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  • Author or Editor: W. E. Heilman x
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W. E. Heilman and E. S. Takle

Abstract

A two4Mensional second-order turbulence-closure model based on Mellor-Yamada level 3 is used to examine the nocturnal turbulence characteristics over Rattlesnake Mountain in Washington. Simulations of mean horizontal velocities and potential temperatures agree well with data. The equations for the components of the turbulent kinetic energy (TKE) show that anisotropy contributes in ways that are counter to our intuition developed from mean now considerations: shear production under stable conditions forces the suppression of the vertical component proportion of loud TKE, while potential-temperature variance under stable conditions leads to a positive (countergradient) contribution to the heat flux that increases the vertical component proportion of total TKE. This paper provides a qualitative analysis of simulated turbulence fields, which indicates significant variation over the windward and leeward slopes. From the simulation results, turbulence anisotropy is seen to develop in the katabatic flow region where vertical wind shears and atmospheric stability are large. An enhancement of the vertical component proportion of the total TKE takes place over the leeward slope as the downslope distance increases. The countergradient portion of the turbulent heat flux plays an important role in producing regions of anisotropy.

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Lisi Pei, Nathan Moore, Shiyuan Zhong, Lifeng Luo, David W. Hyndman, Warren E. Heilman, and Zhiqiu Gao

Abstract

Extreme weather and climate events, especially short-term excessive drought and wet periods over agricultural areas, have received increased attention. The Southern Great Plains (SGP) is one of the largest agricultural regions in North America and features the underlying Ogallala-High Plains Aquifer system worth great economic value in large part due to production gains from groundwater. Climate research over the SGP is needed to better understand complex coupled climate–hydrology–socioeconomic interactions critical to the sustainability of this region, especially under extreme climate scenarios. Here the authors studied growing-season extreme conditions using the Weather Research and Forecasting (WRF) Model. The six most extreme recent years, both wet and dry, were simulated to investigate the impacts of land surface model and cumulus parameterization on the simulated hydroclimate. The results show that under short-term climate extremes, the land surface model plays a more important role modulating the land–atmosphere water budget, and thus the entire regional climate, than the cumulus parameterization under current model configurations. Between the two land surface models tested, the more sophisticated land surface model produced significantly larger wet bias in large part due to overestimation of moisture flux convergence, which is attributed mainly to an overestimation of the surface evapotranspiration during the simulated period. The deficiencies of the cumulus parameterizations resulted in the model’s inability to depict the diurnal rainfall variability. Both land surface processes and cumulus parameterizations remain the most challenging parts of regional climate modeling under extreme climates over the SGP, with the former strongly affecting the precipitation amount and the latter strongly affecting the precipitation pattern.

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