最新SMOS卫星海表盐度L3/4级产品的误差分析

选取SMOS任务的2个海洋盐度专家中心(法国的CATDS-CECOS和西班牙的BEC)的5种经过再处理的新版SSS L3/4产品作为研究对象,以Argo浮标资料及WOA09资料作为参考标准,对其误差特征进行了细致的分析比较,为将其同化到海洋模式中以及用于其它海洋学的分析应用研究,提供必要的参考。主要结论如下:SMOS年平均海表盐度场与WOA09资料很接近,一些已知的重要的分布形势都有所体现;大洋中部误差较小,近陆误差大;热带误差较小,高纬地区误差较大;三大洋中太平洋均方根误差最小。随着时空分辨率的降低,SMOS SSS资料的均方根误差显著减小。检验的几种资料中,CATDS/CEC-OS处理制作的月平均海表盐度L3级产品误差最小,全球平均均方根误差(RMSE)为0.314;另外几种高分辨率产品中,除由BEC制作的简单加权平均产品均方根误差最大,全球平均0.543以外,其他3种资料的均方根误差量级相当,差异不太明显,全球平均的RMSE为0.3~0.4;BEC的两种分析产品总体上RMSE更小。     

基于Aquarius 卫星数据的孟加拉湾海表盐度分析

海洋表面盐度(Sea Surface Salinity,SSS)是海洋的重要物理和化学参量,SSS的时空分布与全球大洋环流和水汽循环密切相关。本文基于美国国家航空航天局(NASA)发射的Aquarius卫星3 a的SSS遥感数据,给出了孟加拉湾及其附近海域海表盐度的空间分布特征,并重点分析了影响孟加拉湾海表盐度变化的可能因素。研究结果从一个侧面说明了利用Aquarius卫星遥感观测海洋大尺度盐度变化的可行性。     

遥感海表盐度分析产品的有效分辨率研究

针对SMOS和Aquarius海表盐度误差分析没有区分不同空间频谱信噪特征的问题,基于6种主要的遥感盐度分析产品,根据定性图像、纬向波数谱、均方根误差等指标,分析产品的有效分辨率并探讨其原因机制。研究表明:CATDS-0.25°分析产品所描述的盐度场中小尺度结构失真,其较高谱能量密度在热带海域以噪音为主,而在西边界流等海域以信号为主;BEC-L3-0.25°有着较小的均方根误差、清晰的盐度图像、显著的中尺度能量,最适于描绘中尺度(25~100 km)盐度特征;BEC-L4-0.25°被奇异谱分析方法过度平滑了盐度场;Aquarius-V2-1.00°通过局部平滑处理,在描述大尺度(＞100 km)盐度现象方面表现较好;Aquarius-CAP-1.00°通过主动-被动联合算法(CAP)减小了均方根误差,但图像中卫星轨道形态明显;CATDS-1.00°的图像形态、能量分布和误差特征与Aquarius-V2-1.00°相当。这些结论可为用户正确使用产品进行地球物理学研究提供参考。     

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.     

孟加拉湾海表盐度变化特征的卫星遥感应用研究

本文利用2011年8月至2014年3月Aquarius卫星盐度产品结合Argo等实测盐度资料,探讨了孟加拉湾海表盐度的季节及年际变化特征。结果显示,Aquarius与Argo盐度呈显著线性正相关,总体较Argo盐度值低,偏差为-0.13,其中在孟加拉湾北部海域负偏差值比南部海域更大,分别为-0.28和-0.10。Aquarius卫星与Argo浮标在表层盐度观测深度上的差别是造成此系统偏差的主因。Aquarius盐度资料清晰显示了孟加拉湾海表盐度具有明显的季节变化特征,包括阿拉伯海高盐水的入侵引起湾南部海域盐度的变化以及湾北部淡水羽分布范围的季节性迁移等主要特征。此外,分析还揭示了2011(2012)年春季整个湾内出现异常高盐(低盐)现象。研究表明,2010(2011)年湾北部夏季降雨减少(增加)导致该海域海水盐度偏高(偏低),并通过表层环流向南输运引起次年春季湾内表层盐度出现异常高盐(低盐)现象,春季风应力旋度正(负)距平通过影响盐度垂直混合过程对同期表层盐度异常高盐(低盐)变化也有影响。     

SMOS卫星遥感海表盐度资料处理应用研究进展

土壤湿度和海洋盐度卫星首次提供了覆盖全球的高频率、高精度、业务化的海表盐度产品,但其处理和延伸应用仍处于初级阶段,后续校准校正工作还将持续数年,如何及时把握其发展轨迹成为一个重要的科学问题.本研究从SMOS计划、数据概况、盐度反演算法、格点产品制作、多源数据融合和产品应用等方面,介绍和评述了SMOS计划及其海表盐度产品应用研究进展,着重分析了反演算法中的各种误差来源,对在轨2 a的运行情况进行了回顾、对未来的发展重点进行了展望,旨在为开发和应用SMOS产品提供参考.     

