A Reconstruction of Observed Profiles in the Sea East of Japan Using Vertical Coupled Temperature-Salinity EOF Modes

It is important to estimate hard-to-observe parameters in the ocean interior from easy-to-observe parameters. This study therefore demostrates a reconstruction of observed temperature and salinity profiles of the sea east of Japan (30°≈40°N, 140°≈150°E). The reconstruction was done by estimating suboptimal state from several values of the observed profiles and/or sea surface dynamic height (SDH) calculated from the profiles. The estimation used a variational method with vertical coupled temperature-salinity empirical orthogonal function (EOF) modes. Profiles of temperature and salinity in the subtropical region are effectively reconstructed from in situ temperature profile data, or sea surface temperature (SST) and SDH. For example, the analyzed temperature field from SST and SDH has an accuracy to within 1°C in the subtropical region. Salinity in the sea north of Kuroshio, however, is difficult to estimate because of its complex variability which is less correlated with temperature than in the subtropical region. Sea surface salinity is useful to estimate the subsurface structure. We also show the possibility that the estimation is improved by considering nonlinearity in the equation calculating SDH from temperature and salinity analysis values in order to examine the misfit between analysis and observation. Analysis using TOPEX/POSEIDON altimetry data instead of SDH was also performed. This revised version was published online in July 2006 with corrections to the Cover Date.     

Seakeeping assessment of fishing vessels in conceptual design stage

The main idea of this paper is to identify functional relations between seakeeping characteristics and hull form parameters of Mediterranean fishing vessels. Multiple regression analysis is used for quantitative assessment through a computer software that is based on the SQL Server Database. The seakeeping attributes under investigation are the transfer functions of heave and pitch motions and of absolute vertical acceleration at stern, while the ship parameters influencing motion dynamics have been classified into two groups: displacement (Δ) and main dimensions (L, B, T), coefficients that define the details of the hull form (C_WP, C_VP, LCB, LCF, etc.).Four multiple regression models having different parameter combinations are here investigated and discussed, giving way to the so-called ‘Simple Model’, ‘Intermediate Model’, ‘Enhanced 1 Model’ and ‘Enhanced 2 Model’. The obtained results are more than satisfactory for seakeeping predictions during the conceptual design stage.     

Preconditioned Optimizing Utility for Large-dimensional analyses (POpULar)

I present the derivation of the Preconditioned Optimizing Utility for Large-dimensional analyses (POpULar), which is developed for adopting a non-diagonal background error covariance matrix in nonlinear variational analyses (i.e., analyses employing a non-quadratic cost function). POpULar is based on the idea of a linear preconditioned conjugate gradient method widely adopted in ocean data assimilation systems. POpULar uses the background error covariance matrix as a preconditioner without any decomposition of the matrix. This preconditioning accelerates the convergence. Moreover, the inverse of the matrix is not required. POpULar therefore allows us easily to handle the correlations among deviations of control variables (i.e., the variables which will be analyzed) from their background in nonlinear problems. In order to demonstrate the usefulness of POpULar, we illustrate two effects which are often neglected in studies of ocean data assimilation before. One is the effect of correlations among the deviations of control variables in an adjoint analysis. The other is the nonlinear effect of sea surface dynamic height calculation required when sea surface height observation is employed in a three-dimensional ocean analysis. As the results, these effects are not so small to neglect.     

山东半岛东部,北部地区冬季冷流低云的探讨

本文主要对山东半岛东部、北部及渤海海峡地区的地方性天气－冷流低云，作了初步的分析。着重分析产生这种低云的环流特点及殊地理环境，分析归纳出这种低云的预测方法及判据。     

Coupling a two-way nested primitive equation model and a statistical SST predictor of the Ligurian Sea via data assimilation

A primitive equation model and a statistical predictor are coupled by data assimilation in order to combine the strength of both approaches. In this work, the system of two-way nested models centred in the Ligurian Sea and the satellite-based ocean forecasting (SOFT) system predicting the sea surface temperature (SST) are used. The data assimilation scheme is a simplified reduced order Kalman filter based on a constant error space. The assimilation of predicted SST improves the forecast of the hydrodynamic model compared to the forecast obtained by assimilating past SST observations used by the statistical predictor. This study shows that the SST of the SOFT predictor can be used to correct atmospheric heat fluxes. Traditionally this is done by relaxing the model SST towards the climatological SST. Therefore, the assimilation of SOFT SST and climatological SST are also compared.     

基于GIS空间统计分析的广电收视率评估研究——以北京市广电收视覆盖分析系统为例

本文将GIS空间信息统计分析手段应用于广播电视收视率的评估,以层次分析法为基础进行改进,采用抽样调查的方法确定权重,提出较为合理的基于空间信息统计方法的广电人口覆盖分布模型并加以软件实现。以北京市为研究区域,经导入多个版本人口数据实验,发现这种评估方式较好地修正了传统的仅仅按照面积评估覆盖率的评估模型,对于提高广电行业的信息化管理水平具有一定意义。     

北京市地铁暗挖施工沉降控制探讨

仅以北京市地铁4#线张自忠车站主体暗挖施工的沉降监控项目为研究背景,对穿越城市主干道的车站结构暗挖施工进行连续性的沉降变形监测;从地下构筑物暗挖施工的工序变化与地表沉降的关系进行试验研究.在预测基础上对获取的数据进行多层次的统计与分析,以研究其变形规律与稳定性,实现了信息化施工.     

中国西部环境与生态科学数据中心：面向西部环境与生态科学的数据集成与共享

国家自然科学基金委员会“中国西部环境与生态科学研究计划”自2001年启动以来,有力地推动了西部区域环境演化、陆地生态过程和人类活动对环境的影响等方面的研究,积累了丰富的基础数据。国家自然科学基金委员会“中国西部环境与生态科学数据中心”承担“西部计划”项目数据产出的收集、管理、集成,并面向西部环境与生态科学的各个领域提供科学数据服务。“西部数据中心”的总体框架是以“西部计划”和地球系统科学发展的科学需求为导向,建成以数据共享、知识积累、合作交流和数据科学为主要内容的集成平台,为“西部计划”多学科的交叉研究和成果的综合集成及地球科学发展的长远目标服务。以共建共享的指导思想集成针对西部环境与生态现状与变化的基础背景数据、环境与生态观测数据、模型数据集、数据工具及数据文档;并以完全与开放的数据共享原则为用户提供推送式数据服务。目前,“西部数据中心”已完成了数据共享平台、知识积累平台与合作交流平台的开发和集成,初步形成了科学数据平台,整理并发布了大量的西部环境生态科学所需的关键数据集。     

国土资源数据分析及其整合与集成

本文详细分析了国土资源数据特点、综合数据来源、产生方式和使用方法等方面,对数据进行了分类,比较了不同的数据格式。在此基础上,阐述了国土资源数据的集成框架,讨论了数据整合和集成的方法和流程,提出了数据整合与集成的三种框架问题。