4.4 Article

Mesoscale boundary layer and heat flux variations over pack ice-covered Lake Erie

期刊

出版社

AMER METEOROLOGICAL SOC
DOI: 10.1175/2007JAMC1479.1

关键词

-

向作者/读者索取更多资源

The development of extensive pack ice fields on the Great Lakes significantly influences lake-effect storms and local airmass modification, as well as the regional hydrologic cycle and lake water levels. The evolution of the ice fields and their impacts on the atmospheric boundary layer complicates weather forecasters' ability to accurately predict late-season lake-effect snows. The Great Lakes Ice Cover-Atmospheric Flux (GLICAF) experiment was conducted over Lake Erie during February 2004 to investigate the surface-atmosphere exchanges that occur over midlatitude ice-covered lakes. GLICAF observations taken by the University of Wyoming King Air on 26 February 2004 show a strong mesoscale thermal link between the lake surface and the overlying atmospheric boundary layer. Mesoscale atmospheric variations that developed over the lake in turn influenced heat exchanges with the surface. Boundary layer sensible and latent heat fluxes exhibited different relationships to variations in surface pack ice concentration. Turbulent sensible heat fluxes decreased nonlinearly with increases in underlying lake-surface ice concentration such that the largest decreases occurred when ice concentrations were greater than 70%. Latent heat fluxes tended to decrease linearly with increasing ice concentration and had a reduced correlation. Most current operational numerical weather prediction models use simple algorithms to represent the influence of heterogeneous ice cover on heat and moisture fluxes. The GLICAF findings from 26 February 2004 suggest that some currently used and planned approaches in numerical weather prediction models may significantly underestimate sensible heat fluxes in regions of high-concentration ice cover, leading to underpredictions of the local modification of air masses and lake-effect snows.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.4
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据