卫星散射计与海面平台所测的风场数据比较

基于南海北部海面PY30-1石油平台气象站测风仪2011年7月19日—2012年9月17日实测的风场数据,分别开展了对卫星搭载的ASCAT和HY-2散射计所测风场数据的比较研究,分析散射计的测风能力(选取的时空窗口为30 min和25 km)。结果表明:在南海北部海域,ASCAT 散射计所测风速和PY30-1石油平台气象站观测风速的均方根误差为2.53 m/s,风向偏差较大,均方根误差为47.87°;HY-2散射计所测风速和PY30-1石油平台气象站观测风速的均方根误差为3.41 m/s,风向的均方根误差为58.66°。分别按低、中和高风速的不同条件将ASCAT和HY-2散射计所测的风场数据与PY30-1石油平台气象站观测的风场数据加以比较可知,ASCAT和HY-2散射计都具有较好的测风能力, 前者所测风速与PY30-1石油平台气象站测风仪观测风速的均方根误差稍小于后者。在150 min和15 km的时空窗口下,ASCAT与HY-2散射计所测风速的均方根误差为0.72 m/s,风向的均方根误差为8.50°。     

基于ERA-interim再分析资料的ASCAT风场产品在南海的精度评估及南海月平均风场特征分析

利用ERA-interim再分析资料作为参照,统计分析了南海季风盛行时ASCAT散射计L2B和L3风场产品的误差特征。结果表明:季风盛行时,南海中南部大部分海域,ASCAT两种散射计风场产品精度较好,与设计精度一致,风速标准偏差小于2 m/s,偏差小于1 m/s,风向标准偏差小于20°,偏差小于5°,ASCATL2B相对L3产品表现更好,西南风盛行时,风场相关性强,在0.8以上,ASCAT与ERA-interim一致性好,东北风盛行时,风场也具有强相关,不过在南海东部海域,风向相关性较弱,小于0.7。另外,利用ASCAT L2B分析了南海月平均风场分布特征,结果表明:南海季风盛行时,存在两个风速大值中心,分别位于南海中南部和台湾海峡及巴士海峡一带,其位置和大小随时间而变化。     

中国近海ASCAT风场反演结果验证分析

利用NCEP风场产品和dropsonde探测资料,对中国近海ASCAT全场和单点的风速反演精度进行验证分析.研究发现ASCAT反演风场与NCEP风场的风速、风向平均绝对偏差分别为2.06 m/s和21.98°;均方根误差分别为2.87 m/s和34.29°.两者风速反演精度较一致,风向误差相对偏大.ASCAT反演风场与dropsonde探测资料的风速、风向平均绝对偏差分别为1.55 m/s和3.43°;均方根误差分别为1.73 m/s和4.15°.ASCAT资料可以较好的反演台风风场.     

HY-2B卫星散射计海面风场产品质量分析 *

星载微波散射计是获取全球海面风场信息的主要手段, HY-2B卫星散射计的成功发射为全球海面风场数据获取的持续性提供了重要保障。本文利用欧洲中期天气预报中心(European Center for Medium-Range Weather Forecasts, ECMWF)再分析风场数据、热带大气海洋观测计划(Tropical Atmosphere Ocean Array, TAO)和美国国家数据浮标中心(National Data Buoy Center, NDBC)浮标获取的海面风矢量实测数据, 对HY-2B散射计海面风场数据产品的质量进行统计分析。分析表明, HY-2B风场与ECMWF再分析风场对比, 在4~24m·s^-1风速区间内, 风速和风向均方根误差(root mean square error, RMSE)分别为1.58m·s^-1和15.34°; 与位于开阔海域的TAO浮标数据对比, 风速、风向RMSE分别为1.03m·s^-1和14.98°, 可见HY-2B风场能较好地满足业务化应用的精度要求(风速优于2m·s^-1, 风向优于20°)。与主要位于近海海域的NDBC浮标对比, HY-2B风场的风速、风向RMSE分别为1.60m·s^-1和19.14°, 说明HY-2B散射计同时具备了对近海海域风场的良好观测能力。本文还发现HY-2B风场质量会随风速、地面交轨位置等变化, 为用户更好地使用HY-2B风场产品提供参考。     

