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基于1980-2018年山西省太行山南麓晋城市5个站点的降水资料,利用小波分析和Mann-Kendall方法,研究了太行山南麓暴雨时空变化特征。结果表明:39年平均年暴雨日数有0.9个,年际变化幅度较小。暴雨量与暴雨日数的空间分布并不一致,1980、1981、1982、1995和1996年暴雨日数较多。分析50-59、60-69、70-79、80-89、90-99、100以上6个暴雨量区间空间分布发现,暴雨的空间分布在不同区间并没有明显的趋同性。年暴雨降水量有不同程度的增加趋势,晋城市下辖5站暴雨量趋势均存在突变,突变时间存在差异,暴雨量在19811983年和19921995年为两个峰值时段,之后有所缓和。39年暴雨时间序列的小波波谱显示,太行山南麓暴雨呈现0~3 a、3~7 a、8~24 a等3类周期准振荡变化规律,各波动周期稳定性和显著性不同。3~7 a出现5个多少准周期振荡,该周期表现较为显著,8~24 a出现2个准振荡周期,且这两个周期非常稳定,具有全域性的特征。晋城市3~7 a的暴雨振荡周期和8~24 a特大暴雨振荡周期与现实非常吻合。 相似文献
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利用宜昌市9个观测站1966-2010年的逐13降水资料,采用线性趋势分析、小波分析和Mann-Kendall法等方法分析了近45a来宜昌市暴雨日数的变化趋势、多时间尺度的时频特征和气候突变特征。结果表明：宜昌市暴雨日数空间分布存在明显差异,基本表现为南多北少;近45a暴雨日数呈现不明显增加趋势,且存在明显的两次波动;暴雨日数存在2-4a、6a、准10a、准16a的4类尺度的周期变化规律,其中在2-4a时间尺度下周期振荡最强,从准10a和准16a时间尺度来看,在2010年后一段时间宜昌市暴雨日数还是处于相对偏多期;突变检测显示,暴雨日数在1979年前后发生了气候突变。 相似文献
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利用中国2400余个国家级气象台站观测数据插值得到的1961~2018年逐日网格降水资料,综合运用回归分析和Morlet小波变换等方法,分析了华南多年暴雨和区域性暴雨的时空变化特征,揭示了华南暴雨的变化规律。结果表明:1961~2018年,华南全年暴雨日数和暴雨雨量大值区域分布在广东、广西、福建沿海一带及海南省和广西北部,夏季暴雨日数和暴雨雨量最大,其次是春季。在广西北部至广西、湖南、广东三省交界处、广东南部、福建和海南省,全年暴雨日数、雨量和强度的增加趋势最显著,夏季的区域平均值增长速率最大,其次是秋季。华南区域性暴雨日数和过程数呈单峰型分布,一年中均可出现,最大值出现在6月。区域性暴雨日数和过程数多年平均值为28 d a^(-1)和16.5 a^(-1),上升速率分别为0.15 d a^(-1)和0.097 a^(-1),四季中夏季的上升速率最快,最慢的是秋季。平均单次过程持续日数和最大单次过程持续日数在冬季均以0.015 d a^(-1)的速率显著上升,在春季却呈现下降的趋势。华南暴雨和区域性暴雨各要素在全年和四季的波动变化中不同程度地表现出准3 a、准14 a和准18 a的周期变化,2000年后,全年暴雨和区域性暴雨各要素准18 a的长周期和准3 a的短周期振荡非常显著。 相似文献
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1961-2012年山东汛期暴雨气候特征分析 总被引：1，自引：0，他引：1
利用1961-2012年山东省35个气象站汛期逐日降水资料,采用常规统计法分析了山东省汛期暴雨日数和暴雨强度的时空变化特征,运用均生函数建立山东省汛期暴雨日数和暴雨强度的预测模型,并进行试报和预报检验。结果表明：1961-2012年山东省汛期暴雨日数和暴雨强度均呈减小趋势,但减小趋势不明显,未通过0.05信度的显著性检验。1961-2012年山东省汛期平均暴雨日数为2.2 d,存在3.4 a与准8.0 a周期振荡|暴雨平均强度为67.8 mm·d-1,有2.3 a、3.3 a、6.9 a与准12.0 a的变化周期。1961-2012年山东省汛期暴雨日数和暴雨强度未出现气候突变|山东省暴雨日数和暴雨强度自20世纪70年代中末期至80年代末期出现年代际减小的变化。山东省汛期多年暴雨平均日数和暴雨强度呈自西北向东南逐渐增加的分布趋势。鲁南、山东半岛南部和东部地区是山东省汛期暴雨（连续性暴雨）的多发地带及暴雨强度大值区域。对2003-2012年山东汛期暴雨预测表明,均生函数预测模型可较好拟合山东省汛期暴雨日数和暴雨强度的变化趋势,对山东汛期暴雨有较好的预测能力。 相似文献
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1964-2013年大连地区暴雨气候特征及变化规律 总被引：2，自引：0，他引：2
利用1964—2013年大连地区6个气象站逐日降水资料,采用线性倾向估计、Mann-Kendall和 Yamamoto检验及最大熵谱分析等方法,对大连地区暴雨的气候特征及变化规律进行分析。结果表明：大连地区3—12月均可出现暴雨,72.7％的暴雨出现在7—8月,7月暴雨出现最多,8月上旬是暴雨出现最集中的时期。4—8月各月暴雨日数均呈上升趋势,且4月暴雨日数上升最明显,8月次之,至9月暴雨日数呈下降趋势。大连地区各站年平均暴雨日数为1.8—2.8 d,暴雨日数分布从大连西北内陆向东南沿海地区逐渐增多,暴雨日数大值中心在庄河（2.8 d）。各站年暴雨日数均呈上升趋势,大连北部普兰店、东部长海和西南部旅顺地区年暴雨日数呈显著上升趋势,其他地区年暴雨日数上升趋势较弱。近50 a来,大连地区暴雨初日有所提前,暴雨终日变化不明显。年暴雨日数、暴雨强度和暴雨贡献率均呈不显著的上升趋势,且均从2003年以来增加明显,均有2.0—3.0 a的周期振荡;此外,年暴雨日数、暴雨强度和暴雨贡献率分别存在准12.5 a、准5.0 a和准16.7 a的振荡周期,仅年暴雨贡献率在1977年和1978年发生显著突变。 相似文献
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《干旱气象》2015,(4)
利用1961~2013年宁夏25个常规观测站资料和2007~2013年220个区域自动站暴雨观测资料,对宁夏暴雨的气候特征进行分析,发现宁夏暴雨呈"南多北少"的整体分布特征,暴雨易发区为中卫市以南和贺兰山东麓。近53 a来,宁夏暴雨有3个集中期,尤其是21世纪后暴雨剧增,其中宁夏南部山区和银川平原暴雨频数增加明显。宁夏暴雨存在明显的月、日变化特征,暴雨主要发生在7月上旬到9月上旬,短时暴雨主要集中在午后到夜间(即下午16时至次日02时),这一特征与气温(热量)及对流发展的日变化比较一致。宁夏暴雨发生频数存在明显的准16 a、20 a年代际、2~3 a年际变化特征和准10 d的低频振荡特征。 相似文献
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四川盆地区域性暴雨时空变化特征及其前兆信号研究 总被引：1，自引：0，他引：1
