9711号台风暴雨及其环流特征分析

1引言9711号台风是1997年全国登陆台风中影响范围最大、强度最强的一个,造成的大风、暴雨等灾害也是历史上罕见的。本文对9711号台风的移动路径、环流特征、西风带系统特征、水汽条件等进行了初步分析,并对欧洲气象中心、日本气象厅、中央气象台三家的数值预报产品进行了对比分析,分析结果可供今后台风暴雨预报参考。29711号台风基本特征11号台风于8月10日在西北太平洋上生成。13日02h开始编报,受副热带高压南侧的偏东气流引导向西偏北方向移动,于18日20h后在浙江温岭附近登陆,登陆时台风中心强度960hPa,中心最大风力40m／S,登陆时…     

影响山东半岛的两次台风暴雨对比分析

利用常规气象观测资料和NCEP 1°×1°再分析资料,对比分析了2011年影响山东半岛的两次台风暴雨过程。结果表明,台风影响产生的强降水除与台风本身的强弱、移动速度、登陆后减弱快慢有关外,还与副高的强弱、位置有关。台风"米雷"影响时,副热带高压中心呈纬向分布,并与东北高压脊同位相叠加形成高压坝,造成"米雷"在荣成市成山镇登陆并缓慢西进;台风"梅花"影响时,副热带高压中心呈经向分布,华北地区有弱槽活动,造成"梅花"在朝鲜半岛北部海岸登陆。两个台风产生的暴雨多发生在台风中心路径左侧2个经度内,暴雨区与水汽辐合中心基本吻合,但"米雷"产生的暴雨基本是自身的能量产生的,"梅花"产生的暴雨是由台风和西风带弱的系统相互作用造成的。两次台风均给山东半岛带来了暴雨,降水中心均出现在台风中心左侧2个经度内和500h Pa上θse大于72℃的高能区内,"梅花"较"米雷"的能量场中高能区的水平范围要大得多。"米雷"强度明显弱于"梅花",但两者强降水强度和落区却相似,主要是因"米雷"在荣成市登陆后西行缓慢,有利于降水的增加和持续;山东半岛东部的山地地形对暴雨起到了增幅作用。     

9711号、8509号两个台风的成功预报及对两个台风天气相似点的分析

本文在对9711号和8509号两个台风暴雨成功预报的基础上,对两个台风时空尺度相似,路径相似,环流背景相似进行分析,总结出有益于台风暴雨预报的相似特征和着眼点。     

华东地区台风暴雨的诊断研究

本文利用一个空间滤波器将物理量分解为天气尺度和次天气尺度,以两个路径相似但环流形势不同的台风暴雨作为例子,对不同尺度的环流及其动能平衡进行诊断研究,结果表明:中、低纬环流系统能否进行相互作用是决定台风暴雨强度和范围的一个重要条件。在8209号台风过程中,这种相互作用是明显的,因而,8209号台风暴雨的强度和范围均显著大于8304号台风。同时,8209号台风暴雨的能量过程突出地反映在次天气尺度的动能场上,相反,在8304号台风过程中,以天气尺度的能量过程为主要的能量特征。本文还特别指出,不能简单地认为登陆台风是一个动能的“准封闭系统”,在8209号台风过程中,台风从中、低层向外输出的次天气尺度动能是造成台风环流之外暴雨的一个重要物理过程。     

西江流域台风暴雨面雨量分布特征及天气概念模型

通过对1971-2015年登陆华南的台风与西江流域暴雨落区的关联分析,将造成西江流域暴雨的台风按路径分为四类,对每类路径的台风暴雨时空分布特征进行分析研究;通过对台风暴雨典型个例合成分析,建立各类路径台风暴雨天气概念模型。结果表明:(1)西江流域台风暴雨出现在每年6-11月,8月最多,11月最少,6月、9月以Ⅱ类路径为主,7月Ⅱ类、Ⅲ类和Ⅳ类路径较多,8月以Ⅳ类路径为主,10月、11月以Ⅰ类路径为主;(2)Ⅰ类路径台风暴雨集中出现在郁江干流,Ⅱ类路径在黔浔江河段和郁江干流,Ⅲ类路径在红水河河段、黔浔江河段和郁江干流、桂江干流,Ⅳ类路径在红水河河段;(3)Ⅰ、Ⅱ类路径台风暴雨强度最强,范围较大,Ⅲ类路径台风暴雨范围最大,强度较Ⅰ、Ⅱ类稍弱,Ⅳ类路径台风暴雨范围和强度都比其他类型要小(弱)。     

