基于全极化微波辐射计WindSat亮温数据的海面风场反演

根据全极化微波辐射传输理论,利用双尺度模式建立了海面辐射亮温的反演算法,并且利用美国发射的全球第一个星载全极化微波辐射计(WindSat)在轨运行期间的亮温数据进行了海面风场的反演,重点分析了风向反演的模糊度问题,并对风场反演结果进行了评估。研究结果初步验证了全极化辐射计在卫星上遥感海面风场的能力:与美国国家环境预报中心(NECP)的数据进行比较,反演的风速误差为1.15m/s,5m/s 以上风速下的风向误差为21°;与 TAO 浮标数据进行比较,风速误差为1.4m/s,风速5m/s 以上的风向误差为20.5°。

WindSat海温产品与SVP浮标资料对比分析

本文利用SVP漂流浮标海面观测资料对Wind Sat SST进行了对比验证。全球尺度的对比分析显示平均偏差为0.01℃,标准偏差约为0.56℃。总体看,Wind Sat海温高于SVP海温,标准偏差随着年份增加有增大趋势。

全极化微波辐射成像仪WindSat海面风矢量反演简化正向模型

WindSat是世界上第1个星载全极化微波辐射成像仪,其海面风矢量的反演基于较复杂的双尺度海面辐射正向模型以及非线性优化算法。1个简单而足够准确的正向模型将可提供快速的反演算法。本文利用WindSat提供的Stokes参数以及同步的QuikScat海面风矢量、TMI海表温度/大气水汽总量/液态水总量构成的数据集,建立了WindSat的简化正向模型。其中,垂直/水平极化亮温风速模型基于改进的Wentz模型,Stokes3/4参数风向模型基于统计回归方法。简化正向模型模拟结果与WindSat观测数据比较,以10.7GHz为例,4个Stokes参数的均方根误差分别为0.52、0.42、0.34、0.22K。

WindSat海表面温度产品误差的空间分布研究

将2006-2015年的WindSatSST数据与Hadley中心的HadISST海温数据进行对比分析,探究误差的空间分布。WindSat海温与Hadley海温总体误差较小,西北大西洋和西北太平洋的沿岸海域偏差较大。北半球较大误差分布区域7月份向北移动,1月份向南移动。

一种针对Windsat极化辐射计的海面风场反演方法

星载极化微波辐射计(Windsat)是美国于2003年1月6日发射的全球第一颗星载极化微波辐射计卫星,目的是为了验证极化辐射计在卫星上遥感海面风场的能力。针对Windsat在轨运行期间的数据,研究了风场反演的海洋和大气算法,进行了全球海面风场的反演,同时反演出其它地球物理参数。最后利用同步的其它数据对反演结果进行了验证。本文在极化辐射计风场反演方法和算法研究方面做了初步的尝试。

WindSat satellite comparisons with nearshore buoy wind data near the U.S. west and east coasts

Nearshore wind speeds retrieved by WindSat are validated by a comparison with the moored buoy observations near the U.S. west and east coasts. A 30 min and 25 km collection window is used for the WindSat wind data and buoy measurements from ]anuary 2004 to December 2014. Comparisons show that the overall root-mean-square error is better than 1.44 m/s near the U.S. coasts, and the result for the east coast is better than that for the west coast. The retrieval accuracy of the descending portions is slightly better than that of the ascending portions. Most buoy-to-buoy variations are not significantly correlated with the coastal topography, the longitude and the distance from the shore or satellite-buoy separation distance. In addition, comparisons between a polarimetric microwave radiometer and a microwave scatterometer are accomplished with the nearshore buoy observations from 2007 to 2008. The WindSat-derived winds tend to be lower than the buoy observations near the U.S. coasts. In contrast, the QuikSCAT-derived winds tend to be higher than the buoy observations. Overall, the retrieval accuracy of WindSat is slightly better than that of QuikSCAT, and these satellite-derived winds are sufficiently accurate for scientific studies.

