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Land cover, lightning frequency, and turbulent fluxes over Southern Louisiana

机译:路易斯安那州南部的陆地覆盖,雷电频率和湍流势倍

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Lightning is one of the most impactful weather phenomena but little precise and accurate information is known about how its frequency is impacted by changes in land use/land cover (LULC). Similarly, little is known about the spatial and temporal variability of the convective (i.e., turbulent) fluxes of sensible and latent heat that fuel the updrafts associated with lightning under changing LULC. This research applies NOAA's gridded annual lightning data from the National Lightning Detection Network (NLDN), gridded annual mean convective flux data from the North American Regional Reanalysis database (NARR), and LULC classification data from the NOAA Coastal Change Analysis Program (C-CAP) to analyze frequency changes in lightning and the turbulent fluxes across a swath of Louisiana, U.S.A., over the years 1996-2011. Results suggest that urban areas have the highest means of CG lightning with a consistent mean of approximately 152 flashes per km(-2). However, urban areas support less convective flux of sensible (latent) heat than five (four) of six other generalized land classes tested. Lightning frequency tends to be positively related to latent heat fluctuations but less directly related to sensible heat, at least at the annual time scale, as we see positive relationships in all land classes except un-consolidated shore (p & 0.05). Collectively, these results suggest that land use change should be considered carefully in models of future projections of severe weather events and climate.
机译:闪电是最有影响力的天气现象之一,但如何了解其频率如何受到土地使用/陆地覆盖(LULC)的变化的影响几乎没有准确和准确的信息。同样地,关于具有在变化的LULC下燃料相关的对流和潜热的对流和潜热的空间和时间可变性的空间和时间可变性很少。本研究适用于来自国家避雷检测网络(NLDN)的NOAA网格的年雷电数据,来自北美区域再分析数据库(NAR)的网格年度平均对流助焊剂数据,以及来自NOAA沿海变更分析计划的LULC分类数据(C-Cap )在1996 - 2011年,在美国路易斯安那州路易斯安那州的闪电和湍流通量的频率变化。结果表明,城市地区具有最高的CG闪电手段,持续平均值约152千米(-2)。然而,城市地区支持少于五(四个)六个其他普遍的陆级课程的明智(潜伏)热量的对流助焊剂。闪电频率往往与潜热波动呈正相关,但至少在年度时间范围内与明智的热量直接相关,因为除了未巩固的岸边(P& 0.05)中看到所有土地类中的正面关系。总的来说,这些结果表明,土地利用变化应仔细考虑在恶劣天气事件和气候的未来预测的模型中。

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