海面盐度卫星遥感的大气影响仿真

海面盐度是描述海洋的重要参量之一,基于星载微波辐射计的海面盐度探测也是海洋遥感研究的重要内容.在影响盐度遥感的误差中,大气是重要的影响因素.在辐射传输理论的基础上,仿真计算了大气透射率和上、下行辐射随地面大气温度、压强和辐射计入射角的变化关系,进而得到大气对接收辐射亮温的影响.仿真结果表明,大气对盐度遥感的影响很大,但当地面大气温度和压强精度分别达到2℃和1000 Pa时,可以消除大气的影响.     

粗糙海面L 和C 双波段的代价函数多参量遥感反演分析

利用代价函数(cost function)方法,通过分析粗糙海面L和C双波段多极化遥感亮温对海表盐度、温度、风速和有效波高等参数的敏感性以及L和C双波段多极化的代价函数收敛特性,建立了反演海表盐度、温度、风速和有效波高等多参数的L和C双波段多极化代价函数模式。双波段遥感模式分析结果表明:(1)对于双参数的联合反演,L和C双波段垂直极化代价函数联合反演海表盐度和温度可以获得较好的反演结果。(2)L波段垂直极化和C波段水平极化代价函数联合反演海表盐度和风速较好。(3)对于三参数联合反演,L波段垂直极化和C波段的双极化联合反演盐度、温度和风速的精度较高。(4)L波段亮温对有效波高的敏感性较低(C波段经验模式不含有效波高),使得有效波高反演误差较大,L和C波段经验模式不适合反演有效波高参数。另外,为了定量分析L和C双波段代价函数的多参量遥感反演结果,采用加性噪音模拟亮温方法,对上述L和C双波段多极化模式的盐度、温度和风速等多参数联合反演误差进行了分析,均得出较好的结果。结论表明L和C双波段代价函数联合反演多参量可以明显提高参量反演精度,为粗糙海表面多参量的反演提供了新的方法和途径。     

海表面盐度遥感技术的发展与应用

文章回顾了近40 a来海水盐度遥感技术的发展,包括遥感原理、反演算法、影响因素、航空实验、卫星遥感等方面取得的进展。指出,以1.400~1.427 GH z为海水盐度遥感的首选波段,采用L波段(1.4 GH z)和S波段(2.65 GH z)组成的双波段、双极化、较大入射角的天线工作方式有助于提高盐度遥感的精度。影响盐度遥感的三个主要因素是:太空、海面和大气。目前,航空盐度遥感的反演精度已经达到了0.3 psu。     

中国海及邻近海域卫星观测资料同化试验

利用1个基于POMgcs海洋模式和多重网格三维变分同化方法建立的中国海及邻近海域海面高与三维温盐流数值预报模型,通过一系列数值试验,研究了同化卫星测高和卫星遥感海面温度观测资料对该模型预报能力的影响。试验结果表明,同化卫星测高资料可明显改善海面高度与三维温度和盐度的分析预报效果,使1 200 m以上的温度预报误差减小0.16℃,并能有效提高对海洋中尺度现象的预报能力;同化卫星遥感海面温度对100 m以上的温度和盐度的预报效果有所改善,可使海面温度的预报误差减小10%。     

基于Argo浮标数据的星载微波辐射计Aquarius数据产品质量评估

Aquarius是专门用于海洋盐度监测的L波段辐射计,于2011年6月发射入轨,目前已进入业务化运行阶段。本文以太平洋为研究区域,利用Argo盐度现场数据对星载微波辐射计Aquarius的2012年2级数据产品质量进行了分析与讨论,结果表明:与Argo数据比较,Aquarius数据盐度存在0.1的负偏差,标准差约为0.7,升轨和降轨数据差异不明显;受亮温陆地污染和无线电射频干扰的影响,近岸海域反演误差较大;海面温度较高的低纬海域反演结果优于中纬度海域;受亮温敏感性及粗糙海面发射率模型的影响,Aquarius在低温水域以及高风速条件下盐度反演误差较大,标准差可达1以上。     

基于SMOS卫星数据的海表面盐度模型

海表面盐度是研究海洋对全球气候影响以及大洋环流的重要参量之一,而卫星遥感技术是获取海表面盐度数据的最有效方法.目前,L波段的SMOS和Aquarius/SAC-D遥感卫星正在用于探测海表面盐度,并根据卫星观测数据和物理机制反演出海表面盐度的产品.但在某些近陆地区域,由于淡水流入及陆地射频(RFI)等因素影响,卫星反演盐度的产品精度较低.文中利用“东方红2号”科学考察船的实测数据、SMOS卫星数据,首次针对中国南海海域提出了用贝叶斯网络模型计算海表面盐度,并用验证数据集(实测Argo盐度)对模型进行适应性评估.经过计算,模型误差和验证误差分别为0.47 psu和0.45 psu,而相应的SMOS Level 2产品的精度分别为1.90 psu和1.82 psu.此模型为海表面盐度的计算提供了一个新方法.     

