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1.
This study investigates the long-term changes of monthly sea surface wind speeds over the China seas from 1988 to 2015. The 10-meter wind speeds products from four major global reanalysis datasets with high resolution are used: Cross-Calibrated Multi-Platform data set(CCMP), NCEP climate forecast system reanalysis data set(CFSR),ERA-interim reanalysis data set(ERA-int) and Japanese 55-year reanalysis data set(JRA55). The monthly sea surface wind speeds of four major reanalysis data sets have been investigated through comparisons with the longterm and homogeneous observation wind speeds data recorded at ten stations. The results reveal that(1) the wind speeds bias of CCMP, CFSR, ERA-int and JRA55 are 0.91 m/s, 1.22 m/s, 0.62 m/s and 0.22 m/s, respectively.The wind speeds RMSE of CCMP, CFSR, ERA-int and JRA55 are 1.38 m/s, 1.59 m/s, 1.01 m/s and 0.96 m/s,respectively;(2) JRA55 and ERA-int provides a realistic representation of monthly wind speeds, while CCMP and CFSR tend to overestimate observed wind speeds. And all the four data sets tend to underestimate observed wind speeds in Bohai Sea and Yellow Sea;(3) Comparing the annual wind speeds trends between observation and the four data sets at ten stations for 1988-1997, 1988–2007 and 1988–2015, the result show that ERA-int is superior to represent homogeneity monthly wind speeds over the China seaes.  相似文献   

2.
全极化合成孔径雷达近岸风场反演研究   总被引:2,自引:2,他引:0  
Coastal winds are strongly influenced by topology and discontinuity between land and sea surfaces. Wind assessment from remote sensing in such a complex area remains a challenge. Space-borne scatterometer does not provide any information about the coastal wind field, as the coarse spatial resolution hampers the radar backscattering. Synthetic aperture radar (SAR) with a high spatial resolution and all-weather observation abilities has become one of the most important tools for ocean wind retrieval, especially in the coastal area. Conventional methods of wind field retrieval from SAR, however, require wind direction as initial information, such as the wind direction from numerical weather prediction models (NWP), which may not match the time of SAR image acquiring. Fortunately, the polarimetric observations of SAR enable independent wind retrieval from SAR images alone. In order to accurately measure coastal wind fields, this paper proposes a new method of using co-polarization backscattering coefficients from polarimetric SAR observations up to polarimetric correlation backscattering coefficients, which are acquired from the conjugate product of co-polarization backscatter and cross-polarization backscatter. Co-polarization backscattering coefficients and polarimetric correlation backscattering coefficients are obtained form Radarsat-2 single-look complex (SLC) data.The maximum likelihood estimation is used to gain the initial results followed by the coarse spatial filtering and fine spatial filtering. Wind direction accuracy of the final inversion results is 10.67 with a wind speed accuracy of 0.32 m/s. Unlike previous methods, the methods described in this article utilize the SAR data itself to obtain the wind vectors and do not need external wind directional information. High spatial resolution and high accuracy are the most important features of the method described herein since the use of full polarimetric observations contains more information about the space measured.This article is a useful addition to the work of independent SAR wind retrieval. The experimental results herein show that it is feasible to employ the co-polarimetric backscattering coefficients and the polarimetric correlation backscattering coefficients for coastal wind field retrieval.  相似文献   

3.
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.  相似文献   

4.
Rain effect and lack of in situ validation data are two main causes of tropical cyclone wind retrieval errors. The National Oceanic and Atmospheric Administration's Climate Prediction Center Morphing technique (CMORPH) rain rate is introduced to a match-up dataset and then put into a rain correction model to remove rain effects on "Jason-1" normalized radar cross section (NRCS); Hurricane Research Division (HRD) wind sPeed, which integrates all available surface weather observations, is used to substitute in situ data for establishing this relationship with "Jason-l" NRCS. Then, an improved "Jason-l" wind retrieval algorithm under tropical cyclone conditions is proposed. Seven tropical cyclones from 2003 to 2010 are studied to validate the new algorithm. The experimental results indicate that the standard deviation of this algorithm at C-band and Ku-band is 1.99 and 2.75 m/s respectively, which is better than the existing algorithms. In addition, the C-band algorithm is more suitable for sea surface wind retrieval than Ku-band under tropical cyclone conditions.  相似文献   

