A new method for multi-point pollution source identification

对于污染源源项特征(数量,位置及排放强度)的快速且精确估计是污染事件应急响应的关键.与单点源估计相比,多点源的重建更具挑战性.本文提出了一种新的针对于多点源污染事件的两步反演算法,该算法通过结合大气化学模式与有限的浓度观测数据以实现对于未知数量的多点源的准确估计.在其计算过程中,无需任何未知量的先验信息,且可以自动识别污染源的数量,并确定每一个污染源的位置及强度.本文通过使用若干组理想试验验证了算法的适用性,试验结果表明,该算法可准确估计单点源,两点源和三点源的个数及位置,强度的估计误差可基本忽略.且该算法的估计精度和计算效率不会随着点源个数的增加而减弱.     

一种新型西南低涡逐步订正识别方法

西南低涡是形成于青藏高原东侧的特殊天气系统,国内学者目前对于西南低涡的识别没有统一的标准。通过分析西南低涡的主要特征,结合高度场、涡度场、风场,设计了一种适应于西南低涡的HVW识别方法,将其应用于2014年6—8月GRAPES-MESO高分辨率格点分析资料,对比与西南低涡天气图实况的差异。通过对西南低涡的识别、低涡生成和消亡时间、低涡中心位置以及低涡中心强度这几方面的具体分析,得到以下几点结论:1)HVW识别方法能够有效识别出高精度格点资料中的西南低涡过程,与格点实况的吻合率达到87.5%;对于天气图和格点资料都能够再现的西南低涡个例,HVW识别方法的准确度能够达到90.9%,说明HVW识别方法能够有效捕捉西南低涡。2)以天气图实况资料为西南低涡生命时长检验标准,HVW识别方法能够合理分析低涡的生成和消亡时间。3)对西南低涡中心位置偏差进行分析发现,HVW识别的西南低涡中心位置不仅位于西南低涡气压低值附近,更位于风场辐合中心。4)对西南低涡中心强度的评估发现,格点实况与HVW识别方法分析的西南低涡强度差异几乎可以忽略,充分说明了HVW识别方法包含了格点实况的高度场信息,也说明该识别方法的西南低涡中心强度可以用来代替格点实况结果。通过对2014年6—8月西南低涡过程的具体分析,验证了HVW逐步循环定位方法的可行性、合理性以及准确性。     

地面成像光谱数据的田间杂草识别

地面成像光谱数据兼具高光谱分辨率与高空间分辨率,在田间杂草识别中具有很好的应用前景。目前基于机器视觉的杂草识别方法以形状特征为主,当作物杂草形态相似时识别的困难和利用高光谱特征以像元为单元识别时效率较低,不利于实时自动化除草,因此,本文提出一种综合面向对象与高光谱特征匹配的杂草识别方法,在对作物杂草对象样本的形状特征和光谱曲线提取分析的基础上,建立基于形状特征规则与光谱角匹配的植物对象识别决策树,用于识别实验田中的作物杂草对象。实验结果表明,当场景中某些不同种类植物对象的形态相似时,基于形状特征规则与光谱角匹配的杂草识别方法可借助高光谱特征精细区分植物对象的种类,且在形状特征规则约束下使用高光谱特征匹配法识别植物对象,可克服"同物异谱"和"同谱异物"现象带来的不确定性,该方法识别精度可优于仅使用光谱角匹配法的情况,并优于使用颜色和形状分析技术的情况。     

GIS中面向对象时空数据模型

由于当前的地理信息系统软件难以处理时态现象，时态数据模型已忧为ＧＩＳ领域的一个研究热点。许多学者提出了多种时态数据模型。本文作者在提出了矢量栅格一体化的面向对象数据模型之后，再一次对时态问题进行了分析研究，净面向对象的数据模型扩充到时间维。有三种方法表达空间对象的历史变化。第一种是将版本信息记录在关系表上；第二种是将版本信息标记在记录上；第三种是将版本信息标记在属性上。本文采用面向对象的方法，将版     

Aliphatic alkanes and polycyclic aromatic hydrocarbons in atmospheric PM10 aerosols from Baoji, China: Implications for coal burning

Normal alkanes and PAHs in atmospheric PM₁₀ aerosols collected during 2008 winter and spring in Baoji, a mid-scale inland city of China, were determined on a molecular level. Concentrations of n-alkanes ranged from 232 to 3583 ng/m³ with an average of 1733 ng/m³ in winter and from 124 to 1160 ng/m³ with an average of 449 ng/m³ in spring, while PAHs in the PM₁₀ samples were 594 ± 405 and 128 ± 82 ng/m³ in the two seasons. Molecular compositions showed that CPI (odd/even) values of n-alkanes were close to unity for all the samples especially in winter, and diagnostic ratios of PAHs (e.g., Phe/(Phe + Ant), CPAH/ΣPAHs and IcdP/(IcdP + BghiP)) were found similar to those in coal burning smoke with a strong linear relationship (R² ≥ 0.85) between PAHs and fossil fuel derived n-alkanes, demonstrating that coal burning is the main source of n-alkanes and PAHs in the city, especially in winter due to house heating. Concentrations of the determined compounds in Baoji are much higher than those in Chinese mega-cities, suggesting that air pollution in small cities in the country is more serious and need more attention.     

