山东降雪含水比统计特征分析
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杨成芳,女,博士,研究员级高级工程师,主要从事天气预报业务及降雪研究,cf_yang70@hotmail.com。

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国家自然科学基金项目(41475038,41975055);中国气象局预报员专项(CMAYBY2018-042)


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Analysis on statistical characteristics of snow-to-liquid ratio in Shandong
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    摘要:

    降雪含水比(snow-to-liquid ratio,SLR)是指积雪深度与降雪融化后等量液体深度(降雪量)的比值,可用来计算积雪深度。山东有两种产生机制不同的降雪,冷流降雪主要分布在山东半岛北部沿海地区,其他类降雪在全省范围均可发生,二者的降雪含水比有明显差异。利用山东122个国家级气象观测站自建站以来至2018年12月的逐12 h降水量、日积雪深度、降水性质、日最高气温及1999—2018年的MICAPS高空、地面图资料,通过限定条件进行质量控制,统计分析了山东不同地区的降雪含水比气候特征,为积雪深度预报提供参考。结果表明:1)山东降雪含水比的变化范围为0.1~3.0 cm·mm-1,全省大部地区多年平均降雪含水比为0.9 cm·mm-1,主要集中在0.3~1.1 cm·mm-1之间;山东半岛北部沿海地区(强冷流降雪区域)的多年平均降雪含水比为1.3 cm·mm-1,主要集中在0.9~2.0 cm·mm-1之间。2)降雪含水比的大小与降雪量等级有关,且存在明显月变化。全省大部地区从中雪至暴雪随着降雪量等级的增大,降雪含水比依次减小;各等级的降雪含水比月最大值均出现在1月或12月,最小值出现在11月或2月;山东半岛北部沿海地区的降雪含水比表现出更为复杂的特征,在以冷流降雪为主的11月—次年1月,中雪、大雪和暴雪的降雪含水比基本相当;2月和3月冷流降雪不明显,降雪含水比表现出与其他地区降雪类似的特征。3)不同天气系统暴雪的降雪含水比有差异。江淮气旋暴雪过程平均降雪含水比为0.69 cm·mm-1,总体上呈现“北大南小,山区大沿海小”分布,中雪、大雪和暴雪的降雪含水比中位数分别为0.8、0.7和0.5 cm·mm-1;回流形势暴雪过程的全省平均降雪含水比为0.67 cm·mm-1,中雪的降雪含水比中位数为0.8 cm·mm-1,大雪和暴雪均为0.6 cm·mm-1;冷流暴雪的降雪含水比明显大于其他两类暴雪,中位数在1.1~1.6 cm·mm-1之间变化,中雪、大雪和暴雪的降雪含水比中位数分别为1.4、1.6和1.3 cm·mm-1。

    Abstract:

    The snow-to-liquid ratio (SLR) is the ratio of the depth of snow to the depth of liquid water contained in that snow (if it were to be melted), which is used for calculating snow depth. There are two types of snowfall with different mechanisms in Shandong. The ocean-effect snow mainly occurs in the northern coastal areas of Shandong Peninsula, while the other type can occur over the whole province, and there are remarkable differences in their SLR. Based on the data of 12-h precipitation, daily snow depth, precipitation type, and daily maximum temperature from 122 national meteorological stations in Shandong from their establishment to December 2018 and MICAPS synoptic charts from 1999 to 2018, this study statistically analyzes climatic characteristics of SLR in different areas of Shandong, providing reference for forecasting snow depth. To eliminate errors, some quantitative calculation rules are brought forward. The results are as follows. 1) The SLR in Shandong ranges between 0.1 cm·mm-1 and 3.0 cm·mm-1 (mainly between 0.3 cm·mm-1 and 1.1 cm·mm-1) and the average value over many years is 0.9 cm·mm-1 in most areas; in the northern coastal areas of Shandong Peninsula (areas of heavy ocean-effect snow), the SLR is mainly between 0.9 cm·mm-1 and 2.0 cm·mm-1 and the average value over many years is 1.3 cm·mm-1. 2) SLR is relevant to grade of snow with obvious monthly variation. SLR decreases with the grade of snow rising from moderate snow to snowstorm for most areas of Shandong; the monthly maximum SLR of each grade of snow appears in January or December, while the minimum appears in November or February; in the northern coastal areas of Shandong Peninsula, SLR presents more complicated characteristics in that SLRs of moderate snow, heavy snow, and snowstorm are roughly equivalent from November to the next January when ocean-effect snow prevails, while SLR shows similar characteristics as in other areas in February and March when ocean-effect is not that obvious. 3) SLRs of snowstorms produced by different synoptic systems exhibit diversity. SLR of snowstorm produced by Changjiang-Huaihe cyclone is higher (lower) in the northern (southern) areas and mountainous (coastal) areas as a whole, the process-averaged value is 0.69 cm·mm-1, and the median SLRs of moderate snow, heavy snow, and snowstorm are 0.8 cm·mm-1, 0.7 cm·mm-1, and 0.5 cm·mm-1, respectively; the average SLR of backflow snowstorm in the whole province is 0.67 cm·mm-1, and the median SLRs of moderate snow, heavy snow, and snowstorm are 0.8 cm·mm-1, 0.6 cm·mm-1, and 0.6 cm·mm-1, respectively; SLR of ocean-effect snowstorm, whose median value ranges between 1.1 cm·mm-1 and 1.6 cm·mm-1, is remarkably higher than that of the other two types of snowstorms mentioned above, and the median SLRs of moderate snow, heavy snow, and snowstorm are 1.4 cm·mm-1, 1.6 cm·mm-1, and 1.3 cm·mm-1, respectively.

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杨成芳,朱晓清.山东降雪含水比统计特征分析[J].海洋气象学报,2020,40(1):47-56.

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  • 收稿日期:2020-02-12
  • 最后修改日期:2020-02-27
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  • 在线发布日期: 2020-04-28
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