• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.638-643
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• 2002

MODIS (MODerate-resolution Imaging Spectroradiometer) onboard the first Earth Observing System (EOS) satellite, Terra, was launched successfully at the end of 1999. The direct broadcast MODIS data has been received and utilized in Korea Meteorological Administration (KMA) since february 2001. This study introduces utilizations of this data, especially for the derivation of sea surface temperature (SST). To produce the MODIS SST operationally, we used a simple cloud mask algorithm and MCSST algorithm. By using a simple cloud mask algorithm and by assumption of NOAA daily SST as a true SST, a new set of MCSST coefficients was derived. And we tried to analyze the current NASA's PFSST and new MCSST algorithms by using the collocated buoy observation data. Although the number of collocated data was limited, both algorithms are highly correlated with the buoy SST, but somewhat bigger bias and RMS difference than we expected. And PFSST uniformly underestimated the SST. Through more analyzing the archived and future-received data, we plan to derive better MCSST coefficients and apply to MODIS data of Aqua that is the second EOS satellite. To use the MODIS standard cloud mask algorithm to get better SST coefficients is going to be prepared.

• Korean Journal of Remote Sensing
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• v.5 no.1
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• pp.41-56
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• 1989

Improvement of the multichannel sea surface temperature(MCSST) software, which had been developed for the purpose of operating under mainframe computer system, was seeked in order to operate effectively in a mini computer system. CPU time and processing time, which is not a major factor under mainframe computer system, become a critical factor in real time image processing under mini computer system. Due to fixed kernel size(3$\times$4) of the old MCSST software, high spatial resolution characteristics of the original image received from satellites were apparently degraded when images are transformed into a cartesian coordinate system after geometrical distortions of the image due to earth curvature are removed. CPU and processing time were reduced to 0.13 and 0.15~0.22 comparing with the old MCSST's, respectively, by applying disk block I/O and M/T queue I/O method under VAX-11/750 computer. The high resolution quality (1.1km in AVHRR) of the processed image was guaranted using 2$\times$2 kernel size and applying moving window techniques without sacrificing CPU and processing time much.

• Ocean and Polar Research
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• v.23 no.2
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• pp.181-188
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• 2001

We describe here the Korea ocean prediction system that closely resembles operational numerical weather prediction systems. This prediction system will be served for real-time forecasts. The core of the system is a three-dimensional primitive equation numerical circulation model, based on ${\sigma}$-coordinate. Remotely sensed multi-channel sea surface temperature (MCSST) is imposed at the surface. Residual subsurface temperature is assimilated through the relationship between vertical temperature structure function and residual of sea surface height (RSSH) using an optimal interpolation scheme. A unified grid system, named as [K-E-Y], that covers the entire seas around Korea is used. We present and compare hindcasting results during 1990-1999 from a model forced by MCSST without incorporating RSSH data assimilation and the one with both MCSST and RSSH assimilated. The data assimilation is applied only in the East Sea, hence the comparison focuses principally on the mesoscale features prevalent in the East Sea. It is shown that the model with the data assimilation exhibits considerable skill in simulating both the permanent and transient mesoscale features in the East Sea.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.518-521
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• 2007

To retrieve Sea Surface Temperature(SST) from NOAA-AVHRR imagery the spilt window atmospheric correction algorithm is generally used. Recently, there have been various new algorithms developed to process these data, namely the variable-coefficient split-window, the R54 transmittance-ratio method, fixed-coefficient nonlinear algorithm, dynamic water vapour (DWV) correction method, Dynamic Water Vapour and Temperature algorithm (DWVT). We used MCSST (Multi-Channel Sea surface temperature) and NLSST(Non linear sea surface temperature) algorithms in this study. The study area is around the Korea sea area (Yellow Sea). We compared and analyzed with various methods by applying each Ocean in-situ data and satellite data. The primary aim of study is to verify and optimize algorithms. Finally, this study proposes an optimized algorithm for SST retrieval.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.38 no.5
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• pp.323-330
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• 2005

Monthly distributions of fishing boats at night in the East Sea are described, using defense meteorological satellite program (DMSP)/operational linescan system (OLS) images and common squid (Todarodes pacificus) catches data. We also estimated temperature in 50m, which is main catch depth of the squid, by MCSST (multi-channel sea surface temperature). We examined DMSP/OLS, MCSST and other observation data from 1993 to 2000. We assumed that squid were caught in areas where fishing boats were located. Fishing boats at night appeared only near the Korea/Tsushima Strait from January to March. Fishing boats moved to the northward from April to Jun, distribution of fishing boats in spring appeared greater than those in winter. In summer (July-September), center of fishing grounds was formed near the Uleung Island in the south east coast of Korea. The north-south distribution range of fishing boats in October appeared to be greater than that in other months. In particular, we estimated main season of squid catches based on distribution range reflecting the number of fishing boats of north-south and east-west directions from September to December. Relationship between satellite estimate SST and in situ SST showed high correlation (0.91). The correlation between the SST and 50m depth temperature, estimated based on the satellite SST, was relatively high in February, April and October.