基于海岛站探空资料的WindSat海洋大气可降水量产品检验

利用时空匹配的15个海岛站的探空资料对WindSat 2007—2015年的海洋大气可降水量产品(total precipitable water,TPW)进行了检验,并分析了造成两者差异的原因。结果表明:WindSat反演的海洋大气可降水量产品与探空比对的一致性较好,两者平均偏差为-0.43mm,均方根误差为3.14mm,标准偏差为3.11mm,相关系数达到了0.98;WindSat在中高纬度地区反演效果较好,均方根误差在各个站点均小于3mm;在低纬度地区WindSat反演精度较差,均方根误差大于5mm。低风速对WindSat可降水反演精度影响较大;海面温度和云中液态水含量与大气可降水量产品之间无明显相关关系;WindSat反演精度随纬度降低下降明显;利用白天探空释放所得到的水汽数据存在干性偏差。

Comparison of sea surface wind field measured by HY-2A scatterometer and WindSat in global oceans

In this study, we present a comprehensive comparison of the sea surface wind ?eld measured by scatterometer(Ku-band scatterometer) aboard the Chinese HY-2 A satellite and the full-polarimetric radiometer WindSat aboard the Coriolis satellite. The two datasets cover a four-year period from October2011 to September 2015 in the global oceans. For the sea surface wind speed, the statistical comparison indicates good agreement between the HY-2 A scatterometer and WindSat with a bias of nearly 0 m/s and a root mean square error(RMSE) of 1.13 m/s. For the sea surface wind direction, a bias of 1.41° and an RMSE of 20.39° were achieved after excluding the data collocated with opposing directions. Furthermore,discrepancies in sea surface wind speed measured by the two sensors in the global oceans were investigated.It is found that the larger dif ferences mainly appear in the westerlies in the both hemispheres. Both the bias and RMSE show latitude dependence, i.e., they have signi?cant latitudinal ?uctuations.

基于WindSat数据的南极区域海冰密集度反演研究

海冰作为全球气候系统的重要组成部分,对大气环流、气候变化、海洋环境等方面有着显著影响,其密集度是反映海冰物理性质的一个重要参数。文章针对海冰密集度反演存在的误差问题,提出采用NASA-Team算法对相关参数进行重新修正和计算,再采用阈值滤波器和动态滤波器对空气滤波器进行适当改进,联合减小海冰反演误差。利用南极区域的WindSat亮温数据,重新计算了算法中的相关参数,对算法的误差进行了分析,并设计了新的空气滤波器。反演得到以10%为边缘的海冰密集度分布,海冰密集度的边缘误差和气候造成的偶发性误差得到一定减少,与SSMI数据相关系数为0.951,并且与国家冰雪中心资料具有较高的一致性,阐明了基于WindSat亮温数据修正的NT算法用于反演海冰密集度的可行性。

Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model

WindSat/Coriolis is the first satellite-borne polarimetric microwave radiometer, which aims to improve the potential of polarimetric microwave radiometry for measuring sea surface wind vectors from space. In this paper, a wind vector retrieval algorithm based on a novel and simple forward model was developed for WindSat. The retrieval algorithm of sea surface wind speed was developed using multiple linear regression based on the simulation dataset of the novel forward model. Sea surface wind directions that minimize the difference between simulated and measured values of the third and fourth Stokes parameters were found using maximum likelihood estimation, by which a group of ambiguous wind directions was obtained. A median filter was then used to remove ambiguity of wind direction. Evaluated with sea surface wind speed and direction data from the U.S. National Data Buoy Center (NDBC), root mean square errors are 1.2 m/s and 30° for retrieved wind speed and wind direction, respectively. The evaluation results suggest that the simple forward model and the retrieval algorithm are practicable for near-real time applications, without reducing accuracy.

Three-dimensional variational data assimilation of WindSat ocean surface winds for the genesis and forecasting of tropical storm Henri

With available high-resolution ocean surface wind vectors retrieved from the U.S.Naval Research Laboratory's WindSat on Coriolis,the impact of these data on genesis and forecasting of tropical storm Henri is examined using the non-hydrostatic,fifth-generation mesoscale model(MM5) of Pennsylvania State University-National Center for Atmospheric Research plus its newly released three-dimensional variational data assimilation(3DVAR) system.It is shown that the assimilation of the WindSat-retrieved ocean surface wind vectors in the 3DVAR system improves the model initialization fields by introducing a stronger vortex in the lower troposphere.As a result,the model reproduces the storm formation and track reasonably close to the observations.Compared to the experiment without the WindSat surface winds,the WindSat assimilation reduced an error between the model simulated track and observations of more than 80 km and also improved the storm intensity by nearly 2 hPa.It suggests that these data could provide early detection and prediction of tropical storms or hurricanes.

