基于交互式分割技术和决策级融合的SAR图像变化检测

为免去去除斑点噪声的预处理操作及克服选择分布模型的限制,本文结合差异图的特点和一种不涉及分布模型的交互式分割方法,产生不同“种子点”下的变化检测结果后,再利用投票策略对其进行决策级的融合给出最终的变化检测结果。分割中,将每个像素的特征设为差异图及由静态小波变换分解差异图再丢弃高频系数后重构得到的各层表示中,其对应位置上的灰度值构成的矢量。此特征及决策级融合的策略使本文的变化检测技术对SAR图像中的斑点噪声具有一定的鲁棒性。在无需对SAR图像做预处理的情况下,对真实SAR图像数据集的变化检测结果,其效果优于其他相关技术的。     

基于感兴趣区域在区域层面上决策的SAR图像变化检测的方法研究

在现有的变化检测方法中,针对差分图像的方法得到了广泛的应用,但现有该框架下的技术都在在像素层面上决策产生的变化检测结果,这易使结果中存在诸如有噪声般散落的杂点、连通区域内有孔洞及边缘不平滑等缺陷。为此,本文给出一种在区域层面上决策生成变化检测结果的技术,其核心为抽取并处理感兴趣区域,而关键在于获取合适的抽取感兴趣区域的标签和如何在区域层面上生成变化检测结果。为使抽取的感兴趣区域包含几乎所有的变化类信息,我们用平稳小波变换和模糊C-均值(Fuzzy C-Means, FCM)算法分两步获取抽取感兴趣区域的标签;为在区域层面上生成变化检测结果,我们依据标签搜索感兴趣区域内所有的连通区域,并把每个连通区域看作为一个数据点,再由阈值技术处理这些数据点生成最终的变化检测结果。对真实SAR图像数据集的变化检测结果表明,其主观效果和客观性能都优于其他相关技术的。     

复杂战场电磁态势三维可视化研究

战场电磁环境的透明化及其态势可视化是一个急需解决的问题。分析复杂战场电磁环境可视化构建内容,针对不同军用电磁辐射特点,介绍体绘制和基于粒子系统的电磁态势可视化方法,实现地理环境和电磁环境的一体化显示,为指挥员提供及时、全面动态的电磁态势信息。     

土地变更调查遥感监测中QuickBird遥感影像处理方法的讨论

遥感技术在全国土地变更调查工作中得到广泛应用,主要通过从最新获取的遥感影像上提取所需信息,再对比之前的数据库信息进行分析得到一定时间阶段的土地变更情况。文章针对在全国土地变更项目中使用较广的QuickBird影像,以QuickBird影像的全色影像、多光谱影像、前时相基础底图及高程数据为基础,对影像进行拼接处理、降位处理、数据融合、影像彩色合成,以及几何纠正等处理过程,并通过实验确定一个最佳处理流程。最终得到地类清晰可见、容易判读的影像,为之后的影像信息提取打好了基础。     

北斗RDSS用户机测试系统指标监测校准方案设计

北斗卫星无线电测定业务(RDSS)用户机是我国北斗卫星导航试验系统的应用终端,测试系统可为用户机研制和使用提供可靠保障。鉴于RDSS体制的特殊性,测试系统的重要性能指标包括:用户机入站信号测量指标、用户机时延测量指标、以及用户机测试系统出站信号。为确保测试系统测试标准的统一,需要对测试系统的这些指标进行监测和校准。通过对普通用户机提出技术改进设计,为测试系统校准提供可行性方案,实现测试系统指标向标准测试仪器的溯源。     

A View of Earth System Model Development

This paper gives a definition of earth system model and shows three development phases of it, including physical climate system model, earth climate system model, and earth system model, based on an inves- tigation of climate system models in the world. It provides an expatiation on the strategic significance of future development of earth system model, an introduction of some representative scientific research plans on development of earth system model home and abroad, and a review of its status and trends based on the models of the fourth assessment report （AR4） ofthe Intergovernmental Panel on Climate Change （IPCC）. Some suggestions on future development of earth system model in China are given, which are expected to be helpful to advance the development.     

Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004)

We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework.     

与广东持续性干旱事件有关的两类海温异常型

利用1961\_2005年广东省86个常规地面观测站降水资料、 ERSST全球月平均海温资料以及NCEP/NCAR 大气多要素再分析资料, 分析了广东跨季节持续性干旱事件(下称广东干旱)与海温外强迫之间的关系及其物理过程。结果表明, 与广东干旱相关的海温异常有赤道东太平洋海温负异常型（即La Nia型）、 赤道中太平洋海温正异常型两种类型。约在1975年之前, 广东干旱基本上属于La Nia型; 在1975年之后, 除了La Nia型外, 赤道中太平洋海温正异常型干旱出现概率明显增加。可见, 在全球气候变暖的背景下, 广东干旱成因更复杂。近几年来广东干旱频繁发生, 与赤道中太平洋海温正异常型出现概率增加有关。La Nia型、 赤道中太平洋海温正异常型与广东干旱之间都存在物理上的联系, 不同的海温异常型对广东干旱影响的物理过程有所不同, 即使同一种海温异常型对不同季节的影响过程也有所不同, 然而所有物理过程最终都是通过改变和削弱降水所需的动力条件或水汽条件, 从而造成局地干旱的发生。     

陆面气温资料的一种推广三维变分同化方法及其在简化情形的检验

发展了一种同化窗口为1个月的陆面气温推广三维变分资料同化方法,并建立了逐日资料到月平均资料的观测算子.作为对该同化方法的初步检验,以2000年1月中国陆地区域时间分辨率低、空间分辨率较高且在高原地区与台站观测资料比较接近的CRU(Climate Research Unit)月平均陆面气温资料作为观测,以时间分辨率高、空间分辨率较低且在高原地区与台站资料有较大误差的NCEP逐日气温资料作为背景场,采用背景误差协方差矩阵(简称B矩阵)的两种简化对角形式进行同化试验,得到高时空分辨率的格点陆面气温分析场,并进行对比分析和均方根误差检验.结果显示,由两种简化形式得到的逐日资料和月平均资料的质量均得到改善,在我国东南和中部大部分地区与台站观测基本一致,在青藏高原、新疆等台站稀疏地区与地形对应良好,为M-SDGVM模型或其他陆面过程模式高质量陆面气象驱动场的准备提供了一种新的手段.尤其在B矩阵中考虑了方差逐日变化的简化形式所得到的分析场整体上要优于不考虑的,这为最终在B矩阵中考虑协方差(即对推广三维变分同化不作简化)进一步改进同化效果打下了基础.