ASCAT洋面风资料在中国北方海域的真实性检验

采用北方海域6个海洋观测站风资料对2009年3月—2013年6月ASCAT卫星反演洋面风(10 m)资料进行了检验。ASCAT反演洋面风与测站风向、风速偏差均较小,二者风速平均偏差为0.99 m/s,ASCAT风速略高于测站风速,二者风向平均偏差为-12.97°,表明ASCAT洋面风资料在北方海域具有可信性;分风级统计表明,在北方海域,风力为0—7级(0—17.1 m/s)时,利用ASCAT风速代表洋面风速是可行的(其中风力为4—5级时,ASCAT与测站风速误差最小,为0.10 m/s),当风力达到8级以上时ASCAT的可信度较低。     

海洋二号卫星散射计风场产品真实性检验及分析

利用国际海-气综合数据集(ICOADS)中的海面风场实测数据作为真实值,对海洋二号卫星散射计风场产品进行真实性检验,得到初步结论:(1)在中、低风速条件下,海洋二号散射计风速与ICOADS实测风速具有较好一致性,但在较高风速条件下海洋二号散射计会出现风速低估现象;(2)海洋二号散射计风向与ICOADS实测风向的误差主要集中在-15°—15°范围内,在低风速条件下,海洋二号散射计与ICOADS两者风向存在较大偏差,风向多解也主要发生在低风速时;(3)在2—24 m/s风速条件下,剔除超过3个标准偏差风速样本后,海洋二号与ICOADS两者风速的平均绝对误差为1.36 m/s,均方根误差为1.92 m/s,若忽略风向多解的影响,两者风向的平均绝对误差为14.98°,均方根误差为20.21°。     

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。     

一种基于BP神经网络方法的HY-2A散射计反演风场偏差订正方案

针对HY-2A散射计风矢量场数据,利用BP神经网络方法,引入NDBC浮标的降水海温等环境要素,对HY-2A散射计风场进行偏差订正。实验结果表明:BP神经网络方法对HY-2A散射计的风速风向均有较好的订正效果,能有效修正HY-2A的风速高估现象,风速平均偏差由2.32 m/s改善至0.25 m/s;同时通过敏感性试验,发现了各样本输入量以及各环境要素对实验结果的敏感性。     

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。     

HY-2A散射计风场在南海海域的检验和分析

利用南海浮标及海洋观测站的实测资料作为真实值对HY-2A散射计反演的风矢量作多角度对比分析,结果表明:HY-2A散射计风速与浮标(海洋站)实测风速数据具有良好的相关性,散射计观测风速普遍大于浮标(海洋站)实测风速;风速误差符合正态分布,风力≤3级时,风向的平均绝对误差最大;4~5级时风速平均偏差和平均绝对偏差均最小。逐月统计发现:1—3月的风速平均偏差最小,两者基本吻合。7—9月的风速平均偏差最大,12月的风向平均偏差最小。另外,东北向的风速平均偏差最小,西北向风速平均偏差最大;远海站点的风速和风向检验误差均小于近海站点。以上结论表明HY-2A散射计风场资料在南海海域具有可信性,为HY-2A散射计风场在南海的应用和研究提供依据。     

Evaluation of marine surface winds observed by SeaWinds and AMSR on ADEOS-II

Marine surface winds observed by two microwave sensors, SeaWinds and Advanced Microwave Scanning Radiometer (AMSR), on the Advanced Earth Observing Satellite-II (ADEOS-II) are evaluated by comparison with off-shore moored buoy observations. The wind speed and direction observed by SeaWinds are in good agreement with buoy data with root-mean-squared (rms) differences of approximately 1 m s⁻¹ and 20°, respectively. No systematic biases depending on wind speed or cross-track wind vector cell location are discernible. The effects of oceanographic and atmospheric environments on the scatterometry are negligible. Though the wind speed observed by AMSR also showed agreement with buoy observations with rms difference of 1.27 m s⁻¹, the AMSR wind speed is systematically lower than the buoy data for wind speeds lower than 5 m s⁻¹. The AMSR wind seems to have a discontinuous trend relative to the buoy data at wind speeds of 5–6 m s⁻¹. Similar results have been obtained in an intercomparison of wind speeds globally observed by SeaWinds and AMSR on the same orbits. A global wind speed histogram of the AMSR wind shows skewed features in comparison with those of SeaWinds and European Centre for Medium-range Weather Forecasts (ECMWF) analyses.     