利用1961-2015年四川盆地逐日降水量资料、NOAA海表温度资料、青藏高原积雪资料,采用多锥度-奇异值分解等方法,研究了四川盆地区域性暴雨的时空变化特征,并对其前兆信号进行了初步探讨。结果表明:四川盆地区域性暴雨可以分为三种类型,其中盆西型出现频率最高,盆东北型次之,盆南型相对较少。四川盆地区域性暴雨年际变化中准3 a周期最为显著,典型循环体现了盆西型区域性暴雨"偏少-调整-偏多"的年际变化过程。年代际变化中准16 a周期最明显,典型循环表现为盆东北型和盆南型的交替演变。通过MTM-SVD协同变化分析,发现在盆西型区域性暴雨的准3 a周期循环中,前冬ENSO事件和前冬青藏高原积雪日数是其显著的异常前兆信号。在年代际的准16 a振荡过程中,前冬太平洋年代际振荡(PDO)是盆东北型和盆南型区域性暴雨的异常前兆信号。 相似文献
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采用小波分析、Lanczos时间滤波器、合成分析等方法分析了2011年夏季广东季风槽暴雨与大气低频振荡的关系。结果表明,2011年夏季降水主要存在准20 d的周期振荡,季风槽暴雨过程对应强的准双周振荡。选取与夏季降水显著相关的区域（102.5~117.5 °E,20~27.5 °N）平均的500 hPa高度场作为影响广东夏季降水的“500 hPa关键区”指数,同样选取区域（110~120 °E,15~22.5 °N）平均的850 hPa风场作为“850 hPa关键区”指数;2011年夏季500 hPa关键区与850 hPa关键区分别存在显著的准23 d、准22 d周期振荡,季风槽暴雨发生在500 hPa关键区准双周振荡的波谷、850 hPa关键区准双周振荡的波峰附近。从南海南部开始的3次低频纬向风、OLR、湿度的北传与从菲律宾以东的西太平洋向西传播的低频中心相遇,导致3次季风槽暴雨过程。利用典型个例的合成分析,对2011年6—8月广东3次季风槽暴雨的准双周振荡不同位相的大气环流场的共同演变特征进行分析,它们反映了季风槽暴雨从间歇-开始-旺盛-减弱-结束期的大气环流场演变特征,为广东季风槽暴雨的中期预报提供参考依据。 相似文献
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The spatial and temporal variations of daily maximum temperature(Tmax), daily minimum temperature(Tmin), daily maximum precipitation(Pmax) and daily maximum wind speed(WSmax) were examined in China using Mann-Kendall test and linear regression method. The results indicated that for China as a whole, Tmax, Tmin and Pmax had significant increasing trends at rates of 0.15℃ per decade, 0.45℃ per decade and 0.58 mm per decade,respectively, while WSmax had decreased significantly at 1.18 m·s~(-1) per decade during 1959—2014. In all regions of China, Tmin increased and WSmax decreased significantly. Spatially, Tmax increased significantly at most of the stations in South China(SC), northwestern North China(NC), northeastern Northeast China(NEC), eastern Northwest China(NWC) and eastern Southwest China(SWC), and the increasing trends were significant in NC, SC, NWC and SWC on the regional average. Tmin increased significantly at most of the stations in China, with notable increase in NEC, northern and southeastern NC and northwestern and eastern NWC. Pmax showed no significant trend at most of the stations in China, and on the regional average it decreased significantly in NC but increased in SC, NWC and the mid-lower Yangtze River valley(YR). WSmax decreased significantly at the vast majority of stations in China, with remarkable decrease in northern NC, northern and central YR, central and southern SC and in parts of central NEC and western NWC. With global climate change and rapidly economic development, China has become more vulnerable to climatic extremes and meteorological disasters, so more strategies of mitigation and/or adaptation of climatic extremes,such as environmentally-friendly and low-cost energy production systems and the enhancement of engineering defense measures are necessary for government and social publics. 相似文献
