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首页> 外文期刊>Journal of hydrometeorology >'It's Raining Bits': Patterns in Directional Precipitation Persistence across the United States
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'It's Raining Bits': Patterns in Directional Precipitation Persistence across the United States

机译:“这是下雨的比特”:美国方向降水持久性的模式

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The spatial and temporal ordering of precipitation occurrence impacts ecosystems, streamflow, and water availability. For example, both large-scale climate patterns and local landscapes drive weather events, and the typical speeds and directions of these events moving across a basin dictate the timing of flows at its outlet. We address the predictability of precipitation occurrence at a given location, based on the knowledge of past precipitation at surrounding locations. We identify "dominant directions of precipitation influence" across the continental United States based on a gridded daily dataset. Specifically, we apply information theory-based measures that characterize dominant directions and strengths of spatial and temporal precipitation dependencies. On a national average, this dominant direction agrees with the prevalent direction of weather movement from west to east across the country, but regional differences reflect topographic divides, precipitation gradients, and different climatic drivers of precipitation. Trends in these information relationships and their correlations with climate indices over the past 70 years also show seasonal and spatial divides. This study expands upon a framework of information-based predictability to answer questions about spatial connectivity in addition to temporal persistence. The methods presented here are generally useful to understand many aspects of weather and climate variability.
机译:降水发生的空间和时间顺序影响生态系统、径流和水的可用性。例如,大规模气候模式和当地景观都会驱动天气事件,而这些事件在流域内移动的典型速度和方向决定了流域出口处的流量时间。基于对周围地区过去降水的了解,我们讨论了给定地点降水发生的可预测性。我们基于网格化的每日数据集,确定了整个美国大陆“降水影响的主要方向”。具体来说,我们采用基于信息论的措施来描述空间和时间降水依赖性的主导方向和强度。从全国平均水平来看,这一主导方向与全国各地普遍存在的从西向东的天气运动方向一致,但区域差异反映了地形差异、降水梯度和降水的不同气候驱动因素。在过去70年中,这些信息关系的趋势及其与气候指数的相关性也显示出季节和空间差异。本研究扩展了一个基于信息的可预测性框架,以回答除了时间持久性之外的空间连通性问题。这里介绍的方法通常有助于理解天气和气候变化的许多方面。

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