辽宁省滨海城市“十二五”国内旅游发展预测——基于ＧＭ （１，１）模型

灰色系统理论已成为旅游研究的热点之一。基于灰色系统理论,以辽宁省滨海６城市２０００—２００９年国内旅游统计资料为原始数据,建构旅游指标ＧＭ （１,１）灰色预测动态模型,并进行模型精度检验;预测结果分析表明,模型预测结果与实际数据基本一致,进而利用该模型对辽宁省滨海６城市“十二五”期间国内旅游指标进行预测。     

海水水质评价方法比较分析

以实测数据为依据,分别利用灰色关联分析法、灰色聚类分析法、模糊综合评判法及单项污染指数法对样本水质进行分析。对比分析评价结果,发现各评价方法均有其自身的侧重点及局限,提出在实际水质评价工作中综合利用各种评价方法是较好的选择。     

基于Matlab和灰色模型GM（1,1）预测海岸线变化*

使用灰色模型的“少数据”建模寻求现实规律的良好特性用于海岸线变迁预测,结合 MATLAB 强大的计算能力,解决了灰色预测模型在矩阵计算方面复杂的问题.利用1975-2005年珠江口内伶仃洋海区6个时期的海图提取局部时序海岸线,设置原点和28条侧线,使用侧线与海岸线的交点,建立灰色模型 GM (1,1)模型原始数列,使用 MATLAB 编制程序计算出海岸线变化的预测点,并使用2005年实际海岸线对预测结果进行验证,结果表明灰色模型 GM (1,1)进行海岸线预测是合理可靠的.     

灰色系统GM（1，1）模型改进的建模方法

分析灰色系统ＧＭ（１，１）模型建模中所存在的问题，提出以现代非线性回归分析的方法改进ＧＭ（１，１）模型的建模方法．实例计算表明该方法优于传统的建模方法。     

辽宁省滨海城市入境旅游发展研究——基于ＧＭ （１，１）模型

旅游指标建模与预测研究起始于20世纪60年代,已成为旅游研究的热点之一。基于灰色系统理论,以辽宁省滨海6城市2000-2009年入境旅游统计资料为原始数据,建构旅游指标GM(1,1)灰色预测动态模型并进行模型精度检验;对预测结果分析表明,模型预测结果与实际数据基本一致,进而利用该模型对辽宁省滨海6城市2011—2015年入境旅游指标进行预测;最后提出辽宁省滨海6城市入境旅游可持续发展对策。     

浙江省滨海城市入境旅游发展研究——基于GM(1,1)模型

作为区域旅游业发展研究的一项重要内容,旅游指标建模与预测研究起始于20世纪60年代,已成为旅游研究的热点之一。基于灰色系统理论,以浙江省滨海7城市1995—2007年入境旅游统计资料为原始数据,建构旅游指标GM(1,1)灰色预测动态模型,并进行模型精度检验;对预测结果分析表明,模型预测结果与实际数据基本一致,进而利用该模型对浙江省滨海7城市2008—2012年入境旅游指标进行预测;最后,提出浙江省滨海7城市入境旅游可持续发展对策。     

基于灰色系统的准公共性基础设施融资效果评价模型

针对准公共性基础设施融资效果评价问题,通过综合运用AHP法、灰色理论和模糊决策等,从指标体系的确立到模型的构建,提出了1种切实可行的定量、直观的评价方法,并通过实际的项目事例验证了评价模型的可行性和实用性。     

海水营养状态的灰色关联评价模型及应用

基于计算海水营养状态评价指标的参考序列(u0)和其比较序列(ui)之间的关联度这一思路,建立了一种新的海水营养状态的灰色关联评价模型,并应用这个模型对2001年8月东海区10个主要河口、海湾的水体进行了营养状态评价,结果表明:应用灰色关联海水营养状态评价模型所进行的海水营养状态等级的评价和划分,与实际该区水体的营养状态有很好的吻合。同时,该模型在进行营养状态评价时,能避免人为的主观臆断,并能适应营养状态受PO4-P限制的中国主要近岸河口、海湾等特殊海域的要求,因而可以作为一种科学、实用的海水营养程度评价模型,推广应用于水质评价等研究领域和实际评价中。     

