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 the GMF plays a...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 the GMF plays an important role in an ocean wind vector retrieval. The performance of the existing Ku-band model function QSCAT-1 is considered to be effective at low and moderate wind speed ranges. However, in the conditions of higher wind speeds, the existing algorithms diverge alarmingly, owing to the lack of in situ data required for developing the GMF for the high wind conditions, the QSCAT-1 appears to overestimate the a0, which results in underestimating the wind speeds. Several match-up QuikSCAT and special sensor microwave/imager （SSM/I） wind speed measurements of the typhoons occurring in the west Pacific Ocean are analyzed. The results show that the SSM/I wind exhibits better agreement with the ＂best track＂ analysis wind speed than the QuikSCAT wind retrieved using QSCAT-1. On the basis of this evaluation, a correction of the QSCAT-1 model function for wind speed above 16 m/s is proposed, which uses the collocated SSM/I and QuikSCAT measurements as a training set, and a neural network approach as a multiple nonlinear regression technologytechnology.In order to validate the revised GMF for high winds, the modified GMF was applied to the QuikSCAT observations of Hurricane IOKE. The wind estimated by the QuikSCAT for Typhoon IOKE in 2006 was improved with the maximum wind speed reaching 55 m/s. An error analysis was performed using the wind fields from the Holland model as the surface truth. The results show an improved agreement with the Holland model wind when compared with the wind estimated using the QSCAT-1. However, large bias still existed, indicating that the effects of rain must be considered for further improvement.展开更多
It is one of the most important part to build an accurate gravity model in geophysical exploration.Traditional gravity modelling is usually based on grid method,such as difference method and finite element method wide...It is one of the most important part to build an accurate gravity model in geophysical exploration.Traditional gravity modelling is usually based on grid method,such as difference method and finite element method widely used.Due to self-adaptability lack of division meshes and the difficulty of high-dimensional calculation.展开更多
This paper presents the TDS-1 GNSS reflectometry wind Geophysical Model Function(GMF)response to GPS block types.The observables were extracted from Delay Doppler Maps(DDMs)after taking the receiver antenna gains effe...This paper presents the TDS-1 GNSS reflectometry wind Geophysical Model Function(GMF)response to GPS block types.The observables were extracted from Delay Doppler Maps(DDMs)after taking the receiver antenna gains effects and GNSS-R geometry effects into account.Since the DDM is affected by GPS EffectiveIsotropic Radiated Power(EIRP),we first investigate the sensitivity of observables to the GPS block.Additionally,the observables at high SNRs are more sensitive to wind speed,but the spatial coverage at high signal to noise ratios(SNRs)is lower,while DDMs at low SNRs have the opposite characteristics.To balance the accuracy and spatial coverage,the DDM datasets are divided into two parts:high SNR(>0 dB)and low SNR(>−10 dB and≤0 dB)to develop wind GMF.Then,the influences of GPS block on wind speed retrieval both at high and low SNR is analyzed.Results show that the block types have impacts on wind GMF and the use of a prior GPS block can contribute to a better wind speed retrieval both at high and low SNR.Compared with ASCAT,the Root Mean Square Error(RMSE)value of wind speed retrieval at high and low SNR are 2.19 m/s and 3.13 m/s,respectively,when all TDS data are processed without distinguishing GPS block types.However,if the TDS data are separately processed and used to develop wind GMF through different blocks,both the accuracy and correlation coefficient can be improved to some extent.Finally,the influence of significant height of the swell(Hs)on SNR observables is analyzed,and it is demonstrated that there is no obvious linear or nonlinear relationship between them.展开更多
利用散射计测量海面后向散射系数,并通过地球物理模型函数(geophysical model function, GMF)反演得到海面风场。目前散射计风场反演所采用的GMF一般只考虑雷达极化方式、雷达入射角、风速和相对风向对海面后向散射系数的影响,而相关研...利用散射计测量海面后向散射系数,并通过地球物理模型函数(geophysical model function, GMF)反演得到海面风场。目前散射计风场反演所采用的GMF一般只考虑雷达极化方式、雷达入射角、风速和相对风向对海面后向散射系数的影响,而相关研究表明海表温度(sea surface temperature, SST)对Ku波段散射计风场反演具有不可忽略的影响。文章利用海洋二号A卫星散射计(Haiyang-2AScatterometer,HY2A-SCAT)后向散射系数观测值、欧洲中期天气预报中心(European Center for Medium-Range Weather Forecasts, ECMWF)再分析风矢量和SST数据,采用人工神经网络方法,建立起一种SST相关的GMF (TNGMF)。对TNGMF进行分析后发现,海面后向散射系数随着SST的增加而增加,并且其增加幅度与雷达极化方式、风速有关。为了对比,文章使用相同数据集和相同方法建立了不包含SST的GMF (NGMF),将美国国家航天航空局散射计-2 (National Aeronautics and Space Administration Scatterometer-2, NSCAT2) GMF、TNGMF和NGMF分别用于HY2A-SCAT风场反演实验。试验结果表明,采用NSCAT2 GMF、NGMF反演得到的风速在低温时系统性偏小,在高温时系统性偏大;而TNGMF可较好地纠正SST对风速偏差均值的影响,从而提高反演风场质量。展开更多