影响河北两次相似路径台风的湿位涡对比分析

利用常规的探空和地面资料以及NCEP／NCAR全球再分析资料，对9711号台风温妮和0509号台风麦莎的变性过程和影响河北暴雨过程的湿位涡场进行了诊断分析。结果表明：温妮变性再加强过程是一个温带气旋强烈发展的过程，主要与高层湿位涡扰动下传、热带气旋低压环流两者之间的相互作用有关。而麦莎变性过程中，没有高层湿位涡扰动下传和热带气旋低压环流之间的相互作用过程，无再加强过程。对流层中低层MPV1〈0、MPV2〉0区域对应暴雨区，对此类暴雨具有较好的指示意义；对流层高层高值湿位涡下传，有利于位势不稳定能量的储存和释放，使降水增幅。     

0812号台风鹦鹉分析

利用常规观测资料、雷达资料分析了台风＂鹦鹉＂影响过程的环流形势特征,发现：（1）台风鹦鹉的影响期间,南压高压中心位置稳定在青藏高原东部,有利于台风右侧高层的辐散流场;（2）副高强度偏弱位置偏东,不利台风东侧水汽输送。700hPa槽后的偏北气流大举南下与台风前的偏北气流合并,台风中层冷空气的入侵影响有利暴雨的发生;（3）暴雨期间,低层切变在广西南部频繁摆动为当地暴雨提供动力条件,台风强度和云系的维持主要来自于南海20°N以北的一条较狭窄的急流带为台风前西侧降水提供水汽条件;（4）南宁多普勒雷达速度图上的γ中尺度系统与强度场上雨团强度分布有很好的对应关系,造成暴雨的原因是不断发展的强对流单体。     

0509号台风麦莎影响山东分析

分析了0509号台风的移动路径、强降水分布及物理量场分布,重点分析台风在安徽突然转向东北和强降水不在中心附近而是偏于中心东北侧的原因。结果表明：副高突然南落及200hPa辐散偏向台风中心右侧是台风在安徽转向进入江苏的原因;台风进入西风带以后没有明显的冷空气侵入;台风进入山东前,半岛大暴雨是由台风右侧东南急流和切变线引起,而台风进入山东境内时,鲁北、鲁中强降水由台风倒槽引起。     

9711号台风的移动路径和影响山东的特大暴雨分析

对９７１１号台风的移动路径及其造成的暴雨进行了分析。分析表明,副高外围环流在台风移动过程中有明显的引导作用;台风的移动路径和暴雨落区与正涡度轴线及高能轴线都有较好的对应关系;对流层中层变能场的分布对台风移动路径有明显的预报指示意义。     

“麦莎”远距离台风暴雨的排熵指数分析

对2005年8月5日山东省一次远距离台风暴雨过程进行了诊断分析。从引起远距离台风暴雨的几个因素,分析此次山东地区强降水过程与台风“麦莎”及中纬度西风带系统的关系,判别台风暴雨的类型;应用耗散结构理论,分析了排熵指数与远距离台风暴雨区的关系。结果表明：该次暴雨过程是在远距离台风（0509号“麦莎”台风）和西风槽相互作用下产生的;非纬向的高、低空急流对本次暴雨过程起着重要作用;负熵变区（IRE〈0）对应着暴雨区,负熵变区的汇合反映远距离台风暴雨的落区,排熵指数对远距离台风暴雨分析预报有一定的指示作用。     

CHARACTERISTICS AND TRENDS OF CLIMATIC EXTREMES IN CHINA DURING 1959–2014

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.     

ANALYSIS OF “BIMODAL PATTERN” STORM ACTIVITY CHARACTERISTICS OF THE BAY OF BENGAL

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.     

LOW-FREQUENCY CIRCULATION AND EXTENDED-RANGE FORECAST IN CONNECTION WITH AUTUMN EXTREME HEAVY RAINFALL PROCESS IN HAINAN

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.     

AN ATTRIBUTION ANALYSIS OF CHANGES IN POTENTIAL EVAPOTRANSPIRATION IN THE BEIJING–TIANJIN–HEBEI REGION UNDER CLIMATE CHANGE

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.     

Could the Recent Taal Volcano Eruption Trigger an El Ni?o and Lead to Eurasian Warming?

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.     

Revisiting the Concentration Observations and Source Apportionment of Atmospheric Ammonia

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     

COMPARATIVE ANALYSIS OF VARIOUS DATA OF AIR–SEA TEMPERATURE DIFFERENCE AND ITS VARIATION ACROSS SOUTH CHINA SEA IN THE PAST 35 YEARS

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 Nio3.4 index in summer and autumn but negatively correlated in spring and winter.     

Information for Authors

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     

Information for Authors

AIMS AND SCOPE Atmospheric and Oceanic Science Letters （AOSL） pub- lishes short research letters on all disciplines of the atmos- phere sciences and physical oceanography. Contributions from all over the world are welcome.