不等时距的灰色模型GM（1，1）──建模方法及其在黄河河道长度预测中的应用

提出不等时距的灰色模型ＧＭ（１，１）的建模方法，依据１９７６～１９８９年的现行黄河河道长度数据，假设在现行清水沟流路不变的前提下，利用该模型对２０００年前的黄河河道长度进行预测。     

基于数列灰预测的海洋经济预测

灰色理论自20世纪80年代被广泛应用于社会、经济、科技、农业、生物等各个领域。灰色预测是灰色理论的主要内容之一,其中数列灰预测是灰色预测的主要类型之一。针对海洋经济数据缺乏等特点,本文运用数列灰预测对2007年和2008年海洋总产值和各个产业产值做出预测。     

FY-1C/1D全球海上气溶胶业务反演算法研究

介绍了基于FY-1C/1D气象卫星数据进行全球海洋气溶胶反演的业务算法,主要论述了海上气溶胶反演的单通道算法基本原理.该算法的核心内容是建立气溶胶光学厚度查算表,该查算表基于6S辐射传输模式,在假定气溶胶模式条件下,卫星的表观反射率是卫星观测几何(太阳卫星角)和气溶胶光学厚度函数,最终由卫星观测表观反射率反演出气溶胶光学厚度.业务反演过程中还考虑到数据质量检验、云检测处理和太阳耀斑区去除等.利用该算法对FY-1C卫星自2001年起的部分资料进行反演试验,并在FY-1D卫星发射后投入业务应用,自2002年8月开始能实时得到每天和每月的全球气溶胶光学厚度产品.从两颗卫星两年多连续反演结果分析可以非常清晰地发现全球气溶胶主要排放源地和全球海上气溶胶分布的季节变化,与国外卫星反演结果十分接近.     

A note on sea-breeze-induced seasonal variability in the K1 tidal constants in Cádiz Bay, Spain

The response of Cádiz Bay to sea-breeze wind stress and tidal boundary forcing—individually and in combination—is studied using a 2D depth-averaged, non-linear, high-resolution hydrodynamic model. Linear superposition of the solution for the K₁ and S₁ constituents, like the solution obtained with an allowance for both the input functions together, is shown to give rise to a modulation of the K1 tidal dynamics. It is precisely this modulation which is responsible for the observed seasonal variations in the K₁ tidal constants in Cádiz Bay.     

东方气田海管悬跨段安全评估分析

从工程实际问题出发,利用实际测量资料,通过建立近底床悬跨海管的有限元模型,计算了不同底床形状下海管最大静挠度随海流流速变化的规律,对东方1-1气田某标段海管进行了安全评估．筛选出了危险悬跨段。这一计算结果对海管的防护维护工程实践具有切实的指导意义。     

基于GF-1 WFV影像的黄河口湿地植被盖度估测及分析

植被盖度是植被生长状况的重要定量指标,而目前开展的植被盖度遥感估测工作主要集中在陆地区域,对河口湿地植被盖度遥感估测工作比较少见。本文基于国产GF-1 WFV遥感影像开展了黄河口湿地植被盖度估算,并结合植被类型、土壤盐度和植被指数分布状况开展了植被盖度分布特征分析,得到如下结论:（1）基于GF-1 WFV卫星影像的NDVI、SRI、SAVI、MSAVI和DVI 5种植被指数,分别建立植被盖度估测模型,其中基于NDVI、SRI、MSAVI和DVI 4种植被指数建立的多变量线性回归模型估测精度最好,其决定系数（R2）最大,为0.904,均方根误差RMSE最小,为0.14;（2）植被盖度估算模型的精度与植被盖度本身有一定的关系,其中各植被盖度回归模型中,盖度大于0.8时估算精度要优于盖度小于0.6的区域,RMSE最大相差0.04;（3）以潮滩碱蓬和潮滩芦苇为主的植被覆盖区属于低植被盖度区,盖度位于0.03~0.5,盐度在1.5 g/L左右;芦苇草甸、互花米草和柽柳灌丛植被覆盖区属于高植被盖度区,盖度位于0.8~1.0,其中芦苇草甸土壤盐度小于1.2 g/L,柽柳灌丛土壤盐度在1.4~2.0 g/L,在高盖度植被区混生有中等盖度的植被,盖度在0.5~0.8,土壤盐度在1.8 g/L左右。     

