近50年新疆地区降水变化及天气分型

发布时间：2019-04-17 05:14

【摘要】：在全球气候变化背景下,研究新疆地区降水变化的区域特征和影响降水的天气形势,具有重要的科学和社会意义。本文选取新疆47个气象站点1961-2013年的逐日降水资料,分析了新疆降水的时空分布演变特征,并利用EOF、REOF方法对新疆夏季降水进行气候分区。结果表明:新疆地区1961-2013年降水量呈现出持续增长的趋势,1987年后进入降水偏多期。一年中,夏季是对新疆全年降水量贡献最大的季节,主导了全年降水量的空间分布及全年降水的变化趋势。新疆年降水量呈现北多南少,全疆降水量上升的趋势,西北部增长较多。EOF的四个模态体现了全疆夏季降水变化的一致性,天山山脉的阻挡造成了南、北疆的变化差异,新疆东部与西部,中部天山山区及附近与其他地区夏季降水存在反相变化。REOF结果进一步给出了新疆夏季降水异常的敏感区域,可将新疆地区细分为五个区域。五个区内站点的平均年降水量均呈增长趋势,夏季增长趋势最大,2区的平均年降水量最大,3、5区次之,且5区增长趋势最快。应用SOM神经网络算法将包含新疆在内的研究区域1961-2013年5-9月逐日的海平面气压场进行天气分型,得到35类天气型。结合降水资料确定出影响新疆地区,尤其是北疆夏季降水的主要天气系统是发生在5月、7月和9月地面气旋和地面高压前的锋面系统。每个天气型对年降水量的贡献表明,新疆近50%的平均年降水量期望值集中在对新疆降水贡献较大的13个结点,体现了 SOM天气分型对研究新疆降水的重要性。NAO是新疆降水年际变化的重要因子之一,NAO指数与新疆年降水量呈负相关,与年降水增量呈显著负相关。在NAO异常年份,冰岛低压和北大西洋高压强度异常,通过配合高空以及地面天气系统的发展移动进而影响了新疆地区的降水活动。因而掌握好NAO指数的异常变化规律及预测,对于新疆地区的降水预报具有重要意义。
[Abstract]:Under the background of global climate change, it is of great scientific and social significance to study the regional characteristics of precipitation changes and the weather situation affecting precipitation in Xinjiang. In this paper, the daily precipitation data of 47 meteorological stations in Xinjiang from 1961 to 2013 are selected to analyze the temporal and spatial distribution of precipitation in Xinjiang, and the climatic zoning of summer precipitation in Xinjiang is carried out by using EOF,REOF method. The results show that the precipitation in Xinjiang region shows a continuous increasing trend from 1961 to 2013. After 1987, the precipitation is on the high side. Summer is the season that contributes most to the annual precipitation in Xinjiang in one year, which dominates the spatial distribution of annual precipitation and the changing trend of annual precipitation. The annual precipitation in Xinjiang is more and less in the north and south, and the precipitation in the whole of Xinjiang is rising. The four modes of EOF reflect the consistency of the summer precipitation changes in Xinjiang, and the blocking of the Tianshan Mountains causes the difference between the changes in the south and the north of Xinjiang, and the four modes of EOF reflect the consistency of the summer precipitation in Xinjiang. There are inverse variations of summer precipitation in eastern and western Xinjiang, central Tianshan mountain area and other areas. The REOF results further show the sensitive areas of summer precipitation anomaly in Xinjiang, which can be divided into five regions. The average annual precipitation of the five regions all showed an increasing trend, with the largest increasing trend in summer, the largest average annual precipitation in the two regions, the third in the fifth region and the fastest in the fifth region. The SOM neural network algorithm is used to classify the daily sea level pressure field from May to September from 1961 to 2013 in the study area including Xinjiang, and 35 types of weather patterns are obtained. Combined with precipitation data, the main weather systems affecting summer precipitation in Xinjiang, especially in northern Xinjiang, are the frontal systems which occur before the surface cyclones and surface high in May, July and September. The contribution of each climate type to the annual precipitation indicates that nearly 50% of the expected annual precipitation in Xinjiang is concentrated in the 13 nodes which contribute more to the precipitation in Xinjiang. NAO is one of the important factors in the inter-annual variation of precipitation in Xinjiang. The NAO index is negatively correlated with the annual precipitation in Xinjiang and negatively correlated with the annual precipitation increment. The SOM weather classification is of great importance to the study of precipitation in Xinjiang. In the year of NAO anomaly, the intensity anomaly of Icelandic low and North Atlantic high influenced the precipitation activity in Xinjiang by cooperating with the development and movement of high altitude and surface weather system. Therefore, it is of great significance to master the abnormal variation law and forecast of NAO index for precipitation forecast in Xinjiang.
【学位授予单位】：南京大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：P426.6

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