A wind speed retrieval algorithm by combining 6 and 10 GHz data from Advanced Microwave Scanning Radiometer: Wind speed inside hurricanes

A wind speed retrieval algorithm was developed using 6 and 10 GHz h-pol (6H and 10H) data of the Advanced Microwave Scanning Radiometer (AMSR) aboard the Advanced Earth Observation Satellite-II (ADEOS-II) and AMSR-E aboard AQUA, for the purpose of retrieving wind speed inside rainstorms, primarily hurricanes and typhoons. The h-pol was used rather than the v-pol, because the brightness temperature sensitivity to the ocean wind at h-pol is larger than v-pol. The microwave emission change of 6H and 10H corresponding to ocean wind was evaluated in no-rain areas by combining AMSR and SeaWinds data aboard the ADEOS-II (SeaWinds was NASA’s scatterometer), and it was found that the ratio of the two 6H to 10H increments due to ocean wind is 0.9. Assuming that this result also holds with higher wind speeds and under rainy conditions, the brightness temperatures at 6H and 10H were simulated using a microwave radiative transfer model. A parameter W6 (unit; Kelvin) was then defined, representing an increment at 6H due to ocean wind. W6 is applicable to rainy areas, and to all ranges of sea surface temperature. W6 was compared with wind speed reported by the National Hurricanes Center for several hurricanes in the Western Atlantic Ocean during three years (2002 to 2004). W6 averaged around centers of hurricanes was found to exhibit a sensitivity to wind speed, such as increasing from 22 K to 65 K as the wind speed rose from 65 to 140 knots (33 to 72 m/s), and an empirical relationship relating the averaged W6 to wind speed in hurricanes was derived.     

Effect of air-sea temperature difference on ocean microwave brightness temperature estimated from AMSR,SeaWinds, and buoys

The effect of air-sea temperature differences on the ocean microwave brightness temperature (Tb) was investigated using the Advanced Microwave Scanning Radiometer (AMSR) aboard the Advanced Earth Observing Satellite-II (ADEOS-II) during a period of seven months. AMSR Tb in the global ocean was combined with wind data supplied by the scatterometer SeaWinds aboard ADEOS-II and air temperature given by a weather forecast model. Tb was negatively correlated with air-sea temperature difference, its ratio lying around −0.4K/°C at the SeaWinds wind speed of 14 m/s for the 6 GHz vertical polarization. Tb of AMSR-E aboard AQUA during 3.5 years was combined with ocean buoy data, and similar results were obtained.     

Difference characteristics of sea surface temperature observed by GLI and AMSR aboard ADEOS-II

This study compares infrared and microwave measurements of sea surface temperature (SST) obtained by a single satellite. The simultaneous observation from the Global Imager (GLI: infrared) and the Advanced Microwave Scanning Radiometer (AMSR: microwave) aboard the Advanced Earth Observing Satellite-II (ADEOS-II) provided an opportunity for the intercomparison. The GLI-and AMSR-derived SSTs from April to October 2003 are analyzed with other ancillary data including surface wind speed and water vapor retrieved by AMSR and SeaWinds on ADEOS-II. We found no measurable bias (defined as GLI minus AMSR), while the standard deviation of difference is less than 1°C. In low water vapor conditions, the GLI SST has a positive bias less than 0.2°C, and in high water vapor conditions, it has a negative (positive) bias during the daytime (nighttime). The low spatial resolution of AMSR is another factor underlying the geographical distribution of the differences. The cloud detection problem in the GLI algorithm also affects the difference. The large differences in high-latitude region during the nighttime might be due to the GLI cloud-detection algorithm. AMSR SST has a negative bias during the daytime with low wind speed (less than 7 ms⁻¹), which might be related to the correction for surface wind effects in the AMSR SST algorithm.     