人工增雨农业减灾技术研究

该项目在河南、山东、吉林3省建立了层状云人工增雨外场观测作业试验区，研制了层状云数值模式，结合分析得出了层状云人工增雨新的概念模型、相应的催化条件判据和作业指标；研制了催化条件的飞机实时识别技术和地面综合集成识别技术；建立了中尺度层状云系数值模拟预报的实时业务和省级的人工增雨综合技术系统（催化条件综合识别判据、实时监测识别技术、催化指标和作业决策系统等），提出了相应的作业流程。 在鄂西北（十堰市）对流云人工增雨作业试验区进行了大量外场观测和作业试验，用研制的三维对流云催化模式进行实时预报和催化数值试验，初步提出了催化的最佳部位、时机和剂量；开发应用了高成核率火箭；用雷达识别作业条件并建立了指挥作业的技术流程，因而大大减少了作业的盲目性，提高了对流云人工增雨作业的科学性。     

冰雹云雷达识别方法及防雹作业经验

总结了辽宁省40余年人工防雹工作积累的成果和经验,归纳了3种类型冰雹云的雷达回波特征以及利用回波形态、回波参量和综合指标对其进行雷达识别的方法,并结合冰雹云数值模拟结果,总结了对具有液态水累积区的雹云、对超级单体冰雹云和对多单体冰雹云实施人工防雹作业的一些经验,对于各地的科学防雹作业具有参考作用。     

Gust Front Statistical Characteristics and Automatic Identification Algorithm for CINRAD

Gust front is a kind of meso-and micro-scale weather phenomenon that often causes serious ground wind and wind shear. This paper presents an automatic gust front identification algorithm. Totally 879 radar volume-scan samples selected from 21 gust front weather processes that occurred in China between 2009 and 2012 are examined and analyzed. Gust front echo statistical features in reflectivity, velocity, and spectrum width fields are obtained. Based on these features, an algorithm is designed to recognize gust fronts and generate output products and quantitative indices. Then, 315 samples are used to verify the algorithm and 3 typical cases are analyzed. Major conclusions include： 1） for narrow band echoes intensity is between 5 and 30 dBZ, widths are between 2 and 10 km, maximum heights are less than 4 km （89.33%are lower than 3 km）, and the lengths are between 50 and 200 km. The narrow-band echo is higher than its surrounding echo. 2） Gust fronts present a convergence line or a wind shear in the velocity field;the frontal wind speed gradually decreases when the distance increases radially outward. Spectral widths of gust fronts are large, with 87.09% exceeding 4 m s-1 . 3） Using 315 gust front volume-scan samples to test the algorithm reveals that the algorithm is highly stable and has successfully recognized 277 samples. The algorithm also works for small-scale or weak gust fronts. 4） Radar data quality has certain impact on the algorithm.     

基于雷达三维组网数据的对流性地面大风自动识别

应用雷达三维组网数据和地面加密自动站风场资料,统计分析了对流性地面大风的6个主要雷达识别指标：风暴最大反射率因子、风暴最大垂直积分液态水含量、垂直积分液态水含量随时间变率、风暴最大反射率因子下降高度、风暴体移动速度和垂直积分液态水含量密度等参数。根据雷达识别指标和地面大风的相关程度,给出了识别指标的隶属函数和权重系数;采用不等权重法,建立了具有模糊逻辑的对流性地面大风识别方法。并将对流性地面大风的出现概率分为3级：当识别风暴单体的判据小于0.3时,出现对流性地面大风的概率小;当判据在0.3—0.5时,产生对流性地面大风的概率较大;当判据大于0.5时,出现对流性地面大风的概率很大。通过对河北省2012年6月21日线状风暴和2009年7月23日孤立单体风暴引发的灾害大风典型个例的识别效果检验,证明这种方法识别到的风暴单体跟踪效果良好,识别出的大风范围与实况风场基本吻合,命中率、虚警率和临界成功指数分别达81.8%、25.0%和64.3%,利用模糊逻辑原理建立对流性大风的识别算法是切实可行的。     

Observation of edge waves trapped on the continental shelf in the vicinity of Makurazaki Harbor, Kyushu, Japan

Sea-bottom pressure gauges were used to measure sea levels at two points on the shelf off the southern coast of Satsuma Peninsula, Kyushu, Japan. Spectral analysis of the observed records and the tide-gauge record of Makurazaki Harbor revealed several predominant common peaks. At the same time, the eigenmodes for the trapped waves on the shelf and inside Makurazaki Bay were obtained numerically using a two-dimensional model, and the periods and the spatial distribution of amplitudes of the proper modes were obtained. A comparison of the calculated modes with the periods and phase patterns of the observed peaks clarified that peaks with periods of 19.5, 16, 13.3, and 12.2 minutes in the shelf region were the modes of standing-edge waves, and the peak with the period of 16 minutes in Makurazaki Harbor was the fundamental mode of the harbor. Among the modes of standing-edge waves, the mode of the period 16 minutes on the shelf had nearly the same period as that of the fundamental mode of Makurazaki Harbor. An analysis of changes of spectral densities of these two modes confirmed that the fundamental mode of the Makurazaki Harbor was induced by this standing-wave mode.