Comparison of the Ku-Band Range Noise Level and the Relative Sea-State Bias of the Jason-1, TOPEX,and Poseidon-1 Radar Altimeters Special Issue: Jason-1 Calibration/Validation

TOPEX/Poseidon is a well known success, with the operational altimeter (TOPEX) and the experimental one (Poseidon-1), providing data of unprecedented quality. However, there are two major differences between the TOPEX and Poseidon-1 radar altimeters on board TOPEX/Poseidon. The first is related to the estimated range noise; the second is linked to the sea-state bias (SSB) model estimates. Since the recent launch of the Jason-1 radar altimeter (also called Poseidon-2), we have been cross-comparing these three systems to better characterize each of them. Analyzing standard user products, we have found that Jason-1 is behaving like Poseidon-1 and thus shows the same observed differences when compared with TOPEX. A comparative analysis of their features was performed, starting from the on-board acquisition of the ocean return and ending with the ground generation of the high level accuracy oceanographic product. The results lead us to believe that the sources for these differences lie in both the waveform tracking processing and the presence or abscence of a retracking procedure whether on-board or on ground. Because Poseidon-1 and Jason-1 waveforms are retracked while TOPEX waveforms are not in the products distributed to the users, we have applied the same ground retracking algorithm to the waveforms of the three radar altimeters to get consistent data sets. The analysis of the outputs has shown that: (a) the noise level for the three radar altimeters is definitively the same, and (b) the source of the relative SSB between Jason-1 and TOPEX lies in the different behavior of the on-board tracking softwares.     

Improving the Jason-1 Ground Retracking to Better Account for Attitude Effects

After two years of verification and validation activities of the Jason-1 altimeter data, it appears that all the mission specifications are completely fulfilled. Performances of all instruments embarked onboard the platform meet all the requirements of the mission. However, the star tracker system has shown some occasional abnormal behavior leading to mispointing angles out of the range of Jason-1 system specification which states that the altimeter antenna shall be pointed to the nadir direction with an accuracy below 0.2 degree (3 sigma). This article discusses the platform attitude angle and its consequences on the altimetric estimates. We propose improvements of the Jason-1 retracking process to better account for attitude effects.

The first star tracker anomalies for the Jason-1 mission were detected in April 2002. The Poseidon-2 algorithms were specified assuming an antenna off-nadir angle smaller than 0.3 degree. For higher values, the current method to estimate the ocean parameters is known to be inaccurate. Thus, the algorithm has to be reviewed, and more specifically, the present altimeter echo model has to be modified to meet the desired instrument performance.

Therefore, we derive a second order analytical model of the altimeter echo to take into account attitude angles up to 0.8 degree, and consequently, we adapt the retracking algorithm. This new model is tested on theoretical simulated data using a maximum likelihood estimation. Biases and noise performance characteristics are computed for the different estimated parameters. They are compared to the ones obtained with the current algorithm. This new method provides highly improved estimations for high attitude angles. It is statistically validated on real data by applying it on several cycles of Poseidon-2 raw measurements. The results are found to be consistent with those obtained from simulations. They also fully agree with the TOPEX estimates when flying along the same ground track. Finally, the estimates are also in agreement with the ones available in the current I/GDR (Intermediate Geophysical Data Record) products when mispointing lies in the mission specifications.     

Improving the Jason-1 Ground Retracking to Better Account for Attitude Effects

After two years of verification and validation activities of the Jason-1 altimeter data, it appears that all the mission specifications are completely fulfilled. Performances of all instruments embarked onboard the platform meet all the requirements of the mission. However, the star tracker system has shown some occasional abnormal behavior leading to mispointing angles out of the range of Jason-1 system specification which states that the altimeter antenna shall be pointed to the nadir direction with an accuracy below 0.2 degree (3 sigma). This article discusses the platform attitude angle and its consequences on the altimetric estimates. We propose improvements of the Jason-1 retracking process to better account for attitude effects.

