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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。 相似文献
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海表面盐度(Sea Surface Salinity, SSS)是研究大洋环流和海洋对全球气候影响的关键参数之一。目前借助卫星遥感技术获取全天候和连续的SSS是最有效的方法,但是SSS的反演精度在大部分海域达不到预期目标。众所周知,海表面亮温是反演SSS的关键因素之一,海面粗糙度导致了亮温增量的产生,亮温正演模型的误差会影响盐度反演的精度。本文首次提出了依据6个风带划分全球海域,利用Argo实测盐度数据、SMOS卫星数据和相关辅助数据,通过LASSO统计方法在各风带覆盖的海域构建了一个全新的二次曲线亮温增量模型,再通过贝叶斯迭代反演算法计算出了各个海域的SSS产品。与Argo实测SSS对比,新模型下6部分海域反演SSS的绝对平均误差分别为0.76、0.88、0.93、0.92、1.28和1.21,均显著优于修正前(SMOS L2 SSS)产品的误差(0.98、1.61、2.82、1.50、2.35和3.13)。 相似文献
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针对传统海表盐度遥感反演精度不高、影响因素较少等问题,本文基于SMAP（Soil Moisture Active Passive）卫星L2C（Level 2 C）数据、Argo（Array for Real-time Geostrophic Oceanography）数据和其他辅助数据,以太平洋部分海域（160°E～120°W,0°～30°N）为研究区域,综合考虑海面粗糙度以及白冠覆盖率等参量,利用径向基神经网络建立RBF亮温增量模型,并对平静海面亮温进行修正,然后基于Meissner-Wentz介电常数模型得到反演后的盐度值。验证结果表明：模型预测盐度和SMAP卫星盐度相对于Argo实测盐度的均方根误差分别为0.4和0.5,平均绝对误差分别为0.3和0.4。实验证明,利用RBF神经网络建立的亮温增量模型可以提高海表盐度反演的精度,对海表盐度反演具有实用意义。 相似文献
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以西太平洋为研究区域,利用Argo浮标表层盐度观测值(5 m)对SMAP卫星获得的2016年海表面盐度反演质量进行了评估。首先将西太平洋2016-01—12期间的每日和每月SMAP卫星SSS数据与Argo实测SSS数据进行匹配,然后利用最小二乘线性回归法对其进行相关性分析,并对误差的分布特征进行了研究。结果表明:SMAP SSS与Argo SSS之间具有极显著的正相关关系;每日Argo浮标数据(WMO ID:2901520,WMO ID:2901548)和SMAP SSS的变化趋势基本一致,前者均方根误差(RMSE)、偏差(Bias)和相关系数(r)分别为0.43, 0.34和0.71,后者RMSE,Bias和r分别为0.41,0.26和0.69;研究区域内全年RMSE值处于0～0.35,在西太平洋南部海域偏差较大,这可能是由于该海域小岛众多,缺少Argo实测数据,导致其网格化的盐度存在较大误差。除夏季外,研究区域的大部分海域,RMSE都小于0.25。在海表盐度较低的海域,两者的对比结果误差较大,该现象在夏秋两季尤为显著。 相似文献
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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。 相似文献
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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. 相似文献
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基于多元线性回归方法,利用2013-01-06的AMSR2辐射计亮温数据和红外-微波融合SST数据产品,开展了近海面气温反演算法研究,并用TAO,RAMA和PIRATA等浮标实测数据对近海面气温的反演结果进行检验。近海面气温反演结果误差情况:均方根误差为0.66℃,偏差为0.02℃,相关系数R为0.91,该误差结果表明所建立近海面气温反演算法较好的反映在60°S~60°N纬度范围内的近海面气温分布情况;同时为进一步确定不同纬度近海面气温反演的误差分布,将近海面气温反演结果与ECMWF再分析数据进行了对比分析,结果表明,从赤道起算,纬度每升高或降低1°,反演均方根误差约增大0.1℃。 相似文献
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射频干扰RFI(Radio-Frequency Interference)的校正和抑制一直是微波遥感研究领域的重要问题之一。本文基于SMAP卫星2015年5月1日至5日L波段辐射计亮温数据对中国近海及沿岸的射频干扰进行了特征分析。研究发现RFI分布不均匀,主要集中在城市群及其周边地区;通过对比辐射计天线接收的亮温数据与射频干扰校正和抑制后的数据,发现研究区域主要的射频干扰(95%)的平均值为1.4 K,整体射频干扰平均值为2.5 K,标准差为6.5;研究区域中不同波段和极化的数据受到射频干扰影响较为相似,受射频干扰影响最大的是升轨的垂直极化数据,受影响最小的是升轨的水平极化数据。 相似文献
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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. 相似文献
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自欧洲土壤湿度和盐度卫星SMOS和美国宝瓶座盐度卫星Aquarius相继发射之后,多个数据中心发布了两颗卫星的海表盐度网格化产品,其中包括法国海洋研究院SMOS卫星数据小组发布SMOS Locean L3盐度产品、西班牙巴塞罗那专家中心发布SMOS BEC L4盐度产品和美国宇航局喷气动力实验室发布AquariusV3.0 CAP L3盐度产品。本文利用精确盐度现场观测资料从产品精度和模拟海洋现象能力两个方面对以上3种产品质量进行了评估。研究表明:(1) 在精度方面,与盐度现场资料相比,Aquarius CAP 产品质量最高,产品盐度偏差和均方根误差全年稳定且偏差较小,部分海域达到了设计精度;SMOS两种卫星产品在全球海域偏差较不稳定,个别月份出现异常偏差值;SMOS产品在低纬和开阔海域的数据质量相对较高,但在高纬海域仍存在较大误差,需要进一步提升;(2) 在刻画海洋现象方面,Aquarius产品在热带太平洋较好刻画了淡池东缘盐度锋,SMOS BEC产品的刻画能力次之,SMOS Locean产品在热带太平洋充满了小尺度噪音,描述物理现象方面表现偏差。 相似文献
