• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.323-324
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• 1999

• Proceedings of the KSRS Conference
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• v.1
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• pp.278-281
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• 2006

Korea Meteorological Administration(KMA) has issued the tropical storm(typhoon) warning or advisories when it was developed to tropical storm from tropical depression and a typhoon is expected to influence the Korean peninsula and adjacent seas. Typhoon information includes current typhoon position and intensity. KMA has used the Dvorak Technique to analyze the center of typhoon and it's intensity by using available geostationary satellites' images such as GMS, GOES-9 and MTSAT-1R since 2001. The Dvorak technique is so subjective that the analysis results could be variable according to analysts. To reduce the subjective errors, QuikSCAT seawind data have been used with various analysis data including sea surface temperature from geostationary meteorological satellites, polar orbit satellites, and other observation data. On the other hand, there is an advantage of using the Subjective Dvorak Technique(SDT). SDT can get information about intensity and center of typhoon by using only infrared images of geostationary meteorology satellites. However, there has been a limitation to use the SDT on operational purpose because of lack of observation and information from polar orbit satellites such as SSM/I. Therefore, KMA has established Advanced Objective Dvorak Technique(AODT) system developed by UW/CIMSS(University of Wisconsin-Madison/Cooperative Institude for Meteorological Satellite Studies) to improve current typhoon analysis technique, and the performance has been tested since 2005. We have developed statistical relationships to correct AODT CI numbers according to the SDT CI numbers that have been presumed as truths of typhoons occurred in northwestern pacific ocean by using linear, nonlinear regressions, and neural network principal component analysis. In conclusion, the neural network nonlinear principal component analysis has fitted best to the SDT, and shown Root Mean Square Error(RMSE) 0.42 and coefficient of determination($R^2$) 0.91 by using MTSAT-1R satellite images of 2005. KMA has operated typhoon intensity analysis using SDT and AODT since 2006 and keep trying to correct CI numbers.

• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.56.1-56.1
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• 2011

The solar and meteorological resources map is calculated using by one-layer solar radiation model (GWNU model), satellites data and numerical model output on the Korean peninsula. The Meteorological input data to perform the GWNU model are retrieved aerosol optical thickness from MODIS (TERA/AQUA), total ozone amount from OMI (AURA), cloud fraction from geostationary satellites (MTSAT-1R) and temperature, pressure and total precipitable water from output of RDAPS (Regional Data Assimilation and Prediction System) and KLAPS (Korea Local Analysis and Prediction System) model operated by KMA (Korea Meteorological Administration). The model is carried out every hour using by the meteorological data (total ozone amount, aerosol optical thickness, temperature, pressure and cloud amount) and the basic data (surface albedo and DEM). And the result is analyzed the distribution in time and space and validated with 22 meteorological solar observations. The solar resources map is used to the solar energy-related industries and assessment of the potential resources for solar plant. The National Institute of Meteorological Research in KMA released $4km{\times}4km$ solar map in 2008 and updated solar map with $1km{\times}1km$ resolution and topological effect in 2010. The meteorological resources map homepage (http://www.greenmap.go.kr) is provided the various information and result for the meteorological-solar resources map.

• Atmosphere
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• v.33 no.1
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• pp.33-47
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• 2023

In this study, for the application of observation errors to the Korean Integrated Model (KIM) to utilize the Constellation Observing System for Meteorology, Ionosphere & Climate-2 (COSMIC-2) new satellites, the observation errors were diagnosed based on the Desroziers method using the cost function in the process of variational data assimilation. We calculated observation errors for all observational species being utilized for KIM and compared with their relative values. The observation error of the calculated the Global Navigation Satellite System Radio Occultation (GNSS RO) was about six times smaller than that of other satellites. In order to balance with other satellites, we conducted two experiments in which the GNSS RO data expanded by about twice the observation error. The performance of the analysis field was significantly improved in the tropics, where the COSMIC-2 data are more available, and in the Southern Hemisphere, where the influence of GNSS RO data is significantly greater. In particular, the prediction performance of the Southern Hemisphere was improved by doubling the observation error in global region, rather than doubling the COSMIC-2 data only in areas with high density, which seems to have been balanced with other observations.

