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.     

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。     

复合雷达后向散射模型与合成孔径雷达、散射计和高度计海面雷达后向散射观测的比较分析

海洋微波散射模型相比于以经验统计建立的地球物理模式函数具有不受特定微波频率限制的优势。组合布拉格散射模型和几何光学模型形成了复合雷达后向散射模型。利用南海北部气象浮标2014年海面风速风向实测值作为散射模型输入，分别比较了复合雷达后向散射模型与RADARSAT-2卫星C波段SAR、HY-2A卫星Ku波段微波散射计的海面后向散射系数，偏差分别为(?0.22±1.88) dB (SAR)、(0.33±2.71) dB (散射计VV极化）和(?1.35±2.88) dB (散射计HH极化)；以美国浮标数据中心(NDBC)浮标2011年10月1日至2014年9月30日共3年的海面风速、风向实测值作为散射模型输入，分别比较了复合雷达后向散射模型与Jason-2、HY-2A卫星Ku波段高度计海面后向散射系数，偏差分别为(1.01±1.15) dB和(1.12±1.29) dB。中等入射角和垂直入射下的卫星传感器后向散射系数观测值与复合雷达后向散射模型模拟值比较，具有不同的偏差，但具有相同的海面风速检验精度，均方根误差小于1.71 m/s。结果表明，复合雷达后向散射模型可模拟计算星载SAR、散射计和高度计观测条件下的海面雷达后向散射系数，且与CMOD5、NSCAT-2、高度计业务化海面风速反演的地球物理模式函数的计算结果具有一致性；复合雷达后向散射模型可用于微波遥感器的定标与检验、海面雷达后向散射的模拟。     

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.     

基于微波遥感技术海面盐度反演方法

海面盐度(sea surface salinity,SSS)是研究海洋变化及其气候效应重要的物理量,对海洋生态环境、海洋可持续发展至关重要.为了提高海面盐度反演精度,本文通过对SMAP卫星L波段微波辐射计测量的亮温数据进行海面盐度反演研究,考虑风、浪等影响海面粗糙度的环境因子对Klein-Shift模型(简称K-S模型...     

Wind- and wave-field measurements using marine X-band radar-image sequences

This paper describes two algorithms for the retrieval of high-resolution wind and wave fields from radar-image sequences acquired by a marine X-band radar. The wind-field retrieval algorithm consists of two parts. In the first part, wind directions are extracted from wind-induced streaks, which are approximately in line with the mean surface wind direction. The methodology is based on the retrieval of local gradients from the mean radar backscatter image and assumes the surface wind direction to be oriented normal to the local gradient. In the second part, wind speeds are derived from the mean radar cross section. Therefore, the dependence of the radar backscatter on the wind vector and imaging geometry has to be determined. Such a relationship is developed by using neural networks (NNs). For the verification of the algorithm, wind directions and speeds from nearly 3300 radar-image sequences are compared to in situ data from a colocated wind sensor. The wave retrieval algorithm is based on a methodology that, for the first time, enables the inversion of marine radar-image sequences to an elevation-map time series of the ocean surface without prior calibration of the acquisition system, and therefore, independent of external sensors. The retrieved ocean-surface elevation maps are validated by comparison of the resulting radar-derived significant wave heights, with the significant wave heights acquired from three colocated in situ sensors. It is shown that the accuracy of the radar-retrieved significant wave height is consistent with the accuracy of the in situ sensors.     

Geophysical Model Function for Wind Speed Retrieval from SARAL/AltiKa

With the launch of SARAL/AltiKa altimeter, efforts have been made to develop wind speed retrieval algorithms. Here we present two algorithms for estimating and validating wind speed from AltiKa. The first method is based on a theoretical Geophysical Model Function (GMF) using forward model simulations for Ka band specifications. The second is the model function developed using the matched database of input and output vectors of Normalized Radar Cross Section (NRCS) from AltiKa and wind speed measurements from concurrent Jason-2 altimeters. Since the NRCS depends on both the surface roughness due to surface wind speed and on mean square slope of the surfaces, the significant wave height is used along with wind speed for model development as an proxy variable. Both the theoretical and empirical GMFs are evaluated for retrieval of wind speed from AltiKa and validated with NDBC buoys data. The empirical model provide wind speed retrieval accuracy of 1.4 m/s. The accuracy of wind retrievals from theoretical model is also in the similar range (1.6 m/s), indicating the sound physical basis applicable for the future altimeters with various incidence angles. The retrieved wind speed is applied for various case studies, bringing out all the regional and global features quite well.     

