CMIP5模式对南海SST的模拟和预估

分析了32个CMIP5模式对南海历史海表温度(SST)的模拟能力和不同排放情景下未来SST变化的预估。通过检验各气候模式对南海历史SST增温趋势和均方差的模拟,发现大部分模式都能较好地模拟出南海20世纪历史SST的基本特征和变化规律,但也有部分模式的模拟存在较大偏差。尽管这些模拟偏差较大的模式对SST多模式集合平均的影响不大,但会增加未来情景预估的不确定性。剔除15个模式后,分析了南海SST在RCP26、RCP45和RCP85三种排放情景下的变化趋势,发现在未来百年呈明显的增温趋势,多模式集合平均的增温趋势分别为0.42、1.50和3.30℃/(100a)。这些增温趋势在空间上变化不大,但随时间并不是均匀变化的。在前两种排放情景下,21世纪前期的增温趋势明显强于后期,而在RCP85情景下,21世纪后期的增温趋势强于前期。     

海-陆-气耦合模式中海洋分量的温盐模拟分析

基于OASIS耦合器,发展了一套具有较高分辨率并行化的海-陆-气耦合模式,并进行了30年以上的耦合积分。为了评估模式中海洋分量的基本性能,首先考察了耦合模式对"气候漂移"的控制,然后对比分析了耦合与未耦合海洋环流模式模拟温度和盐度的气候态特征和季节变化,结果显示耦合模式的模拟结果中没有出现明显的"气候漂移现象",同时能较好的模拟全球大洋温盐分布的基本特征和季节变化。     

杭州湾中部实测波浪特性分析

利用杭州湾中部一年实测波浪资料,分析该地区的波参数统计变化特性,采用最小二乘法拟合得出相关参数之间的关系式,并统计分析了频谱特性。结果表明:杭州湾中部以小浪和轻浪为主,在各月分布较为均匀,年平均周期为2.97 s;最大一次波浪过程为冷空气影响所致,影响强度大于台风莫拉克;常浪向分布在东北至东南向,其中又以小浪出现较多,对应的波周期以2~4 s为主;强浪向主要分布在西北和东北方向,对应的波周期主要分布在3~5 s;多数特征波参数之间相关关系较好;由谱分析得知风浪占多数,且以单峰为主。研究结果可为相关工程和理论研究提供基础资料。     

渤海的平均余环流

利用72个渤海石油平台、站点测流资料,阐明了渤海的平均余环流特征,并用已有的数值计算结果阐明了其形成机制。观测表明在黄河三角洲近海存在一支北-东北向的流动,可抵达秦皇岛附近水域,数值计算表明该支流动主要是潮生的;在辽东湾北部存在一顺时针向的涡旋运动,在冬半年该涡旋是风生的;在渤海湾北部存在潮生的逆时针向的余环流。     

Global Statistical Assessment and Cross-Calibration with Jason-2 for Reprocessed HY-2 A Altimeter Data

HY-2 A (Haiyang-2 A) satellite was launched on August 16, 2011 and radar altimeter is one of its main payloads. We reprocessed two years of HY-2 A altimeter sensor geophysical dataset records (SGDR) data. This paper presents the main results in terms of reprocessed HY-2 A altimeter data quality: verification of data availability and validity, monitoring several relevant altimeter parameters, and assessment of the HY-2 A altimeter system performances. A cross-calibration analysis of reprocessed HY-2 A altimeter data with Jason-2 was conducted. The reprocessed HY-2 A altimeter data show good quality and have a low level of noise with respect to Jason-2. The same geophysical correction methods were used to calculate the sea surface height (SSH) for the two missions. The mean standard deviations of the crossover differences for HY-2 A and Jason-2 are 5.24 cm and 5.34 cm, respectively. The mean standard deviation of the crossover differences between HY-2 A and Jason-2 is 5.37 cm. These show that HY-2 A can provide SSH measurements at almost the same level of accuracy as Jason-2. The relative SSH bias between HY-2 A and Jason-2 due to the Ultra Stable Oscillator (USO) drift is obviously observed, and it can affect the calculation of mean sea level and should be further studied and corrected.     

利用超快速精密星历约束的北斗卫星实时精密定轨

以广播星历为起算轨道的北斗卫星实时滤波精密定轨往往需要较长收敛时间,针对此,提出利用超快速精密星历约束的实时精密定轨方法。通过MGEX跟踪网全球分布的51个测站连续7 d的实测数据,利用平方根信息滤波对北斗卫星实时精密轨道进行确定,并以3 d解事后轨道作为参考,评估北斗卫星实时滤波轨道精度。结果表明,利用广播星历作起算轨道时,北斗实时滤波轨道平均需要经过15 h收敛才能达到稳定,而新方法在这段时间内轨道变化较为平稳,未出现明显的收敛现象,并且7 d时间内GEO卫星在切向、法向和径向上RMS分别优于2.5 m、20 cm和30 cm,IGSO和MEO卫星在3个方向上分别优于30 cm、15 cm和10 cm。
     

