台风"麦莎"的天气过程总结

利用全市21个自动气象站的逐时资料，分析了台风“麦莎”对苏州市的风雨影响，并将其与20世纪90年代以来对苏州造成严重影响的9015号和9711号两个台风，分别从登陆时强度、登陆后移动路径、影响苏州市的持续时间和形成的风雨实况等方面进行了对比分析。通过分析比较得出，台风“麦莎”是20世纪90年代以来对苏州影响最严重的一次台风过程。     

基于图像退化模型的天气现象识别

在不同的天气条件下,由于气溶胶对光波的散射作用,通过成像系统获得的图像都会有一定程度的退化。利用图像的退化模型可以获取与天气现象相关的参量,从而达到识别天气的目的。对不同天气条件下得到的同一场景图进行研究,运用多垂线检测法,检测出不同天气条件下直线模糊边缘上的灰度值,采用signloid函数拟合出刃边函数,进而求出线扩散函数。通过分析线扩散函数的变化规律,总结出其与天气现象之间的紧密联系。测试结果表明,应用该方法对于晴天、薄雾、雨、霾、沙尘暴等天气现象具有一定的判别效果。     

基于关联规则的故障诊断方法研究

提高自动气象站设备运行能力和观测数据的质量状况成为影响气象预测预报服务的准确性和气象事业发展的关键。然而中国现在业务运行的自动气象站设备并没有内置的故障检测装置,也没有完善的自检系统。但是气象要素(温湿压风雨)的变化往往是相互关联的,为了提高自动气象站数据质量,及时有效地判断设备故障。从大量观测数据试验结果进行研究,提出一种基于关联规则的故障综合诊断方法。通过使用Apriori挖掘算法,得到各气象要素及其变化量之间的强关联规则,通过实际观测数据验证2012年7月26日08时江西南昌站湿度传感器发生故障。     

半Lagrange法在数值预报中的应用和近年来的发展(Ⅰ)

本文就五十年代以来半Lagrange法在数值预报中的应用和近年来的发展进行评述,介绍我国在这方面的工作。还就此法和Euler法的优缺点作了比较;指出了存在的问题。     

Multi-band Solar Full-disk Extreme Ultraviolet Imager

Solar Extreme Ultraviolet (EUV) imaging observation is an important measure for the researches of solar activities and coronal plasma physics. But the traditional EUV imager and spectrograph can hardly achieve simultaneously the high spectral resolution and wide field-of-view of solar imaging. This paper has designed a new type of solar EUV multi-band imager, by adopting a kind of slitless grating and grazing incidence structure, it can realize the solar full-disk imaging of high spectral and spatial resolution. The field-of-view of the imager can be as broad as 47′. The spectral resolution is 2×10^?3nm per pixel, and the spatial resolution is 1.4′ per pixel. The temporal resolution of the solar full-disk is better than 60 s. The analysis of the solar full-disk spectral image and system response shows that the imager can observe the morphological evolutions of various solar activities, and can provide more comprehensive data for the researches of solar physics and space weather forecast.     

Automated Prediction of CMEs Using Machine Learning of CME – Flare Associations

Machine-learning algorithms are applied to explore the relation between significant flares and their associated CMEs. The NGDC flares catalogue and the SOHO/LASCO CME catalogue are processed to associate X and M-class flares with CMEs based on timing information. Automated systems are created to process and associate years of flare and CME data, which are later arranged in numerical-training vectors and fed to machine-learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Properties representing the intensity, flare duration, and duration of decline and duration of growth are extracted from all the associated (A) and not-associated (NA) flares and converted to a numerical format that is suitable for machine-learning use. The machine-learning algorithms Cascade Correlation Neural Networks (CCNN) and Support Vector Machines (SVM) are used and compared in our work. The machine-learning systems predict, from the input of a flare’s properties, if the flare is likely to initiate a CME. Intensive experiments using Jack-knife techniques are carried out and the relationships between flare properties and CMEs are investigated using the results. The predictive performance of SVM and CCNN is analysed and recommendations for enhancing the performance are provided.     

