The comparison of altimeter retrieval algorithms of the wind speed and the wave period

With the launch of altimeter,much effort has been made to develop algorithms on the wind speed and the wave period.By using a large data set of collocated altimeter and buoy measurements,the typical wind speed and wave period algorithms are validated.Based on theoretical argument and the concept of wave age,a semi-empirical algorithm for the wave period is also proposed,which has the wave-period dimension,and explicitly demonstrates the relationships between the wave period and the other variables.It is found that Ku and C band data should be applied simultaneously in order to improve either wind speed or wave period algorithms.The dual-band algorithms proposed by Chen et al.(2002) for the wind speed and Quilfen et al.(2004) for the wave period perform best in terms of a root mean square error in the practical applications.     

Global air-sea surface carbon-dioxide transfer velocity and flux estimated using ERS-2 data and a new parametric formula

Using data from the European remote sensing scatterometer（ERS-2） from July 1997 to August 1998,global distributions of the air-sea CO₂ transfer velocity and flux are retrieved.A new model of the air-sea CO₂ transfer velocity with surface wind speed and wave steepness is proposed.The wave steepness（5） is retrieved using a neural network（NN） model from ERS-2 scatterometer data,while the wind speed is directly derived by the ERS-2 scatterometer.The new model agrees well with the formulations based on the wind speed and the variation in the wind speed dependent relationships presented in many previous studies can be explained by this proposed relation with variation in wave steepness effect.Seasonally global maps of gas transfer velocity and llux are shown on the basis of the new model and the seasonal variations of the transfer velocity and llux during the 1 a period.The global mean gas transfer velocity is 30 cm/h after area-weighting and Schmidt number correction and its accuracy remains calculation with in situ data.The highest transfer velocity occurs around 60°N and 60°S,while the lowest on the equator.The total air to sea CO₂ llux（calculated by carbon） in that year is 1.77 Pg.The strongest source of CO₂ is in the equatorial east Pacific Ocean, while the strongest sink is in the 68°N.Full exploration of the uncertainty of this estimate awaits further data.An effectual method is provided to calculate the effect of waves on the determination of air-sea CO₂ transfer velocity and fluxes with ERS-2 scatterometer data.     

A semiempirical algorithm for SAR wave height retrieval and its validation using Envisat ASAR wave mode data

Many synthetic aperture radar(SAR) wave height retrieval algorithms have been developed.However,the wave height retrievals from most existing methods either depend on other input as the first guess or are restricted to the long wave regime.A semiempirical algorithm is presented,which has the objective to estimate the wave height from SAR imagery without any prior knowledge.The proposed novel algorithm was developed based on the theoretical SAR ocean wave imaging mechanism and the empirical relation between two types of wave period.The dependency of the proposed model on radar incident and wave direction was analyzed.For Envisat advanced synthetic aperture radar(ASAR) wave mode data,the model can be reduced to the simple form with two input parameters,i.e.,the cutoff wavelength and peak wavelength of ocean wave,which can be retrieved from SAR imagery without any prior knowledge of wind or wave.Using Envisat ASAR wave mode data and the collocated buoy measurements from NDBC,the semiempirical algorithm is validated and compared with the Envisat ASAR level 2 products.The root-mean-square-error(RMSE) and scatter index(SI) in respect to the in situ measurements are 0.52 m and 19% respectively.Validation results indicate that,for Envisat ASAR wave mode data,the proposed method works well.     