利用南极走航观测评估卫星遥感海表面温度

利用1989-2005年间南极走航观测的海表面温度,对目前3个主要的卫星反演的SST产品AVHRR(Advanced Very High Resolution Radiometer),TMI(TRMM Microwave Imager)和AMSR-E(Advanced Microwave Scanning Radiometer for the Earth Observing System)进行了较为系统的评估,并着重检验了它们在南大洋的准确性.结果表明,AVHRR SST比观测数据偏冷,白天的偏差为-0.12℃,夜晚的偏差为-0.04℃,而且南大洋的冷偏差更为显著.TMI SST比观测数据明显偏暖,白天的偏差为0.48℃,夜晚的偏差为0.57℃,其温差ΔT受37GHz风速影响,在强风速(>6m/s)下这种影响仍然存在.AMSR-ESST比观测数据偏暖,白天的偏差为0.34℃,夜晚的偏差为0.27℃,而且南大洋的暖偏差相对较大.AMSR-E SST温差受水汽影响,并在南大洋随着水汽的增加而增加.通过进一步比较微波(AMSR-E和TMI)和红外(AVHRR)遥感的SST在2004年北半球冬季(即南半球夏季)的差别,发现微波遥感在热带(15°S-15°N)和南大洋区域(45°S以南)比红外遥感偏暖,而且在南大洋区域的偏差相对较大,相反在北半球中纬度区域(15°~40°N)偏冷.AMSR-E与AVHRR SST的温差,从白天到夜晚有减小的趋势,而TMI与AVHRR SST的温差无明显的变化.     

六种遥感海表温度产品的比对分析

海表温度(sea surface temperature, SST)产品是研究全球海洋大气系统的重要数据源, 在海洋相关领域的研究和应用方面具有重要价值。对2007年1和7月的六种不同SST产品(AVHRR-only, AMSR+AVHRR, NCODA, RSS, RTG-HR和OSTIA)在南大洋阿古拉斯回流(Agulhas Return Current, ARC)与绕极环流交汇区域的产品特性进行了比对统计分析, 包括SST分析、SST梯度分析、统计参数分析和波数谱分析。分析结果表明产品之间SST时空变化分布的整体趋势一致, RTG-HR在空间分布上过于平滑, OSTIA解析的大洋锋面最弱, RSS包含噪声较多, NCODA相对其他产品存在较大偏差。发现在AVHRR数据的覆盖率较好的情况下与以红外数据构建的AVHRR-only相比, AMSR数据并不能提供更多的SST信息, 反而会降低产品的空间解析能力。     

基于Himawari-8卫星的逐时次海表温度融合

Himawari-8卫星是日本气象厅发射的新一代地球同步静止气象卫星,为获取逐时次海表温度产品提供了有力数据支持。本文以Himawari-8 AHI海表温度为基础,利用最优插值法融合GCOM-W1 AMSR2海表温度和NERA-GOOS现场观测资料,生成逐时次海表温度融合产品。为了充分利用邻近时刻的海表温度观测资料,利用Himawari-8 AHI海表温度和欧洲中期天气预报中心海面风速数据建立匹配数据集,研究建立海表温度日变化模型,实现邻近时刻海表温度的订正;为了消除多源海表温度间的系统偏差,以Himawari-8 AHI海表温度为目标数据,利用泊松方程对GCOM-W1 AMSR2海表温度进行偏差订正。实验验证结果表明,利用逐时次海表温度融合产品计算的日增温情况与海面风速具有较好的相关性,间接证实了逐时次海表温度融合产品的准确性;另外,逐时次海表温度融合产品与现场观测海表温度的偏差为0.09℃、均方根误差为0.89℃,二者具有较好的一致性,说明逐时次海表温度融合产品具有较高的精度。     

Satellite-derived SST validation based on in-situ data during summer in the East China Sea and western North Pacific

Satellite-derived sea surface temperature (SST) is validated based on in-situ data from the East China Sea (ECS) and western North Pacific where most typhoons, which make landfall on the Korean peninsula, are formed and pass. While forecasting typhoons in terms of intensity and track, coupled ocean-typhoon models are significantly influenced by initial ocean condition. Potentially, satellite-derived SST is a very useful dataset to obtain initial ocean field because of its wide spatial coverage and high temporal resolution. In this study, satellite-derived SST from various sources such as Tropical Rainfall Measuring Mission Microwave Imager (TMI), Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and New Generation Sea Surface Temperature for Open Ocean (NGSST-O) datasets from merged SSTs were compared with in-situ observation data using an indirect method which is using near surface temperature for validation of satellite derived SST. In-situ observation data included shipboard measurements such as Expendable Bathythermograph (XBT), and Conductivity, Temperature, Depth (CTD), and Argo buoy data. This study shows that in-situ data can be used for microwave derived SST validation because homogeneous features of seawater prevail at water depths of 2 m to 10 m under favorable wind conditions during the summer season in the East China Sea. As a result of validation, root-mean-square errors (RMSEs) are shown to be 0.55 °C between microwave SST and XBT/CTD data mostly under weak wind conditions, and 0.7 °C between XBT/CTD measurement and NGSST-O data. Microwave SST RMSE of 0.55 °C is a potentially valuable data source for general application. Change of SST before and after typhoon passing may imply strength of ocean mixing due to upwelling and turbulent mixing driven by the typhoon. Based on SST change, ocean mixing, driven by Typhoon Nari, was examined. Satellite-derived SST reveals a significant SST drop around the track immediately following the passing of Typhoon Nari in October, 2007.     