Impact of Aquarius Sea-Surface Salinity Assimilation in Improving the Ocean Analysis Over Indian Ocean

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.     

一种QuikSCAT大风地球物理模型及其在台风IOKE风场反演中的应用

The geophysical model function (GMF) describes the relationship between a backscattering and a sea surface wind, and enables a wind vector retrieval from backscattering measurements. It is clear that t...     

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.     

用19.35 GHz星载微波辐射计(SSM/I)亮温反演海面风速

我们发展了一种用19．35GHz星载微波辐射计(SSM／I)亮温反演海面风速的模式，并利用同步的卫星亮温和海面浮标数据反演出海面风速，并且和浮标风速进行比较。为了说明反演算法的可用性，我们分别与目前国际上的通用反演算法的反演结果进行了比较。文章提供了一种新的、用单一波段亮温反演海面风速的方法。     

Evaluation of NSCAT-2 Wind Vectors by Using Statistical Distributions of Wind Speeds and Directions

In order to validate wind vectors derived from the NASA scatterometer (NSCAT), statistical distributions of wind speeds and directions retrieved by the NSCAT-2 geophysical model function have been investigated by comparison with wind data retrieved by the other model functions such as SASS-2 and NSCAT-1 and those derived from the wind analyses of the European Centre for Medium Range Weather Forecasts (ECMWF). The histogram of the NSCAT-2 wind speeds has a similar shape to those of the ECMWF and NSCAT-1 winds, but is slightly shifted toward higher wind speed to adjust negative bias which has been found in the NSCAT-1 winds by previous buoy comparison studies. Variations of the standard deviation of the NSCAT-2 wind speeds with incidence angle are greater than those of the ECMWF and NSCAT-1 winds. The frequency distribution of wind directions relative to spacecraft flight direction has been calculated to assess the self-consistency of the wind directions. It was found that the NSCAT-2 wind vectors exhibit systematic directional preference relative to antenna beams. This artificial directivity is considered to be caused by imperfections in the antenna beam balancing and the geophysical model function. The skill of the ambiguity removal procedure is discussed as a function of wind speed and incidence angle, and is found to be improved compared to the NSCAT-1 winds, especially at high incidence angles. It is concluded that systematic errors in wind directions might be increased by modifications from NSCAT-1 to NSCAT-2, though the wind speed bias is removed and the ambiguity removal skill is improved.     

基于现场观测资料的卫星海表盐度网格化产品误差分析与质量评估

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。     

SMOS卫星盐度数据在中国近岸海域的准确度评估

盐度是描述海洋的关键变量,对海表面盐度进行观测可以推进对全球水循环的理解。本文的主要目的是在中国近海海域对SMOS卫星盐度数据进行准确度评估。主要方法是将SMOS卫星L2海洋盐度数据产品(V317)与实测ARGO数据和走航数据进行匹配,并采用统计学的方法对SMOS卫星数据准确度进行评估。结果表明:匹配数据的线性关系不显著,SMOS卫星盐度数据(V317)在南海和东海的均方根误差分别约为1.2和0.7,应用海表面粗糙度修正模型得到的3组海表盐度数据准确度都相对较低,尤其在近岸强风场区域,海表盐度卫星数据相对于实测数据偏高,这可能是由于海表粗糙度和陆地射频干扰(RFI)作用影响的结果;SMOS卫星数据在东海的均方根误差比南海高0.5左右,这可能是由于东海海域为相对开阔海域,受陆地RFI影响相对南海较小;在中国近岸海域,应用SSS1和SSS3模型得到的盐度数据准确度相对较高,可以对模型进行地球物理参数修正,进行局地化改进,预计可以提高近岸海域盐度反演的准确度。     

Radiometric observations of sea temperature at 2.65 GHZ over the chesapeake bay

The present work describes the various corrections necessary in order to deduce ocean surface temperature fromS-band microwave radiometer measurements and applies these results to a series of data obtained with a high absolute accuracy radiometer. Measurements made with a 2.65 GHz radiometer from an aircraft flown over the Chesapeake Bay area are presented and compared in detail with accurately obtained sea truth data. For the calm sea, it was found that the observed brightness temperature agreed well with that calculated from the known sea surface and atmospheric properties over a fairly wide range of surface salinity values (0.2 per mille to 25 per mille). For cases where the surface wind speeds are of the order of 7 to 15 knots, an excess brightness temperature was observed which is attributable to surface roughness and microscale surface disturbances. The excess brightness temperature dependence on wind speed was found to correlate to a certain extent with the rms wave slope dependence on wind speed.     

