历年影响广西沿海的热带气旋及其灾害成因分析(英文)

登陆或经过广西沿海的热带气旋是一种严重的自然灾害,每年热带气旋所伴随的大风、大雨、风暴潮等灾害造成沿海地区严重的财产损失或人员伤亡。通过对1950~2012年影响广西沿海的热带气旋的统计分析发现,影响广西沿海的热带气旋数量年际变化明显,最多的年份达9个,最少的年份为0个;热带气旋季节分布具有明显规律性,每年的7、8、9三个月为影响高峰月,其次为6、10月;热带气旋从菲律宾以东洋面进入南海后穿过海南省和雷州半岛再次登陆广西沿海的次数最多,该类热带气旋引起的风暴增水平均值为111.2 cm,到达非登陆台风增水的2.6倍。风暴潮灾害的形成与强台风天气系统、全日大潮、河流下泄洪水直接有关。强台风产生巨浪及降雨,使入海河口水位上升,与风暴潮叠加后产生明显的增水,造成巨大的潮灾。     

历年影响广西沿海的热带气旋及其灾害成因分析

通过对1950-2012年影响广西沿海的热带气旋的统计分析发现,影响广西沿海的热带气旋数量年际变化明显,最多的年份达9个,最少的年份为0个;热带气旋季节分布具有明显规律性,每年的7、8、9三个月为影响高峰月,其次为6、10月;热带气旋从菲律宾以东洋面进入南海后穿过海南省和雷州半岛再次登陆广西沿海的次数最多,该类热带气旋引起的风暴增水平均值为111.2 cm,到达非登陆台风增水的2.6倍。风暴潮灾害的形成与强台风天气系统、全日大潮、河流下泄洪水直接有关。强台风产生巨浪及降雨,使入海河口水位上升,与风暴潮叠加后产生明显的增水,造成巨大的潮灾。     

高光谱大气红外探测仪(AIRS)反演大气不稳定度指数在强对流天气个例中的应用试验

使用探空、NCEP-FNL,数据和高光谱分辨率大气垂直探测仪(AIRS)标准反演数据计算大气不稳定度指数,对2011年6月23日北京强对流天气发生前的本地及上游大气中不稳定能量进行分析研究。分析发现:利用08、14和20时探空数据计算的北京站不稳定度指数显示了在"6·23暴雨"过程发生前后北京上空不稳定能量变化,上游关键区无探空数据;利用NCEP和AIRS数据计算的不稳定度指数显示,强对流天气发生前,在北京的上游关键区大气处于极端不稳定状态(K指数大于40,SI指数小于一5),有利于强对流天气发生。文章的研究结果表明,探空数据时空分辨率较低,不利于监测强对流天气的发生;质量控制后AIRS数据计算的不稳定度指数可以监测对流天气的发生;空间分辨率较低的NCEP数据监测小范围大气不稳定层结能力较低。综上所述,AIRS反演产品具有弥补探空资料时空分辨率不足的优势,利用AIRS L2反演产品计算晴空大气不稳定度指数产品可以监测到"6·23暴雨"天气发生前上游关键区大气层结稳定度状态,为预报员决策提供有效的辅助信息。     

应用样条插值提高GRAPES模式物理过程反馈精度

GRAPES (Global/Regional Assimilation and PrEdiction System) 模式动力框架中垂直方向变量的跳层设置采用Charney-Phillips分布,在整层上进行位温、水物质的计算,物理过程中在半层上对其进行处理。这样在GRAPES模式中,进入物理过程之前和物理过程计算完毕之后,都要采用线性插值进行整层和半层之间物理量的转换。由于线性插值精度欠佳,为提高上述反馈过程的精度,并保证水物质的正定性,该研究引入样条插值,并在水物质的插值过程中进行保单调处理,有效减小了位温场、水物质场的预报偏差,并提升了模式的综合预报性能。     

相控阵雷达扫描方式对回波强度测量的影响

有源数字阵列雷达的波束设计非常灵活,可变的波束宽度和多波束模式不仅可以满足不同任务的观测需求,还可有效节约扫描时间,但阵列天线参数随波位改变的特性对相控阵天气雷达回波强度的精确定标提出了挑战。2013年4—6月,中国气象科学研究院与安徽四创电子股份有限公司联合研发的X波段一维有源相控阵天气雷达 (X-PAR) 进行了装配后的首次测试观测。对比该雷达与相同位置的C波段双线偏振雷达 (C-POL) 观测资料发现,X-PAR不同宽度的扫描波束均能取得较合理的观测资料。但由于各模式的雷达方程及其标定方法不尽相同,并受到阵列天线照射体积、展宽波束和发射增益的影响,造成X-PAR回波强度存在一定的测量偏差。结合天线参数分析和实际数据统计将测量偏差订正至±1 dB内,为雷达的进一步调试和改进提供了依据。     

