TOPEX Constrained Solution of Mean Sea Surface by Joint Processing of TOPEX,ERS-1 and GEOSAT Altimetric Measurements

We present an improved crossover adjustment procedure to determine mean sea surface height using TOPEX, 35-day repeat phase ERS-1, Geosat, and 168-day repeat phase ERS-1 satellite altimeter data. The mean sea surface frame defined by the TOPEX data is imposed as certain constraints in our crossover adjustment procedure rather than held fixed as in some other procedures. The new procedure is discussed in detail. Equations are developed to incorporate the a priori information of Topex data as well as other satellite altimeter data. The numerical computation result shows that the rms crossover discrepancies are reduced by an order of 1 cm when the Topex data is not fixed. Furthermore, the computed mean sea surface is less noisy and more realistic than that computed by the traditional procedure.     

利用经验正交函数数据插值法重构东中国海叶绿素a质量浓度场

利用经验正交函数数据插值法(Data INterpolating Empirical Orthogonal Functions)重构由MODIS-Aqua卫星提供的三级产品叶绿素a质量浓度,得到了从2003—2009年东中国海叶绿素a质量浓度的月平均场。东中国海近岸叶绿素a质量浓度7 a平均值明显高于外海。对叶绿素a质量浓度季节平均场进行分析研究表明,东中国海叶绿素a质量浓度主要受长江径流、海表水温和季风的影响。对叶绿素a质量浓度异常场进行EOF分析表明,第一模态方差贡献为37.8％,空间分布显示,在长江口东北部出现叶绿素a质量浓度异常高值区,时间变化以半年和半年以下周期为主;第二模态方差贡献为21.4％,空间分布显示,在长江口东部出现叶绿素a质量浓度异常高值区,时间系数主要表现为年际变化。     

基于AutoCAD二次开发的矢量地形数据检查

基于CAD的矢量地形数据是一类基本的重要的基础地理数据，其应用十分广泛，同时也是各类地理信息系统重要的数据源，其属性精度、逻辑一致性是其重要质量元素，本文论述了基于AuotoCAD二次开发实现矢量地形数据计算机自动化高效检查的技术方法。     

评价储层含油性的热解参数校正方法及其应用

评价储层含油性时，热解参数Ｓ１＋Ｓ２是一项有效参数。其中Ｓ２相对稳定，Ｓ１则随样品类别或露置时间的不同而发生变化。岩心样品的随钻解分析值比较接近真实，岩屑和井壁取心样品的Ｓ１会明显损失。而目前多数探井心进尺少，大多为岩屑录井。     

A tool for site prioritisation of marine protected areas under data poor conditions

A challenge of managing data poor fisheries lies in overcoming uncertainties associated with a lack of information on biological and socio-economic conditions. This paper focuses on site prioritisation for marine protected areas, a process that usually imposes moderate to heavy demands on data, time, local expertise, and funds. A fuzzy logic decision support tool for zoning marine areas that is suitable for use in data poor conditions is developed. This tool, the Protected Area Suitability Index (PASI), assesses the suitability of sites for protection based on fishers’ preferences for that site and the site’s conservation value. Only eight input attributes are required to run the PASI, which operates on a series of heuristic rules to estimate a site suitability score that ranges from 0 to 10, where 10 indicates that a site is highly suitable for being protected from fishing. Sensitivity analysis reveals that the PASI is relatively robust, and produces reliable results even as the system is degraded by the loss of data. Eighteen actively fished sites and 11 sites that are closed to fishing are assessed to evaluate how well the PASI is able to distinguish differences between sites. The estimated scores are significantly different (p<0.05), whereby non-fished sites are scored as being more suitable for protection than fished sites. The PASI can be used as a decision support tool to facilitate systematic marine spatial management under data poor conditions, especially in the task of identifying suitable sites for protection.     

Discrimination of spurious self-correlation in nondimensionalized analyses of fluid dynamical data

Nondimensionalization of variables enables us to treat experiment data much more simply and efficiently by decreasing the number of variables. In some cases, trivial conclusions (which Kenney, 1982, called spurious self-correlation) result from a formal application of dimensional analyses. In contrast, in some cases fully significant conclusions can be derived. We first discuss how to construct nondimensional variables retaining the physical meanings of variables. We then propose simple and efficient methods, especially the use of “spurious triangle (SpT)”, to discriminate between significant conclusions and spurious self-correlations in the analysis of nondimensionalized variables.     

Deterministic non-linear wave prediction using probe data

The accurate prediction of non-linear sea states represents a great challenge, with a number of applications in oceanography, marine engineering, security of people and marine transportation, etc. In this paper, we report on the development of two efficient deterministic prediction models for 2D irregular wave-fields. These models are based on the exploitation of wave elevation time series given by one or more probes and on the use of two different numerical models for the wave-fields simulation. Two effective data assimilation processes are developed to improve the wave-field estimates obtained from the study of one or several probe signals, so that we obtain proper initial conditions for the forecast. The assimilation schemes have been validated on the reconstruction of model-generated observations and accurate predictions of the corresponding synthetic wave-fields evolution have been obtained.     

ArcSDE压缩二进制存储格式的研究与改进

ArcSDE压缩二进制是ArcSDE在关系型数据库(RDBMS)中保存地理信息中二进制几何图形的存储格式,这种格式采用将浮点几何数据整形化的方法进行数据压缩,减少了数据存储空间,同时也加快了数据在网络上传输的速度。本文通过对ArcSDE压缩二进制存储格式的研究与分析,指出了这种格式在存取和处理效率上的不足之处,并提出了更为高效的改进方法。     

2015年济南地区雾霾天气过程大气物理量特征初步分析

文中对比分析了2015年29个雾、霾及雾霾混合天气过程中,章丘探空站L波段探空雷达和山东省气象局院内德国14通道地基微波辐射计观测的温度资料。对观测数据实施了质量控制,检验了精度和可信度,统计分析了宏观物理参量特征和日变化规律。针对雾、霾及雾霾天气过程各选取了一个个例进行分析,分析了大气中PM_2.5、PM₁₀、SO₂、NO₂、O₃、CO含量的变化情况,分析了相对湿度、液态水路径和综合水汽含量等的变化情况。结果表明:两种观测数据一致性较好,拟合优度高于0.97;贴地逆温层存在一定的季节变化,悬垂逆温层存在一定的差异,逆温层的变化、污染参量变化与雾霾的形成有密切关系;不同天气背景对大气物理参量有较大的影响,PM₁₀、AQI（空气质量指数）和CO均在相同时间段出现峰值,有明显的起伏;CO峰值雾霾天气中尤为明显,由早到晚随时间峰值逐渐增大,雾天和霾天峰值较小,雾霾天气明显大于雾天或霾天。     

Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing

As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral Imaging (HSI). Consequently, a novel Folded-PCA is proposed, where the spectral vector is folded into a matrix to allow the covariance matrix to be determined more efficiently. With this matrix-based representation, both global and local structures are extracted to provide additional information for data classification. Moreover, both the computational cost and the memory requirement have been significantly reduced. Using Support Vector Machine (SVM) for classification on two well-known HSI datasets and one Synthetic Aperture Radar (SAR) dataset in remote sensing, quantitative results are generated for objective evaluations. Comprehensive results have indicated that the proposed Folded-PCA approach not only outperforms the conventional PCA but also the baseline approach where the whole feature sets are used.