• 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.

• Proceedings of the KSRS Conference
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• v.1
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• pp.150-152
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• 2006

The DISCOVER Project (${\underline{D}}istributed$ ${\underline{I}}nformation$ ${\underline{S}}ervices$ for ${\underline{C}}limate$ and ${\underline{O}}cean$ products and ${\underline{V}}isualizations$ for ${\underline{E}}arth$ ${\underline{R}}esearch$) is a NASA funded Earth Science REASoN project that strives to provide highly accurate, carefully calibrated, long-term climate data records and near-real-time ocean products suitable for the most demanding Earth research applications via easy-to-use display and data access tools. A key element of DISCOVER is the merging of data from the multiple sensors on multiple platforms into geophysical data sets consistent in both time and space. The project is a follow-on to the SSM/I Pathfinder and Passive Microwave ESIP projects which pioneered the simultaneous retrieval of sea surface temperature, surface wind speed, columnar water vapor, cloud liquid water content, and rain rate from SSM/I and TMI observations. The ocean products available through DISCOVER are derived from multi-sensor observations combined into daily products and a consistent multi-decadal climate time series. The DISCOVER team has a strong track record in identifying and removing unexpected sources of systematic error in radiometric measurements, including misspecification of SSM/I pointing geometry, the slightly emissive TMI antenna, and problems with the hot calibration source on AMSR-E. This in-depth experience with inter-calibration is absolutely essential for achieving our objective of merging multi-sensor observations into consistent data sets. Extreme care in satellite inter-calibration and commonality of geophysical algorithms is applied to all sensors. This presentation will introduce the DISCOVER products currently available from the web site, http://www.discover-earth.org and provide examples of the scientific application of both the diurnally corrected optimally interpolated global sea surface temperature product and the 4x-daily global microwave water vapor product.

• Journal of the korean society of oceanography
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• v.37 no.3
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• pp.154-159
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• 2002

Satellite remote sensing technology for ocean observation has evolved considerably in these last twenty years. Ocean color is one of the most important parameters of ocean satellite measurements. This paper describes a remote sensing of ocean color data project - Asian I-Lac Project; it also introduces several case studies using satellite images in the Asian waters. The Asian waters are related to about 30 Asian countries, representing about 60% of the world population. The project aims at generating long-term time series images (planned for 10 years from 1996 to 2006) by combining several ocean color satellite data, i.e., ADEOS-I OCTS and SeaWiFS, and some other sensors. Some typical parameters that could be measured include Chlorophyll- a (Chl-a), Colored Dissolved Organic Matter (CDOM), and Suspended Material (SSM). Reprocessed OCTS images display spatial variation of Chl-a, CDOM, and SSM in the Asian waters; a short term variability of phytoplankton blooms was observed in the Gulf of Oman in November 1996 by analyzing OCTS and NOAA sea surface temperature (SST)； Chl-a concentrations derived from OCTS and SeaWiFS have also been evaluated in coastal areas of the Taiwan Strait, the Gulf of Thailand, the northeast Arabian Sea, and the Japan Sea. The data system provides scientists with capability of testing or developing ocean color algorithms, and transferring images for their research. We have also analyzed availability of OCTS images. The results demonstrate the potential of long-term time series of satellite ocean color data for research in marine biology, and ocean studies. The case studies show multiple applications of satellite images on monitoring of coastal environments in the Asian Waters.

• Proceedings of the KSRS Conference
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• v.2
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• pp.660-663
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• 2006

Rainfall estimation is important to weather forecast, flood control, hydrological plan. The empirical and statistical methods by measured data(surface rain gauge, rainfall radar, Satellite) is commonly used for rainfall estimation. In this study, the rainfall intensity for East Asia region was estimated using the empirical relationship between SSM/I data of DMSP satellite and brightness temperature of GEOS-9(10.7${\mu}m$) with cloud types(ISCCP and MSG classification). And the empirical formula for rainfall estimation was produced by PMM (Probability Matching Method).

