• Atmosphere
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• v.22 no.2
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• pp.137-147
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• 2012

The technique of Brightness Temperature Difference (BTD) between 11 and $12{\mu}m$ separates yellow sand dust from clouds according to the difference in absorptive characteristics between the channels. However, this method causes consistent false alarms in many cases, especially over the desert. In order to reduce these false alarms, we should eliminate the background noise originated from surface. We adopted the Background BTD (BBTD), which stands for surface characteristics on clear sky condition without any dust or cloud. We took an average of brightness temperatures of 11 and $12{\mu}m$ channels during the previous 15 days from a target date and then calculated BTD of averaged ones to obtain decontaminated pixels from dust. After defining the BBTD, we subtracted this index from BTD for the Yellow Sand Index (YSI). In the previous study, this method was already verified using the geostationary satellite, MTSAT. In this study, we applied this to the polar orbiting satellite, MODIS, to detect yellow sand dust over Northeast Asia. Products of yellow sand dust from OMI and MTSAT were used to verify MODIS YSI. The coefficient of determination between MODIS YSI and MTSAT YSI was 0.61, and MODIS YSI and OMI AI was also 0.61. As a result of comparing two products, significantly enhanced signals of dust aerosols were detected by removing the false alarms over the desert. Furthermore, the discontinuity between land and ocean on BTD was removed. This was even effective on the case of fall. This study illustrates that the proposed algorithm can provide the reliable distribution of dust aerosols over the desert even at night.

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.607-615
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• 2017

The aerosol optical thickness data retrieved by Moderate Resolution Imaging Spectrometer (MODIS) of Terra & Aqua and Meteorological Imager (MI) of Communication Ocean and Meteorological Satellite (COMS) are analyzed and compared with the measurement data of Aerosol Robotic Network (AERONET) in Northeast Asia. As the result, the aerosol optical thickness retrieved by MODIS and MI were well agreed at ocean region but quite different at cloud edge and barren surface. The reason was that MODIS aerosol optical thickness was retrieved using the visible and infrared channels but MI was retrieved with the visible channel only. Consequentially, the thin cloud be misinterpreted as aerosol by MI and the difference between MODIS and MI aerosol optical thicknesses could be occurred with Normal Distribution Vegetation Index (NDVI) and land surface property. Therefore, the accuracies of clear/cloud region and surface reflectivity are required in order to improve the aerosol optical thickness algorithm by MI.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.469-483
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• 2006

An algorithm was developed to estimate stability indices (SI) over the Korean peninsula using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) infrared brightness temperatures (TBs). The SI is defined as the stability of the atmosphere in the hydrostatic equilibrium with respect to the vertical displacements and is used as an index for the potential severe storm development. Using atmosphere temperature and moisture profiles from Regional Data Assimilation and Prediction System (RDAPS) as initial guess data for a nonlinear physical relaxation method, K index (KI), KO Index (KO), lifted index (LI), and maximum buoyancy (MB) were estimated. A fast radiative transfer model, RTTOV-7, is utilized for reducing the computational burden related to the physical relaxation method. The estimated TBs from the radiative transfer simulation are in good agreement with observed MODIS TBs. To test usefulness for the short-term forecast of severe storms, the algorithm is applied to the rapidly developed convective storms. Compared with the SIs from the RDAPS forecasts and NASA products, the MODIS SI obtained in this research predicts the instability better over the pre-convection areas. Thus, it is expected that the nowcasting and short-term forecast can be improved by utilizing the algorithms developed in this study.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.109-126
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• 2014

We improved the Land Surface Emissivity (LSE) data (Kongju National University LSE v.2: KNULSE_v2) over the Communication, Ocean and Meteorological Satellite (COMS) observation region using recent(2009-2012) Moderate Resolution Imaging Spectroradiometer (MODIS) data. The surface emissivity was derived using the Vegetation Cover Method (VCM) based on the assumption that the pixel is only composed of ground and vegetation. The main issues addressed in this study are as follows: 1) the impacts of snow cover are included using Normalized Difference Snow Index (NDSI) data, 2) the number of channels is extended from two (11, 12 ${\mu}m$) to four channels (3.7, 8.7, 11, 12 ${\mu}m$), 3) the land cover map data is also updated using the optimized remapping of the five state-of-the-art land cover maps, and 4) the latest look-up table for the emissivity of land surface according to the land cover is used. The updated emissivity data showed a strong seasonal variation with high and low values for the summer and winter, respectively. However, the surface emissivity over the desert or evergreen tree areas showed a relatively weak seasonal variation irrespective of the channels. The snow cover generally increases the emissivity of 3.7, 8.7, and 11 ${\mu}m$ but decreases that of 12 ${\mu}m$. As the results show, the pattern correlation between the updated emissivity data and the MODIS LSE data is clearly increased for the winter season, in particular, the 11 ${\mu}m$. However, the differences between the two emissivity data are slightly increased with a maximum increase in the 3.7 ${\mu}m$. The emissivity data updated in this study can be used for the improvement of accuracy of land surface temperature derived from the infrared channel data of COMS.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1631-1645
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• 2021

