TMI、AMSR-E全球海表温度与Argo近海表温度的比较

本文将TMI（Tropical Rainfall Measuring Mission （TRMM）Microwave Imager）和AMSR-E（Advanced Microwave Scanning Radiometer for the Earth Observing System）卫星观测的全球海表温度与Argo浮标观测的近海表温度进行了比较。并检验了影响海温变化的因素,包括风速、水汽含量、液态云和地理位置。结果显示,TMI、AMSR-E海表温度与Argo近海表温度均明显相关。在低风速时,TMI、AMSR-E海表温度整体比Argo近海表温度高。在低风速时,TMI比AMSR-E海表温度更接近Argo近海表温度,但TMI海表温度在高纬可能没有经过良好校正。温度差异显示,在低水汽含量时,TMI和AMSR-E海表温度显示出暖的差异,代表TMI和AMSR-E海表温度在高纬均没有经过良好校正。黑潮延伸区的海表温度变化要比海潮区明显。春季在黑潮延伸区,卫星观测的海表温度与Argo近海表温度差异较小。在低风速时,TMI和AMSR-E海表温度均经过了良好校正,而TMI比AMSR-E效果更好。     

利用南大洋漂流浮标数据评估AMSR-E SST

利用AOML(Atlantic Oceanographical and Meteorological Laboratory)SVP漂流浮标的海表面温度数据,针对30°S以南的南大洋海域,对目前主要使用的微波遥感产品(AMSR-E,Ad-vanced Microwave Scanning Radiometer for the Earth Observing System)反演的SST进行了较为系统的评估。结果表明,AMSR-E SST比浮标数据偏冷,偏差为-0.01℃,标准差为0.70℃。夏季的偏差为0.004℃,标准差为0.64℃;冬季的偏差为-0.06℃,标准差为0.75℃,冬季的偏差和标准差较大。温差ΔT受流速影响,随着流速的增大而减小,且这种趋势在夏季更为显著。具备托伞结构的浮标与总体情况基本一致,而无托伞结构的浮标受流速的影响要大一些。同时,温差ΔT受水汽的影响,随着水汽的增加而减小,且这种影响在冬季更大一些。进一步对4个穿极和绕极浮标的追踪分析表明,温差ΔT受大洋海流系统的影响显著。在海流大的大西洋边界流和南极绕极流中,温差ΔT的不确定性要明显大于总体情况。     

VIIRS和AVHRR海表温度场空间精细度估算

对上层海洋次中尺度过程研究至关重要的卫星海表温度(Sea Surface Temperature, SST)场的空间精细度一直未受到足够重视。由于卫星SST产品反演噪声的影响和实测数据的缺乏等原因,目前对卫星SST场空间精细度的研究受到较大限制。本研究开发了一套估算卫星SST场空间精细度的方法,将Suomi National Polar-orbiting Partnership卫星Visible Infrared Imager Radiometer Suite(Suomi-NPP/VIIRS)和NOAA-15卫星Advanced Very High Resolution Radiometer(NOAA-15/AVHRR)Level-2 SST场的空间能量谱与长时间在同一航线反复观测的高空间分辨率实测海温数据的空间能量谱进行了比较。研究发现VIIRS SST场夜间沿扫描方向在1.5～50 km尺度对海表温度空间能量的分布特征和变化趋势描述准确,日变化导致VIIRS白天场次中尺度空间谱能量相对夜晚有所增加。AVHRR SST场空间谱能量在次中尺度相比VIIRS有较大升高。     

西北太平洋NOAA AVHRR海表温度日变化校正研究

白天,太阳辐射将海面上层加热,会出现海表温度日变化的情况,该变化对海气热交换以及海洋生态等的研究具有重要意义,且在不同海域有着不尽相同的变化规律。文章首先介绍了海表温度日变化经验和数值模型,然后在西北太平洋海域范围内,利用美国国家海洋和大气管理局(National Oceanic and Atmospheric Administration,NOAA)的改进型甚高分辨率辐射计(Advanced Very High Resolution Radiometer,AVHRR)海表温度数据、美国宇航局"水"卫星Aqua上先进微波扫描辐射计(Advanced Microwave Scanning Radiometer for EOS,AMSR-E)的海面风速和经计算得到的太阳辐射强度数据,通过对已有经验模型系数进行重新回归拟合,得到在该海域NOAAAVHRR海表温度数据日变化的经验模型。验证结果显示,重新回归系数后的模型在西北太平洋海域内计算所得的海表温度日变化大小与AVHRR数据本身计算所得结果相比,其平均偏差为0.01℃,标准偏差为0.22℃,可以在该海域内较好地对NOAAAVHRR海表温度数据进行日变化校正。     

利用遗传算法反演实时近海面气温和比湿

使用Advanced Microwave Scanning Radiometer for EOS(AMSR-E)的海洋产品数据海表温度、风速,大气水蒸气、云液态水,通过遗传算法建立其与近海面气温和比湿之间的经验关系,进行近海面气温和比湿的实时反演.反演结果与The Tropical Ocean-Atmosphere(TAO)和The National Data Buoy Center(NDBC)的浮标实测资料进行比较,实时近海面气温和比湿的均方根误差分别为1.18℃和1.36 g/kg.分析结果表明,利用遗传算法采用AMSR-E海洋产品数据可以较好地反演近海面气温和比湿.     