稳健的回光反射平面靶心定位算法

针对因回光反射平面标靶点云数据缺失或冗余而难以准确计算靶心坐标的问题,本文提出一种基于距离标靶重心最远点的边缘点提取和靶心定位算法。首先,进行点云数据预处理,先人工大概选取出标靶点云所在位置,并根据回光反射强度信息提取出标靶点云,对标靶点云进行粗差剔除、投影以及坐标旋转等工作;然后,进行边缘点提取,应用所提的边缘点提取算法对投影到二维平面的标靶点云进行边缘点提取;最后,进行靶心定位,先应用抗差最小二乘对边缘点进行拟合计算圆心坐标,然后将其旋转回三维空间作为靶心坐标计算值。实验结果表明,本文提出的边缘点提取算法能高效、准确地提取出标靶边缘点,比文献[12]中的边缘点提取算法节约了大量时间,并且应用所提取出的边缘点能稳健地计算出靶心坐标,与基准值的偏差在亚毫米以内,优于文献[11]、[12]算法靶心计算精度,有效地解决了残缺或冗余的回光反射平面标靶点云靶心定位问题。     

2016年夏季南海海盆水体颗粒物粒径分布特征*

颗粒物粒径分布(Particle Size Distribution, PSD)代表了颗粒物浓度与颗粒物粒径之间的关系, 影响着海洋生态环境和水体光学特性等。文章基于2016年夏季航次调查的生物光学剖面数据, 研究了南海海盆海域PSD的分布特征。研究发现, 幂律函数可以较好地拟合南海海盆区域的PSD, 对数空间中的实测的PSD与模拟的PSD平均决定系数高达0.95。PSD斜率(ξ)的分布范围为[1.27, 7.65], 均值为3.93±0.56。南海海盆区域表层水体的ξ均值与全球大洋表层水体的ξ均值相近, 但高于海湾等表层水体的ξ均值。ξ能较好地表征颗粒物平均粒径D_A的大小, 两者存在明显负相关关系, 即ξ值越高, D_A越小; 反之, D_A越大。通过分析T1断面的生物光学剖面数据及总体平均的PSD剖面数据, 发现PSD剖面分布特征如下: 1)表层水体的ξ值相对较高, 且D_A值相对较低, 推测可能是由于微微型藻类为主导颗粒物所致; 2) ξ值极小值层出现在次表层叶绿素浓度极大值层(Subsurface Chlorophyll Maximum Layer, SCML)中, 并伴随D_A极大值层的出现, 其原因可能是SCML中的大粒径浮游植物占比显著增加; 3)弱光层中的ξ值较SCML中的高, 但略低于表层的ξ值, 而D_A则位于表层与SCML的D_A之间, 这可能与浮游植物及其碎屑的絮凝、分解、沉降等过程相关。PSD特征影响着海水的固有光学特性, 分析发现: 由于SCML中的叶绿素浓度增加, 颗粒物散射系数(b_p(532))和颗粒物后向散射系数(b_bp(532))也相应呈现显著增加的趋势。弱光层中的平均b_p(532)与平均b_bp(532)最小。ξ与颗粒物衰减光谱斜率之间呈高分散性, Boss 等(2001b)的模型适合用于粗略估算区域性的ξ分布范围及均值。     

Using advanced soft computing techniques for regional shoreline geoid model estimation and evaluation

This study aims at evaluating the global geoid model for a regional shoreline fitting using advanced soft computing techniques and global navigation satellite system/leveling measurements. Artificial neural networks, fuzzy logic, and least square support vector machine models are developed and used to fit the global geoid model for the north coastal Egyptian line. In addition, a novel estimation geoid model is designed and evaluated based on the latest global geoid models. The results of the three estimation models show that they can be used to correct the shoreline geoid model, in terms of root mean square error that ranges from 1.7 to 8.5?cm. Moreover, it is found that the least square vector machine model is a competitive approach with certain advantage in solving complex problems represented by missing data.     

Observations and analyses of floc size and floc settling velocity in coastal salt marsh of Luoyuan Bay, Fujian Province, China

In coastal environments, fine-grain sediments often aggregate into large and porous flocs. ElectroMagnetic Current Meters (EMCM) and Laser In Situ Scattering and Transmissometry (LISST-ST) have been deployed within a Spartina alterniflora marsh of the Luoyuan Bay in Fujian Province, China, to measure the current velocity, the floc size and the settling velocity between 15 and 22 January 2008. During the observations, the near-bed water was collected in order to obtain the suspended sediment concentration (SSC) and constituent grain size. Data show that: (1) the nearbed current velocities vary from 0.1 to 5.6 cm/s in the central Spartina alterniflora marsh and 0.1–12.5 cm/s at the edge; (2) the SSCs vary from 47 to 188 mg/dm 3 . The mean grain size of constituent grains varies from 7.0 to 9.6 μm, and the mean floc sizes (MFS) vary from 30.4 to 69.4 μm. The relationship between the mean floc size and settling velocity can be described as: w s =ad b , in which w s is the floc settling velocity (mm/s), a and b are coefficients. The floc settling velocity varies from 0.17 to 0.32 mm/s, with a mean value of 0.26 mm/s, and the floc settling velocity during the flood tide is higher than that during the ebb tide. The current velocity and the SSC are the main factors controlling the flocculation processes and the floc settling velocity.