To retrieve wind field from SAR images, the development for surface wind field retrieval from SAR images based on the improvement of new inversion model is present. Geophysical Model Functions （GMFs） have been widel...To retrieve wind field from SAR images, the development for surface wind field retrieval from SAR images based on the improvement of new inversion model is present. Geophysical Model Functions （GMFs） have been widely applied for wind field retrieval from SAR images. Among them CMOD4 has a good performance under low and moderate wind conditions. Although CMOD5 is developed recently with a more fundamental basis, it has ambiguity of wind speed and a shape gradient of normalized radar cross section under low wind speed condition. This study proposes a method of wind field retrieval from SAR image by com-bining CMOD5 and CMOD4 Five VV-polarisation RADARSAT2 SAR images are implemented for validation and the retrieval re-suits by a combination method （CMOD5 and CMOD4） together with CMOD4 GMF are compared with QuikSCAT wind data. The root-mean-square error （RMSE） of wind speed is 0.75 m s-1 with correlation coefficient 0.84 using the combination method and the RMSE of wind speed is 1.01 m s-1 with correlation coefficient 0.72 using CMOD4 GMF alone for those cases. The proposed method can be applied to SAR image for avoiding the internal defect in CMOD5 under low wind speed condition.展开更多
基金The National Natural Science Foundation of China under contract No.41106152the National Science and Technology Support Program under contract No.2013BAD13B01+3 种基金the National High Technology Research and Development Program(863 Program)of China under contract No.2013AA09A505the International Science and Technology Cooperation Program of China under contract No.2011DFA22260the National High Technology Industrialization Project under contract No.[2012]2083the Marine Public Projects of China under contract Nos 201105032,201305032 and 201105002-07
文摘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 the GMF plays an important role in an ocean wind vector retrieval. The performance of the existing Ku-band model function QSCAT-1 is considered to be effective at low and moderate wind speed ranges. However, in the conditions of higher wind speeds, the existing algorithms diverge alarmingly, owing to the lack of in situ data required for developing the GMF for the high wind conditions, the QSCAT-1 appears to overestimate the a0, which results in underestimating the wind speeds. Several match-up QuikSCAT and special sensor microwave/imager （SSM/I） wind speed measurements of the typhoons occurring in the west Pacific Ocean are analyzed. The results show that the SSM/I wind exhibits better agreement with the ＂best track＂ analysis wind speed than the QuikSCAT wind retrieved using QSCAT-1. On the basis of this evaluation, a correction of the QSCAT-1 model function for wind speed above 16 m/s is proposed, which uses the collocated SSM/I and QuikSCAT measurements as a training set, and a neural network approach as a multiple nonlinear regression technologytechnology.In order to validate the revised GMF for high winds, the modified GMF was applied to the QuikSCAT observations of Hurricane IOKE. The wind estimated by the QuikSCAT for Typhoon IOKE in 2006 was improved with the maximum wind speed reaching 55 m/s. An error analysis was performed using the wind fields from the Holland model as the surface truth. The results show an improved agreement with the Holland model wind when compared with the wind estimated using the QSCAT-1. However, large bias still existed, indicating that the effects of rain must be considered for further improvement.
基金provided by China Geological Survey with the project(Nos.DD20190707,DD20190012)the Fundamental Research Funds for China Central public research Institutes with the project(No.JKY202014)
文摘It is one of the most important part to build an accurate gravity model in geophysical exploration.Traditional gravity modelling is usually based on grid method,such as difference method and finite element method widely used.Due to self-adaptability lack of division meshes and the difficulty of high-dimensional calculation.