VIIRS与MODIS海表面温度产品观测能力对比分析

以2015—2016年白天和夜间的VIIRS,MODIS-Terra和MODIS-Aqua三个红外SST产品为研究对象,探讨了3个红外SST的全球覆盖情况,包括统计全年有效观测天数和每日全球海洋覆盖率,将3个红外SST与Argo浮标在全球范围以及大西洋、印度洋、太平洋进行匹配统计分析,同时对匹配点平均偏差与标准偏差随纬度的变化进行研究,最后将3个红外SST进行交叉比对。结果表明:VIIRS在白天和夜间全年观测到的最大有效观测天数高于MODIS-Terra和MODIS-Aqua,该数据白天观测到的范围最大、全球海洋覆盖率也最高,夜间3个红外SST观测范围和覆盖率差别不大;VIIRS SST产品在全球以及3个大洋统计中数据质量较MODIS-Terra和MODIS-Aqua更接近Argo浮标;3个数据白天的SST平均偏差和标准差随纬度变化除了南北高纬度地区,整体浮动相差不大,VIIRS整体偏差在0℃附近,MODIS-Aqua次之,MODIS-Terra大部分都在0℃以下。夜间偏差与标准差随纬度变化平缓,在南北半球高纬度地区波动也小于白天。VIIRS白天和夜间SST值都高于其他2个红外SST。白天,VIIRS与MODIS-Aqua的温度值接近;夜间,则与MODIS-Terra的温度值接近。     

南北半球副热带高压对赤道东太平洋海温变化的响应

本文利用1974年1月到1996年12月重分析(NOAANCEP-NCARCDAS-1)全球500hPa位势高度场资料,及同期赤道太平洋各海区SST资料,研究了南北半球副热带高压的变化特征及其对赤道东太平洋SST变化的响应。结果表明,全球副热带高压的变化及对SST的响应,在南北两个半球有很好的一致性。全球副热带高压强度的变化与超前3个月SST的正相关最为显着。对SST响应最强烈的区域主要在南北纬30°之间的低纬,低纬地区局地SST对副热带高压也有强烈的影响。从10°到30°纬度,对SST的响应分别落后于赤道2～9个月。在中、高纬大气环流的响应表现为波列特征,对暖SST及冷SST的响应波列基本相反,但对暖SST的响应更为显着。海温和副热带高压的月际持续性有明显的季节变化,副热带高压9-10月的相关障碍可能与NinoC区SST8-9月的相关障碍低点有关。     

风应力桥梁作用下热带太平洋和热带印度洋相互作用的数值试验

利用中等复杂程度全球热带大气和热带海洋模式的数值试验,模拟分析了热带太平洋和热带印度洋通过风应力桥梁的相互作用过程.利用NCEP再分析的1958～1998年SST强迫大气模式得到的风应力与NCEP再分析的同期热通量共同驱动海洋模式,作为控制试验;和控制试验平行,但强迫大气模式的SST在某一海盆取为多年气候平均值的试验作为敏感性试验.比较控制试验与敏感性试验模拟的SST变率,揭示了热带某海盆SST异常通过风应力桥梁作用对其他海盆SST的影响及其过程.数值试验结果表明:热带某海盆SST暖(冷)异常一般总是引起该海盆上空西部西(东)风异常和东部东(西)风异常;热带太平洋SST暖(冷)异常导致年际尺度上印度洋上空东(西)风异常和年代际尺度上热带印度洋风场辐散(合),该风应力导致热带印度洋年际SST暖(冷)异常以及年代际SST冷(暖)异常,但这种异常均较弱;热带印度洋SST暖(冷)异常导致热带太平洋上空东(西)风异常,该风应力异常在年际和年代际尺度上均导致热带太平洋SST冷(暖)异常,但年代际尺度上异常更明显.考虑到热带印度洋SSTA受热带太平洋SSTA影响大,并且热带太平洋SST暖(冷)异常主要通过表面热通量导致热带印度洋SST变暖(冷)的观测事实,文中揭示的热带印度洋SST暖(冷)异常通过风应力桥梁作用导致热带太平洋SST冷(暖)异常的结果表明,热带印度洋SSTA对于热带太平洋SSTA主要起着一种负反馈作用,并且这种负反馈作用在年代际尺度上更为明显.     

