1/32° real-time global ocean prediction and value-added over 1/16° resolution

A 1/32° global ocean nowcast/forecast system has been developed by the Naval Research Laboratory at the Stennis Space Center. It started running at the Naval Oceanographic Office in near real-time on 1 Nov. 2003 and has been running daily in real-time since 1 Mar. 2005. It became an operational system on 6 March 2006, replacing the existing 1/16° system which ceased operation on 12 March 2006. Both systems use the NRL Layered Ocean Model (NLOM) with assimilation of sea surface height from satellite altimeters and sea surface temperature from multi-channel satellite infrared radiometers. Real-time and archived results are available online at http://www.ocean.nrlssc.navy.mil/global_nlom. The 1/32° system has improvements over the earlier system that can be grouped into two categories: (1) better resolution and representation of dynamical processes and (2) design modifications. The design modifications are the result of accrued knowledge since the development of the earlier 1/16° system. The improved horizontal resolution of the 1/32° system has significant dynamical benefits which increase the ability of the model to accurately nowcast and skillfully forecast. At the finer resolution, current pathways and their transports become more accurate, the sea surface height (SSH) variability increases and becomes more realistic and even the global ocean circulation experiences some changes (including inter-basin exchange). These improvements make the 1/32° system a better dynamical interpolator of assimilated satellite altimeter track data, using a one-day model forecast as the first guess. The result is quantitatively more accurate nowcasts, as is illustrated by several model-data comparisons. Based on comparisons with ocean color imagery in the northwestern Arabian Sea and the Gulf of Oman, the 1/32° system has even demonstrated the ability to map small eddies, 25–75 km in diameter, with 70% reliability and a median eddy center location error of 22.5 km, a surprising and unanticipated result from assimilation of altimeter track data. For all of the eddies (50% small eddies), the reliability was 80% and the median eddy center location error was 29 km. The 1/32° system also exhibits improved forecast skill in relation to the 1/16° system. This is due to (a) a more accurate initial condition for the forecast and (b) better resolution and representation of critical dynamical processes (such as upper ocean – topographic coupling via mesoscale flow instabilities) which allow the model to more accurately evolve these features in time while running in forecast mode (forecast atmospheric forcing for the first 5 days, then gradually reverting toward climatology for the remainder of the 30-day forecast period). At 1/32° resolution, forecast SSH generally compares better with unassimilated observations and the anomaly correlation of the forecast SSH exceeds that from persistence by a larger amount than found in the 1/16° system.     

组合预测模型在上海内河港口吞吐量预测中的应用

建立时间序列和二元线性回归的组合预测模型,对上海内河港口2010年、2015年和2020年的货物吞吐量水平进行了预测。研究发现,组合预测模型相比单个预测方法具有较高的精度,能够较准确地预测上海内河港口货物吞吐量。     

基于改进BP神经网络的船舶操纵性能预报

以某单桨大型船舶在海上的回转性能为例，探讨了应用改进的BP神经网络（Back-pmpagation Neural Network）建立船舶操纵性预报数学模型的方法，并利用matlab语言对其进行了仿真。研究结果表明，改进的BP算法有更快的收敛速度和更好的计算精度。     

铁路噪声预测计算方法

根据声学基本理论和有关有限长运动线声源指向性、等效时间等声学特性研究成果,结合铁路噪声的特点,总结了比例法和模式法两种主要的噪声预测方法,并给出了相应的计算公式,可供铁路建设项目环境影响评价中预测铁路噪声时参考。     

帆板航速预测计算模型探讨

在帆板运动平衡方程的基础上建立帆板的航速预测,依据最小阻力、最大推力原理求得各条航线上的最大船速,介绍帆板航速预测的计算方法,为帆板航速预测与航线选择的进一步研究做准备。     

安全装备管理分析系统

机务段管理是一个系统工程 ,加强科学管理及增加高新技术硬件设施在安全生产上的投入 ,并用先进的管理科学理论对影响安全的要素进行认真细致的分析、评价及预测 ,从而实现机务段安全生产最优化管理     

公路隧道施工超前地质预报技术方法研究现状综述

本文将公路隧道施工超前地质预报技术方法分为地质分析法、现场测试法、地球物理方法和数值模拟法等4大类,系统阐述了国内外公路隧道施工超前地质预报技术方法的研究现状,并在某些方面提出了一些新的见解。     

波浪弯矩设计值与长期预报理论计算值的比较

本文分析了国际船级社协会（ＩＡＣＳ）纵向强度标准Ｓ１中，波浪弯矩值成采用ＩＡＣＳＷＰ／Ｓ委员会推荐的海浪谱和海况统计资料条件下的长期预报理论计算值的关系，大量计算实践表明后者总体上要比前者大１．５倍。文中建议为了把长期技术应用于船体波浪弯的直接设计计算，对上述预报过程可取１０概率水平时地长期预报值作为船体波浪湾矩设计值。     

向量时序SVM-AR模型在公路软土地基沉降中的预测研究

为提高公路软土地基沉降预测的精度,提高工程的安全性,文中分别探讨了SVM模型和时序AR模型适用范围和特点,并结合2类模型各自优点提出SVM-AR模型。该模型用SVM模型预测趋势沉降量,用AR模型预测随机沉降量,然后组合获得预测沉降量。工程实例表明,SVM-AR比SVM模型预测结果更为准确,更好地反映公路软土地基沉降过程。