High-resolution simulation of upper-ocean submesoscale variability in the South China Sea: Spatial and seasonal dynamical regimes

Submesoscale processes in marginal seas usually have complex generating mechanisms, highly dependent on the local background flow and forcing. This numerical study investigates the spatial and seasonal differences of submesoscale activities in the upper ocean of the South China Sea (SCS) and the different dynamical regimes for sub-regions. The spatial and seasonal variations of vertical vorticity, horizontal convergence, lateral buoyancy gradient, and strain rate are analyzed to compare the submesoscale phenomenon within four sub-regions, the northern region near the Luzon Strait (R1), the middle ocean basin (R2), the western SCS (R3), and the southern SCS (R4). The results suggest that the SCS submesoscale processes are highly heterogeneous in space, with different seasonalities in each sub-region. The submesoscale activities in the northern sub-regions (R1, R2) are active in winter but weak in summer, while there appears an almost seasonal anti-phase in the western region (R3) compared to R1 and R2. Interestingly, no clear seasonality of submesoscale features is shown in the southern region (R4). Further analysis of Ertel potential vorticity reveals different generating mechanisms of submesoscale processes in different sub-regions. Correlation analyses also show the vertical extent of vertical velocity and the role of monsoon in generating submesoscale activities in the upper ocean of sub-regions. All these results suggest that the sub-regions have different regimes for submesoscale processes, e.g., Kuroshio intrusion (R1), monsoon modulation (R2), frontal effects (R3), topography wakes (R4).     

覆盖中国沿海地区的精细化台风风暴潮模型的研究及适用

精细化风暴潮预报是目前风暴潮预报重点发展方向之一,本文首次建立起了一个覆盖整个中国沿海地区的精细化台风风暴潮数值模型,克服了以往分区域数值模型的不足,该模型在中国沿海地区的分辨率达到300m左右。模型采用了并行计算,并对2012年和2013年灾害性台风风暴潮过程进行了数值检验,计算精度和计算所用时间都能够满足业务化运行的要求。本文同时还根据中国气象局、美国国家气象局等5家主要台风预报机构给出的24h台风预报,对2013年度灾害性台风风暴潮过程进行了24h数值预报检验,检验结果表明:根据中国气象局台风登陆前24h预报可以得到更准确的风暴潮预报结果,其预报结果优于其他各家预报结果。该结论可以为今后的台风风暴潮预报中台风路径的选取提供重要的参考。     

国内外雾预报技术研究进展

大雾是引起低能见度的主要天气现象.提高雾的预报技术水平是确保交通安全的重要措施.从统计和数值预报两个方面,回顾了过去几十年来国内外在雾预报技术上的主要研究进展,并总结了各种方法的特点及存在的缺陷.在某些情形下,新统计方法的应用提高了雾的预报准确率,但仍然无法摆脱统计方法本身的缺陷.相比较而言,数值模式在大雾预报方面具有更广泛的应用和更大的潜力.在目前的计算机水平下,使用高分辨率的一维雾模式与中尺度天气模式相结合的方法,在一定程度上可以提高雾的预报准确率,该方法在大雾易发区的机场及高速公路沿线具有重要的应用价值.随着计算机能力的不断提高,包含大雾形成和演变的各种复杂过程、具有先进资料同化过程的高分辨率三维雾模式,以及集合数值预报系统将是未来的发展方向.     

Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping

Despite the high richness of information content provided by airborne hyperspectral data, detailed urban land-cover mapping is still a challenging task. An important topic in hyperspectral remote sensing is the issue of high dimensionality, which is commonly addressed by dimensionality reduction techniques. While many studies focus on methodological developments in data reduction, less attention is paid to the assessment of the proposed methods in detailed urban hyperspectral land-cover mapping, using state-of-the-art image classification approaches. In this study we evaluate the potential of two unsupervised data reduction techniques, the Autoassociative Neural Network (AANN) and the BandClust method – the first a transformation based approach, the second a feature-selection based approach – for mapping of urban land cover at a high level of thematic detail, using an APEX 288-band hyperspectral dataset. Both methods were tested in combination with four state-of-the-art machine learning classifiers: Random Forest (RF), AdaBoost (ADB), the multiple layer perceptron (MLP), and support vector machines (SVM). When used in combination with a strong learner (MLP, SVM) BandClust produces classification accuracies similar to or higher than obtained with the full dataset, demonstrating the method’s capability of preserving critical spectral information, required for the classifier to successfully distinguish between the 22 urban land-cover classes defined in this study. In the AANN data reduction process, on the other hand, important spectral information seems to be compromised or lost, resulting in lower accuracies for three of the four classifiers tested. Detailed analysis of accuracies at class level confirms the superiority of the SVM/Bandclust combination for accurate urban land-cover mapping using a reduced hyperspectral dataset. This study also demonstrates the potential of the new APEX sensor data for detailed mapping of land cover in spatially and spectrally complex urban areas.     