基于Aquarius数据的粗糙海面L波段辐射模型研究

Aquarius是NASA于2011年6月发射的基于主被动遥感技术的盐度观测卫星,其主要载荷是一个工作于L波段的微波辐射计。Aquarius天线三波束扫描刈幅可达390km,在7d内完成对全球海域的盐度观测。海面风浪导致海面粗糙度的变化,进而影响海面微波辐射特性。粗糙海面辐射亮温是盐度信息提取的重要误差源,需要发展相应的海面辐射模型进行修正。本文利用Aquarius观测的海表亮温数据,与扫描微波辐射计WindSat测量数据进行时空匹配,建立了一个描述粗糙海面L波段辐射特性的参数化模型,进而利用该模型进行了海表盐度反演,并将反演结果与Argo实测盐度数据进行了比较。结果表明,本文发天展的参数化模型可以准确描述中低风速条件下的粗糙海面辐射,在12m/s以上高风速条件下对粗糙海面亮温存在高估;采用此模型反演的盐度误差优于0.5,在高风速条件下盐度反演误差可超过1。     

一维综合孔径微波辐射计遥感海面温度的敏感性分析

一维综合孔径微波辐射计能够有效提高观测的空间分辨率，其观测入射角通常在0°~55°范围内变化。为了开发适用于一维综合孔径微波辐射计的海面温度反演算法，需要评估其观测亮温对海洋大气环境要素的敏感性。利用海面发射率模型和大气辐射传输模型，构建了适用于一维综合孔径微波辐射计的微波海洋大气辐射传输模式，研究了C波段垂直和水平极化微波辐射亮温在不同入射角下对海洋大气环境要素的敏感性变化情况，并定量计算了相应的敏感系数。结果表明:垂直和水平极化亮温对海洋大气环境要素的敏感性表现出不同的特性。随着入射角的增大，垂直极化亮温对海面温度的敏感性增强，对海面风场的敏感性相对减弱；水平极化亮温则相反。由大气水汽含量和云液态水含量误差引入的垂直和水平极化亮温误差随入射角增大而增大，但是，即使在55°的大入射角下垂直和水平极化亮温误差仍小于0.12 K。对于海面温度反演精度优于1 K的要求，一维综合孔径微波辐射计的测温精度需优于0.6 K。研究结果对于一维综合孔径微波辐射计海面温度反演算法的研究和载荷设计具有一定的理论指导意义。     

基于MISR多角度光学遥感影像的海面风速估算实验

太阳耀光是来自粗糙海面的直接太阳反射光,其强度与海面粗糙度密切相关,而海面粗糙度主要受海面风场影响。因此,包含太阳耀光信息的光学遥感影像在海洋动力过程和海面风速探测中具有积极意义。本文利用2016年2月到2017年3月期间成像的25幅Terra卫星MISR(Multi-angle Imaging Spectro Radiometer)传感器的多角度遥感影像,分别提取了太阳的高度角和方位角、正视和后视影像的卫星观测角、方位角等信息,校正获得正视和后视影像的太阳耀光辐射强度,进一步反演海表面粗糙度信息,进而计算海面风速。最后利用ECMWF(European Centre for Medium-Range Weather Forecasts)的模式风速数据与反演获得的风速结果进行对比验证。结果表明,两者的相关系数较高(R=0.745),均方根误差和平均绝对偏差值分别为1.514 m·s^-1和1.319 m·s^-1。初步实验结果表明,利用MISR多角度光学遥感影像估算海表面风速是可行性的。     

HY-2微波辐射计降雨条件下海面风速反演算法研究

由于降雨改变了海洋-大气的辐射/散射特性,长期以来星载遥感器在降雨条件下进行海面风速信息提取存在困难。本文针对自主海洋动力环境卫星海洋2号（HY-2）搭载的扫描微波辐射计,分析了不同频段亮温对降雨和海面风速敏感性,自此基础上获得了一种对降雨不敏感的亮温通道组合,该亮温通道组合对海面风速的敏感性甚至高于原有亮温通道。本文利用该亮温通道组合建立了降雨条件下的风速反演算法,并将反演结果与WindSat全天候风速产品、HY-2微波辐射计原有风速产品以及浮标实测数据进行了比较。结果表明本文算法在降雨条件下的反演误差小于2m/s,明显优于原有HY-2微波辐射计风速产品,验证了本文发展的算法在降雨条件下的风速反演能力。     