Evaluation of ENVISAT ASAR data for sea surface wind retrieval in Hong Kong coastal waters of China

The C-band wind speed retrieval models, CMOD4, CMOD - IFR2, and CMOD5 were applied to retrieval of sea surface wind speeds from ENVISAT （European environmental satellite） ASAR （advanced synthetic aperture radar） data in the coastal waters near Hong Kong during a period from October 2005 to July 2007. The retrieved wind speeds are evaluated by comparing with buoy measurements and the QuikSCAT （quick scatterometer） wind products. The results show that the CMOD4 model gives the best performance at wind speeds lower than 15 m/s. The correlation coefficients with buoy and QuikSCAT winds are 0.781 and 0.896, respectively. The root mean square errors are the same 1.74 m/s. Namely, the CMOD4 model is the best one for sea surface wind speed retrieval from ASAR data in the coastal waters near Hong Kong.     

Evaluation of reanalysis surface wind products with quality-assured buoy wind measurements along the north coast of the South China Sea

Three archived reanalysis wind vectors at 10 m height in the wind speed range of 2–15 m/s, namely, the second version of the National Centres for Environmental Prediction(NCEP) Climate Forecast System Reanalysis(CFSv2), European Centre for Medium-Range Weather Forecasting Interim Reanalysis(ERA-I) and NCEPDepartment of Energy(DOE) Reanalysis 2(NCEP-2) products, are evaluated by a comparison with the winds measured by moored buoys in coastal regions of the South China Sea(SCS). The buoy data are first quality controlled by extensive techniques that help eliminate degraded measurements. The evaluation results reveal that the CFSv2 wind vectors are most consistent with the buoy winds(with average biases of 0.01 m/s and 1.76°). The ERA-I winds significantly underestimate the buoy wind speed(with an average bias of –1.57 m/s), while the statistical errors in the NCEP-2 wind direction have the largest magnitude. The diagnosis of the reanalysis wind errors shows the residuals of all three reanalysis wind speeds(reanalysis-buoy) decrease with increasing buoy wind speed, suggesting a narrower wind speed range than that of the observations. Moreover, wind direction errors are examined to depend on the magnitude of the wind speed and the wind speed biases. In general, the evaluation of three reanalysis wind products demonstrates that CFSv2 wind vectors are the closest to the winds along the north coast of the SCS and are sufficiently accurate to be used in numerical models.     

海浪对ASCAT散射计反演风场的影响研究

To improve retrieval accuracy, this paper studies wave effects on retrieved wind field from a scatterometer. First, the advanced scatterometer(ASCAT) data and buoy data of the National Data Buoy Center(NDBC) are collocated. Buoy wind speed is converted into neutral wind at 10 m height. Then, ASCAT data are compared with the buoy data for the wind speed and direction. Subsequently, the errors between the ASCAT and the buoy wind as a function of each wave parameter are used to analyze the wave effects. Wave parameters include dominant wave period(dpd), significant wave height(swh), average wave period(apd) and the angle between the dominant wave direction(dwd) and the wind direction. Collocated data are divided into sub-datasets according to the different intervals of each wave parameter. A root mean square error(RMSE) for the wind speed and a mean absolute error(MAE) for the wind direction are calculated from the sub-datasets, which are considered as the function of wave parameters. Finally, optimal wave conditions on wind retrieved from the ASCAT are determined based on the error analyses. The results show the ocean wave parameters have correlative relationships with the RMSE of the retrieved wind speed and the MAE of the retrieved wind direction. The optimal wave conditions are presented in terms of dpd, swh, apd and angle.     

