基于环境因子的东、黄海鲐鱼剩余产量模型及应用

海洋环境是影响中上层鱼类资源的重要因子之一。本文根据东、黄海鲐鱼推断的产卵场海域平均海表面温度(SST)和适宜SST(15~21℃)范围,假设环境容量K和内禀自然增长率r与环境因子呈线性关系,建立了基于SST的剩余产量模型(Environmentally Dependent Surplus Produc-tion Model,EDSP模型),分析环境因子对东、黄海鲐鱼资源的影响。用Akaike信息法则(Akaike Information Criterion,AIC)对不同模型进行了比较。研究结果表明,产卵场适宜SST面积与平均SST成反比,基于产卵场适宜SST面积的ESDP模型为最优模型,并证实产卵场适宜SST面积大小与Kr、呈线性关系,进而对鲐鱼繁殖成功率和补充量的影响。研究结果可为鲐鱼资源量的预测和科学管理提供参考。     

地图扫描数字化系统误差分析及对策探讨

较全面地分析了地图扫描数字化的系统误差来源及影响。在系统误差纠正模型的选取过程中所采用的数据存在粗差,纠正模型的误差和模型数据的粗差是值得讨论的问题。利用粗差拟准检定法探测粗差准确的优点和判断函数模型的准则理论,提出顾及粗差的系统误差回归函数分析,先确定回归函数,在此基础上进行粗差探测与剔出,然后再进行回归分析,以达到消弱系统误差最优的目的。     

AIC与BIC在亲体-补充量模型选择中的应用及比较

由于渔业资源评估中补充量的剧烈变动、亲体量的测量误差以及时间序列的偏差常常使亲体一补充量(SR)关系模型的确定存在很大偏差问题。本文以7种SR(Stock-Recruitment)模型的模拟数据作为观测数据，研究了AIC(Akaike Information Criterion)与BIC(Bayesian Information Criterion)在SR模型选择中的应用。作为例证，文中采用AIC和BIC对8组实际的SR数据进行了SR模型的选择，并对其结果进行了比较。参数的估计方法为最大似然法(Maximum likelihood method)。结果表明，AIC和BIC在SR模型选择中是有效的。但是，对于嵌套模型，BIC可能比AIC更有效。     

海洋测线网平差中拟稳测线系统误差的影响

分析了海洋测线网平差中拟稳测线系统误差对参数解和系统误差检验的影响，提出了选择拟稳测线的t检验选取法。并通过仿真分析了拟稳测线系统误差的影响和选择方法的有效性。     

海洋重力测量中系统误差的补偿研究

本文在分析总结海洋重力测量中的主要误差源基础上,从纯数学角度建立起能够反映海洋重力观测中系统误差变化规律的误差模型及其相应的测线网平差模型,分别提出两种补偿系统误差的解算方法,即自检校平差法和验后补偿法,并详细讨论了求解过程中的秩亏现象、误差模型的选择、附加参数权的确定以及系统误差补偿效果的显著性检验等一系列应用中的具体问题,最后本文使用一个实际观测网数据验证了两种补偿方法的有效性和可靠性。     

基于空间自相关的阿根廷滑柔鱼CPUE标准化研究

CPUE的观测往往不是独立的,而是存在空间相关性的。但是,大多数的CPUE标准化方法通常都假设名义CPUE在空间上是相互独立的。为此,本研究以西南大西洋阿根廷滑柔鱼为例,采用2000-2014年1-5月中国大陆鱿钓生产统计数据以及对应的海表温度和叶绿素浓度数据,选择广义线性模型（general linear model,GLM）为基础模型,将空间自相关加入到GLM中,比较标准GLM和4种加入空间自相关的空间GLM的CPUE标准化。根据最小信息准则（Akaike Information Criterion,AIC）及贝叶斯信息准则（Bayesian Information Criterion,BIC）,空间自相关的GLM的CPUE标准化结果优于标准GLM,其中指数模型的CPUE标准化结果最佳。同时,标准GLM与空间自相关的GLM相比,存在精确度过高估计的问题。因此,在CPUE标准化中,应充分考虑空间自相关这一因素。     