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Roughness-induced emission from ocean surfaces is one of the main issues that affects the retrieval accuracy of sea surface salinity remote sensing. In previous studies, the correction of roughness effect mainly depended on wind speeds retrieved from scatterometers or those provided by other means, which necessitates a high requirement for accuracy and synchronicity of wind-speed measurements. The aim of this study is to develop a novel roughness correction model of ocean emissivity for the salinity retrieval application. The combined active/passive observations of normalized radar cross-sections (NRCSs) and emissivities from ocean surfaces given by the L-band Aquarius/SAC-D mission, and the auxiliary wind directions collocated from the National Centers for Environmental Prediction (NCEP) dataset are used for model development. The model is validated against the observations and the Aquarius standard algorithms of roughness-induced emissivity correction. Comparisons between model computations and measurements indicate that the model has better accuracy in computing wind-induced brightness temperature in the upwind/downwind directions or for the surfaces with smaller NRCSs, which can be better than 0.3 K. However, for crosswind directions and larger NRCSs, the model accuracy is relatively low. A model using HH-polarized NRCSs yields better accuracy than that using VV-polarized ones. For a fair comparison to the Aquarius standard algorithms using wind speeds retrieved from multi-source data, the maximum likelihood estimation is employed to produce results combining our model calculations and those using other sources. Numerical simulations show that combined results basically have higher accuracy than the standard algorithms. 相似文献
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The distribution of ocean salinity controls the density field and thereby plays a major role in influencing the ocean dynamics. It has been a challenging task to understand the variability of salinity structure in the regions of large fresh water discharge and high precipitation such as Bay of Bengal (BoB). Recent advancement in satellite technology has made possible the measurement of sea surface salinity (SSS). Aquarius is the satellite which measured the global SSS for the period 2011 to 2015. In the present study, we assimilated Aquarius SSS in the Global Ocean Data Assimilation System based on 3DVAR technique. The assimilation of Aquarius SSS resulted in reduced biases in salinity not only at the surface, but also in the vertical distribution of salinity and better captured the temporal variations of salinity structure in sensitive regions, such as the Bay of Bengal. In addition, the assimilation of SSS showed marginal improvement in ocean thermal structure over data sparse regions of Indian Ocean. It is also shown that the assimilation of Aquarius SSS has improved the stratification in the upper Ocean which is the key factor in the observed improvement in ocean analysis. 相似文献
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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. 相似文献
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水下滑翔机可以高效地观测海水的温度、盐度和压强等海洋参数,但由于热滞后效应,盐度数据,特别是在温度梯度较大的温跃层,会出现一定程度的偏差。本研究选取了3种目前常用的盐度热滞后订正方法,对带泵的“海翼号”水下滑翔机,于2019年8月在中北太平洋所观测的盐度数据因热滞后效应引起的偏差进行订正处理,与船载911型温盐深测量仪(Instrument for Measuring Conductivity Temperature and Depth,CTD)观测盐度进行对比,在比较了3种方法对滑翔机盐度订正前后下降和上升剖面偏差的减少程度、订正后剖面与船载CTD观测剖面的偏差大小和下降上升温盐曲线(T-S曲线)的一致程度后,得出了水下滑翔机盐度订正的最优方法,即在订正电导池中实际温度的前提下,采用计算机图形分割方法,最小化滑翔机机载CTD测得的下降和上升两个剖面T-S曲线围成面积所确定的目标函数,来确定合适的热滞后修正振幅和时间常数,从而修正下降和上升两个剖面之间盐度偏差。 相似文献
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基于ROMS模式利用集合最优插值同化沿轨海表面高度异常数据的研究 总被引：2，自引：1，他引：1