一种利用SMOS卫星观测的海表面盐度反演海水盐度廓线的方法

This paper proposes a new method to retrieve salinity profiles from the sea surface salinity(SSS) observed by the Soil Moisture and Ocean Salinity(SMOS) satellite. The main vertical patterns of the salinity profiles are firstly extracted from the salinity profiles measured by Argo using the empirical orthogonal function. To determine the time coefficients for each vertical pattern, two statistical models are developed. In the linear model, a transfer function is proposed to relate the SSS observed by SMOS(SMOS_SSS) with that measured by Argo, and then a linear relationship between the SMOS_SSS and the time coefficient is established. In the nonlinear model, the neural network is utilized to estimate the time coefficients from SMOS_SSS, months and positions of the salinity profiles. The two models are validated by comparing the salinity profiles retrieved from SMOS with those measured by Argo and the climatological salinities. The root-mean-square error(RMSE) of the linear and nonlinear model are 0.08–0.16 and 0.08–0.14 for the upper 400 m, which are 0.01–0.07 and 0.01–0.09 smaller than the RMSE of climatology. The error sources of the method are also discussed.     

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.     

SMAP卫星海表盐度产品精度评定与全球海表盐度特征分析

海表盐度是研究海洋变化及其气候效应重要的物理量。本文将2018年SMAP卫星的月均、日均海表盐度产品分别与Argo月均网格化产品、实时散点盐度数据进行比较,评定其精度,并分析全球海表盐度分布特征。结果表明：SMAP卫星月均产品RMSE为0.17,BIAS为0.11,STD为0.17,R为0.98,t检验呈显著相关;SMAP卫星日均产品RMSE为0.28,BIAS为0.23,STD为0.26,R为0.81,相较月均产品,精度较低。SMAP卫星月均产品偏差在中纬度海域较小,在高纬度海域较大;SMAP卫星日均产品偏差在太平洋海域为-0.6~0.6,在地中海海域超过1.0。全球海表盐度在25.0~40.0之间,沿纬度方向呈带状分布,其中大西洋海表盐度普遍高于太平洋和印度洋。     

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...     

降雨导致的海面粗糙度对Aquarius卫星盐度反演的影响研究

Rainfall has two significant effects on the sea surface, including salinity decreasing and surface becoming rougher,which have further influence on L-band sea surface emissivity. Investigations using the Aquarius and TRMM 3B42 matchup dataset indicate that the retrieved sea surface salinity(SSS) is underestimated by the present Aquarius algorithm compared to numerical model outputs, especially in cases of a high rain rate. For example, the bias between satellite-observed SSS and numerical model SSS is approximately 2 when the rain rate is 25 mm/h. The bias can be eliminated by accounting for rain-induced roughness, which is usually modeled by rain-generated ring-wave spectrum. The rain spectrum will be input into the Small Slope Approximation(SSA) model for the simulation of sea surface emissivity influenced by rain. The comparison with theoretical model indicated that the empirical model of rain spectrumis more suitable to be used in the simulation. Further, the coefficients of the rain spectrum are modified by fitting the simulations with the observations of the 2–year Aquarius and TRMM matchup dataset. The calculations confirm that the sea surface emissivity increases with the wind speed and rain rate. The increase induced by the rain rate is rapid in the case of low rain rate and low wind speed. Finally, a modified model of sea surface emissivity including the rain spectrum is proposed and validated by using the matchup dataset in May 2014. Compared with observations, the bias of the rain-induced sea surface emissivity simulated by the modified modelis approximately 1e–4, and the RMSE is slightly larger than 1e–3. With using more matchup data, thebias between model retrieved sea surface salinities and observationsmay be further corrected,and the RMSE may be reduced to less than 1 in the cases of low rain rate and low wind speed.     

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.     

无线射频干扰对SMOS卫星盐度数据产品准确度的影响研究

SMOS卫星数据发布以来,相关学者针对海表盐度数据开展了大量的真实性检验工作,但是在受无线射频干扰（RFI）影响海域开展的相关工作很少。本文以西太平洋海域为研究区域,选择合理的时空匹配窗口,将WOD13实测海表盐度数据与SMOS卫星单轨海表盐度数据进行数据匹配,采用统计学方法开展SMOS卫星数据真实性检验,并分析RFI对SMOS卫星数据的影响。结果表明,SMOS卫星受分布在西太平洋沿岸射频干扰源的影响,RFI污染高风险区单轨L2数据准确度相对较低,最优仅为3.45,RFI污染低风险区的卫星数据准确度最优为1.07,可见,RFI对单轨卫星数据准确度的影响很大,最终导致西太平洋海域西部大面积海域数据缺失,尤其是中国近海海域,如何检测和减缓RFI对卫星数据的影响是亟待解决的问题。