利用卫星资料分析黄海海表温度的年际与年代际变化

海表温度长期变化在一定程度上反映了海域的气候变化信号,卫星遥感资料是获取高时空分辨率水温长期变化的有效手段。基于国家海洋局1982—1999年黄海断面监测器测数据的2 954组水温数据对时空匹配的卫星(NOAA/AVHRR)反演海表温度(SST)进行校验,计算得到卫星反演SST系统偏差为(0.18±1.00)℃。卫星反演的水温空间分布以及长期变化趋势与器测趋势较为一致,可以用来研究海域SST长期变化规律。利用校验后1982-01～2011-08NOAA/AVHRR的SST数据,分析了该时段黄海冬夏季代表月2、8月海表水温的变化规律。结果显示:(1)近30a,黄海冬季水温有2次跃迁:1989—1990年由冷至暖的状态跃迁,2000-2001年出现由暖至冷的状态转变;1990年代冬季水温达最高,相比1880年代,水温升高1.07℃,新世纪水温稍有降低,水温较1990年代下降了0.53℃,温度变化较大区域位于北黄海、山东半岛沿岸,苏北浅滩毗邻海区,该区SST与局地经向风场存在显著正相关,且北极涛动通过影响冬季风间接影响黄海水温变化;(2)夏季海表水温在1994—1995年呈现由冷至暖的状态跃迁,冷、暖期水温相差0.57℃,水温变化较显著的区域为黄东海分界处,其具体变化机制需深入研究。     

南半球微波遥感SST与Argo浮标NST的异同分析

利用Argo剖面浮标观测得到的近表层温度数据(NST),与两种卫星微波传感器(TMI和AMSRE)反演的海表温度(SST)进行较为系统的对比分析。结果表明,在南半球海域SST与NST虽存在显著的线性关系,但两者之间的差异(△T)还是十分明显的。无论是TMI还是AMSR-E反演的SST,与Argo NST相比,△T均存在昼夜和季节变化:△T夜间较白天大,冬季达到最大,而春季则是最小。此外,△T还表现出沿纬线呈带状分布的特征。进一步研究表明,造成南半球海域SST与NST的差异主要由风速所致,且与海面流速和大气水汽含量也有一定的关系。为此,建议改进卫星遥感SST反演方法,缩小其与实测NST之间的差异,从而为南半球乃至全球海域多源SST融合提供更加可靠的统计学依据。     

Merging satellite infrared and microwave SSTs: Methodology and evaluation of the new SST

The Global Ocean Data Assimilation Experiment (GODAE) requires the availability of a global analyzed SST field with high-resolution in space (at least 10 km) and time (at least 24 hours). The new generation SST products would be based on the merging of SSTs from various satellites data and in situ measurements. The merging of satellite infrared and microwave SST data is investigated in this paper. After pre-processing of the individual satellite data, objective analysis was applied to merge the SST data from NOAA AVHRR (National Oceanic and Atmospheric Administration, Advanced Very High Resolution Radiometer), GMS S-VISSR (Geostationary Meteorological Satellite, Stretched-Visible Infrared Spin Scan Radiometer), TRMM MI (Tropical Rainfall Measuring Mission, Microwave Imager: TMI) and VIRS (Visible and Infrared Scanner). The 0.05° daily cloud-free SST products were generated in three regions, viz., the Kuroshio region, the Asia-Pacific Region and the Pacific, during one-year period of October 1999 to September 2000. Comparisons of the merged SSTs with Japan Meteorological Agency (JMA) buoy SSTs show that, with considerable error sources from individual satellite data and merging procedure, an accuracy of 0.95 K is achieved. The results demonstrate the practicality and advantages of merging SST measurements from various satellite sensors.     

SST Availabilities of Satellite Infrared and Microwave Measurements

To investigate the feasibility and methodology of new generation sea surface temperature (SST) maps that combine various satellite measurements, we have quantitatively evaluated SST availabilities of NOAA AVHRR (National Oceanic and Atmospheric Administration, Advanced Very High Resolution Radiometer), GMS S-VISSR (Geostationary Meteorological Satellite, Stretched-Visible Infrared Spin Scan Radiometer) and TRMM MI (Tropical Rainfall Measuring Mission, Microwave Imager: TMI), during the one-year period from October 1999 to September 2000. The advantage of satellite microwave SST measurements is the ability to penetrate the clouds that contaminate satellite infrared measurements. Daily SST availabilities were calculated in the overlapping coverage from 20°N to 38°N and 120°E to 160°E. The annual-mean SST availabilities of AVHRR, S-VISSR and TMI are 48%, 56% and 78%, respectively. There are large seasonal variations in the availabilities of infrared measurements. The latitude-time plots of one-degree zonal mean SST availabilities of S-VISSR and TMI in the region from 38°S to 38°N and 80°E to 160°W show significant zonal variations, which are influenced by the atmospheric circulation such as the Subtropical High and the Intertropical Convergence Zone. The SST availabilities of S-VISSR and TMI in the five selected regions have large regional variations, ranging from 35% to 74% and 62% to 88% for S-VISSR and TMI, respectively. The present statistical analyses of SST availabilities in the infrared and microwave measurements indicate that 1) a daily cloud-free high-spatial resolution may be achieved by merging various SST measurements since their deficiencies compensate each other, and 2) nevertheless, it is necessary to take account of the seasonal and regional variations of SST availabilities of different satellite sensors for the development of merging technology. This revised version was published online in July 2006 with corrections to the Cover Date.     

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.     

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.     

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.     

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.     

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。     

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.