使用参数化初猜测谱算法提取VV极化Sentinel-1 SAR图像的海浪要素并与ECMWF再分析数据以及浮标数据进行验证对比

The purpose is to study the accuracy of ocean wave parameters retrieved from C-band VV-polarization Sentinel-1Synthetic Aperture Radar(SAR) images, including both significant wave height(SWH) and mean wave period(MWP), which are both calculated from a SAR-derived wave spectrum. The wind direction from in situ buoys is used and then the wind speed is retrieved by using a new C-band geophysical model function(GMF) model,denoted as C-SARMOD. Continuously, an algorithm parameterized first-guess spectra method(PFSM) is employed to retrieve the SWH and the MWP by using the SAR-derived wind speed. Forty–five VV-polarization Sentinel-1 SAR images are collected, which cover the in situ buoys around US coastal waters. A total of 52 subscenes are selected from those images. The retrieval results are compared with the measurements from in situ buoys. The comparison performs good for a wind retrieval, showing a 1.6 m/s standard deviation(STD) of the wind speed, while a 0.54 m STD of the SWH and a 2.14 s STD of the MWP are exhibited with an acceptable error.Additional 50 images taken in China's seas were also implemented by using the algorithm PFSM, showing a 0.67 m STD of the SWH and a 2.21 s STD of the MWP compared with European Centre for Medium-range Weather Forecasts(ECMWF) reanalysis grids wave data. The results indicate that the algorithm PFSM works for the wave retrieval from VV-polarization Sentinel-1 SAR image through SAR-derived wind speed by using the new GMF C-SARMOD.     

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.     

Measurements of ocean wave spectrum from airborne radar at small incidence angles

This paper proposes the retrieval method of ocean wave spectrum for airborne radar observations at small incidence angles, which is slightly modified from the method developed by Hauser. Firstly, it makes use of integration method to estimate total mean square slope instead of fitting method, which aims to reduce the affects of fluctuations superposed on normalized radar cross-section by integration. Secondly, for eliminating the noise spectrum contained in signal spectrum, the method considers the signal spectrum in certain look direction without any long wave components as the assumed noise spectrum, which would be subtracted from signal spectrum in any look direction for linear wave spectrum retrieval. Estimated ν from the integration method are lower than the one from fitting method and have a standard deviation of 0.004 between them approximately. The assumed noise spectrum energy almost has no big variations along with the wave number and is slightly lower to the high wave number part of signal spectrum in any look direction, which follows that the assumption makes sense. The retrieved directional spectra are compared with the buoy records in terms of peak wavelength, peak direction and the significant wave height. Comparisons show that the retrieved peak wavelength and significant wave height are slightly higher than the buoy records but don’t differs significantly (error less than 10%). For peak direction, the swell waves in first case basically propagate in the wind direction 6 hours ago and the wind-generated waves in second case also propagate in the wind direction, but the 180? ambiguity remains. Results show that the modified method can carry out the retrieval of directional wave spectrum.     

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.     

The long-term trend of the sea surface wind speed and the wave height (wind wave, swell, mixed wave)in global ocean during the last 44 a

Utilizing the 45 a European Centre for Medium-Range Weather Forecasts(ECMWF)reanalysis wave data(ERA-40),the long-term trend of the sea surface wind speed and(wind wave,swell,mixed wave)wave height in the global ocean at grid point 1.5×1.5 during the last 44 a is analyzed.It is discovered that a majority of global ocean swell wave height exhibits a significant linear increasing trend(2–8 cm/decade),the distribution of annual linear trend of the significant wave height(SWH)has good consistency with that of the swell wave height.The sea surface wind speed shows an annually linear increasing trend mainly concentrated in the most waters of Southern Hemisphere westerlies,high latitude of the North Pacific,Indian Ocean north of 30 S,the waters near the western equatorial Pacific and low latitudes of the Atlantic waters,and the annually linear decreasing mainly in central and eastern equator of the Pacific,Juan.Fernandez Archipelago,the waters near South Georgia Island in the Atlantic waters.The linear variational distribution characteristic of the wind wave height is similar to that of the sea surface wind speed.Another find is that the swell is dominant in the mixed wave,the swell index in the central ocean is generally greater than that in the offshore,and the swell index in the eastern ocean coast is greater than that in the western ocean inshore,and in year-round hemisphere westerlies the swell index is relatively low.     

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.     

基于浮标实测数据的WindSat海洋反演产品精度分析

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.     

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。     

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。     

Validation and accuracy analysis of wind products from scatterometer onboard the HY-2B satellite

The Chinese marine dynamic environment satellite HY-2B was launched in October 2018 and carries a Ku-band scatterometer. This paper focuses on the accuracies of HY-2B scatterometer wind data during the period from November 2018 to May 2021. The HY-2B wind data are validated against global moored buoys operated by the U.S. National Data Buoy Center and Tropical Atmosphere Ocean, numerical model data by the National Centers for Environmental Prediction, and the Advanced Scatterometer data issued b...     