The first star tracker anomalies for the Jason-1 mission were detected in April 2002. The Poseidon-2 algorithms were specified assuming an antenna off-nadir angle smaller than 0.3 degree. For higher values, the current method to estimate the ocean parameters is known to be inaccurate. Thus, the algorithm has to be reviewed, and more specifically, the present altimeter echo model has to be modified to meet the desired instrument performance.

Therefore, we derive a second order analytical model of the altimeter echo to take into account attitude angles up to 0.8 degree, and consequently, we adapt the retracking algorithm. This new model is tested on theoretical simulated data using a maximum likelihood estimation. Biases and noise performance characteristics are computed for the different estimated parameters. They are compared to the ones obtained with the current algorithm. This new method provides highly improved estimations for high attitude angles. It is statistically validated on real data by applying it on several cycles of Poseidon-2 raw measurements. The results are found to be consistent with those obtained from simulations. They also fully agree with the TOPEX estimates when flying along the same ground track. Finally, the estimates are also in agreement with the ones available in the current I/GDR (Intermediate Geophysical Data Record) products when mispointing lies in the mission specifications.     

Sea surface wind speed retrieval from Sentinel-1 HH polarization data using conventional and neural network methods

Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed(SSWS) from HH-polarized Sentinel-1(S1) SAR images. The Polarization Ratio(PR) models combined with the CMOD5.N Geophysical Model Function(GMF) is used for SSWS retrieval from the HH-polarized SAR data. We compared different PR models developed based on previous C-band SAR data in HH-polarization for their applications to the S1 SAR data. The recently proposed CMODH, i.e., retrieving SSWS directly from the HHpolarized S1 data is also validated. The results indicate that the CMODH model performs better than results achieved using the PR models. We proposed a neural network method based on the backward propagation(BP)neural network to retrieve SSWS from the S1 HH-polarized data. The SSWS retrieved using the BP neural network model agrees better with the buoy measurements and ASCAT dataset than the results achieved using the conventional methods. Compared to the buoy measurements, the bias, root mean square error(RMSE) and scatter index(SI) of wind speed retrieved by the BP neural network model are 0.10 m/s, 1.38 m/s and 19.85%,respectively, while compared to the ASCAT dataset the three parameters of training set are –0.01 m/s, 1.33 m/s and 15.10%, respectively. It is suggested that the BP neural network model has a potential application in retrieving SSWS from Sentinel-1 images acquired at HH-polarization.     

基于GF-1卫星的海上溢油定量监测——以青岛溢油事故为例

基于GF-1卫星数据对2013年11月22日青岛黄潍输油管道爆炸造成的海面溢油进行定量监测。研究首先采用2013年11月26日GF-1卫星数据对海面溢油进行提取,其统计面积为0.67 km2;其次,结合肖剑伟提出的油膜厚度单波段反演模型估算油膜厚度,最终计算得到溢油量为0.28 t;最后,对Band 1反射率与油膜厚度结果进行灰度分割,将溢油污染程度分为6级,对比发现两者分级结果较为一致。由此得出,通过反射率直接进行灰度分割可以对溢油污染进行有效的等级评定。     

HY-1C卫星影像中太阳耀斑区域的预测技术研究

对卫星影像太阳耀斑区域的预测是保障船-星同步观测有效实施的基础性工作。本文在对HY-1C卫星影像各像元观测几何分析的基础上,简化构建卫星影像各像元的网格化模型,介绍了利用卫星轨道报估算的卫星-太阳-像元位置的观测几何,进而估算卫星影像中太阳耀斑区域的技术方法。经与HY-1C实际卫星影像的应用比对,表明本文预测的像元位置、太阳位置以及卫星观测天底角/方位角均与实际影像数据具有很好的重合度,预测的太阳耀斑区域与实际影像耀斑掩码区重合度良好,验证了该方法能够预测影像中太阳耀斑区域,保障船-星同步观测的能力。