基于现场观测与MyOcean模式数据的印度洋东南部Aquarius反演海表盐度的评价与纠正

The in situ sea surface salinity(SSS) measurements from a scientific cruise to the western zone of the southeast Indian Ocean covering 30°–60°S, 80°–120°E are used to assess the SSS retrieved from Aquarius(Aquarius SSS).Wind speed and sea surface temperature(SST) affect the SSS estimates based on passive microwave radiation within the mid- to low-latitude southeast Indian Ocean. The relationships among the in situ, Aquarius SSS and wind-SST corrections are used to adjust the Aquarius SSS. The adjusted Aquarius SSS are compared with the SSS data from My Ocean model. Results show that:(1) Before adjustment: compared with My Ocean SSS, the Aquarius SSS in most of the sea areas is higher; but lower in the low-temperature sea areas located at the south of 55°S and west of 98°E. The Aquarius SSS is generally higher by 0.42 on average for the southeast Indian Ocean.(2) After adjustment: the adjustment greatly counteracts the impact of high wind speeds and improves the overall accuracy of the retrieved salinity(the mean absolute error of the Zonal mean is improved by 0.06, and the mean error is-0.05 compared with My Ocean SSS). Near the latitude 42°S, the adjusted SSS is well consistent with the My Ocean and the difference is approximately 0.004.     

海表面盐度的高精度预测模型

为了建立高精度的海洋表面盐度预测模型,采用BP神经网络的方法,针对SMOS卫星level 1C级亮度温度数据和辅助数据建立了一种海表面盐度预测模型,以ARGO浮标观测值作为海表盐度实测值来检验新模型预测结果的准确度,同时利用验证集对模型的精度进行验证.结果表明:通过新模型预测的海表盐度(SSS0)比SMOS卫星的3个粗...     

利用海表盐度等遥感观测产品重构三维盐度场的性能评估

Several remotely sensed sea surface salinity(SSS) retrievals with various resolutions from the soil moisture and ocean salinity(SMOS) and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profiles(S) using multilinear regressions. The performance is evaluated using a total root mean square(RMS) error, different error sources, and the feature resolutions of the retrieved S fields. In the mixed layer of the salinity, the SSS-S regression coefficients are uniformly large. The SSS inputs yield smaller RMS errors in the retrieved S with respect to Argo profiles as their spatial or temporal resolution decreases. The projected SSS errors are dominant, and the retrieved S values are more accurate than those of climatology in the tropics except for the tropical Atlantic, where the regression errors are abnormally large. Below that level, because of the influence of a sea level anomaly, the areas of high-accuracy S values shift to higher latitudes except in the high-latitude southern oceans, where the projected SSS errors are abnormally large. A spectral analysis suggests that the CATDS-0.25° results are much noisier and that the BEC-L4-0.25° results are much smoother than those of the other retrievals. Aquarius-CAP-1° generates the smallest RMS errors, and Aquarius-V2-1° performs well in depicting large-scale phenomena. BEC-L3-0.25°,which has small RMS errors and remarkable mesoscale energy, is the best fit for portraying mesoscale features in the SSS and retrieved S fields. The current priority for retrieving S is to improve the reliability of satellite SSS especially at middle and high latitudes, by developing advanced algorithms, combining both sensors, or weighing between accuracy and resolutions.     

An analysis on the error structure and mechanism of soil moisture and ocean salinity remotely sensed sea surface salinity products

For the application of soil moisture and ocean salinity（SMOS） remotely sensed sea surface salinity（SSS） products,SMOS SSS global maps and error characteristics have been investigated based on quality control information.The results show that the errors of SMOS SSS products are distributed zonally,i.e.,relatively small in the tropical oceans,but much greater in the southern oceans in the Southern Hemisphere（negative bias） and along the southern,northern and some other oceanic margins（positive or negative bias）.The physical elements responsible for these errors include wind,temperature,and coastal terrain and so on.Errors in the southern oceans are due to the bias in an SSS retrieval algorithm caused by the coexisting high wind speed and low temperature; errors along the oceanic margins are due to the bias in a brightness temperature（TB） reconstruction caused by the high contrast between L-band emissivities from ice or land and from ocean; in addition,some other systematic errors are due to the bias in TB observation caused by a radio frequency interference and a radiometer receivers drift,etc.The findings will contribute to the scientific correction and appropriate application of the SMOS SSS products.     