• Journal of Astronomy and Space Sciences
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• v.35 no.3
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• pp.175-183
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• 2018

Many recent satellites have mission periods longer than 10 years; thus, satellite-based local space weather monitoring is becoming more important than ever. This article describes the instruments and data applications of the Korea Space wEather Monitor (KSEM), which is a space weather payload of the GeoKompsat-2A (GK-2A) geostationary satellite. The KSEM payload consists of energetic particle detectors, magnetometers, and a satellite charging monitor. KSEM will provide accurate measurements of the energetic particle flux and three-axis magnetic field, which are the most essential elements of space weather events, and use sensors and external data such as GOES and DSCOVR to provide five essential space weather products. The longitude of GK-2A is $128.2^{\circ}E$, while those of the GOES satellite series are $75^{\circ}W$ and $135^{\circ}W$. Multi-satellite measurements of a wide distribution of geostationary equatorial orbits by KSEM/GK-2A and other satellites will enable the development, improvement, and verification of new space weather forecasting models. KSEM employs a service-oriented magnetometer designed by ESA to reduce magnetic noise from the satellite in real time with a very short boom (1 m), which demonstrates that a satellite-based magnetometer can be made simpler and more convenient without losing any performance.

• Atmosphere
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• v.28 no.2
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• pp.201-209
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• 2018

Benefits of the next generation geostationary meteorological satellite observation (e.g., GEO-KOMPSAT-2A) are qualitatively and comprehensively described and discussed. Main beneficial phenomena for application can be listed as tropical cyclones (typhoon), high impact weather (heavy rainfall, lightning, and hail), ocean, air pollution (particulate matter), forest fire, fog, aircraft icing, volcanic eruption, and space weather. The next generation satellites with highly enhanced spatial and temporal resolution images, expanding channels, and basic and additional products are expected to create the new valuable benefits, including the contribution to the reduction of socioeconomic losses due to weather-related disasters. In particular, the new satellite observations are readily applicable to early warning and very-short time forecast application of hazardous weather phenomena, global climate change monitoring and adaptation, improvement of numerical weather forecast skill, and technical improvement of space weather monitoring and forecast. Several policy plans for expanding the application of the next generation satellite data are suggested.

• Atmosphere
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• v.31 no.3
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• pp.251-265
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• 2021

This study examined the impact of assimilating the bending angle (BA) obtained via the global navigation satellite system radio occultation (GNSS RO) of the three new satellites (KOMPSAT-5, FY-3C, and FY-3D) on analyses and forecasts of a numerical weather prediction model. Numerical data assimilation experiments were performed using a three-dimensional variational data assimilation system in the Korean Integrated Model (KIM) at a 25-km horizontal resolution for August 2019. Three experiments were designed to select the height and quality control thresholds using the data. A comparison of the data with an analysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) integrated forecast system showed a clear positive impact of BA assimilation in the Southern Hemisphere tropospheric temperature and stratospheric wind compared with that without the assimilation of the three new satellites. The impact of new data in the upper atmosphere was compared with observations using the infrared atmospheric sounding interferometer (IASI). Overall, high volume GNSS RO data helps reduce the RMSE quantitatively in analytical and predictive fields. The analysis and forecasting performance of the upper temperature and wind were improved in the Southern and Northern Hemispheres.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.43-48
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• 2001

Many applications in the areas of agricultural, hydrological and environmental resource management require data over very large areas and with a high imaging frequency - monitoring crop growth, water stress, seasonal wetland flooding and natural vegetation development. This precludes the use of fine resolution data (Landsat, Spot) on the grounds of cost, accessibility and low imaging frequency. Meteorological satellites have the potential to fill this need, given their very wide spatial coverage, and high repeat imaging. The Remote Sensing Unit (RSU) at the Zambia Meteorological Department routinely receives, processes and archives imagery from both Meteosat and NOAA AVHRR satellites. Here I wish to present some examples of applications of these data sets that arise from the RSU work - relationships between rainfall and vegetation development as assessed by satellite, derived information and seasonal patterns of flooding in the Barotse floodplain and the Kafue flats. I also wish to outline ways in which a more widespread use of this data by the Zambian institutions canbe achieved.

• 한국태양에너지학회:학술대회논문집
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• 2011.11a
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• pp.342-347
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• 2011

Solar energy is attenuated by absorbing gases (ozone, aerosol, water vapour and mixed gas) and cloud in the atmosphere. And these are measured with solar instruments (pyranometer, phyheliometer). However, solar energy is insufficient to represent detailed energy distribution, because the distributions of instruments are limited on spatial. If input data of solar radiation model is accurate, the solar energy reaches at the surface can be calculated accurately. Recently a variety of satellite measurements are available to TERA/AQUA (MODIS), AURA (OMI) and geostationary satellites (GMS-5, GOES-9, MTSAT-1R, MTSAT-2 and COMS). Input data of solar radiation model can be used aerosols and surface albedo of MODIS, total ozone amount of OMI and cloud fraction of meteorological geostationary satellite. The solar energy reaches to the surface is calculated hourly by solar radiation model and those are accumulated monthly and annual. And these results are verified the spatial distribution and validated with ground observations.

• 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.