两种ASCAT 散射计风产品的比较及评估

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。     

OSMAR-S100便携式高频地波雷达海浪和海面风探测性能分析

OSMAR-S系列便携式高频地波雷达系统采用单极子/交叉环紧凑型天线阵,通过单站雷达即可实现有效探测距离约10km内海浪和海面风的单点观测。为了更好地了解OSMAR-S100雷达系统海浪和海面风的综合探测性能,于2013年1月29日至3月7日在台湾海峡西南部海域进行了雷达与浮标观测的对比试验,得到了有效波高、有效波周期、平均风速和平均风向数据。对比结果表明,OSMAR-S100便携式高频地波雷达可有效观测距雷达10km以内有效波高0.5m以上的海浪平均状况和平均风速5m/s以上的海面风,雷达反演有效波高和有效波周期的均方根误差分别为0.60m和1.60s,反演平均风速和平均风向的均方根误差为1.83m/s和16.7°。在未经区域化标定的情况下,此结果说明了该型雷达产品已初步具备了海浪和海面风的业务化观测水平。     

星载高级合成孔径雷达波模式算法及其反演数据精度验证

搭载在欧洲环境卫星(ENVISAT)上的高级合成孔径雷达(Advanced Synthetic Aperture Radar,ASAR)二级波模式数据提供了诸多海浪信息包括有效波高、波向、波长和二维海浪谱等,在海浪预报模式中具有重要作用。本文拟利用浮标观测数据对ASAR波模式算法及其反演数据精度进行对比验证。由于SAR卫星在海面的特殊成像机制,不同海况下会有不同的测量结果,通过与美国国家浮标中心(NDBC)的浮标数据对比,显示ASAR有效波高在高海况下低估和在低海况下高估的现象,在中等海况下的测量结果较优。通过研究ASAR数据集中对应的海浪谱,按照能量与方向分布可分为四种类型:单一方向海浪谱(Ⅰ类谱),180°方向模糊海浪谱(Ⅱ类谱),海浪两个方向且能量分布杂乱(Ⅲ类谱),多个传播方向且谱型杂乱海浪谱(Ⅳ类谱)。探究在不同类型下的海浪参数的精度,结果表明在单一波向正常海浪谱情况下,有效波高、波向与浮标数据一致性较好,存在180°方向模糊的对称海浪谱仅有效波高精度较高,谱型杂乱的海浪谱海浪有效波高和波向反演结果均较差。     

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。     

Influence of the type of sea waves on the backscattered radar cross section at medium incidence angles

We consider the influence of the sea surface state on the backscattered radar cross section and the accuracy of the wind speed retrieval from the scatterometer data. We used a joint set of radars and buoys to determine the type of sea waves. Three types of sea waves were distinguished: developing wind waves, fully developed wind waves, and mixed sea. It is shown that the retrieval error of the near surface wind speed using a one-parameter algorithm is minimal in the case of fully developed wind waves. We compared these data with the results of radio-altimeter data analysis and showed that in both cases underestimation of the retrieval wind speed exists for developing wind waves and overestimation occurs for mixed sea. A variety of swell parameters (length of the dominating wave, swell height, swell age) significantly influence the backscattered radar cross section, leading to a growth in the mean square error of the retrieved wind speed during vertical sounding (radio-altimeter data), and only slightly influence the mean square error of the scatterometer data (medium incidence angles). It is necessary to include the information about the parameters of sea waves in the algorithms and take into account the regional wave properties to increase the accuracy of wind speed retrieval.     

A simple meteorological buoy with satellite telemetry

A small, inexpensive, and easily deployable meteorological buoy is described. Buoy motion is greatly reduced by appropriate ballast techniques; vector averaging further removes buoy motion effects from wind data. Data is transmitted to the GOES satellite and is retrieved by telephone. Measurements are vector-averaged wind components, wind speed, wind direction, water temperature, air temperature, and compass direction. Data from two field trials are discussed. Speed comparisons averaged 0.2 m sec⁻¹ with a standard deviation of 0.6 m sec⁻¹. Direction comparisons were different due to local topography, but they indicate a probable accuracy of ±5°.