• Journal of the Korean earth science society
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• v.29 no.3
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• pp.280-289
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• 2008

An extensive set of both in-situ and satellite data regarding oceanic sea surface temperatures in Northeast Asian seas, collected over a 10-year period, was collocated and surveyed to assess the accuracy of satellite-observed sea surface temperatures (SST) and investigate the characteristics of satellite measured SST errors. This was done by subtracting insitu SST measurements from multi-channel SST (MCSST) measurements. 845 pieces of collocated data revealed that MCSST measurements had a root-mean-square error of about 0.89$^{\circ}C$ and a bias error of about 0.18$^{\circ}C$. The SST errors revealed a large latitudinal dependency with a range of $\pm3^{\circ}C$ around 40$^{\circ}N$, which was related to high spatial and temporal variability from smaller eddies, oceanic currents, and thermal fronts at higher latitudes. The MCSST measurements tended to be underestimated in winter and overestimated in summer when compared to in-situ measurements. This seasonal dependency was discovered from shipboard and moored buoy measurements, not satellite-tracked surface drifters, and revealed the existence of a strong vertical temperature gradient within a few meters of the upper ocean. This study emphasizes the need for an effort to consider and correct the significant skin-bulk SST difference which arises when calculating SST from satellite data.

• Proceedings of the KSEEG Conference
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• 2007.04a
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• pp.538-540
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• 2007

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.37-42
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• 1998

Latent and sensible heat fluxes over the global oceans are estimated using SSM/I (Special Sensor Microwave Imager) and AVHRR MCSST (Advanced Very High Resolution Radiometer Multi-Channel Sea Surface Temperature). The heat fluxes are computed from SSM/I wind speeds and surface humidity, the MCSST, and NCEP Reanalysis sea level pressures and 2-m temperatures from 1996 to 1997. The diabatic conditions bulk formula (Kondo, 1975) is used to compute the heat fluxes. To validate, the derived fluxes are compared to the measurements of 3 JMA buoys. The wind speeds and surface humidity derived from SSM/I have accuracies of 1.37m/s and 1.7g/kg, respectively. The heat fluxes were estimated these factors and the standard error of the latent and sensible heat fluxes are 5.53 W/m$^2$ and 3.33 W/m$^2$. The latest El-Nino phenomenon started at the beginning of 1997 and this event was larger than any others. We compare the heat fluxes in 1997 with the fluxes in 1996 and investigate the spatial movement of meteorological factors as well as the heat fluxes associated with El-Nino appearance.

• Korean Journal of Remote Sensing
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• v.33 no.3
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• pp.275-285
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• 2017

National Meteorological Satellite Center(NMSC) has produced Sea Surface Temperature (SST) using Communication, Ocean, and Meteorological Satellite(COMS) data since April 2011. In this study, we have developed a new regional COMS SST algorithm optimized within the North-West Pacific Ocean area based on the Multi-Channel SST(MCSST) method and made a composite SST using polar orbit satellites as well as the COMS data. In order to retrieve the optimized SST at Northwest Pacific, we carried out a colocation process of COMS and in-situ buoy data to make coefficients of the MCSST algorithm through the new cloud masking including contaminant pixels and quality control processes of buoy data. And then, we have estimated the composite SST through the optimal interpolation method developed by National Institute of Meteorological Science(NIMS). We used four satellites SST data including COMS, NOAA-18/19(National Oceanic and Atmospheric Administration-18/19), and GCOM-W1(Global Change Observation Mission-Water 1). As a result, the root mean square error ofthe composite SST for the period of July 2012 to June 2013 was $0.95^{\circ}C$ in comparison with in-situ buoy data.

• Korean Journal of Remote Sensing
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• v.24 no.4
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• pp.273-287
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• 2008

Characteristics of skin-bulk sea surface temperature (SST) differences in the Northeast Asia seas were analyzed by utilizing 845 collocated matchup data between NOAA/AVHRR data and oceanic in-situ temperature measurements for selected months from 1994 to 2003. In order to understand diurnal variation of SST within a few meters of the upper ocean, the matchup database were classified into four categories according to day-night and drifter-shipboard measurements. Temperature measurements from daytime drifters showed a good agreement with satellite MCSST (Multi-Channel Sea Surface Temperature) with an RMS error of about $0.56^{\circ}C$. Poor accuracy of SST with an rrns error of $1.12^{\circ}C$ was found in the case of daytime shipboard CTD (Conductivity, Temperature, Depth) measurements. SST differences between MCSST and in-situ measurements are caused by various errors coming from atmospheric moist effect, coastal effect, and others. Most of the remarkable errors were resulted from the diurnal variation of vertical temperature structure within a few meters as well as in-situ oceanic temperatures at different depth, about 20 cm for a satellite-tracked drifting buoy and a few meters for shipboard CTD or moored buoy. This study suggests that satellite-derived SST shows significant errors of about ${\pm}3^{\circ}C$ in some cases and therefore it should be carefully used for one's purpose on the base of in-depth understanding of skin-bulk SST difference and vertical temperature structure in regional sea.