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1.
We have examined accuracies of nine nighttime National Oceanic and Atmospheric Administration/National Environmental Satellite, Data and Information Service (NOAA/NESDIS) equations for SST estimation using the Advanced Very High Resolution Radiometer (AVHRR)/NOAA-11 dataset produced by Sakaida and Kawamura (1992). Among the nine equations, the revised triple-window CPSST algorithm gives the smallest rms error, which is 0.38°C. The dual-window MCSST algorithm gives the largest rms error 0.56°C. Rms errors of the other algorithms are smaller than 0.5°C.  相似文献   

2.
A new set of multi-channel sea surface temperature (MCSST) equations for the Advanced Very High Resolution Radiometer (AVHRR) on NOAA-9 is derived from regression analyses between two-channel brightness temperatures andin situ SST obtained from moored buoys around Japan. Two equations are derived: one for daytime and the other for nighttime. They are linear split window type and both the equations contain a term dependent on satellite zenith angle, which has not been accounted for in the previous daytime split window equations for NOAA-9. It is shown that the new set of equation can give SSTs in much better precision than those without the zenith-angle-dependent terms. It is also found that the split window equation for NOAA-9 provided by the National Oceanographic and Atmospheric Administration/National Environmental Satellite, Data and Information Service (NOAA/NESDIS) considerably underestimates the daytime SSTs; sometimes nighttime SSTs are evenhigher than daytime SSTs. This is because the zenith angle effect to the radiation deficiet is neglected in the daytime equation by NOAA/NESDIS. By using the new MCSST equations, it is expected that the quality of satellite MCSST would be much improved, at least in regional applications around Japan, for the period of NOAA-9's operation.  相似文献   

3.
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   

4.
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   

5.
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   

6.
We have developed an algorithm to estimate the wide-ranging Sea Surface Temperature (SST) data from the GMS-5 (Geostationary Meteorological Satellite) S-VISSR (Stretched-Visible Infrared Spin Scan Radiometer). Better SST estimates are realized by averaging the temporal variation of the VISSR calibration table and decreasing noise of the split-window terms using a spatial filter. The effects of the satellite zenith angle were examined in detail for better estimates, and VISSR-derived SSTs with root-mean-square (rms) error of 0.8 K were achieved using a new algorithm. The accuracy of SST estimates has been improved by using the temporal-spatial average of the split-window terms. Using the new techniques, we demonstrate that the hourly wide-ranging SST image data can be used to study the daily variations of SSTs in the Northern and Southern Pacific Oceans.  相似文献   

7.
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.  相似文献   

8.
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.  相似文献   

9.
In the previous study, merged sea surface temperature (SST) dataset called “New Generation SST” has been produced from several infrared and microwave satellite SSTs through an objective mapping. Here we examine the merged SST by comparison with moored buoy SST at 1 m depth, which is treated as true sea surface temperature. Comparison between wavelet spectra of merged and buoy SSTs shows that the former have larger amplitudes than those of the latter, which is partly explained as an aliasing effect due to TRMM Microwave Imager (TMI) aboard Tropical Rainfall Measuring Mission (TRMM) sampling on merged products. Coherency between wavelet-decomposed merged and buoy SSTs has high values in autumn and low ones in winter to spring. In winter, phase differences between them are positive, meaning that wavelet components of merged SST lag those of buoy SST. Reasons for delay and low coherency are: (1) seasonal components of merged SSTs are strongly affected by a lack of infrared SSTs due to clouds in winter, and (2) small-scale oceanic features, undetectable by coarse-resolution microwave SSTs, are blurred by the merging process. Improvements of merging methodology are discussed with regard to present study results.  相似文献   

10.
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   

11.
Sea surface temperature 1871-2099 in 38 cells in the Caribbean region   总被引：1，自引：0，他引：1  
Sea surface temperature (SST) data with monthly resolution are provided for 38 cells in the Caribbean Sea and Bahamas region, plus Bermuda. These series are derived from the HadISST1 data set for historical time (1871-1999) and from the HadCM3 coupled climate model for predicted SST (1950-2099). Statistical scaling of the forecast data sets are performed to produce confluent SST series according to a now established method. These SST series are available for download. High water temperatures in 1998 killed enormous amounts of corals in tropical seas, though in the Caribbean region the effects at that time appeared less marked than in the Indo-Pacific. However, SSTs are rising in accordance with world-wide trends and it has been predicted that temperature will become increasingly important in this region in the near future. Patterns of SST rise within the Caribbean region are shown, and the importance of sub-regional patterns within this biologically highly interconnected area are noted.  相似文献   