应用栖息地适宜性指数研究海州湾小黄鱼的空间分布特征

为研究海州湾小黄鱼(Larimichthys polyactis)的空间分布特征。于2011、2013及2014年的春、秋两季在海州湾进行了渔业资源与栖息环境综合调查,根据调查结果和同步采集的底层水温、底层盐度及水深数据,构建了海州湾小黄鱼的栖息地适宜性指数(Habitat suitability index,HSI)模型,利用赤池信息量准则值(Akaike information criterion,AIC)检验算术平均法(AM)和几何平均法(GM)的拟合度,对模型输出结果进行分析和比较。研究表明:除秋季的水深外,其余各环境因子与适宜性指数间均呈现明显的正态或偏正态关系。通过AIC检验发现,春季用几何平均法拟合栖息地适宜性指数的效果较好,而秋季用算术平均法拟合的效果较好。小黄鱼的最适栖息地存在明显的年间变化和季节变化,春季HSI高值区域多集中于34.7°N,120.2°E附近海域;秋季HSI高值中心位于34.5°N~34.7°N,120.0°E~120.2°E附近海域。研究结果表明,栖息地适宜性指数模型能够较好地反映海州湾小黄鱼栖息地的分布和变化情况,在单因素SI极小值出现频率较高的情况下,算术平均法要优于几何平均法。     

基于变化信息量的海图改版需求评估

在对海图信息量概念阐述基础上,着重介绍了语义、位置和注记信息量及其变化信息量的量测方法,并提出基于信息量变化程度的海图改版需求评估方案。实例分析表明,该评估方法可为海图改版提供有力的决策依据。     

海面非对称航迹对海底定位精度影响分析

海底基准点定位精度和可靠性均与定位几何构型有关。为探讨海面非对称走航条件下声学测距误差对海底基准定位的影响,从声速误差影响函数出发,构建了顾及定位精度与系统误差影响的海面-海底控制网综合优化准则。考虑声速场常系统误差和声速场时变系统误差,开展了非对称走航观测仿真实验。结果表明：关于海底基准点中心对称的海面控制网,可以将声速场常系统误差的影响降到最低,而声速时变系统误差对海底基准点定位的影响,会随着定位构型几何强度的衰减在水平、高程分量间相互转化。     

厦门港潮汐水流及浑水悬沙整体模型设计

厦门港潮汐水流及浑水悬沙整体物理模型于1989年建成后，已进行了不少有意义的试验研究工作，取得较好效果。本文主要介绍模型的设计思想、相似准则、浑水悬沙相似理论的探讨、模型设计方法以及模型沙的选择等。     

On the Compensation of Systematic Errors in Marine Gravity Measurements

Based on an analysis of the source of errors in marine gravity measurements, an error model, firstly, is constructed mathematically which can characterize the change of systematic errors and with which a new crossover adjustment model is presented in this paper. Then, two methods of compensating the systematic errors are proposed, i.e., the self-calibrating adjustment and the a-posteriori compensation. Some questions involved in solving the adjustment problem, such as the rank deficiency, the choice of error model, the weighting of model parameters and the significance test of compensation efficiency, etc., are discussed in detail. Finally, a practical survey network is used as a case study to test the efficiency and reliability of the two compensation methods.     

On the Compensation of Systematic Errors in Marine Gravity Measurements

Based on an analysis of the source of errors in marine gravity measurements, an error model, firstly, is constructed mathematically which can characterize the change of systematic errors and with which a new crossover adjustment model is presented in this paper. Then, two methods of compensating the systematic errors are proposed, i.e., the self-calibrating adjustment and the a-posteriori compensation. Some questions involved in solving the adjustment problem, such as the rank deficiency, the choice of error model, the weighting of model parameters and the significance test of compensation efficiency, etc., are discussed in detail. Finally, a practical survey network is used as a case study to test the efficiency and reliability of the two compensation methods.     