The ensemble optimal interpolation （EnOI） is applied to the regional ocean modeling system （ROMS） with the ability to assimilate the along-track sea level anomaly （TSLA）. This system is tested with an eddy-resolving system of the South China Sea （SCS）. Background errors are derived from a running seasonal ensemble to account for the seasonal variability within the SCS. A fifth-order localization function with a 250 km localization radius is chosen to reduce the negative effects of sampling errors. The data assimilation system is tested from January 2004 to December 2006. The results show that the root mean square deviation （RMSD） of the sea level anomaly decreased from 10.57 to 6.70 cm, which represents a 36.6% reduction of error. The data assimilation reduces error for temperature within the upper 800 m and for salinity within the upper 200 m, although error degrades slightly at deeper depths. Surface currents are in better agreement with trajectories of surface drifters after data assimilation. The variance of sea level improves significantly in terms of both the amplitude and position of the strong and weak variance regions after assimilating TSLA. Results with AGE error （AGE） perform better than no AGE error （NoAGE） when considering the improvements of the temperature and the salinity. Furthermore, reasons for the extremely strong variability in the northern SCS in high resolution models are investigated. The results demonstrate that the strong variability of sea level in the high resolution model is caused by an extremely strong Kuroshio intrusion. Therefore, it is demonstrated that it is necessary to assimilate the TSLA in order to better simulate the SCS with high resolution models. 相似文献
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A Reconstruction of Observed Profiles in the Sea East of Japan Using Vertical Coupled Temperature-Salinity EOF Modes 总被引：4，自引：0，他引：4
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. 相似文献
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The impact of assimilating Argo data into an initial field on the short-term forecasting accuracy of temper- ature and salinity is quantitatively estimated by using a forecasting system of the western North Pacific, on the base of the Princeton ocean model with a generalized coordinate system （POMgcs）. This system uses a sequential multigrid three-dimensional variational （3DVAR） analysis scheme to assimilate observation da- ta. Two numerical experiments were conducted with and without Argo temperature and salinity profile data besides conventional temperature and salinity profile data and sea surface height anomaly （SSHa） and sea surface temperature （SST） in the process of assimilating data into the initial fields. The forecast errors are estimated by using independent temperature and salinity profiles during the forecasting period, including the vertical distributions of the horizontally averaged root mean square errors （H-RMSEs） and the horizontal distributions of the vertically averaged mean errors （MEs） and the temporal variation of spatially averaged root mean square errors （S-RMSEs）. Comparison between the two experiments shows that the assimila- tion of Argo data significantly improves the forecast accuracy, with 24% reduction of H-RMSE maximum for the temperature, and the salinity forecasts are improved more obviously, averagely dropping of 50% for H-RMSEs in depth shallower than 300 m. Such improvement is caused by relatively uniform sampling of both temperature and salinity from the Argo drifters in time and space. 相似文献