Correction of the effect of relative wind direction on wind speed derived by advanced microwave scanning radiometer

The sea surface wind speed (SSWS) derived by a microwave radiometer can be contaminated by changes of the brightness temperature owing to the angle between the sensor azimuth and the wind direction (Relative Wind Direction effect: RWD effect). We attempt to apply the method proposed by Konda and Shibata (2004) to the SSWS derived by Advanced Microwave Scanning Radiometer (AMSR) on Advanced Earth Observing Satellite II (ADEOS-II), in order to correct for the RWD effect. The improvement of accuracy of the SSWS estimation amounts to roughly 60% of the error caused by the RWD effect. Comparison with in situ observation at the Tropical Atmosphere Ocean (TAO) array shows that the root mean square error of the corrected SSWS is 1.1 ms⁻¹. It is found that the impact of the RWD effect on the estimation of the latent heat flux can amount to about 30 Wm⁻² on average. We applied the method to the SSWS derived by AMSR for Earth Observing System (AMSR-E) and obtained a similar result.     

多源星载辐射计SST数据对比分析

基于2018年4种红外辐射计(MODIS-Aqua,MODIS-Terra,VIIRS和AVHRR)的SST数据和3种微波辐射计(GMI,WindSat和AMSR2)的SST数据,分析了7种星载辐射计SST数据的全球覆盖情况,利用Argo数据对7种辐射计SST数据进行了真实性检验,并开展了微波产品、红外产品和Argo的...     

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.     

Ocean-atmosphere momentum coupling in the Kuroshio Extension observed from space

Using a combination of Quick Scatterometer (QuikSCAT), Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and Lagrangian drifter measurements, we demonstrate that wind data alone are not sufficient to derive ocean surface stress (momentum flux) over mid-latitude ocean fronts, specifically the Kuroshio Extension. There was no continuous and large-scale stress measurement over ocean until the launch of the scatterometers. Stress had been derived from winds through a drag coefficient, and our concept of stress distribution may be largely influenced by our knowledge of wind distribution. QuikSCAT reveals that the variability of stress could be very different from wind. The spatial coherence between the magnitude of stress and sea surface temperature (SST), between the divergence of surface stress and the downwind SST gradient, and between the vorticity of stress and crosswind SST gradient, are the inherent characteristics of stress (turbulence production by buoyancy) that would exist even under a uniform wind field. The coherence between stress vorticity and SST gradient is masked by the rotation of ocean currents over the Kuroshio meanders. Surface stress rotates in the opposite direction to surface currents because stress is the vector difference between wind and current. The results are in agreement with a previous study of the Agulhas Extension and confirm the unique stress measuring capability of the scatterometer.     

The sea surface temperature product algorithm of the Ocean Color and Temperature Scanner (OCTS) and its accuracy

The Ocean Color and Temperature Scanner (OCTS) aboard the Advanced Earth Observing Satellite (ADEOS) can observe ocean color and sea surface temperature (SST) simultaneously. This paper explains the algorithm for the OCTS SST product in the NASDA OCTS mission. In the development of the latest, third version (V3) algorithm, the OCTS match-up dataset plays an important role, especially when the coefficients required in the MCSST equation are derived and the equation form is adjusted. As a result of the validation using the OCTS match-up dataset, the algorithm has improved the root mean square (rms) error of the OCTS SST up to 0.698°C although some problems remain in the match-up dataset used in the present study.     

A study on retrieval algorithms in oceanic passive microwave remote sensing using 1.4 GHz and 10.7 GHz bands

To retrieve sea-surface salinity (SSS) from radiometer data at 1.4 GHz, auxiliary data of sea-surface temperature (SST), surface roughness and meteorological variables are needed. The authors study oceanic passive polarimetric microwave remote sensing using 1.4 GHz and 10.7 GHz bands. A set of algorithms are developed for 1.4 GHz and 10.7 GHz microwave polarimetric radiometer at 50° incidence angle to retrieve wind vector, as well as other geophysical parameters, such as SSS, SST, atmospheric volumes of water vapor and liquid water. Idealized retrievals are conducted using 2324 simulated brightness temperatures of full Stokes parameters at 1.4 GHz and 10.7 GHz. Results indicate that SSS, SST, sea-surface wind speed, direction, atmospheric volumes of water vapor and liquid water can be inversed at the same time. This suggests an alternative way for SSS remote sensing.     