5.
Sea-surface acoustic backscattering measurements at moderate to high frequencies were performed in the shallow water of the south Yellow Sea, using omnidirectional spherical sources and omnidirectional hydrophones. Sea-surface backscattering data for frequencies in the 6–25 k Hz range and wind speeds of(3.0±0.5)and(4.5±1.0) m/s were obtained from two adjacent experimental sites, respectively. Computation of sea-surface backscattering strength using bistatic transducer is described. Finally, we calculated sea-surface backscattering strengths at grazing angles in the range of 16°–85°. We find that the measured backscattering strengths agree reasonably well with those predicted by using second order small-roughness perturbation approximation method with "PM" roughness spectrum for all frequencies at grazing angles ranged from 40° to 80°. The backscattering strengths varied slightly at grazing angles of 16°–40°, and were much stronger than roughness scattering. It is speculated that scattering from bubbles dominates the backscattering strengths at high wind speeds and small grazing angles. At the same frequencies and moderate to high grazing angles, the results show that the backscattering strengths at a wind speed of(4.5±1.0) m/s were approximately 5 d B higher than those at a wind speed of(3.0±0.5) m/s. However, the discrepancies of backscattering strength at low grazing angles were more than 10 d B. Furthermore the backscattering strengths exhibited no significant frequency dependence at 3 m/s wind speed. At a wind speed of 4.5 m/s, the scattering strengths increased at low grazing angles but decreased at high grazing angles with increasing grazing angle.  相似文献   

6.
A scanning microwave radiometer(RM) was launched on August 16,2011,on board HY-2 satellite.The six-month long global sea surface wind speeds observed by the HY-2 scanning microwave radiometer are preliminarily validated using in-situ measurements and WindSat observations,respectively,from January to June 2012.The wind speed root-mean-square(RMS) difference of the comparisons with in-situ data is 1.89 m/s for the measurements of NDBC and 1.72 m/s for the recent four-month data measured by PY30-1 oil platform,respectively.On a global scale,the wind speeds of HY-2 RM are compared with the sea surface wind speeds derived from WindSat,the RMS difference of 1.85 m/s for HY-2 RM collocated observations data set is calculated in the same period as above.With analyzing the global map of a mean difference between HY-2 RM and WindSat,it appears that the bias of the sea surface wind speed is obviously higher in the inshore regions.In the open sea,there is a relatively higher positive bias in the mid-latitude regions due to the overestimation of wind speed observations,while the wind speeds are underestimated in the Southern Ocean by HY-2 RM relative to WindSat observations.  相似文献   

7.
基于浮标实测数据的WindSat海洋反演产品精度分析   总被引:1,自引:1,他引:0  
To evaluate the ocean surface wind vector and the sea surface temperature obtained from Wind Sat, we compare these quantities over the time period from January 2004 to December 2013 with moored buoy measurements. The mean bias between the Wind Sat 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 of WSPD_LF is better than that of the medium frequency wind speed product. The crosstalk analysis indicates that the Wind Sat 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 Wind Sat 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 Wind Sat and buoy measurements in the tropical Pacific Ocean; the overall RMS error is only 0.36°C, and the retrieval accuracy of the low latitudes is better than that of the middle and high latitudes.  相似文献   

8.
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.  相似文献   

9.
全球有效波高和风速的时空变化及相关关系研究   总被引:2,自引:1,他引:1  
The climatology of significant wave height(SWH) and sea surface wind speed are matters of concern in the fields of both meteorology and oceanography because they are very important parameters for planning offshore structures and ship routings. The TOPEX/Poseidon altimeter, which collected data for about 13 years from September 1992 to October 2005, has measured SWHs and surface wind speeds over most of the world's oceans. In this paper, a study of the global spatiotemporal distributions and variations of SWH and sea surface wind speed was conducted using the TOPEX/Poseidon altimeter data set. The range and characteristics of the variations were analyzed quantitatively for the Pacific, Atlantic, and Indian oceans. Areas of rough waves and strong sea surface winds were localized precisely, and the correlation between SWH and sea surface wind speed analyzed.  相似文献   

