南海波高熵和风速熵

根据风速的统计分布，给出了有因次风速熵和无因次风速熵的定义及其计算方法，使用ＧＥＯＳＡＴ高度计１９８６年１１月－１９８９年２月的有效波高和风速的资料，计算，分析了南海海域上的波高熵，风速熵，给出它们的时间变化特征和空间变化特征，并对不同随机量的无因次熵，即随机度进行了比较。     

受陆岸影响浅水区台风风浪的计算方案

在我国港口工程技术规范的波浪成长理论基础上，考虑到台风波浪的基本特点、等效水深的概念、浅水区波浪的折射、不规则波的能量分布特性、受陆岸和岛屿影响角度范围内的有效能量风区长度等因素，提出了受陆岸、岛屿影响浅水区（包括海湾区）台风风浪的计算方案。本方案只需台风中心位置、中心气压和计算点于各方位的风区长度（受陆岸影响部分）等资料，便可快速地得到计算点的波浪要素。经实测资料验证，效果良好。     

青岛局地风特征的分析

本文根据位于胶州湾东岸的青岛沧口和流亭两机场气象台和唠山区气象台资料及现场实验资料对胶州湾东岸的海陆风气候特征,来自胶州湾与来自南面黄海水域的两支海陆风相互作用及其对沧口地区的影响,崂山西坡下坡风的气候特征及影响进行了分析。文中还提出了一种根据常规气象观测资料估算海陆风发生频率的方法。     

好望角附近海域春季大风的几种主要形势分析

通过普查和分析，概括出了春季好望角附近海域三种主要大风出现时的模型图，并以一个月中逐日4次船舶测风报告为例，分析了好望角大风的某些主要特征。     

短周期仪器记录在远震分析中的应用

地震泼由多种频率成份组成,在其传播过程中。由于地球介质的滤波作用,随着传播距离的增大。高频成份逐渐被衰减。周期越大的成份传播越远。因此,在地震观测上,不同频带的地震仪用于观测不同震中距范围的地震,使仪器较好地响应,就我国台网而言,短周期仪用于地方震、近震的监测,中长周期仪和长周期仪用于远震,极远震的监测,其中长周期仪偏重于记录极远震。但作者在实际分析工作中注意到,短周期仪的记录应用于远震,极远震的分析,显示出独特的作用,充分挖掘和利用短仪资料,将会更加丰富远震、极远震的震相资料。本文以高台地震台的资料为依据,从震中距和震级角度,与中长仪和长仪作比较,对短仪记录远震、极远震的震相及特征作了统计分析与初步探讨,并对新疆地区“影区”地震S波的记录、日本地区地震_PPcP等震相的出现提出了现象的存在,给出了相应的观测结果,以待步一步研究。     

Average recurrence intervals of strong earthquakes and potential risky segments along the Taiyuan-Linfen portion of the Shanxi graben system

Introduction In AD 1303, the great Hongtong, Shanxi, earthquake of magnitude 8 caused a very serious disaster, which killed over one hundred thousands people at least (Department of Earthquake Dis- *aster Prevention, State Seismological Bureau, 1995). On the occasion of commemorating this ca-tastrophe having occurred for 700 years, we have important problems that need to be answered: How long the average recurrence interval of the grea…     

Assessing the Comparability of Ammonium,Nitrate and Sulfate Concentrations Measured by Three Air Quality Monitoring Networks

Airborne fine particulate matter across the United States is monitored by different networks, the three prevalent ones presently being the Clean Air Status and Trend Network (CASTNet), the Interagency Monitoring of PROtected Visual Environment Network (IMPROVE) and the Speciation and Trend Network (STN). If combined, these three networks provide speciated fine particulate data at several hundred locations throughout the United States. Yet, differences in sampling protocols and samples handling may not allow their joint use. With these concerns in mind, the objective of this study is to assess the spatial and temporal comparability of the sulfate, nitrate and ammonium concentrations reported by each of these networks. One of the major differences between networks is the sampling frequency they adopted. While CASTNet measures pollution levels on seven-day integrated samples, STN and IMPROVE data pertain to 24-hour samples collected every three days. STN and IMPROVE data therefore exhibit considerably more short-term variability than their CASTNet counterpart. We show that, despite their apparent incongruity, averaging the data with a window size of four to six weeks is sufficient to remove the effects of differences in sampling frequency and duration and allow meaningful comparison of the signals reported by the three networks of concern. After averaging, all the sulfate and, to a lesser degree, ammonium concentrations reported are fairly similar. Nitrate concentrations, on the other hand, are still divergent. We speculate that this divergence originates from the different types of filters used to collect particulate nitrate. Finally, using a rotated principal component technique (RPCA), we determined the number and the geographical organization of the significant temporal modes of variation (clusters) detected by each network for the three pollutants of interest. For sulfate and ammonium, the clusters’ geographical boundaries established for each network and the modes of variations within each cluster seem to correspond. RPCA erformed on nitrate concentrations revealed that, for the CASTNet and IMPROVE networks, the modes of variation do not correspond to unified geographical regions but are found more sporadically. For STN, the clustered areas are unified and easily delineable. We conclude that the possibility of jointly using the data collected by CASTNet, IMPROVE and STN has to be weighed pollutant by pollutant. While sulfate and ammonium data show some potential for joint use, at this point, combining the nitrate data from these monitoring networks may not be a judicious choice.