草原火险等级预报数据库子系统

介绍了用Ｃ语言和图形图象处理等方法所编制的火险数据库子系统的原理，方法，还介绍了为来源及系统的查询，统计，打印等功能及实现方法。     

如何在APS＋SQL中实现文件的上传和查看

介绍了建立SQL数据库所需字段的设置，注册上传组件的方法，组件方法介绍，组件属性介绍。通过一个具体的实例详细把文件完成上传和查看的过程一一说明。     

机载微波辐射计测云中液态水含量(Ⅰ): 仪器和标定

简要介绍为研制机载对空测云微波辐射计所做的预研究，结果确认了研制这样的单频机载微波辐射计的可行性，并明确了仪器的技术难点，给出了针对技术难点提出的设计方案和技术指标。本文还介绍了对仪器进行灵敏度测试的实验方法和结果。实验室测试和晴空飞行测试表明，在一定的条件下，仪器灵敏度指标达到0．2K；还较详细地介绍了几种实用的标定方法，并讨论了它们的适用范围和不确定性。     

局域网中ARP的欺骗攻击

介绍了ARP（Address Resolution Protocol）地址解析协议的含义和工作原理，并通过分析ARP协议的工作原理，探讨了ARP协议从IP地址到MAC（Media Access Control）地址解析过程中的安全性，以及ARP协议所存在的安全漏洞，介绍了ARP欺骗的实现过程和基于ARP欺骗的中间人攻击方法。最后，从气象系统网络安全的维护工作出发，介绍了IP地址和MAC地址绑定、交换机端口和MAC地址绑定、静态配置路由ARP条目等技术能够有效防御ARP欺骗攻击的安全防范策略。     

内蒙古极轨气象卫星的应用领域及方法

介绍了自治区极轨气象卫星的应用原理和方法，同时也介绍了地理信息系统技术与遥感技术的结合方法。     

711雷达观测及摄取回波资料方法的探讨

本文根据有关资料，结合作用多年的实践经验，对711雷达的探测内容、探测方法等进行了全面论述。文中介绍了最基本的观测内容，强调了雷达回波与其他有关资料相配合的问题。详细介绍了整个探测程序，特别指出了探测过程中容易出现的一些问题和可能遇到的困难，并根据711雷达的性能及雷达气象学原理，提出了解决方法和建议。文章着重讨论了回波资料的摄取方法，按照不同性质的降水回波或不同拍摄目的。分门别类做了介绍。     

乌鲁木齐地区大降水自动预报系统

介绍了乌鲁木齐地区大降水自动预报系统的设计思路和系统的主要功能，并介绍了系统所具有的智能化、自动化、系统化等特点，力图通过研制和使用这一系统，使单点大降水预报准确率有所提高，为实现天气预报的自动化、准定量化和现代化做一点工作。     

计算机病毒的防范

针对计算机病毒日益猖獗严重威胁信息网络安全的事实，介绍了计算机病毒的概念、特征和主要分类及其防范对策，并介绍了目前国内外主流杀毒软件的特点和网址及市场占有率较高的KV3000杀毒软件的使用说明。     

701雷达收发系统的检修

根据701雷达工作的基本原理，利用雷达现有的各种仪表，结合本人多年的检修工作经验，系统介绍了701雷达接收、发射系统的检修特点和技巧，并介绍若干故障检修实例。     

1713MHZ微波干扰判断分析和对策

本文对在无专业仪器条件下如何判明干扰性质，查清干扰来源作了介绍；干扰强度估算和载波频带分析是正确决策的基础，本文介绍了计算分析方法。     

黄、渤海沿海大风变化特征及影响系统

利用1981—2010年黄、渤海沿海44个气象站大风资料,根据中央气象台对近海海区的划分,分析了近30 a黄、渤海近海5海区大风的气候特征,以及通过天气分型对2008—2012年黄、渤海沿海大风的影响系统进行了统计,结果表明:近30 a黄、渤海沿海5海区日最大风速≥6级和≥8级日数呈递减趋势,1980s大风日数较多,各海区≥6级大风在1981年和1987年前后均有两个峰值。≥6级大风日数随季节变化的峰值,渤海海区出现在春季,黄海南部海区是春季、夏季8月和秋季11月,渤海海峡、黄海北部和中部海区则主要是春季和冬季。渤海海区以偏北风和南南西风为主导风向,与其他海区以北或西北风为主的特征明显不同。冷锋是黄、渤海沿海大风最主要的影响系统,其次是气旋型和高低压型大风。另外以850 h Pa温度平流的强度、冷/暖中心的强度、等温线密集带梯度、地面高/低压强度、地面大风前3 h/24 h最强变压中心强度和地面气压梯度等要素为着眼点,对不同类型的大风指标进行了分析。     