10.
A new 0.1° gridded daily sea surface temperature(SST) data product is presented covering the years 2003–2015. It is created by fusing satellite SST data retrievals from four microwave(Wind Sat, AMSR-E, ASMR2 and HY-2 A RM)and two infrared(MODIS and AVHRR) radiometers(RMs) based on the optimum interpolation(OI) method. The effect of including HY-2 A RM SST data in the fusion product is studied, and the accuracy of the new SST product is determined by various comparisons with moored and drifting buoy measurements. An evaluation using global tropical moored buoy measurements shows that the root mean square error(RMSE) of the new gridded SST product is generally less than 0.5℃. A comparison with US National Data Buoy Center meteorological and oceanographic moored buoy observations shows that the RMSE of the new product is generally less than 0.8℃. A comparison with measurements from drifting buoys shows an RMSE of 0.52–0.69℃. Furthermore, the consistency of the new gridded SST dataset and the Remote Sensing Systems microwave-infrared SST dataset is evaluated, and the result shows that no significant inconsistency exists between these two products.  相似文献   

11.
基于SAR图像雨团足印的海面风向提取方法   总被引:1,自引:1,他引:0  
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。  相似文献   

12.
海浪微波散射理论模式   总被引:4,自引:1,他引:3  
何宜军 《海洋与湖沼》2000,31(2):178-185
在假设海面白帽为球形气泡层的基础上,利用白帽海面的矢量辐射传输方程各随机粗糙面散射模型建立了海面的微波散射模型。辐射传输方程利用迭代法求解,随机粗糙面散射模型采用双尺度散射模型,利用白帽覆盖率的经验公式计算海面的微波散射特性。数值计算结果表明,随着气泡厚度的增加球形气泡散射系数越来越接近球形粒子散射系数;白帽对散射同的贡献随风速增大而增大;侧风情况比逆风和顺风情况影响均大;水平极化比垂直极化影响大  相似文献   

13.
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。  相似文献   

14.
本文提出了一种白冠海面的小入射角星载雷达后向散射模型,模型包括海面非波浪破碎部分和波浪破碎部分的后向散射。在风的作用下,海浪破碎形成白冠,对星载雷达的后向散射信号造成影响。文中利用TRMM PR和ECMWF的时空匹配数据集,拟合得出小入射角下星载雷达海面波浪破碎部分的后向散射模型,并分别与高斯分布/非高斯的海浪斜率分布海面的准镜面散射模型组成了白冠海面小入射角星载雷达后向散射模型。经实测数据对比,本文提出的由非高斯准镜面散射和考虑波浪破碎组合模型有效。  相似文献   

15.
16.
国内外对海上阵风的研究并不多,且大多集中在阵风预报和应用研究方面,对于海洋阵风数据的获取技术未见文献系统论述。本文利用HY-2B卫星雷达高度计观测的后向散射系数,结合校正微波辐射计观测的亮度温度信息,提出联合反演阵风风速的方法。两个遥感载荷联合反演得到的阵风风速与2019–2021年美国国家浮标数据中心(NDBC)浮标数据进行真实性检验,结果显示:阵风风速均方根误差(RMSE)为0.98 m/s,相关系数为0.82;基于本方法利用国外同类卫星Jason-3得到的阵风风速与2016–2018年NDBC浮标数据的RMSE为0.96 m/s,相关系数为0.88。本文在HY-2B卫星雷达高度计海面风速观测的基础上,纳入同一卫星平台校正微波辐射计的同步观测信息联合实现了海面阵风的观测,数据的比对结果证明文中方法具有较高的观测精度。同时,该方法对于具有相同观测体制的国内外卫星也适用。  相似文献   

17.
星载SAR对雨团催生海面风场的观测研究   总被引:2,自引:1,他引:1  
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。  相似文献   

18.
“龙王”台风SAR遥感研究   总被引:1,自引:0,他引:1       下载免费PDF全文
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。  相似文献   

19.
A Spectral Approach for Determining Altimeter Wind Speed Model Functions   总被引:9,自引:0,他引:9  
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.  相似文献   

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