首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 156 毫秒
1.
A sea surface temperature (SST) retrieval algorithm for Global Imager (GLI) aboard the ADEOS-II satellite has been developed. The algorithm is used to produce the standard SST product in the Japan Aerospace Exploration Agency (JAXA). The algorithm for cloud screening is formed by combinations of various types of tests to detect cloud-contaminated pixels. The combination is changed according to the solar zenith angle, which enables us to detect clouds even in the sun glitter region in daytime. The parameters in the cloud-detection tests have been tuned using the GLI global observations. SST is calculated by the Multi-Channel SST (MCSST) technique from the detected clear pixels. Using drifting buoy measurements, match-up data are produced to derive the coefficients of the MCSST equations and to examine their performance. The bias and RMSE of the GLI SST are 0.03 K and 0.66 K for daytime and, −0.01 K and 0.70 K for nighttime, respectively.  相似文献   

2.
A regional algorithm to estimate SST fields in the western North Pacific, where small oceanographic disturbance are often found, has been developed using Moderate Resolution Imaging Spectroradiometers (MODIS) aboard Terra and Aqua. Its associated algorithm, which includes cloud screening and SST estimation, is based on an algorithm for the Global Imager (GLI) aboard Advanced Earth Observing Satellite-II (ADEOS-II) and is tuned for MODIS sensors. For atmospheric correction, we compare Multi-Channel SST (MCSST), Nonlinear SST (NLSST), Water Vapor SST (WVSST) and Quadratic SST (QDSST) techniques. For NLSST, four first-guess SSTs are investigated, including the values for MCSST, climatology with two different spatial resolutions, and near-real-time objective analysis. The results show that the NLSST method using high-resolution climatological SST as a first-guess has both good quality and high efficiency. The differences of root-mean-square error (RMSE) between the NLSST models using low-resolution climatology and those using high-resolution climatology are up to 0.25 K. RMSEs of the new algorithm are 0.70 K/0.65 K for daytime (Aqua/Terra) and 0.65 K/0.66 K for nighttime, respectively. Diurnal warming and the stratification of the ocean surface layer under low wind are discussed.  相似文献   

3.
利用南极走航观测评估卫星遥感海表面温度   总被引：3，自引：1，他引：2  
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.  相似文献   

4.
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.  相似文献   

5.
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.  相似文献   

6.
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.  相似文献   

7.
Ocean microwave emissions changed by the ocean wind at 6 GHz were investigated by combining data of the Advanced Microwave Scanning Radiometer (AMSR) and SeaWinds, both aboard the Advanced Earth Observation Satellite-II (ADEOS-II). This study was undertaken to improve the accuracy of the sea surface temperature (SST) retrieved from the AMSR 6 GHz data. Two quantities, 6V*(H*), were defined by the brightness temperature of the AMSR at 6 GHz with two polarizations (V-pol and H-pol), adjusted for atmospheric effects and with a calm ocean surface emission removed. These quantities represent a microwave emission change due to the ocean wind at 6 GHz. 6V* does not change in a region where 6H* is less than around 4 K (referred to as z0). Both 6V* and 6H* increase above z0. The 6V* to 6H* ratio, sp, varies with the relative wind directions. Furthermore, the sp values vary with the SST, between the northern and southern hemisphere, and seasonally. By specifying appropriate values for z0 and sp, the SST error between AMSR and buoy measurement became flat against 6H*, which is related to the ocean wind. Two extreme cases were observed: the Arabian Sea in summer and the Northwestern Atlantic Ocean in winter. The air-sea temperature difference in the former case was largely positive, while it was largely negative in the latter. The 6V* and 6H* relations differed from global conditions in both cases, which resulted in incorrect SSTs in both areas when global coefficients were applied.  相似文献   

8.
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的交叉比对分析。结果表明:VIIRS SST数据的覆盖率、有效覆盖天数均高于MODIS-Aqua、MODIS-Terra和AVHRR;AMSR2微波辐射计SST数据的覆盖率和有效覆盖天数均高于GMI和WindSat;4种红外辐射计SST数据与Argo浮标数据的平均偏差在-0.27~0℃,均方根误差小于0.76℃,其中VIIRS数据质量最好;3种微波辐射计SST数据与Argo浮标数据的平均偏差在-0.04~0.22℃,均方根误差小于0.88℃,其中AMSR2绝对偏差、标准偏差和均方根误差均小于其他2个微波辐射计数据。AMSR2和VIIRS的SST数据交叉对比发现,AMSR2与APDRC Argo、VIIRS与APDRC Argo的平均偏差分别小于0.15和-0.20℃,标准偏差分别小于0.52和0.60℃;AMSR2与VIIRS平均偏差在-0.23~-0.10℃,标准偏差小于0.41℃,两者具有较高的一致性。  相似文献   

9.
An algorithm has been developed for retrieving sea surface temperature (SST) from hourly data transmitted from the Japanese Advanced Meteorological Imager (JAMI) aboard a Japanese geostationary satellite, Multi-functional Transport Satellite (MTSAT)-1R. Threshold tests screening cloudy pixels are empirically adjusted to cases of daytime with/without sun glitter, and nighttime. The Non-Linear SST (NLSST) equation, including several new additional terms, is used to calculate MTSAT SST. The estimated SST is compared with drifting and moored buoy measurements, with the result that the bias of the MTSAT SST is nearly 0.0°K. The root mean square (rms) error is about 0.8°K, and it is 0.7°K under the condition that the satellite zenith angle is less than 50°. It is demonstrated that the hourly MTSAT SST produced by the algorithm developed here captures diurnal SST variations in the equatorial sea in mid-November 2006.  相似文献   