北太平洋冬季SST、风场及环流对淡水强迫的响应

通过海气耦合模式CCSM3(The Community Climate System Model version 3),研究在北大西洋高纬度淡水强迫下,北太平洋冬季的海表温度SST、风场及流场的响应及其区域性差异。结果表明:淡水的注入使北太平洋整体变冷,但有部分区域异常增暖;在太平洋东部赤道两侧,SST的变化出现北负南正的偶极子型分布。阿留申低压北移的同时中纬度西风减弱,热带附近东北信风增强。黑潮和南赤道流减弱,北太平洋副热带逆流和北赤道流增强,日本海被南向流控制。风场及流场的改变共同导致了北太平洋SST异常出现复杂的空间差异:北太平洋中高纬度SST的降温主要由大气过程决定,海洋动力过程主要影响黑潮、日本海及副热带逆流区域的SST,太平洋热带地区SST异常由大气与海洋共同主导。     

Validation of interannual simulations from the 1/8° global Navy Coastal Ocean Model (NCOM)

A 1/8° global version of the Navy Coastal Ocean Model (NCOM) is used for simulation of upper-ocean quantities on interannual time scales. The model spans the global ocean from 80°S to a complete Arctic cap, and includes 19 terrain-following σ- and 21 fixed z-levels. The global NCOM assimilates three-dimensional (3D) temperature and salinity fields produced by the Modular Ocean Data Assimilation System (MODAS) which generates synthetic temperature and salinity profiles based on ocean surface observations. Model-data intercomparisons are performed to measure the effectiveness of NCOM in predicting upper-ocean quantities such as sea surface temperature (SST), sea surface salinity (SSS) and mixed layer depth (MLD). Subsurface temperature and salinity are evaluated as well. An extensive set of buoy observations is used for this validation. Where possible, the model validation is performed between year-long time series obtained from the model and time series from the buoys. The statistical analyses include the calculation of dimensionless skill scores (SS), which are positive if statistical skill is shown and equal to one for perfect SST simulations. Model SST comparisons with year-long SST time series from all 83 buoys give a median SS value of 0.82. Model subsurface temperature comparisons with the year-long subsurface temperature time series from 24 buoys showed that the model is able to predict temperatures down to 500 m reasonably well, with positive SS values ranging from 0.18 to 0.97. Intercomparisons of MLD reveal that the model MLD is usually shallower than the buoy MLD by an average of about 15 m. Annual mean SSS and subsurface salinity biases between the model and buoy values are small. A comparison of SST between NCOM and a satellite-based Pathfinder data set demonstrates that the model has a root-mean-square (RMS) SST difference of 0.61 °C over the global ocean. Spatial variations of kinetic energy fields from NCOM show agree with historical observations. Based on these results, it is concluded that the global NCOM presented in this paper is able to predict upper-ocean quantities with reasonable accuracy for both coastal and open ocean locations.     

北极星载微波辐射计海表温度遥感观测能力分析

本文利用2016年的AMSR2、GMI、WindSat和HY-2A RM等星载微波辐射计海表温度（SST）数据，分析了北极卫星遥感SST数据的时空覆盖和产品精度情况。结果表明:北极星载微波辐射计SST冬季覆盖率和有效覆盖天数要低于夏季，GMI的SST有效覆盖率较低，AMSR2较高，联合使用AMSR2、GMI、WindSat和HY-2A RM星载微波辐射计SST数据，2月份覆盖率在12%~15%之间，有效观测天数优于26 d，8月份覆盖率全月高于26%，有效观测天数优于29 d。北极地区星载微波辐射计SST数据的误差均要大于全球平均水平，AMSR2数据精度较好，WindSat与AMSR2的精度相当，GMI的均方根误差约是AMSR2的2倍，HY-2A RM数据精度低于其他星载微波辐射计水平。     

Generation of high resolution sea surface temperature using multi-satellite data for operational oceanography