2004年—2013年WindSat海表面温度产品与浮标观测对比

利用美国国家浮标数据中心NDBC和热带大气海洋计划TAO浮标的海表面温度数据,对Wind Sat 2004年—2013年近10年的海表面温度产品进行了验证。结果表明,在美国沿岸海域,Wind Sat反演得到的海表面温度的平均偏差为0.10°C,标准偏差为0.59°C;在近赤道太平洋海域,反演得到的海表面温度的平均偏差为–0.15°C,标准偏差为0.33°C。Wind Sat海表面温度在夏季相对浮标实测值有正偏差增大和负偏差缩小的趋势,在美国东海岸以及墨西哥湾区域部分站点反演得到的海表面温度的标准偏差较大,其标准偏差超过1°C。在5–10 m/s风速段,Wind Sat海表面温度反演效果比较理想,平均偏差和标准偏差相对恒定。当风速大于12 m/s时,Wind Sat海表面温度反演的不确定性明显增加。与AMSR-E月平均海表面温度产品对比发现,夏季,Wind Sat SST较AMSRE偏低;冬季,Wind Sat SST较AMSR-E偏高。

一种适用于WindSat的地理定位方法

星载全极化微波辐射计WindSat为海面风场的被动微波遥感提供了有效的手段。WindSat亮温需要经过地理定位才能形成包含目标位置信息的有效物理量。地理定位是辐射计数据预处理的重要环节。针对WindSat圆锥扫描方式,将Patt94定位算法进行修改,应用于WindSat地理定位中。详述了定位的计算过程,给出了定位结果,讨论了误差分析方法。通过模拟WindSat轨道,对可能的定位误差来源展开分析,着重分析了辐射计姿态误差、扫描方位误差对定位的影响。最后,就辐射计姿态校正进行了讨论。

一种基于海面背景的WindSat极化通道替代定标方法研究

凭借能够测量海面辐射全部4个Stokes参数的能力,全极化微波辐射计成为测量海面风场的一种新手段。由于风向信号仅仅只有几K甚至更小的振幅,故对全极化微波辐射计极化通道的定标精度要求颇高。使用WindSat实测亮温及其海面参数匹配数据集,基于海面风向信号的谐波特征,对WindSat极化通道亮温的定标偏差和仪器噪声进行了计算和分析。研究结果表明:WindSat的10.7、18.7和37.0GHz这3个频率的极化通道均存在明显的定标偏差,并且该定标偏差会随着升轨和降轨、前向刈幅和后向刈幅等不同观测模态发生变化。另外,WindSat极化通道的仪器噪声较小,其中T3的仪器噪声在0.2K以内,T4的仪器噪声在0.1K以内。

大亚湾表层叶绿素浓度时空特征的遥感分析

利用2003―2014年MODIS卫星遥感数据,采用EOF方法分析叶绿素浓度的时空变化特征,并探讨其影响机制。结果表明,大亚湾海域表层叶绿素浓度具有明显的时空分布特征,夏季最高,春季最低,由大亚湾顶向湾口递减,近岸高于离岸。EOF分析获得前3个模态的累积方差贡献率分别是32.5%,16.8%,8.5%,基本反映了大亚湾海域叶绿素浓度变化的主要过程。第1模态权重占绝对优势,其空间模态表明了大亚湾叶绿素浓度以霞涌至中央列岛一带海域为高值中心,变化较大区域主要位于中央列岛北部海域,第1模态的周期变化是13.3个月。第2模态主要体现为季节变化,功率谱显著周期为6个月和12个月,反映了叶绿素浓度的变化以白鹤洲为界东西两个不同的区域,高值区位于圣筑岛的东边,变率较大区域与第1模态基本一致。第3模态的方差贡献率是8.5%,高值区位于中央列岛中部和范和港南部海域,变率最大区域与第1、第2模态一致,第3模态的变化周期是8.57个月和17.14个月。大亚湾叶绿素浓度的分布及变化与海表面温度、降水、风、潮流,以及人类的影响密切相关。

Comparison of Wind Sat and buoy-measured ocean products from 2004 to 2013

To evaluate the ocean surface wind vector and the sea surface temperature obtained from WindSat, we compare these quantities over the time period from January 2004 to December 2013 with moored buoy measurements. The mean bias between the WindSat wind speed and the buoy wind speed is low for the low frequency wind speed product （WSPD_LF）, ranging from -0.07 to 0.08 m/s in different selected areas. The overall RMS error is 0.98 m/s for WSPD_LF, ranging from 0.82 to 1.16 m/s in different selected regions. The wind speed retrieval result in the tropical Ocean is better than that of the coastal and offshore waters of the United States. In addition, the wind speed retrieval accuracy ofWSPD LF is better than that of the medium frequency wind speed product. The crosstalk analysis indicates that the WindSat wind speed retrieval contains some cross influences from the other geophysical parameters, such as sea surface temperature, water vapor and cloud liquid water. The mean bias between the WindSat wind direction and the buoy wind direction ranges from -0.46° to 1.19° in different selected regions. The overall RMS error is 19.59° when the wind speed is greater than 6 m/s. Measurements of the tropical ocean region have a better accuracy than those of the US west and east coasts. Very good agreement is obtained between sea surface temperatures of WindSat and buoy measurements in the tropical Pacific Ocean; the overall RMS error is only 0.36℃, and the retrieval accuracy of the low latitudes is better than that of the middle and high latitudes.

