多传感器观测下带乘性噪声系统的逆向滤波与反褶积融合算法

针对多传感器观测环境下带乘性噪声系统的逆向最优滤波与反褶积融合估计问题 ,本文提出了 1种基于极大似然准则的最优融合算法。该算法中各单传感器间并行计算 ,并且融合中心与单传感器处理中心间无反向通讯 ,因而执行效率较高。仿真表明 ,该融合算法产生的逆向滤波与反褶积比单传感器处理结果有较明显提高     

海水叶绿素α浓度遥感测量的实验研究

本文描述了1989年11月在青岛即墨岙山养殖基地所做的海水叶绿素σ浓度遥感测量的基础实验。该实验获得了高浓度叶绿素α含量的海水反射光谱。该反射光谱的特征波段为:波谷在485nm;波峰在450nm 和535nm;“节点”在572nm.根据该实验数据选取了叶绿素α浓度两种双波段估算模式.分析结果表明;两种模式的精度都在50%以上,且双波段反射率的差值法优于双波段反射率比值法.     

Data assimilation for a coastal area morphodynamic model: Morecambe Bay

This paper investigates the use of data assimilation in coastal area morphodynamic modelling using Morecambe Bay as a study site. A simple model of the bay has been enhanced with a data assimilation scheme to better predict large-scale changes in bathymetry observed in the bay over a 3-year period. The 2DH decoupled morphodynamic model developed for the work is described, as is the optimal interpolation scheme used to assimilate waterline observations into the model run. Each waterline was acquired from a SAR satellite image and is essentially a contour of the bathymetry at some level within the inter-tidal zone of the bay. For model parameters calibrated against validation observations, model performance is good, even without data assimilation. However the use of data assimilation successfully compensates for a particular failing of the model, and helps to keep the model bathymetry on track. It also improves the ability of the model to predict future bathymetry. Although the benefits of data assimilation are demonstrated using waterline observations, any observations of morphology could potentially be used. These results suggest that data assimilation should be considered for use in future coastal area morphodynamic models.     

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.     

黄海溶解氧垂直分布最大值的成因

本文对黄海溶解氧垂直分布中最大值的成因进行了探讨．认为：浮游植物春花期光合作用产生的大量氧，是溶解氧垂直分布最大值得以在春季（5月）形成的基础；而良好的温、密跃层的存在是氧最大值得以形成的必要条件，夏季叶绿素最大值层中较强的光合作用，是氧最大值在夏季得以维持甚至增强的主要原因．提出：黄海春、夏季溶解氧垂直分布中的最大值并非主要由冬季保持而来．     

Nonparametric Estimates of the Sea State Bias for the Jason-1 Radar Altimeter

The Jason-1 sea state bias (SSB) is analyzed in depth from the first year of GDR products. Compared to previous missions, this work benefits from two aspects of the empirical determination of the SSB from the altimetric data themselves. First, from a methodological point of view, a nonparametric technique (NP) has been developed and largely tested on TOPEX/Poseidon 1, GFO and Envisat data. The NP estimator has proven to be a useful tool in the SSB estimation, and it is now mature enough to be used for a refined analysis. On the other hand, the SSB can be extracted from three different data sets (crossovers, collinear data, and residuals) with different characteristics. It is then possible to cross calibrate various estimations of the SSB models and to determine the most accurate one. A systematic comparison is made between these different estimates for the Jason-1 altimeter. The collinear and crossover data sets yield very similar estimates despite their difference of spatial and temporal distributions. These SSB models assure consistency with the TOPEX mission when comparing Jason-1 and TOPEX residuals during the tandem phase. Thanks to the present work, the impact of the short wavelengths filtering on the SSB estimation is evidenced. More generally, our understanding of potential errors affecting the sea surface height and their impact onto the SSB estimation is also improved.     

State Estimation of the North Pacific Ocean by a Four-Dimensional Variational Data Assimilation Experiment

A four-dimensional variational data assimilation system has been applied to an experiment to describe the dynamic state of the North Pacific Ocean. A synthesis of available observational records and a sophisticated ocean general circulation model produces a dynamically consistent dataset, which, in contrast to the nudging approach, provides realistic features of the seasonally-varying ocean circulation with no artificial sources/sinks for temperature and salinity fields. This new dataset enables us to estimate heat and water mass transports in addition to the qualification of water mass formation and movement processes. A sensitivity experiment on our assimilation system reveals that the origin of the North Pacific Intermediate Water can be traced back to the Sea of Okhotsk and the Bering Sea in the subarctic region and to the subtropical Kuroshio region further south. These results demonstrate that our data assimilation system is a very powerful tool for the identification and characterization of ocean variabilities and for our understanding of the dynamic state of ocean circulation. This revised version was published online in July 2006 with corrections to the Cover Date.