Ship measurements of submicron aerosol size distributions over the Yellow Sea and the East China Sea

During the spring of 2005, the total particle concentrations and the submicron aerosol size distributions were measured on board the research vessel over the south sea of Korea and the Korean sector of the Yellow Sea. Similar measurements were made over the East China Sea in autumn 2005. The aerosol properties varied dynamically according to the meteorological conditions, the proximity to the land masses and the air mass back trajectories. The average total particle concentration was the lowest over the East China Sea, 4335 ± 2736 cm^− 3, but the instantaneous minimum, 837 cm^− 3, for the entire ship measurement was recorded during the Yellow Sea cruise. There was also a long (more than 6 h) stretch of low total particle concentrations that fell as low as 1025 cm^− 3 during the East China Sea cruise when the ship was the farthest from the shores and the air mass back trajectories resided long hours over the sea. These observations lead to the suggestion of ~ 1000 cm^− 3 as the background total particle concentration over the marine boundary layer in the studied region of the Yellow Sea and the East China Sea, implying significant anthropogenic influence even for the background value. In the mean time, average aerosol size distributions were unimodal and the mode diameter ranged between 52 and 86 nm, excluding the fog periods, which suggests that the aerosols measured in this study experienced relatively less aging processes within the marine boundary layer.     

Temporal variability and spatial distribution of Sea Surface Temperatures in the Aegean Sea

Summary Sea Surface Temperature (SST) is a critical factor in the assessment of the climatic structure of a coastal region affected mainly by air temperature (AT). A regionalization of the Aegean Sea based on SST values measured at twenty-one representative stations is presented using a widely accepted clustering method. With this method, the Aegean Sea is divided into sub-regions with similar characteristics, on a seasonal basis.Principal Component Analysis (PCA) shows that SSTs oscillate simultaneously in the Aegean Sea and that the major part of the variance is concentrated in the first mode. Spectral analysis shows a high concentration of energy for periods of one year, as is expected. When the annual oscillation is filtered, two secondary peaks can be distinguished, one representing periods from two to three years and another representing periods from three to five years. The first peak is caused by the Quasi-Biennial Oscillation (QBO) and the second as a result of the Southern Oscillation (SO).Interannual variability of the SSTs, although present in the overall signal, seems to be low compared with the strong annual period signal.With 7 Figures     

北极海冰的气候变化与20世纪90年代的突变

应用英国Had ley气候研究中心1968~2000年的1°×1°的北半球逐月海冰密集度资料,使用EOF分解等统计方法,探讨北极海冰的气候变化趋势、海冰的突变、海冰的季节持续性和各季的特色。结果表明:(1)自1968年以来,北极海冰的减小是北半球海冰变化的总趋势;海冰的趋势变化在海冰的年际总变化中占有相当重要的地位,可达50%左右。冬春季主要减少区域在格陵兰海、巴伦支海和白令海;夏秋季海冰减少是唯一趋势,中心在北冰洋边缘的喀拉海、拉普捷夫海、东西伯利亚海、楚科奇海、波弗特海。(2)20世纪80年代中后期北极海冰已出现减小趋势,在20世纪90年代,海冰又出现范围和面积的突然减少,中心在格陵兰海和巴伦支海;即海冰减少是加速的,其变化程度已远远超过一般的自然变化。(3)海冰有很好的季节持续性,有很强的隔季相关,也有较好的隔年相关;各季节海冰分布型之间有很好的联系,表现为海冰分布型的总体变化趋势是一致的,在海冰的减少中也体现了分布型的特征。     

A Note on the South China Sea Shallow Interocean Circulation

1. IntroductionThe South China Sea (SCS) has many channelsconnecting with the outer oceans/seas (Fig. 1). Thewidest and deepest channel is the Luzón Strait, whichis the main entrance to the SCS from the WesternPacific Ocean, having a sill depth of about 2500 m.On the north, the Taiwan Strait connects with theEast China Sea, with a sill depth of about 70 m. Inthe vicinity of Mindoro Island, there are a numberof channels connecting the SCS with the Sulu Sea.The main channel is the M…     