基金supported by the Funds for Creative Research Groups of China[Grant no.41721003]the National Natural Science Foundation of China[Grant nos.41825009 and 41774034].
文摘This paper presents the TDS-1 GNSS reflectometry wind Geophysical Model Function(GMF)response to GPS block types.The observables were extracted from Delay Doppler Maps(DDMs)after taking the receiver antenna gains effects and GNSS-R geometry effects into account.Since the DDM is affected by GPS EffectiveIsotropic Radiated Power(EIRP),we first investigate the sensitivity of observables to the GPS block.Additionally,the observables at high SNRs are more sensitive to wind speed,but the spatial coverage at high signal to noise ratios(SNRs)is lower,while DDMs at low SNRs have the opposite characteristics.To balance the accuracy and spatial coverage,the DDM datasets are divided into two parts:high SNR(>0 dB)and low SNR(>−10 dB and≤0 dB)to develop wind GMF.Then,the influences of GPS block on wind speed retrieval both at high and low SNR is analyzed.Results show that the block types have impacts on wind GMF and the use of a prior GPS block can contribute to a better wind speed retrieval both at high and low SNR.Compared with ASCAT,the Root Mean Square Error(RMSE)value of wind speed retrieval at high and low SNR are 2.19 m/s and 3.13 m/s,respectively,when all TDS data are processed without distinguishing GPS block types.However,if the TDS data are separately processed and used to develop wind GMF through different blocks,both the accuracy and correlation coefficient can be improved to some extent.Finally,the influence of significant height of the swell(Hs)on SNR observables is analyzed,and it is demonstrated that there is no obvious linear or nonlinear relationship between them.
文摘利用散射计测量海面后向散射系数,并通过地球物理模型函数(geophysical model function, GMF)反演得到海面风场。目前散射计风场反演所采用的GMF一般只考虑雷达极化方式、雷达入射角、风速和相对风向对海面后向散射系数的影响,而相关研究表明海表温度(sea surface temperature, SST)对Ku波段散射计风场反演具有不可忽略的影响。文章利用海洋二号A卫星散射计(Haiyang-2AScatterometer,HY2A-SCAT)后向散射系数观测值、欧洲中期天气预报中心(European Center for Medium-Range Weather Forecasts, ECMWF)再分析风矢量和SST数据,采用人工神经网络方法,建立起一种SST相关的GMF (TNGMF)。对TNGMF进行分析后发现,海面后向散射系数随着SST的增加而增加,并且其增加幅度与雷达极化方式、风速有关。为了对比,文章使用相同数据集和相同方法建立了不包含SST的GMF (NGMF),将美国国家航天航空局散射计-2 (National Aeronautics and Space Administration Scatterometer-2, NSCAT2) GMF、TNGMF和NGMF分别用于HY2A-SCAT风场反演实验。试验结果表明,采用NSCAT2 GMF、NGMF反演得到的风速在低温时系统性偏小,在高温时系统性偏大;而TNGMF可较好地纠正SST对风速偏差均值的影响,从而提高反演风场质量。
基金supported by the National Natural Science Foundation of China (Nos.41376010 and 40830959)the Start-up Foundation of Zhejiang Ocean University (No.21105011913)
文摘To retrieve wind field from SAR images, the development for surface wind field retrieval from SAR images based on the improvement of new inversion model is present. Geophysical Model Functions （GMFs） have been widely applied for wind field retrieval from SAR images. Among them CMOD4 has a good performance under low and moderate wind conditions. Although CMOD5 is developed recently with a more fundamental basis, it has ambiguity of wind speed and a shape gradient of normalized radar cross section under low wind speed condition. This study proposes a method of wind field retrieval from SAR image by com-bining CMOD5 and CMOD4 Five VV-polarisation RADARSAT2 SAR images are implemented for validation and the retrieval re-suits by a combination method （CMOD5 and CMOD4） together with CMOD4 GMF are compared with QuikSCAT wind data. The root-mean-square error （RMSE） of wind speed is 0.75 m s-1 with correlation coefficient 0.84 using the combination method and the RMSE of wind speed is 1.01 m s-1 with correlation coefficient 0.72 using CMOD4 GMF alone for those cases. The proposed method can be applied to SAR image for avoiding the internal defect in CMOD5 under low wind speed condition.