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Storms that occur at the Bay of Bengal (BoB) are of a bimodal pattern, which is different from that of the other sea areas. By using the NCEP, SST and JTWC data, the causes of the bimodal pattern storm activity of the BoB are diagnosed and analyzed in this paper. The result shows that the seasonal variation of general atmosphere circulation in East Asia has a regulating and controlling impact on the BoB storm activity, and the “bimodal period” of the storm activity corresponds exactly to the seasonal conversion period of atmospheric circulation. The minor wind speed of shear spring and autumn contributed to the storm, which was a crucial factor for the generation and occurrence of the “bimodal pattern” storm activity in the BoB. The analysis on sea surface temperature (SST) shows that the SSTs of all the year around in the BoB area meet the conditions required for the generation of tropical cyclones (TCs). However, the SSTs in the central area of the bay are higher than that of the surrounding areas in spring and autumn, which facilitates the occurrence of a “two-peak” storm activity pattern. The genesis potential index (GPI) quantifies and reflects the environmental conditions for the generation of the BoB storms. For GPI, the intense low-level vortex disturbance in the troposphere and high-humidity atmosphere are the sufficient conditions for storms, while large maximum wind velocity of the ground vortex radius and small vertical wind shear are the necessary conditions of storms. 相似文献
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Observed daily precipitation data from the National Meteorological Observatory in Hainan province and daily data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis-2 dataset from 1981 to 2014 are used to analyze the relationship between Hainan extreme heavy rainfall processes in autumn (referred to as EHRPs) and 10–30 d low-frequency circulation. Based on the key low-frequency signals and the NCEP Climate Forecast System Version 2 (CFSv2) model forecasting products, a dynamical-statistical method is established for the extended-range forecast of EHRPs. The results suggest that EHRPs have a close relationship with the 10–30 d low-frequency oscillation of 850 hPa zonal wind over Hainan Island and to its north, and that they basically occur during the trough phase of the low-frequency oscillation of zonal wind. The latitudinal propagation of the low-frequency wave train in the middle-high latitudes and the meridional propagation of the low-frequency wave train along the coast of East Asia contribute to the ‘north high (cold), south low (warm)’ pattern near Hainan Island, which results in the zonal wind over Hainan Island and to its north reaching its trough, consequently leading to EHRPs. Considering the link between low-frequency circulation and EHRPs, a low-frequency wave train index (LWTI) is defined and adopted to forecast EHRPs by using NCEP CFSv2 forecasting products. EHRPs are predicted to occur during peak phases of LWTI with value larger than 1 for three or more consecutive forecast days. Hindcast experiments for EHRPs in 2015–2016 indicate that EHRPs can be predicted 8–24 d in advance, with an average period of validity of 16.7 d. 相似文献