黄渤海登陆热带气旋活动的统计分析

利用中国气象局整编的1949-2000年的热带气旋资料,分析了52年间在黄渤海沿岸登陆的热带气旋的年际和季节分布、强度、移向和移速、源地及变性等气候特征,揭示了此类气旋大部分强度较强并易发生陆上变性,变性后增强的气旋的雏持时间较长等事实,并发现了其变性的高频地区位于黄海北部至中朝边境。     

Subseasonal variability during the South China Sea summer monsoon onset

Analysis of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) data for the period 1998–2007 reveals large subseasonal fluctuations in sea surface temperature (SST) of the South China Sea during the summer monsoon onset. These subseasonal SST changes are closely related to surface heat flux anomalies induced by surface wind and cloud changes in association with the summer monsoon onset. The SST changes feed back on the atmosphere by modifying the atmospheric instability. The results suggest that the South China Sea summer monsoon onset involves ocean–atmosphere coupling on subseasonal timescales. While the SST response to surface heat flux changes is quick and dramatic, the time lag between the SST anomalies and the atmospheric convection response varies largely from year to year. The spatial–temporal evolution of subseasonal anomalies indicates that the subseasonal variability affecting the South China Sea summer monsoon onset starts over the equatorial western Pacific, propagates northward to the Philippine Sea, and then moves westward to the South China Sea. The propagation of these subseasonal anomalies is related to the ocean–atmosphere interaction, involving the wind-evaporation and cloud-radiation effects on SST as well as SST impacts on lower-level convergence over the equatorial western Pacific and atmospheric instability over the Philippine Sea and the South China Sea.     

黄、渤海气旋暴发性发展的个例分析

对1993年6月初的一个在黄、渤海区达到暴发性发展强度，并形成了一个有明显眼区的温带气旋个例进行了诊断研究。结果表明，温度平流和涡度平流、沿岸锋生以及高空急流的动力作用对气旋暴发性发展有重要贡献。     

Sea ice in the Barents Sea: seasonal to interannual variability and climate feedbacks in a global coupled model

Sea ice variability in the Barents Sea and its impact on climate are analyzed using a 465-year control integration of a global coupled atmosphere–ocean–sea ice model. Sensitivity simulations are performed to investigate the response to an isolated sea ice anomaly in the Barents Sea. The interannual variability of sea ice volume in the Barents Sea is mainly determined by variations in sea ice import into Barents Sea from the Central Arctic. This import is primarily driven by the local wind field. Horizontal oceanic heat transport into the Barents Sea is of minor importance for interannual sea ice variations but is important on longer time scales. Events with strong positive sea ice anomalies in the Barents Sea are due to accumulation of sea ice by enhanced sea ice imports and related NAO-like pressure conditions in the years before the event. Sea ice volume and concentration stay above normal in the Barents Sea for about 2 years after an event. This strongly increases the albedo and reduces the ocean heat release to the atmosphere. Consequently, air temperature is much colder than usual in the Barents Sea and surrounding areas. Precipitation is decreased and sea level pressure in the Barents Sea is anomalously high. The large-scale atmospheric response is limited with the main impact being a reduced pressure over Scandinavia in the year after a large ice volume occurs in the Barents Sea. Furthermore, high sea ice volume in the Barents Sea leads to increased sea ice melting and hence reduced surface salinity. Generally, the climate response is smallest in summer and largest in winter and spring.     

Recent Changes in the Climate At the Dead Sea – a Preliminary Study

In the last decade pan evaporation measured at the Southern Dead Sea has significantly increased. Wind, temperature and humidity measurements at the Dead Sea starting in the 1930s as well as 3-D model simulations all seem to indicate a statistically significant change in the local climate of the Dead Sea region. The potential contribution to this climatic change through the weakening of the local land-sea breeze circulation caused by the reduction in the Dead Sea surface area in 1979–1981, is examined. It is suggested that since the breeze tempers the Dead Sea climate, its weakening has caused the air temperature to increase, the relative humidity to decrease and thus increased the pan evaporation. The climatic changes as implied by the MM4 Mesoscale PSU/NCAR model simulations, seem to fit the observed changes and to suggest a local tendency to the more arid climate that now prevails to the south of the study region.