Using McDaniel's model to represent non-Rayleigh reverberation

Reverberation in low-frequency active sonar systems operating in shallow water has often been observed to follow non-Rayleigh statistical distributions. McDaniel's model, generalized to allow noninteger valued parameters, has shown promise as being capable of accurately representing real data with a minimal parameterization. This paper first derives an exact analytical expression for the cumulative distribution function (CDF) of the generalized McDaniel model and then compares it with numerical inversion of the characteristic function. Both methods are seen to provide adequate and equivalent precision; however the characteristic function inversion method is significantly faster. The latter CDF evaluation technique is then applied to the analysis of simulated and real data to show that, when minimal data are available, McDaniel's model can more accurately represent a wide variety of non-Rayleigh reverberation than the K or Rayleigh mixture models. This result arises from the generality of McDaniel's model with respect to the K-distribution (i.e., the K-distribution P_fa estimate can be dominated by model mismatch error) and to its compact parameterization with respect to the Rayleigh mixture (i.e., the Rayleigh mixture model P_fa estimate is usually dominated by parameter estimation error)     

近海生态系统动力学研究中浮游植物采样及分析策略

从理论上对于海洋游植物生态系统动力学中的监测与调查方法进行初步探讨。对浮游植物的采样站位的时空选择、采样方式、样品分析、数据同化和数据库形成等方面进行了讨论。建议在最小化、完整化和标准化的原则下进行近海浮游植物生态系统动力学的策略分析及采样设计，为更好地进行中国海域浮游植物的调查和监测提供理论依据。     

A Modified Quasi-Static Method for CALM System Analysis

The quasi-static analysis method introduced by API RP 2P is well known and accepted as a very useful mooring analysis method. In the early design stage, this method is widely used for preliminary analysis and mooring parameter selection. However, the quasi-static method of API RP 2P is developed for single-floating-body condition, i. e., only one floating body is considered in the computation procedure. Difficulties arise when it is used for the analysis of a CALM system, which is comprised of two floating bodies (tanker and buoy). This paper presents an analysis procedure for a two-floating-body system based on the quasi-static procedure of API RP 2P with some modifications reflecting special characteristics of the CALM system. Finally, the analysis results of a CALM system are given to illustrate the use of this procedure.     

Accuracy Evaluation of A Diagnostic Test by Detecting Outliers and Influential Observations

Logit regression analysis is widely applied in scientific studies and laboratory experiments, where skewed observations on a data set are often encountered. A number of problems with this method, for example, oudiers and influential observations, can cause overdispersion when a model is fitted. In this study a systematic statistical approach, including the plotting of several indices is used to diagnose the lack-of-fit of a logistic regression model. The outliers and influential observations on data from laboratory experiments are then detected. Specifically we take account of the interaction of an internal sohtary wave （ISW） with an obstacle, i.e., an underwater ridge, and also analyze the effects of the ridge height, the lower layer water depth, and the potential energy on the amplitude-based transmission rate of the ISW. As concluded, the goodness-of-fit of the revised logit regression model is better than that of the model without this approach.     

Different approaches to model error formulation in 4D-Var: a study with high-resolution advection schemes

All numerical models are imperfect. Weak constraint variational data assimilation ( VDA ), which provides a treatment of the modelling errors, is studied; building on the approach of Vidard et al. (Tellus, 56 A, pp. 177–188, 2004). The evolution of model error ( ME ) is modelled using ordinary differential equations, which involve a scalar parameter. These approaches were tested using different high-resolution advection schemes. The first set of experiments were constructed to see if it is possible to account for (numerical) discretization error within such a framework. In other set of experiments, a systematic source of modelling error was introduced by deliberately specifying an incorrect value for the Coriolis parameter in the model. Results with observational state at half of the model state resolution, are also presented. We also discuss a method of estimating the scalar parameter in the ME through VDA . In all cases, the inclusion of ME provides reduction in forecasting errors. Also, our experiments indicate that different settings of the model (e.g. using different high-resolution advection schemes) would need different ME formulation. Results presented in this paper could be used to formulate sophisticated ME forms to account for systematic errors in higher dimensional models with complex advection schemes.     

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.