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Based on the measurements obtained at 64 national meteorological stations in the Beijing–Tianjin–Hebei (BTH) region between 1970 and 2013, the potential evapotranspiration (ET0) in this region was estimated using the Penman–Monteith equation and its sensitivity to maximum temperature (Tmax), minimum temperature (Tmin), wind speed (Vw), net radiation (Rn) and water vapor pressure (Pwv) was analyzed, respectively. The results are shown as follows. (1) The climatic elements in the BTH region underwent significant changes in the study period. Vw and Rn decreased significantly, whereas Tmin, Tmax and Pwv increased considerably. (2) In the BTH region, ET0 also exhibited a significant decreasing trend, and the sensitivity of ET0 to the climatic elements exhibited seasonal characteristics. Of all the climatic elements, ET0 was most sensitive to Pwv in the fall and winter and Rn in the spring and summer. On the annual scale, ET0 was most sensitive to Pwv, followed by Rn, Vw, Tmax and Tmin. In addition, the sensitivity coefficient of ET0 with respect to Pwv had a negative value for all the areas, indicating that increases in Pwv can prevent ET0 from increasing. (3) The sensitivity of ET0 to Tmin and Tmax was significantly lower than its sensitivity to other climatic elements. However, increases in temperature can lead to changes in Pwv and Rn. The temperature should be considered the key intrinsic climatic element that has caused the "evaporation paradox" phenomenon in the BTH region. 相似文献
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Fei LIU Chen XING Jinbao LI Bin WANG Jing CHAI Chaochao GAO Gang HUANG Jian LIU Deliang CHEN 《大气科学进展》2020,37(7):663-670
正The Taal Volcano in Luzon is one of the most active and dangerous volcanoes of the Philippines. A recent eruption occurred on 12 January 2020(Fig. 1a), and this volcano is still active with the occurrence of volcanic earthquakes. The eruption has become a deep concern worldwide, not only for its damage on local society, but also for potential hazardous consequences on the Earth's climate and environment. 相似文献
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The moving-window correlation analysis was applied to investigate the relationship between autumn Indian Ocean Dipole (IOD) events and the synchronous autumn precipitation in Huaxi region, based on the daily precipitation, sea surface temperature (SST) and atmospheric circulation data from 1960 to 2012. The correlation curves of IOD and the early modulation of Huaxi region’s autumn precipitation indicated a mutational site appeared in the 1970s. During 1960 to 1979, when the IOD was in positive phase in autumn, the circulations changed from a “W” shape to an ”M” shape at 500 hPa in Asia middle-high latitude region. Cold flux got into the Sichuan province with Northwest flow, the positive anomaly of the water vapor flux transported from Western Pacific to Huaxi region strengthened, caused precipitation increase in east Huaxi region. During 1980 to 1999, when the IOD in autumn was positive phase, the atmospheric circulation presented a “W” shape at 500 hPa, the positive anomaly of the water vapor flux transported from Bay of Bengal to Huaxi region strengthened, caused precipitation ascend in west Huaxi region. In summary, the Indian Ocean changed from cold phase to warm phase since the 1970s, caused the instability of the inter-annual relationship between the IOD and the autumn rainfall in Huaxi region. 相似文献