基于一维综合孔径微波辐射计的海面温度反演研究

Due to the low spatial resolution of sea surface temperature(T_S) retrieval by real aperture microwave radiometers,in this study, an iterative retrieval method that minimizes the differences between brightness temperature(T_B)measured and modeled was used to retrieve sea surface temperature with a one-dimensional synthetic aperture microwave radiometer, temporarily named 1 D-SAMR. Regarding the configuration of the radiometer, an angular resolution of 0.43° was reached by theoretical calculation. Experiments on sea surface temperature retrieval were carried out with ideal parameters; the results show that the main factors affecting the retrieval accuracy of sea surface temperature are the accuracy of radiometer calibration and the precision of auxiliary geophysical parameters. In the case of no auxiliary parameter errors, the greatest error in retrieved sea surface temperature is obtained at low T_S scene(i.e., 0.710 6 K for the incidence angle of 35° under the radiometer calibration accuracy of0.5 K). While errors on auxiliary parameters are assumed to follow a Gaussian distribution, the greatest error on retrieved sea surface temperature was 1.330 5 K at an incidence angle of 65° in poorly known sea surface wind speed(W)(the error on W of 1.0 m/s) over high W scene, for the radiometer calibration accuracy of 0.5 K.     

印度洋海表盐度预报的线性马尔可夫模型及其改进办法

海洋盐度在水循环、海洋环流、海洋生态系统、全球天气和气候变化等方面起着至关重要的作用。然而, 受观测的限制, 以往对海洋盐度的研究相对匮乏, 对其进行预报的工作更为少见。本文采用线性马尔可夫模型对印度洋海表面盐度(sea surface salinity, SSS)开展初步的预报工作。根据混合层盐度收支方程, 选择海表面高度(sea surface height, SSH)、海表面温度 (sea surface temperature, SST)、SSS等物理量的异常值作为模型的组成部分, 对印度洋SSS开展预报工作。结果表明, 马尔可夫模型可提前9个月对印度洋SSS进行较好的预报。此外, 南太平洋海表面温度异常(sea surface temperature anomaly, SSTA), 海表面高度异常(sea surface height anomaly, SSHA)和印度洋偶极子(Indian Ocean dipole, IOD)系数等遥相关因素的加入可将线性马尔可夫预报对印度洋SSS的预报效果(相关系数)平均提高10%。利用改进的模型对印度洋SSS进行提前1~11个月的“实时”预测, 得出预报的SSS时空变化特征与观测场相吻合。综上所述, 改进的线性马尔可夫模型对印度洋SSS具有一定的预测能力, 未来可进一步完善。     

基于神经网络的全球三维温盐场重构技术研究

文章利用果蝇优化广义回归神经网络算法FOAGRNN (fruit fly optimization algorithm, FOA; generalized regression neural network, GRNN)对SODA (simple ocean data assimilation)再分析数据进行训练, 构建海表温度、盐度、海面高度与次表层温盐场之间的投影关系模型, 并在全球范围使用SODA和卫星遥感数据评估了模型的应用性能。首先, 利用独立的2016年SODA海表数据作为模型输入进行理想重构试验, 结果显示全球重构温、盐平均均方根误差(MRMSE)分别为0.36℃和0.08‰, 与世界海洋图集WOA13资料相比减小约50%和60%。然后, 利用卫星观测的海表信息作为模型输入进行实际应用试验, 并与Argo观测剖面进行比较评估。试验结果表明, 重构模型能有效表征海水温、盐特征, 其中重构温、盐MRMSE分别为0.79℃和0.16‰, 相比WOA气候态减小27%和11%。误差的垂向分布显示, 重构温度RMSE从海表向下迅速增大, 至100m达到峰值1.35℃, 而后又迅速回落,至250m处为0.81℃, 跃层往下不断减小; 重构盐度RMSE基本随深度增大而减小, 误差峰值位于25m附近, 约为0.25‰。此外, Argo浮标跟踪分析和区域水团统计结果也表明模型能够较好地刻画海洋三维温盐场的内部结构特征。     

SMAP卫星海表盐度产品的检验与校正

In this study, sea surface salinity(SSS) Level 3(L3) daily product derived from soil moisture active passive(SMAP)during the year 2016, was validated and compared with SSS daily products derived from soil Moisture and ocean salinity(SMOS) and in-situ measurements. Generally, the root mean square error(RMSE) of the daily SSS products is larger along the coastal areas and at high latitudes and is smaller in the tropical regions and open oceans. Comparisons between the two types of daily satellite SSS product revealed that the RMSE was higher in the daily SMOS product than in the SMAP, whereas the bias of the daily SMOS was observed to be less than that of the SMAP when compared with Argo floats data. In addition, the latitude-dependent bias and RMSE of the SMAP SSS were found to be primarily influenced by the precipitation and the sea surface temperature(SST). Then, a regression analysis method which has adopted the precipitation and SST data was used to correct the larger bias of the daily SMAP product. It was confirmed that the corrected daily SMAP product could be used for assimilation in high-resolution forecast models, due to the fact that it was demonstrated to be unbiased and much closer to the in-situ measurements than the original uncorrected SMAP product.