广东2008年低温雨雪冰冻灾害及气象应急响应

2008年初我国南方遭受了百年一遇低温雨雪冰冻灾害,广东的受灾程度属80年一遇,造成了重大经济损失和严重社会影响.分析发现:灾害过程在近年来最严重的一次拉尼娜事件背景下发生的,与欧亚地区持续大气环流异常密切相关.在北脊南槽和西太平洋副高偏北偏强的形势下,冷暖气流在我国南方地区频繁交汇,使对流层中低层形成逆温层和局地经向环流产生异常,造成了此次持续低温雨雪冰冻过程.面对灾害引发的公共事件,广东省气象部门打破常规、准确预警,启动预案,通过媒体和公共事件预警信息发布平台,及时传播权威的公共预警信息,为安定民心、稳定社会、减少灾害起到积极作用.灾后反思发现,山区冰灾的监测、灾害的评估、公共信息发布等的能力和规范均亟待加强.     

The challenge of predicting the occurrence of intense storms

Geomagnetic super-storms of October and November 2003 are compared in order to identify solar and interplanetary variables that influence the magnitude of geomagnetic storms. Although these superstorms (D_ST < -300 nT) are associated with high speed CMEs, their DST indices show large variation. The most intense storm of November 20, 2003 (D_St∼ - 472 nT) had its source in a comparatively small active region and was associated with a relatively weaker, M-class flare, while the others had their origins in large active regions and were associated with strong X-class flares. An attempt has been made to implement a logistic regression model for the prediction of the occurrence of intense/superintense geomagnetic storms. The model parameters (regression coefficients) were estimated from a training data-set extracted from a data-set of 64 geo-effective CMEs observed during 1996–2002. The results indicate that logistic regression models can be effectively used for predicting the occurrence of major geomagnetic storms from a set of solar and interplanetary factors. The model validation shows that 100% of the intense storms (-200 nT < D_St < -100 nT) and only 50% of the super-intense (DST < -200 nT) storms could be correctly predicted.     

Prediction of Space Weather Using an Asymmetric Cone Model for Halo CMEs

Halo coronal mass ejections (HCMEs) are responsible of the most severe geomagnetic storms. A prediction of their geoeffectiveness and travel time to Earth’s vicinity is crucial to forecast space weather. Unfortunately, coronagraphic observations are subjected to projection effects and do not provide true characteristics of CMEs. Recently, Michalek (Solar Phys. 237, 101, 2006) developed an asymmetric cone model to obtain the space speed, width, and source location of HCMEs. We applied this technique to obtain the parameters of all front-sided HCMEs observed by the SOHO/LASCO experiment during a period from the beginning of 2001 until the end of 2002 (solar cycle 23). These parameters were applied for space weather forecasting. Our study finds that the space speeds are strongly correlated with the travel times of HCMEs to Earth’s vicinity and with the magnitudes related to geomagnetic disturbances.     

成都“2008.9.24”暴雨的中尺度数值模拟

为了更好的研究四川成都地区2008年9月23日～25日强降水天气的物理机制,利用中尺度天气预报模式(Weather Research Forecast,WRF)模拟了此次过程,并结合FY-2号C星云图资料,对过程的发生、发展过程进行分析后发现,此次暴雨过程为低层弱低压辐合,同时台风"黑格比"登陆后带来大量水汽,并借助西伸的西太平洋副高源源不断的向四川盆地输送,另外存在来自南方孟加拉湾水汽的持续供应,对流云团形成于地面低压与西伸副高的交界处,并沿着副高边缘由西南向东北移动,雨带分布在副高边缘气压梯度大的地方,模拟结果与观测事实基本相符。利用WRF模式模拟暴雨产生的环流形势,配合卫星云图,能进一步揭示暴雨产生的物理机制,可作为深化暴雨研究的有效手段。