首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到17条相似文献，搜索用时 140 毫秒
1.
HY-2 satellite is the first satellite for dynamic environmental parameters measurement of China,which was launched on 16th August 2011.A scanning microwave radiometer（RM） is carried for sea surface temperature（SST）,sea surface wind speed,columnar water vapor and columnar cloud liquid water detection.In this paper,the initial SST product of RM was validated with in-situ data of National Data of Buoy Center（NDBC） mooring and Argo buoy.The validation results indicate the accuracy of RM SST is better than 1.7 C.The comparison of RM SST and WindSat SST shows the former is warmer than the latter at high sea surface wind speed and the difference between these SSTs is depend on the sea surface wind speed.Then,the relationship between the errors of RM SST and sea surface wind speed was analyzed using NDBC mooring measurements.Based on the results of assessment and errors analysis,the suggestions of taking account of the affection of sea surface wind speed and using sea surface wind speed and direction derived from the microwave scatteromter aboard on HY-2 for SST product calibration were given for retrieval algorithm improvement.  相似文献   

2.
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   

3.
Microwave remote sensing is one of the most useful methods for observing the ocean parameters. The Doppler frequency or interferometric phase of the radar echoes can be used for an ocean surface current speed retrieval,which is widely used in spaceborne and airborne radars. While the effect of the ocean currents and waves is interactional. It is impossible to retrieve the ocean surface current speed from Doppler frequency shift directly. In order to study the relationship between the ocean surface current speed and the Doppler frequency shift, a numerical ocean surface Doppler spectrum model is established and validated with a reference. The input parameters of ocean Doppler spectrum include an ocean wave elevation model, a directional distribution function, and wind speed and direction. The suitable ocean wave elevation spectrum and the directional distribution function are selected by comparing the ocean Doppler spectrum in C band with an empirical geophysical model function(CDOP). What is more, the error sensitivities of ocean surface current speed to the wind speed and direction are analyzed. All these simulations are in Ku band. The simulation results show that the ocean surface current speed error is sensitive to the wind speed and direction errors. With VV polarization, the ocean surface current speed error is about 0.15 m/s when the wind speed error is 2 m/s, and the ocean surface current speed error is smaller than 0.3 m/s when the wind direction error is within 20° in the cross wind direction.  相似文献   

4.
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.  相似文献   

5.
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   

6.
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   

7.
基于一维综合孔径微波辐射计的海面温度反演研究   总被引：1，自引：0，他引：1  
Due to the low spatial resolution of sea surface temperature(T_S) retrieval by real aperture microwave radiometers,in this study, an iterative retrieval method that minimizes the differences between brightness temperature(T_B)measured and modeled was used to retrieve sea surface temperature with a one-dimensional synthetic aperture microwave radiometer, temporarily named 1 D-SAMR. Regarding the configuration of the radiometer, an angular resolution of 0.43° was reached by theoretical calculation. Experiments on sea surface temperature retrieval were carried out with ideal parameters; the results show that the main factors affecting the retrieval accuracy of sea surface temperature are the accuracy of radiometer calibration and the precision of auxiliary geophysical parameters. In the case of no auxiliary parameter errors, the greatest error in retrieved sea surface temperature is obtained at low T_S scene(i.e., 0.710 6 K for the incidence angle of 35° under the radiometer calibration accuracy of0.5 K). While errors on auxiliary parameters are assumed to follow a Gaussian distribution, the greatest error on retrieved sea surface temperature was 1.330 5 K at an incidence angle of 65° in poorly known sea surface wind speed(W)(the error on W of 1.0 m/s) over high W scene, for the radiometer calibration accuracy of 0.5 K.  相似文献   

8.
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   

9.
The principal purpose of this paper is to extract entire sea surface wind＇s information from spaceborne lidar, and particularly to utilize a appropriate algorithm for removing the interference information due to white caps and subsurface water. Wind speeds are obtained through empirical relationship with sea surface mean square slopes. Wind directions are derived from relationship between wind speeds and wind directions im plied in CMOD5n geophysical models function （GMF）. Whitecaps backscattering signals were distinguished with the help of lidar depolarization ratio measurements and rectified by whitecaps coverage equation. Subsurface water backscattering signals were corrected by means of inverse distance weighted （IDW） from neighborhood non-singular data with optimal subsurface water backscattering calibration parameters. To verify the algorithm reliably, it selected NDBC＇s TAO buoy-laying area as survey region in camparison with buoys＇ wind field data and METOP satellite ASCAT of 25 km single orbit wind field data after temporal-spa tial matching. Validation results showed that the retrieval algorithm works well in terms of root mean square error （RMSE） less than 2m/s and wind direction＇s RMSE less than 21 degree.  相似文献   