12.
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.  相似文献   

13.
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   

14.
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   

15.
The effects of sea surface temperature(SST) data assimilation in two regional ocean modeling systems were examined for the Yellow Sea(YS). The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis(OSTIA) were assimilated. The National Marine Environmental Forecasting Center(NMEFC) modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University(KNU) modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error(RMSE) of the SST from 1.78°C(1.46°C) to 1.30°C(1.21°C) for the NMEFC(KNU) modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature(OISST) SST dataset. A comparison with the buoy SST data indicated a 41%(31%) decrease in the SST error for the NMEFC(KNU) modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5°C in the upper 20 m and approximately 3.1°C in the lower layer in October. In contrast, it was less than 1.0°C throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS.  相似文献   

16.
Sea surface temperatures (SSTs) in the southwestern South China Sea have been reconstructed for the past 160 ka using the Uk37 paleothermometer from the core MD01-2392. The temperature differences between glacial times (MISs 6 and 2) and interglacial times (MISs 5.5 and 1) are 2.2~2.5 ℃. Younger Dryas event during the last deglaciation was documented in both the planktonic foraminiferal δ18O and SST records. After MIS 5.5, SSTs displayed a progressive cooling from 28.6 to 24.5 ℃, culminating at the LGM. During this gradual cooling period, warm events such as MISs 5.3, 5.1 and 3 were also clearly documented. By comparison of SST between the study core and Core 17954, a pattern of low or no meridional SST gradients during the interglacial periods and high meridional SST gradients during the glacial periods was exhibited. This pattern indicates the much stronger East Asian winter monsoon at the glacial than at the interglacial periods. Spectral analysis gives two prominent cycles: 41 and 23 ka, with the former more pronounced, suggesting that SSTs in the southern SCS varied in concert with high-latitude processes through the connection of East Asian winter monsoon.  相似文献   

17.
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   

18.
Real-time generation and distribution of the New Generation Sea Surface Temperature for Open Ocean (NGSST-O) product began in September 2003 as a demonstration operation of the Global Ocean Data Assimilation Experiment (GODAE) High-Resolution Sea Surface Temperature Pilot Project. Satellite sea surface temperature (SST) observations from infrared radiometers (AVHRR, MODIS) and a microwave radiometer (AMSR-E) are objectively merged to generate the NGSST-O product, which is a quality-controlled, cloud-free, high-spatial-resolution (0.05° gridded), wide-coverage (13–63° N, 116–166° E), daily SST digital map. The NGSST-O demonstration operation system has been developed in cooperation with the Japanese Space Agency (JAXA) and has produced six years of continuous data without gaps. Comparison to in situ SSTs measured by drifting buoys indicates that the root mean-square error of NGSST-O has been kept at approximately 0.9°C.  相似文献   

19.
Sea surface temperature fields in the East Sea are composed of various spatial structures such as eddies, fronts, filaments, turbulent-like features and other mesoscale variations associated with the oceanic circulations of the East Sea. These complex SST structures have many spatial scales and evole with time. Semi-monthly averaged SST distributions based on extensive satellite observations of SSTs from 1990 through 1995 were constructed to examine the characteristics of their spatial and temporal scale variations by using statistical methods of multi-dimensional autocorrelation functions and spectral analysis. Two-dimensional autocorrelation functions in the central part of the East Sea revealed that most of the spatial SST structures are anisotropic in the shape of ellipsoids with minor axes of about 90–290 km and major axes of 100–400 km. Two dimensional spatial scale analysis demonstrated a consistent pattern of seasonal variation that the scales appear small in winter and spring, increase gradually to summer, and then decrease again until the spring of the next year. These structures also show great spatial inhomogeneity and rapid temporal change on time scales as short as a semi-month in some cases. The slopes in spectral energy density spectra of SSTs show characteristics quite similar to horizontal and geostrophic turbulence. Temporal spectra at each latitude are demonstrated by predominant peaks of one and two cycles per year in all regions of the East Sea, implying that SSTs present very strong annual and semi-annual variations. This revised version was published online in August 2006 with corrections to the Cover Date.  相似文献   

20.
Ten-day mean surface level air-temperature from SSMI precipitable water (SSMI-T a ) has been derived and compared with the temperature from two ocean data buoys (Buoy-T a ) of Japan Meteorological Agency (JMA) for a period of six months (July–December, 1988). Statistical relations between air-temperature and mixing ratio, using data from ocean data buoys are used to derive air-temperature from mixing ratio, obtained from SSMI precipitable water. For getting the mixing ratio from precipitable water, regional mixing ratio-precipitable water relations have been used, instead of global relation proposed by Liu (1986). The rms errors (standard deviation of the difference between SSMI-T a and Buoy-T a ) for two buoy locations are found to be 1.15 and 1.12°C, respectively. Surface level temperature for the two buoy locations are also derived using direct regression relation between Buoy-T a and precipitable water. The rms errors of the SSMI-T a , in this case are found to be reduced to 1.0°C.  相似文献   

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