean horizontal pressure gradient at a point. Order of magnitude calculations suggest that this term can be larger than the so-called thermal wind term when the katabatic flow is sufficiently shallow, as is the case on steep slopes. This model may serve as a test bench for various turbulent closure models that could be implemented to improve large-scale numerical weather predictions in those regions characterized by complex topography.

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