10.
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.  相似文献   

11.
为提高降雨条件下星载全极化微波辐射计海面风场精度，通过匹配WindSat海面风场和降雨率数据以及美国国家浮标中心浮标观测数据，得到18 996组匹配样本，深入分析了降雨对海面风场反演精度的严重影响，构建了风场校正模型。试验结果表明，降雨导致海面风速被严重高估，风向误差随着降雨率的增大而增大。校正后的风速精度在低风速段提升明显。无论降雨率多大，校正后风速精度均比校正前高。风速均方根误差由原来的2.9 m/s降低到了2.1 m/s，风向均方根误差由原来的26.9°降低到了26.3°。  相似文献   

12.
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   

13.
WindSat近海岸风场与美国沿岸浮标对比分析   总被引：1，自引：1，他引：0  
利用美国近海岸2004-2014年的固定浮标数据,本文对比分析了WindSat的近海岸风速产品。匹配时空窗口分别为30分钟和25公里。对比分析结果表明：WindSat反演的美国近海岸风速产品的均方根误差优于1.44 m/s,并且东海岸风速反演结果优于西海岸。WindSat下降轨道的风速反演结果优于上升轨道的结果。通过浮标相互间的对比分析发现,WindSat近海岸的风速反演结果与近岸海水深度、经度及距岸距离等因素并无明显的相关性。此外,利用2007-2008年的固定浮标数据,本文还对比分析了WindSat和QuikSCAT的近海岸风速反演结果,结果表明：相对于浮标数据,WindSat的风速反演值偏低,而QuikSCAT的风速反演值偏高;总体上来看,WindSat的近岸风速反演结果略优于QuikSCAT的近海岸风速反演结果。以上风速反演的精度均达到了传感器设定的指标,其为进一步的科学研究提供了良好的数据支撑。  相似文献   

14.
张扬  李宏  丁扬  余为  许建平 《海洋学报》2019,41(5):12-22
本文应用一个经验证的全球尺度FVCOM海浪模型，模拟了2012年全球海洋海浪场的分布和演变，分析了海表面风场、海浪场与混合层深度的全球尺度分布及相关性。综合观测资料和模型结果显示，海表面10 m风速、有效波高与混合层深度的全球尺度分布随季节发生显著的变化，并且其分布态势存在明显的相似性。从相关系数的全球分布来看，海表面10 m风速在印度洋低纬度海区（纬度0°~20°）与混合层深度间有较强的相关性，相关系数大于0.5；有效波高与混合层深度间相关系数大于0.5的网格分布在北半球高纬度海区和印度洋北部。谱峰周期与混合层深度间在部分海区存在负相关关系，这些网格主要分布在低纬度海区（纬度0°~30°）。统计结果显示，有效波高、海表面10 m风速和谱峰周期与混合层深度间的平均相关系数分别为0.31、0.25和0.12。综合以上结果表明，有效波高较谱峰周期能更有效地表征波浪能对海洋上层混合的影响；相比于海表面风速，有效波高与混合层深度间存在更强的相关关系，其变化对海洋上层混合有更显著的影响。  相似文献   

15.
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。  相似文献   

16.
一种改进海面风速反演的分类神经网络方法   总被引：1，自引：0，他引：1       下载免费PDF全文
为了提高使用SSM/I资料反演全球海面风速的精度,发展了一个新型的神经网络方法.在这个方法中,使用高风速、中、低风速状态和天气状态分类的方法分别训练神经网络,然后根据其类别的不同使用不同的神经网络计算风速.此方法较好地去除了由于高风速和云天天气状态下训练样本数据的缺少所产生的误差,改进了在高风速状态下反演风速值比实际风速偏低的情况,使得反演的高风速值被校正到了正常位置.本方法反演海面风速的值与浮标实测风速值之间的均方根误差达到1.60m/s.  相似文献   

17.
汪栋  张杰  范陈清  孟俊敏 《海洋科学》2016,40(4):108-115
基于浮标和步进频率微波辐射计(SFMR,Stepped-Frequency Microwave Radiometer)数据对NASA JPL(Jet Propulsion Laboratory)和RSS(Remote Sensing Systems)公司分别发布的已广泛应用于全球海面风场观测的ASCAT(Advanced SCATterometer)散射计风产品进行了比较和分析。结果表明,两者风速在中低风速(15 m/s)时基本一致;高风速(15 m/s)时RSS风速整体高于JPL风速。通过浮标数据对比,风速15 m/s时两者风速精度一致;风速15 m/s时两者风速RMS相当,但JPL和RSS风速分别低估和高估。利用SFMR数据检验表明RSS风速与SFMR风速一致性更好。两者风向精度在低风速(5 m/s)时较低,但随风速增加而提高并趋于稳定。该研究结果对相关科研人员的ASCAT散射计风产品选择具有重要的指导意义。  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号