河池新一代天气雷达站防雷接地降阻技术探讨

通过对河池新一代天气雷达站防雷接地工程的设计和施工实践,探讨在高电阻率土壤如何获得低接地电阻值,提出综合降阻法,力争做到经济合理、安全可靠,对类似的山区防雷接地装置设计与施工有一定的参考价值.     

高土壤电阻率条件下烟花仓库的地网设计与测试

介绍了高土壤电阻率条件下烟花仓库地网设计与测试的防雷工程案例,通过分析接地电阻尺与土壤电阻率P、地网占地面积S的关系,以及接地装置的冲击接地电阻Ri与工频接地电阻尺之间的换算关系,探索了高土壤电阻率条件下如何设计地网接地电阻的难题,并通过使用三极法检测,对地网接地电阻的设计结果进行了验证。     

山地民爆小型仓库防雷接地探讨

由于高电阻率的土壤特性,山地民爆小型仓库防雷接地装置的设计与施工一直是难点之一。本文提出了山地民爆小型仓库多支避雷针可以共用同一接地装置、利用仓库的基础接地体作为防雷电感应接地装置及其接地电阻值可适当增大的观点,并分析了其安全性、科学性及合理性。另外,还提出了高土壤电阻率地区可采用砼接地体降低接地电阻值的设计思路,该方法具有减少工程量、降低成本、防水土流失、降阻性能稳定、减少污染等优点,具有一定的实用性和推广价值。     

建筑物防雷装置人工接地体的设计计算

防雷装置在建筑工程中得到了越来越广泛的应用,防雷装置的设计施工已成为建（构）筑物工程设计中的一个重要内容。以西宁市新一代天气雷达站防雷工程设计为例,对人工接地装置的科学计算提出一些建议,以便在今后进行工程设计时作为参考。     

减少屏蔽作用 巧布接地装置

防雷、电力、通信等工程中接地装置的设计安装,总是希望能尽量降低接地电阻以节省材料烈而降低工程成本。这就涉及到接地装置的布局技巧,以及如何减少屏蔽作用。 接地体与土壤的接触面积越大,接地电阻就越小,这是误解。因为接触面积的大小只在一定范围内起作用,当其达到一个不大的数值（通常总长几ｍ的接地体便可达到该数值）,如果继续增大,对降低接地电阻的作用就不明显了［１］。实际上,接地电阻的大小与接地装置的散流条件有直接的关系。当土壤电阻率确定之后,接地体互相之间的屏蔽作用就成为影响散流条件的主要因素。组成接地…     

高土壤电阻率地质条件下接地技术探讨

分析了高土壤电阻率环境条件下降低接地电阻的技术思路,指出高土壤电阻率环境条件下仅依靠扩大地网面积及增加普通垂直接地体、加密均压网格来达到接地电阻要求是十分困难的.接地电阻的大部分在电极附近.在接地体等值半径2倍范围内的土层占接地电阻的一半,如果将接地体等值半径2倍范围内的土壤电阻率降低,就可以使接地体的接地电阻大大降低,取得费用省效果好的结果.新型ALG离子接地系统管内填充剂采用低电阻、高渗透扩展的高能回填料,可形成枝状低阻通道,在ALG防腐离子接地极和大地之间建立电气连接,改善了接地体有效电阻区域介质电阻率,从而大大降低了接地电阻值.工程实践中应用深井离子接地系统成功地解决了高土壤电阻率地区接地难题.     

非均匀电阻率土壤防雷接地工程技术探讨

通过对河池市气象局新建自动观测站防雷接地工程的设计和施工实践,探索出在非均匀电阻率土壤获得低电阻的一些经验,对类似环境下的防雷接地工程设计施工有一定参考价值。     

秭归县凤凰山复建文物建筑的防雷设计与施工质量控制

联系秭归县凤凰山复建文物建筑防雷设计与施工实际,介绍了该防雷工程的设计思路、现场勘察与调查方法以及防雷类别确定方法,简述了复建文物建筑防雷装置的安装技术和高土壤电阻率地质条件下的防雷接地技术,指出了防雷工程施工中的质量控制措施。     

非均匀电阻率土壤防雷接地工程技术探讨

通过对河池市气象局新建自动观测站防雷接地工程的设计和施工实践，探索出在非均匀电阻率土壤获得低电阻的一些经验，对类似环境下的防雷接地工程设计施工有一定参考价值。     

高土壤电阻率地域降阻工程的若干问题探析

接地电阻是接地系统的一项重要技术指标,电阻值是衡量接地系统有效性、安全性的重要参数之一.但是在一些高土壤电阻率地域,如何设计、改造接地装置并有效地降低接地电阻值,一直是广大工程技术人员不断探索、研究的问题.现以已实施的一个高土壤电阻率地域接地装置改造工程为例,从土壤的水平分层结构、土壤的含水量及电解质、接地体之间的屏蔽效应、测试方法与测试设备等几大因素探讨分析了其对接地装置接地电阻的影响,归纳和总结了各种降阻措施在不同情形下的利弊特点;阐明在高土壤电阻率地域进行接地系统改造工程应掌握现场实况,保证人身和设备安全的前提下,采用科学、经济合理的用材、降阻措施和接地结构组合方式来实现预期设计目的.     

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.     

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.     

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.     

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.     

Analysis of Atmospheric Boundary Layer Height Characteristics over the Arctic Ocean Using the Aircraft and GPS Soundings

正ERRATUM to: Atmospheric and Oceanic Science Letters, 4(2011), 124-130 On page 126 of the printed edition (Issue 2, Volume 4), Fig. 2 was a wrong figure because the contact author made mistake giving the wrong one. The corrected edition has been updated on our website. The editorial office is sincerely sorry for any     

Index to Vol.31

正AN Junling;see LI Ying et al.;(5),1221—1232AN Junling;see QU Yu et al.;(4),787-800AN Junling;see WANG Feng et al.;(6),1331-1342Ania POLOMSKA-HARLICK;see Jieshun ZHU et al.;(4),743-754Baek-Min KIM;see Seong-Joong KIM et al.;(4),863-878BAI Tao;see LI Gang et al.;(1),66-84BAO Qing;see YANG Jing et al.;(5),1147—1156BEI Naifang;