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Yuepeng PAN Mengna GU Yuexin HE Dianming WU Chunyan LIU Linlin SONG Shili TIAN Xuemei Lü Yang SUN Tao SONG Wendell W. WALTERS Xuejun LIU Nicholas A. MARTIN Qianqian ZHANG Yunting FANG Valerio FERRACCI Yuesi WANG 《大气科学进展》2020,37(9):933-938
正While China’s Air Pollution Prevention and Control Action Plan on particulate matter since 2013 has reduced sulfate significantly, aerosol ammonium nitrate remains high in East China. As the high nitrate abundances are strongly linked with ammonia, reducing ammonia emissions is becoming increasingly important to improve the air quality of China. Although satellite data provide evidence of substantial increases in atmospheric ammonia concentrations over major agricultural regions, long-term surface observation of ammonia concentrations are sparse. In addition, there is still no consensus on 相似文献
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基于最新的GTAP8 (Global Trade Analysis Project）数据库,使用投入产出法,分析了2004年到2007年全球贸易变化下南北集团贸易隐含碳变化及对全球碳排放的影响。结果显示,随着发展中国家进出口规模扩张,全球贸易隐含碳流向的重心逐渐向发展中国家转移。2004年到2007年,发达国家高端设备制造业和服务业出口以及发展中国家资源、能源密集型行业及中低端制造业出口的趋势加强,该过程的生产转移导致全球碳排放增长4.15亿t,占研究时段全球贸易隐含碳增量的63%。未来发展中国家的出口隐含碳比重还将进一步提高。贸易变化带来的南北集团隐含碳流动变化对全球应对气候变化行动的影响日益突出,发达国家对此负有重要责任。 相似文献
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Using the International Comprehensive Ocean-Atmosphere Data Set(ICOADS) and ERA-Interim data, spatial distributions of air-sea temperature difference(ASTD) in the South China Sea(SCS) for the past 35 years are compared,and variations of spatial and temporal distributions of ASTD in this region are addressed using empirical orthogonal function decomposition and wavelet analysis methods. The results indicate that both ICOADS and ERA-Interim data can reflect actual distribution characteristics of ASTD in the SCS, but values of ASTD from the ERA-Interim data are smaller than those of the ICOADS data in the same region. In addition, the ASTD characteristics from the ERA-Interim data are not obvious inshore. A seesaw-type, north-south distribution of ASTD is dominant in the SCS; i.e., a positive peak in the south is associated with a negative peak in the north in November, and a negative peak in the south is accompanied by a positive peak in the north during April and May. Interannual ASTD variations in summer or autumn are decreasing. There is a seesaw-type distribution of ASTD between Beibu Bay and most of the SCS in summer, and the center of large values is in the Nansha Islands area in autumn. The ASTD in the SCS has a strong quasi-3a oscillation period in all seasons, and a quasi-11 a period in winter and spring. The ASTD is positively correlated with the Nio3.4 index in summer and autumn but negatively correlated in spring and winter. 相似文献
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《大气和海洋科学快报》2013,(1):67
正AIMS AND SCOPE Atmospheric and Oceanic Science Letters (AOSL) publishes short research letters on all disciplines of the atmosphere sciences and physical oceanography. Contributions from all over the world are welcome.SUBMISSIONAll submitted 相似文献