基于SAR图像雨团足印的海面风向提取方法

利用地球物理模式函数进行SAR海面风速反演时，需以风向作为地球物理模式函数的输入。本文应用了一种利用SAR图像上雨团足印顺风一侧比逆风一侧明亮的图像特征的海面风向提取方法，以进行海面风速反演。4景RADARSAT-2卫星SAR示例数据风向提取结果相对于ASCAT散射计的风向均方根误差满足不大于16°。分别以本文方法提取的风向和ASCAT散射计风向作为输入，利用地球物理模式函数CMOD5进行海面风速的SAR反演，两者的风速反演结果基本一致，其均方根误差差值不超过0.3 m/s。本文利用SAR图像雨团足印信息的风向提取方法准确可靠，可应用于SAR海面风速反演。     

基于C-2PO模型和CMOD5.N地球物理模式函数的SAR风速反演性能评估

本文选取142幅RADARSAT-2全极化合成孔径雷达（SAR）影像，在没有入射角输入的情况下，首先利用C-2PO模型进行海面风速反演。随后，将同一时空下的ASCAT散射计风向作为初始风向，提取相应雷达入射角，利用地球物理模式函数（GMF） CMOD5.N对142幅SAR影像进行风速计算。反演结果与美国国家资料浮标中心海洋浮标风速数据对比，结果显示:CMOD5.N GMF和C-2PO模型均可反演出较高精确度的海面风速，其均方根误差分别为1.68 m/s和1.74 m/s。此外，研究发现，在低风速段，CMOD5.N GMF的风速反演精度要明显优于C-2PO模型。针对这一现象，本文以SAR系统成像机理为基础，以低风速SAR图像为具体案例，给出了3种造成这一现象的原因。     

Measurement of ocean surface winds using synthetic aperture radars

A methodology for retrieving high-resolution ocean surface wind fields from satellite-borne synthetic aperture radar (SAR) data is introduced and validated. The algorithms developed are suited for ocean SAR data, which were acquired at the C band of either vertical (VV) or horizontal (HH) polarization in transmission and reception. Wind directions are extracted from wind-induced streaks that are visible in SAR images of the ocean at horizontal scales greater than 200 m. These wind streaks are very well aligned with the mean surface wind direction. To extract the orientation of these streaks, two algorithms are introduced, which are applied either in the spatial or spectral domain. Ocean surface wind speeds are derived from the normalized radar cross section (NRCS) and image geometry of the calibrated SAR images, together with the local SAR-retrieved wind direction. Therefore, several C-band models (CMOD IFR2, CMOD4, and CMODS) are available, which were developed for VV polarization, and have to be extended for HH polarization. To compare the different algorithms and C-band models as well as demonstrate their applicability, SAR-retrieved wind fields are compared to numerical-model results considering advanced SAR (ASAR) data from Environmental Satellite (ENVISAT), a European satellite.     

A Spectral Approach for Determining Altimeter Wind Speed Model Functions

We propose a new analytical algorithm for the estimation of wind speeds from altimeter data using the mean square slope of the ocean surface, which is obtained by integration of a widely accepted wind-wave spectrum including the gravity-capillary wave range. It indicates that the normalized radar cross section depends not only on the wind speed but also on the wave age. The wave state effect on the altimeter radar return becomes remarkable with increasing wind speed and cannot be neglected at high wind speeds. A relationship between wave age and nondimensional wave height based on buoy observational data is applied to compute the wave age using the significant wave height of ocean waves, which could be simultaneously obtained from altimeter data. Comparison with actual data shows that this new algorithm produces more reliable wind speeds than do empirical algorithms. This revised version was published online in July 2006 with corrections to the Cover Date.     

“龙王”台风SAR遥感研究

利用0519号"龙王"台风SAR遥感图像,结合NCEP/QSCAT混合风场海面风向反演了高分辨的台风海面风速。基于高分辨率的SAR台风海面风场的风速剖面,对台风期间台湾海峡及周边海域海面风场的小尺度特征及变化进行了分析,结果表明,地形对风场特征的形成有显著作用,它导致台风海面风场结构发生变形以及澎湖列岛附近低风速尾流区、台湾岛的中央山脉北端下风面"角流"区和台湾岛西北海岸背风槽(或诱生低压)等现象的形成及台风期间福建省沿海区的大风天气。