星载SAR对雨团催生海面风场的观测研究

雨团或对流雨是热带与亚热带地区的主要降雨形式,较易被高分辨率星载合成孔径雷达（SAR）探测到。SAR图像上的雨团足印是由大气中雨滴的散射与吸收、下沉气流等共同导致形成的。本文以RADARSAT-2卫星100 m分辨率的SAR图像上雨团引起的海面风场及其结构反演与解译作为实例进行分析。使用CMOD4地球物理模式函数,分别以NCEP再分析数据、欧洲MetOp-A卫星先进散射计（ASCAT）和中国HY-2卫星微波散射计的风向为外部风向,进行了SAR图像的海面风场反演。反演的海面风速相对于NCEP、ASCAT和HY-2的均方根误差（RMSE）分别为1.48 m/s,1.64 m/s和2.14 m/s。SAR图像上一侧明亮另一侧昏暗的圆形信号图斑被解译为雨团携带的下沉气流对海面风场（海面粗糙度）的改变所致。平行于海面背景风场其通过雨团圆形足印中心的剖面上的风速变化可拟合为正弦或余弦曲线,其拟合线性相关系数均不低于0.80。背景风场的风速大小、雨团引起的风速大小以及雨团足印的直径可利用拟合曲线获得,雨团足印的直径大小一般为数千米或数十千米,本文的8例个例解译与分析均验证了该结论。     

First six months quality assessment of HY-2A SCAT wind products using in situ measurements

The first Chinese microwave ocean environment satellite HY-2A, carrying a Ku-band scatteromenter （SCAT）, was successfully launched in August 2011. The first quality assessment of HY-2A SCAT wind products is presented through the comparison of the first 6 months operationally released SCAT products with in situ data. The in situ winds from the National Data Buoy Center （NDBC） buoys, R/V Polarstern, Aurora Australis, Roger Revelle and PY30-1 oil platform, were converted to the 10 m equivalent neutral winds. The temporal and spatial differences between the HY-2A SCAT and the in situ observations were limited to less than 5 min and 12.5 km. For HY-2A SCAT wind speed products, the comparison and analysis using the NDBC buoys yield a bias of-0.49 m/s, a root mean square error （RMSE） of 1.3 m/s and an increase negative bias with increasing wind speed observation above 3 m/s. Although less accurate of HY-2A SCAT wind direction at low winds, the RMSE of 19.19° with a bias of 0.92° is found for wind speeds higher than 3 m/s. These results are found consistent with those from R/Vs and oil platform comparisons. Moreover, the NDBC buoy comparison results also suggest that the accuracy of HY-2A SCAT winds is consistent over the first half year of 2012. The encouraging assessment results over the first 6 months show that wind products from HY-2A SCAT will be useful for scientific community.     

Sea surface wind speed retrieval from Sentinel-1 HH polarization data using conventional and neural network methods

Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed(SSWS) from HH-polarized Sentinel-1(S1) SAR images. The Polarization Ratio(PR) models combined with the CMOD5.N Geophysical Model Function(GMF) is used for SSWS retrieval from the HH-polarized SAR data. We compared different PR models developed based on previous C-band SAR data in HH-polarization for their applications to the S1 SAR data. The recently proposed CMODH, i.e., retrieving SSWS directly from the HHpolarized S1 data is also validated. The results indicate that the CMODH model performs better than results achieved using the PR models. We proposed a neural network method based on the backward propagation(BP)neural network to retrieve SSWS from the S1 HH-polarized data. The SSWS retrieved using the BP neural network model agrees better with the buoy measurements and ASCAT dataset than the results achieved using the conventional methods. Compared to the buoy measurements, the bias, root mean square error(RMSE) and scatter index(SI) of wind speed retrieved by the BP neural network model are 0.10 m/s, 1.38 m/s and 19.85%,respectively, while compared to the ASCAT dataset the three parameters of training set are –0.01 m/s, 1.33 m/s and 15.10%, respectively. It is suggested that the BP neural network model has a potential application in retrieving SSWS from Sentinel-1 images acquired at HH-polarization.     

中国近海ASCAT和ERA-Interim风场资料的评估

利用中国近海23个浮标对2015年一整年的ASCAT轨道风场和ERA-Interim再分析风场进行质量评估,并对比了ERA-Interim和CFSV2两种再分析风场资料在中国近海的适用性。结果表明:ASCAT在中国近海与浮标风速的一致性优于ERA-Interim,而二者与浮标风向的一致性则相差不大。同时,对比CFSV2与ERA-Interim的误差统计结果发现,CFSV2的风速误差较ERAInterim略小,风向误差相差不大。