基于MapReduce计算模型的气象资料处理调优试验

云计算技术使用分布式的计算技术实现了并行计算的计算能力和计算效率,解决了单机服务器计算能力低的问题。基于长序列历史资料所计算得出的气候标准值对于气象领域实时业务、准实时业务及科学研究中均具有重要的意义。由于长序列历史资料数据量大、运算逻辑较复杂,在传统单节点计算平台上进行整编计算耗时非常长。该文基于Hadoop分布式计算框架搭建了集群模式的云计算平台,以长序列历史资料作为源数据,基于MapReduce计算模型实现了部分整编算法,提高计算时效。同时,由于数据源本身具有文件个数多、单个文件小等特点,对数据源存储形式及数据文件大小进行改造,分别利用SequenceFile方式及文本文件合并方式对同一种场景进行计算时效对比测试,分别测试了10个文件合并、100个文件合并两种情况,使时效性得到了更大程度的提升。     

Signal acquisition of Luojia-1A low earth orbit navigation augmentation system with software defined receiver

Low Earth Orbit (LEO) satellite navigation signal can be used as an opportunity signal in the case of a Global Navigation Satellite System (GNSS) outage, or as ...     

Spatiotemporal filtering of regional GNSS network’s position time series with missing data using principle component analysis

The existing spatiotemporal analysis methods suppose that the involved time series are complete and have the same data interval. However missing data inevitably occur in the position time series of Global Navigation Satellite Systems networks for many reasons. In this paper, we develop a modified principal component analysis to extract the Common Mode Error (CME) from the incomplete position time series. The principle of the proposed method is that a time series can be reproduced from its principle components. The method is equivalent to the method of Dong et al. (J Geophys Res 111:3405–3421, 2006) in case of no missing data in the time series and to the extended ‘stacking’ approach under the assumption of a uniformly spatial response. The new method is first applied to extract the CME from the position time series of the Crustal Movement Observation Network of China (CMONOC) over the period of 1999–2009 where the missing data occur in all stations with the different gaps. The results show that the CMEs are significant in CMONOC. The size of the first principle components for the North, East and Up coordinates are as large as 40, 41 and 37 % of total principle components and their spatial responses are not uniform. The minimum amplitudes of the first eigenvectors are only 41, 15 and 29 % for the North, East and Up coordinate components, respectively. The extracted CMEs of our method are close to the data filling method, and the Root Mean Squared error (RMS) values computed from the differences of maximum CMEs between two methods are only 0.31, 0.52 and 1.55 mm for North, East and Up coordinates, respectively. The RMS of the position time series is greatly reduced after filtering out the CMEs. The accuracies of the reconstructed missing data using the two methods are also comparable. To further comprehensively test the efficiency of our method, the repeated experiments are then carried out by randomly deleting different percentages of data at some stations. The results show that the CMEs can be extracted with high accuracy at the non missing-data epochs. And at the missing-data epochs, the accuracy of extracted CMEs has a strong dependence on the number of stations with missing data.     

福建省GPS基准站数据处理与分析

利用福建省41个GPS基准站2011—2014年的观测资料,运用GAMIT／GLOBKl0．4软件及块体整体旋转线性应变模型,得到了福建省各测站及分块的水平运动速度场和块体上的应变图像。通过分析研究2011—2013和2011-2014两个时段速度场和应变场的特征,为福建省未来地震活动判定提供形变依据。     

The Characteristics of Atmospheric Ice Nuclei Measured at Different Altitudes in the Huangshan Mountains in Southeast China简

The concentration of ice nuclei （IN） and the relationship with aerosol particles were measured and analyzed using three 5-L mixing cloud chambers and a static diffusion cloud chamber at three altitudes in the Huangshan Mountains in Southeast China from May to September 2011.The results showed that the mean total number concentration of IN on the highest peak of the Huangshan Mountains at an activation temperature （Ta） of-20℃C was 16.6 L-1.When the supersaturation with respect to water （Sw） and with respect to ice （Si） were set to 5％,the average number concentrations of IN measured at an activation temperature of-20℃C by the static diffusion cloud chamber were 0.89 and 0.105 L-1,respectively.A comparison of the concentrations of IN at three different altitudes showed that the concentration of IN at the foot of the mountains was higher than at the peak.A further calculation of the correlation between IN and the concentrations of aerosol particles of different size ranges showed that the IN concentration was well correlated with the concentration of aerosol particles in the size range of 1.2-20 μtm.It was also found that the IN concentration varied with meteorological conditions,such as wind speed,with higher IN concentrations often observed on days with strong wind.An analysis of the backward trajectories of air masses showed that low IN concentrations were often related to air masses travelling along southwest pathways,while higher IN concentrations were usually related to those transported along northeast pathways.