A Local Similarity Function for Katabatic Flows Derived From Field Observations Over Steep- and Shallow-Angled Slopes

Katabatic flows are notoriously difficult to model for a variety of reasons. Notably, the assumptions underpinning Monin-Obukhov similarity theory (MOST) are inherently violated by the sloping terrain, causing the traditional flux-gradient relations used in numerical weather prediction models to break down. Focusing on turbulent momentum transport, we show significant flux divergence, further violating MOST assumptions, and that the traditional parameterizations fail even with local scaling for katabatic flow. In response, we propose a modified local-MOST stability-correction function, informed by near-surface turbulence observations collected over two mountainous slopes with inclination angles (alpha) of alpha approximate to 7.8 degrees and alpha approximate to 35.5 degrees. The proposed relation includes alpha directly, making data from both slopes collapse with unprecedented agreement. RMSE between measured fluxes and estimates from the proposed and Businger et al. (1971, ) relations show significant improvement. Results can be used to inform future development of wall-model and turbulence closures in the katabatic flow layer.

Automated summary

The study introduces a modified local-MOST stability-correction function, which can improve the modeling of katabatic flows by addressing the violation of traditional flux-gradient relations. By utilizing turbulence observations from slopes with different inclination angles, the proposed relation demonstrates better convergence and higher accuracy in data application, showing significant improvement compared to traditional parameterizations.