耦合GF-2遥感影像与街景影像的广州市城中村识别

及时准确地获取城中村的空间分布及其环境质量信息对于优化城市空间、改善人居环境具有重要意义。本文以广州市越秀区为例,提出了耦合GF-2高分遥感影像和百度街景影像的城中村识别方法。首先,从街景影像中提取越秀区的街道空间品质特征;其次,在对高分遥感影像预处理并进行多尺度分割的基础上计算光谱、形状、纹理、场景特征和建筑结构5类共计23个特征;最后,融合两种影像的特征用于构建随机森林分类器进行城中村识别。结果表明,基于高分影像和基于街景影像的城中村识别整体精度分别为94.5%和85.7%,Kappa系数分别为0.58和0.31,而两者融合后的分类精度和Kappa系数为96.1%和0.67;其中基于街景影像获取的度量街道空间品质的5个指标贡献了31.6%的特征重要性。鸟瞰视野高分影像和人本视角街景影像提供的信息综合互补,构建了更有区分度的特征空间,减少了城中村的错分现象。本文证实了高分影像和街景影像在特征尺度的融合提升了城中村识别精度。街景影像中的信息可以融入到高分遥感影像等数据源中,辅助进行城中村等非正规居住空间的识别。     

基于高分辨率PS-DInSAR的城郊地表沉降监测

随着城市的扩张,大型城市的郊区逐步成为新的工业生产及居民中心,为满足生产及生活用水需求,地下水被大量开采,进而导致地表沉降。目前对地表沉降的监测多集中在城市区域,城郊的地表沉降较少受到关注。本文提出使用高分辨率永久散射体（PS）雷达盖分干涉（PS-DInSAR）技术对大型城市郊区的沉降进行监测。选取某市城郊为实验区,使用覆盖该区的16幅高分辨率TerraSAR影像为数据进行PS-DIn-SAR沉降建模和解算,获取了谈区大范围的高分辨率沉降信息。谈市效区最大年沉降速度达到62mm/a（即年沉降量）,平均沉降速度为24mm/a。分析表明,该区域的不均匀沉降与地下水的开采密切相关。     

高分辨率图像辅助提取高光谱图像端元

现有的端元提取算法大多是基于凸面单形体假设,对于非单一地物类型,利用这些端元进行丰度反演将会影响混合像元分解精度。本文提出一种利用高分辨率图像判断高光谱像元内是否为同一类型地物的方法。首先,利用图像分割程序对高分辨率图像进行分割,得到光谱均一的斑块矢量图,并叠加到高光谱图像上;然后,通过空间关系分析找出斑块内的高光谱像元,称其为准端元;最后,利用端元提取算法在这些准端元中进行端元提取。实验结果表明,该方法将端元提取结果的误差降低了20%左右。     

涠西南凹陷涠洲10-3油田及围区流三段沉积相研究

为了促进涠西南凹陷涠洲10-3油田及围区流三段油藏的滚动勘探开发,开展了油区沉积相精细研究。利用岩芯、录井、测井和地震等资料,在识别不同级别层序界面和湖泛面的基础上,将涠洲10-3油田及围区流三段划分为2个长期基准面旋回和8个中期基准面旋回,建立了高分辨率层序地层对比格架。在岩芯描述基础上,进行单井沉积相、连井沉积相、平面沉积相分析,认为研究区流三段发育冲积扇、扇三角洲和湖泊3种沉积相,并系统划分了沉积微相类型。探讨了碎屑流沉积的相带归属,将发育在河道中的碎屑流沉积划分为河道型碎屑流沉积,将单独产出的碎屑流沉积根据沉积环境不同划分为水上非河道化碎屑流沉积和水下非河道化碎屑流沉积。最后对各中期基准面旋回的沉积相展布特征和演化规律进行了探讨,并建立了符合研究区的沉积相模式。     

改进角度纹理特征提取高分辨率遥感影像带状道路

针对目前高分辨率遥感影像的道路自动提取算法研究中的不足,该文提出了一种基于并行角度纹理特征的半自动道路提取算法:用户输入完成道路中心线上的起始点、道路方向、道路宽度等初始化工作,利用并行角度纹理特征获取道路前进方向,用抛物线参数方程构建道路轨迹模型来预测道路轨迹点,使用角度纹理特征值构建的紧质度系数和抛物线的曲率变化来约束道路轨迹点,验证失败则转入手工跟踪;往复执行以提取道路中心线。试验证明,本算法是一种稳健的道路半自动提取算法。