• Journal of Korea Foundry Society
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• v.16 no.6
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• pp.459-471
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• 1996

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.171-171
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• 2003

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.301-304
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• 2008

Since longer wavelength microwave radiation can penetrate clouds, satellite passive microwave sensors can observe sea ice of the entire polar region on a daily basis. Thus, it is becoming popular to derive sea ice motion vectors from a pair of satellite passive microwave sensor images observed at one or few day interval. Usually, the accuracies of derived vectors are validated by comparing with the position data of drifting buoys. However, the number of buoys for validation is always quite limited compared to a large number of vectors derived from satellite images. In this study, the sea ice motion vectors automatically derived from pairs of AMSR-E 89GHz images (IFOV = 3.5 ${\times}$ 5.9km) by an image-to-image cross correlation were validated by comparing with sea ice motion vectors manually derived from pairs of cloudless MODIS images (IFOV=250 ${\times}$ 250m). Since AMSR-E and MODIS are both on the same Aqua satellite of NASA, the observation time of both sensors are the same. The relative errors of AMSR-E vectors against MODIS vectors were calculated. The accuracy validation has been conducted for 5 scenes. If we accept relative error of less than 30% as correct vectors, 75% to 92% of AMSR-E vectors derived from one scene were correct. On the other hand, the percentage of correct sea ice vectors derived from a pair of SSM/I 85GHz images (IFOV = 15 ${\times}$ 13km) observed nearly simultaneously with one of the AMSR-E images was 46%. The difference of the accuracy between AMSR-E and SSM/I is reflecting the difference of IFOV. The accuracies of H and V polarization were different from scene to scene, which may reflect the difference of sea ice distributions and their snow cover of each scene.

• Korean Journal of Remote Sensing
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• v.26 no.5
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• pp.511-525
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• 2010

Rainfall intensity was estimated using the MTSAT-1R infrared channels and the microwave satellite precipitation data. Brightness temperature of geostationary satellite is matched temporal and spatial to a variety of microwave satellite(SSM/I, SSMIS, AMSU-B, AMSRE, TRMM) precipitation data. Rainfall intensity was calculated by the look -up table using relationships of MTSAT-1R brightness temperature and microwave precipitation. Estimated rainfall is verified using by precipitation of TRMM satellite(TRMM3B42) and ground rainfall as AWS from Jul. 21 2008 to Jul. 25 2008. The results of rainfall estimated TRMM 2A12(TMI) that validated by AWS and TRMM3B42 precipitation are represented highly 0.38 and 0.61 by correlation coefficient, 5.81 mm/hr and 2.44 mm/hr by RMSE, 0.79 and 0.84 by POD and 0.65 and 0.87 by PC, respectively. Overall, estimated rainfall using by microwave satellite calculated 5 mm/hr or more comparing by AWS and 5 mm/hr or more comparing by TRMM3B42 precipitation, respectively. Validation results of correlation coefficient are shown series of TRMM 2A12, AMSRE, SSM/I, AMSU-B and SSMIS.

• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.123-139
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• 2007

Recently, Super-Super-Market(SSM) is facing more and more difficult situation due to the expansion of hypermarket and target marketing of specialized shop. In this situation, Customer Satisfaction Management(CSM) is emerging as a core business factor to make continuous growth without competitive exclusion. Especially, the first factor in CSM in distribution industry is a Service Quality Satisfaction. In this paper, with a selection of 3-markets as a sample for the research, I have tried to look for necessary Service Quality(SQ) factors in SSM and deduced Service Quality Index(SQI), loyalty and Index of detail factor in SQ through survey. Based on these results, strategic factors required to improve SQ was found and strategic directions for SQ were proposed through matrix portfolio analysis.

• Korean Journal of Remote Sensing
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• v.24 no.4
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• pp.309-324
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• 2008

In this study the retrieval algorithms have been developed to retrieve total precipitable water (TPW) from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) infrared measurements using a physical iterative retrieval method and a split-window technique over East Asia. Retrieved results from these algorithms were validated against Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) over ocean and radiosonde observation over land and were analyzed for investigating the key factors affecting the accuracy of results and physical processes of retrieval methods. Atmospheric profiles from Regional Data Assimilation and Prediction System (RDAPS), which produces analysis and prediction field of atmospheric variables over East Asia, were used as first-guess profiles for the physical retrieval algorithm. We used RTTOV-7 radiative transfer model to calculate the upwelling radiance at the top of the atmosphere. For the split-window technique, regression coefficients were obtained by relating the calculated brightness temperature to the paired radiosonde-estimated TPW. Physically retrieved TPWs were validated against SSM/I and radiosonde observations for 14 cases in August and December 2004 and results showed that the physical method improves the accuracy of TPW with smaller bias in comparison to TPWs of RDAPS data, MODIS products, and TPWs from split-window technique. Although physical iterative retrieval can reduce the bias of first-guess profiles and bring in more accurate TPWs, the retrieved results show the dependency upon initial guess fields. It is thought that the dependency is due to the fact that the water vapor absorption channels used in this study may not reflect moisture features in particular near surface.