An evaluation of new satellite-derived latent and sensible heat fluxes with moored buoy data, OAFlux and NCEP2 reanalysis products

New satellite-derived latent and sensible heat fluxes are performed by using Wind Sat wind speed, Wind Sat sea surface temperature, the European Centre for Medium-range Weather Forecasting（ECMWF） air humidity, and ECMWF air temperature from 2004 to 2014. The 55 moored buoys are used to validate them by using the 30 min and 25 km collocation window. Furthermore, the objectively analyzed air-sea heat fluxes（OAFlux） products and the National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis 2（NCEP2） products are also used for global comparisons. The mean biases of sensible and latent heat fluxes between Wind Sat flux results and buoy flux data are –0.39 and –8.09 W/m^2, respectively. In addition, the rootmean-square（RMS） errors of the sensible and latent heat fluxes between them are 5.53 and 24.69 W/m^2,respectively. The RMS errors of sensible and latent heat fluxes are observed to gradually increase with an increasing buoy wind speed. The difference shows different characteristics with an increasing sea surface temperature, air humidity, and air temperature. The zonal average latent fluxes have some high regions which are mainly located in the trade wind zones where strong winds carry dry air in January, and the maximum value centers are found in the eastern waters of Japan and on the US east coast. Overall, the seasonal variability is pronounced in the Indian Ocean, the Pacific Ocean, and the Atlantic Ocean. The three sensible and latent heat fluxes have similar latitudinal dependencies; however, some differences are found in some local regions.

卫星遥感反演海面风矢量数据真实性检验与质量评价

微波散射计(QuikSCAT)和微波辐射计(WindSAT)的10m风矢量数据是目前覆盖范围最大、持续时间最长的海面风场卫星数据产品。利用QuikSCAT和WindSAT运行时间重叠的风矢量原始轨道资料,分别与同步的全球海洋浮标实测风矢量资料进行比较。结果显示:QuikSCAT平均风速略大于浮标,平均绝对误差约为1m/s;风向平均绝对误差在8°以下。WindSAT平均风速略大于浮标,平均绝对误差不超过0.5m/s,均方根误差约为1m/s;风向平均绝对误差在10°以下。在全球海域,QuikSCAT和WindSAT风矢量数据质量高,可信度极好。QuikSCAT和WindSAT在同一点上空的过境时间为准同步,并且二者的风矢量数据相关性极好,可以互相替代。

海面风矢量对不同极化状态海表面亮温的遥感影响研究

本文从海面风矢量与不同极化状态下海表面亮温参数的关系入手,利用2014年5月1日西北太平洋区域Windsat卫星L2风场数据和SMOS（Soil Moisture and Ocean Salinity）卫星L1C数据,定量分析了风速和风向对亮温的影响。研究结果表明：海表面亮温的变化,风速大于风向的影响;V极化状态下垂直亮温对风速、风向的敏感性最强,Stokes2亮温参数对风速的敏感性最低,20°风向变化对Stokes1亮温参数敏感性最低;海面亮温在3级风速内和0°~150°风向区间受风场影响变化较小,亮温波动显著区域主要集中在6级风速以上和300°~360°风向区间。

不同季节海面风矢量对海表面亮温增益的影响研究

从不同季节海面风矢量对海表面粗糙度亮温的相关性进行研究,选取2012至2016年西北太平洋区域1、4、7、10月Windsat L2级U10风场数据,利用海面粗糙度半经验半理论算法分析了风速引起的亮温增益与不同季节下风矢量的相关性。研究结果表明:风速对水平亮温增益贡献的影响明显大于垂直亮温;风速引起的水平亮温增益变化在1月份最为显著,7月份影响最小;风速对垂直亮温增益最大和最小均值分别为0.19k和0.05k,说明风速对垂直亮温增益量影响较小,通过标准差计算可知风速引起的垂直亮温增益几乎不受季节影响;4月和10月在高纬度区域受太平洋低压和夏威夷高压影响下形成较大范围的气旋现象,并伴随亮温增益变化,表现出较为明显的北太平洋暖流特征。