首页> 外文期刊>International Journal of Biometeorology: Journal of the International Society of Biometeorology >Synoptic climatology of the long-distance dispersal of white pine blister rust. I. Development of an upper level synoptic classification.
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Synoptic climatology of the long-distance dispersal of white pine blister rust. I. Development of an upper level synoptic classification.

机译:天气气候的白松水泡锈病远距离扩散。 I.上层天气分类法的发展。

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摘要

This study developed a methodology to temporally classify large scale, upper level atmospheric conditions over North America, utilizing a newly-developed upper level synoptic classification (ULSC). Four meteorological variables: geopotential height, specific humidity, and u- and v-wind components, at the 500 hPa level over North America were obtained from the NCEP/NCAR Reanalysis Project dataset for the period 1965-1974. These data were subjected to principal components analysis to standardize and reduce the dataset, and then an average linkage clustering algorithm identified groups of observations with similar flow patterns. The procedure yielded 16 clusters. These flow patterns identified by the ULSC typify all patterns expected to be observed over the study area. Additionally, the resulting cluster calendar for the period 1965-1974 showed that the clusters are generally temporally continuous. Subsequent classification of additional observations through a z-score method produced acceptable results, indicating that additional observations may easily be incorporated into the ULSC calendar. The ULSC calendar of synoptic conditions can be used to identify situations that lead to periods of extreme weather, i.e., heat waves, flooding and droughts, and to explore long-distance dispersal of airborne particles and biota across North America.
机译:这项研究开发了一种方法,可以利用新开发的高层天气分类法(ULSC)对北美整个高层高层大气条件进行时间分类。从1965-1974年期间的NCEP / NCAR再分析项目数据集中,获得了北美500 hPa高度的四个气象变量:地势高度,比湿度以及u和v风分量。对这些数据进行主成分分析以标准化和减少数据集,然后使用平均链接聚类算法识别具有相似流模式的观察组。该过程产生了16个簇。 ULSC识别的这些流型代表了预计在研究区域内观察到的所有流型。此外,由此得出的1965-1974年的群集日历显示,群集通常在时间上是连续的。随后通过z评分方法对其他观察结果进行分类产生了可接受的结果,这表明其他观察结果可以轻松地纳入ULSC日历中。 ULSC的天气状况日历可用于识别导致极端天气时期(即热浪,洪水和干旱)的情况,并探索整个北美的空气传播颗粒和生物群的远距离散布。

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