10.
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   

11.
Sea surface temperature SST obtained from the initial version of the Korea Operational Oceanographic System(KOOS) SST satellite have low accuracy during summer and daytime. This is attributed to the diurnal warming effect. Error estimation of SST data must be carried out to use the real-time forecasting numerical model of the KOOS. This study suggests two quality control methods for the KOOS SST system. To minimize the diurnal warming effect, SSTs of areas where wind speed is higher than 5 m/s were used. Depending on the wind threshold value, KOOS SST data for August 2014 were reduced by 0.15°C. Errors in SST data are considered to be a combination of random, sampling, and bias errors. To estimate bias error, the standard deviation of bias between KOOS SSTs and climatology SSTs were used. KOOS SST data yielded an analysis error standard deviation value similar to OSTIA and NOAA NCDC(OISST) data. The KOOS SST shows lower random and sampling errors with increasing number of observations using six satellite datasets. In further studies, the proposed quality control methods for the KOOS SST system will be applied through more long-term case studies and comparisons with other SST systems.  相似文献   

12.
Diurnal Sea Surface Temperature (SST) variations and the near-surface thermal structure of the tropical hot event (HE) have been investigated using advanced in-situ equatorial observations with hourly temporal resolution. The information on the HE area defined by the satellite cloud-free SSTs is used to sample the in-situ observations. The in-situ SSTs sampled for the HE conditions show that a maximum (minimum) SST has a histogram mode at 30.8°C (29.0°C), and frequently appears at 15:00 (07:00) local time. The amplitude of the diurnal SST variation (DSST) is defined by the difference between the maximum and minimum SSTs. The mean DSST during HEs is greater than 0.5°C, and has a maximum of about 0.75°C at the HE peak. The time series of mean DSST gradually increases (rapidly decreases) before (after) the peak. The satellite SST has a systematic positive bias against the corresponding daytime SST measured by the Triangle Trans-Ocean buoy Network. This bias is enhanced under conditions of large in-situ DSST. One-dimensional numerical model simulation suggests that the systematic bias is caused by the sharp vertical temperature gradient in the surface layer of HE. The near-surface thermal structure is generated by conditions of high insolation and low wind speed, which is the typical HE condition.  相似文献   

13.
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.  相似文献   

14.
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.  相似文献   

15.
基于Himawari-8卫星的逐时次海表温度融合   总被引：1，自引：0，他引：1  
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。  相似文献   

16.
An accurate platinum resistance thermometer (PRT) has been installed on a commercial ferry that operates between Hillarys Marina, some 15 km north of Fremantle, and Rottnest Island off the Western Australian coast. The PRT is located in the engine intake system and provides continuous under-way measurements of the bulk sea surface temperature (SST) at a depth of 1 m. The “SeaFlyte” ferry makes the trip to Rottnest Island between 3 and 5 times daily and so a wealth of data is available for comparison with the SST derived using data from the GLI instrument on ADEOS-II. Analyses of the ferry and satellite data confirm the excellent quality of SST estimates from the GLI as well as four other satellite instruments—AVHRR on NOAA-16, AATSR on ENVISAT, and the MODIS instruments on TERRA and AQUA. All satellite instruments showed a comparison standard deviation of better than 0.6°C with GLI being better than 0.4°C. The number of ferry-satellite data coincidences used in this study demonstrates one of the advantages of installing measurement systems on commercial ships that operate regular passenger or freight services rather than infrequent deployments on research vessels. The analyses also demonstrate that satellite-derived SST estimates obtained under low surface wind conditions must be treated with care.  相似文献   

17.
利用南大洋漂流浮标数据评估AMSR-E SST   总被引：8，自引：4，他引：4  
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。  相似文献   

18.
SST Availabilities of Satellite Infrared and Microwave Measurements   总被引：5，自引：1，他引：5  
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.  相似文献   

19.
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。  相似文献   

20.
The Visible and Infrared Scanner (VIRS) aboard the Tropical Rainfall Measuring Mission (TRMM) is a five-channel radiometer with wavelength from 0.6 to 12 μm. Daily 0.125° sea surface temperature (SST) data from VIRS were first produced at the National Space Development Agency (NASDA) for comparison with SST from TRMM Microwave Imager (TMI). In order to obtain accurate high spatial resolution SST for the merging of SST from infrared and microwave measurements, new SST retrieval coefficients of the Multichannel SST (MCSST) algorithm were generated using the global matchups from VIRS brightness temperature (BT) and Global Telecommunications System (GTS) SST. Cloud detection was improved and striping noise was eliminated. One-year global VIRS level-1B data were reprocessed using the MCSST algorithm and the advanced cloud/noise treatments. The bias and standard deviation between VIRS split-window SST and in situ SST are 0.10°C and 0.63°C, and for triple-window SST, are 0.06°C and 0.48°C. The results indicate that the reprocessing algorithm is capable of retrieving high quality SST from VIRS data. This revised version was published online in July 2006 with corrections to the Cover Date.  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