In the present article, we introduce a high resolution sea surface temperature(SST) product generated daily by Korea Institute of Ocean Science and Technology(KIOST). The SST product is comprised of four sets of data including eight-hour and daily average SST data of 1 km resolution, and is based on the four infrared(IR) satellite SST data acquired by advanced very high resolution radiometer(AVHRR), Moderate Resolution Imaging Spectroradiometer(MODIS), Multifunctional Transport Satellites-2(MTSAT-2) Imager and Meteorological Imager(MI), two microwave radiometer SSTs acquired by Advanced Microwave Scanning Radiometer 2(AMSR2), and Wind SAT with in-situ temperature data. These input satellite and in-situ SST data are merged by using the optimal interpolation(OI) algorithm. The root-mean-square-errors(RMSEs) of satellite and in-situ data are used as a weighting value in the OI algorithm. As a pilot product, four SST data sets were generated daily from January to December 2013. In the comparison between the SSTs measured by moored buoys and the daily mean KIOST SSTs, the estimated RMSE was 0.71°C and the bias value was –0.08°C. The largest RMSE and bias were 0.86 and –0.26°C respectively, observed at a buoy site in the boundary region of warm and cold waters with increased physical variability in the Sea of Japan/East Sea. Other site near the coasts shows a lower RMSE value of 0.60°C than those at the open waters. To investigate the spatial distributions of SST, the Group for High Resolution Sea Surface Temperature(GHRSST) product was used in the comparison of temperature gradients, and it was shown that the KIOST SST product represents well the water mass structures around the Korean Peninsula. The KIOST SST product generated from both satellite and buoy data is expected to make substantial contribution to the Korea Operational Oceanographic System(KOOS) as an input parameter for data assimilation.     

利用西北印度洋船测数据评估基于卫星的海表面温度

本文描述了一次夏季在西北印度洋进行的调查船水文测量,用船测数据评估卫星海面表温度,并寻找影响海表面温度误差的主要因素。我们考虑了两种卫星数据,第一种是微波遥感产品——热带降雨测量任务微波成像仪TMI数据,另外一种是融合了微波,红外线,以及少部分观测数据的融合数据产品——可处理海表温度和海冰分析OSTIA数据。结果表明融合数据的日平均海表面温度的平均误差和均方根误差都比微波遥感小。这一结果证明了融合红外线遥感,微波遥感以及观测数据来提高海表面温度数据质量的必要性。此外,我们分析了海表面温度误差与各项水文参数之间的相关关系,包括风速,大气温度,想对湿度,大气压力,能见度。结果表明风速与TMI海表面温度误差的相关系数最大。而大气温度是影响OSTIA海表面温度误差最重要的因素;与此同时,想对湿度与海表面温度误差的相关系数也很高。     

El Nino and the Southern Oscillation

Using the Comprehensive Ocean-Atmosphere Data Set (COADS), wind, surface pressure and SST fields in the Equatorial Eastern Pacific and the Equatorial South Indian Ocean were analysed comprehensively.lt is pointed out that the seesaw between surface pressure in the Equatorial South Indian Ocean and the Equatorial Southeast Pacific causes the seesaw between the wind fields in the two areas, and the seesaw of wind fields results in the seesaw of SST between Indonesia and the Equatorial Eastern Pacific. El Nino is the response of ocean to the forcing of monsoon system in the Indian Ocean and the trade system in the Pacific.     

印度洋波浪诱导的水体输运效应及其对赤道SST异常的影响

波浪诱导的水体输运会对海洋产生大尺度影响。结合波浪大尺度效应的研究现状和印度洋涌浪分布的事实,利用ECMWF-CERA20的波浪、海表面温度(SST)及风场数据,采用多种统计分析方法,研究了波浪输运与赤道印度洋SST的潜在关系。结果显示:中高纬度波浪输运异常的低频信号在空间、周期上与赤道SST异常均有高度相似性;Stokes漂流纬向、经向异常呈现出南—北、东—西的振荡,其第二模态时间序列与印度洋偶极子(Indian Ocean Dipole,IOD)指数存在强相关性并在La Ni a次年的负IOD事件中达到最高:相关系数在ACC区域纬向异常超前6个月时接近0.6,中纬度区域经向异常在超前3个月时达到0.7。在La Ni a次年的负IOD中,波浪经向输运异常的相位(超前三个月)与赤道SST异常相位呈全年反相位,经向浪致输运异常造成的东—西热量输运差异对赤道SST异常分布有不可忽略的贡献。