This research aims to provide the characteristics of the world's first active lidar sensor Atmospheric Laser Doppler Instrument (ALADIN) wind data and Geostationary Korea Multi Purpose Satellite 2A (GK2A) Atmospheric Motion Vector (AMV) data by comparing two wind data. As a result of comparing the data from September 2019 to August 1, 2020, The total number of collocated data for the AMV (using IR channel) and Mie channel ALADIN data is 177,681 which gives the Root Mean Square Error (RMSE) of 3.73 m/s and the correlation coefficient is 0.98. For a more detailed analysis, Comparison result considering altitude and latitude, the Normalized Root Mean Squared Error (NRMSE) is 0.2-0.3 at most latitude bands. However, the upper and middle layers in the lower latitudes and the lower layer in the southern hemispheric are larger than 0.4 at specific latitudes. These results are the same for the water vapor channel and the visible channel regardless of the season, and the channel-specific and seasonal characteristics do not appear prominently. Furthermore, as a result of analyzing the distribution of clouds in the latitude band with a large difference between the two wind data, Cirrus or cumulus clouds, which can lower the accuracy of height assignment of AMV, are distributed more than at other latitude bands. Accordingly, it is suggested that ALADIN wind data in the southern hemisphere and low latitude band, where the error of the AMV is large, can have a positive effect on the numerical forecast model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.4
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• pp.875-884
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• 2000

Sea fog/stratus is very difficult to detect because of the characteristics of air-sea interaction and locality ,and the scantiness of the observed data from the oceans such as ships or ocean buoys. The aim of our study develops new algorism for sea fog detection by using Geostational Meteorological Satellite-5(GMS-5) and suggests the technics of its continuous detection. In this study, atmospheric synoptic patterns on sea fog day of May, 1999 are classified; cold air advection type(OOUTC, May 10, 1999) and warm air advection type(OOUTC, May 12, 1999), respectively, and we collected two case days in order to analyze variations of water vapor at Osan observation station during May 9-10, 1999.So as to detect daytime sea fog/stratus(OOUTC, May 10, 1999), composite image, visible accumulated histogram method and surface albedo method are used. The characteristic value during day showed A(min) .20% and DA < 10% when visible accumulated histogram method was applied. And the sea fog region which is detected is similar in composite image analysis and surface albedo method. Inland observation which visibility and relative humidity is beneath 1Km and 80%, respectively, at OOUTC, May 10,1999; Poryoung for visble accumulated histogram method and Poryoung, Mokp'o and Kangnung for surface albedo method. In case of nighttime sea fog(18UTC, May 10, 1999), IR accumulated histogram method and Maximum brightness temperature method are used, respectively. Maxium brightness temperature method dectected sea fog better than IR accumulated histogram method with the charateristic value that is T_max < T_max_trs, and then T_max is beneath 700hPa temperature of GDAPS(Global Data Assimilation and Prediction System). Sea fog region which is detected by Maxium brighness temperature method was similar to the result of National Oceanic and Atmosheric Administratio/Advanced Very High Resolution Radiometer (NOAA/AVHRR) DCD(Dual Channel Difference), but usually visibility and relative humidity are not agreed well in inland.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.681-691
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• 2016

Using various satellite measurements in UV, visible and IR, diverse algorithms to retrieve aerosol information have been developed and operated to date. Advanced Himawari Imager (AHI) onboard the Himawari 8 weather satellite was launched in 2014 and has 16 channels from visible to Thermal InfRared (TIR) in high temporal and spatial resolution. Using AHI, it is very valuable to retrieve aerosol optical properties over dark surface to demonstrate its capability. To retrieve aerosol optical properties using visible and Near InfRared (NIR) region, surface signal is very important to be removed which can be estimated using minimum reflectivity method. The estimated surface reflectance is then used to retrieve the aerosol optical properties through the inversion process. In this study, we retrieve the aerosol optical properties over dark surface, but not over bright surface such as clouds, desert and so on. Therefore, the bright surface was detected and masked using various infrared channels of AHI and spatial heterogeneity, Brightness Temperature Difference (BTD), etc. The retrieval result shows the correlation coefficient of 0.7 against AERONET, and the within the Expected Error (EE) of 49%. It is accurately retrieved even for low Aerosol Optical Depth (AOD). However, AOD tends to be underestimated over the Beijing Hefei area, where the surface reflectance using the minimum reflectance method is overestimated than the actual surface reflectance.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.370-370
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• 2021

태풍으로 인한 피해를 줄이기 위해 경로, 강도 및 폭풍해일의 사전 예측은 매우 중요하다. 이중, 태풍의 경로와는 달리 강도 및 폭풍해일의 예측에 있어서 바람장은 수치 모델의 초기 입력값으로 요구되기 때문에 정확한 바람장 정보는 필수적이다. 대기 바람장 예측 방법은 크게 해석적 모델링, 라디오존데 측정과 위성 사진을 통한 산출로 구분할 수 있다. Holland의 해석적 모델링은 비교적 적은 입력값이 필요하지만 정확도가 낮고, 라디오존데 측정은 정확도가 높지만 점 측정에 가깝기 때문에 이차원 바람장을 산출하기에 한계가 있다. 위성 사진을 통한 바람장 산출은 위성기술의 고도화로 관측 채널 수 및 시공간 해상도가 크게 증가하고 있기 때문에 다양한 기법들이 개발되고 있다. 본 연구에서는 생성적 적대 신경망 (Generative Adversarial Network, GAN)을 통해 일련의 연속된 과거 적외 채널 위성 사진 흐름의 패턴을 학습시켜 미래 위성 사진을 예측하고, 예측된 연속적인 위성 사진들의 교차상관 (cross-correlation)을 통해 바람장을 산출하였다. GAN을 적용함에 있어 2011년부터 2019년까지 한반도 근방에 접근했던 태풍 중에 4등급 이상인 68개의 태풍의 한 시간 간격으로 촬영된 총 15,683개의 위성 사진을 학습시켜 생성된 이미지들은 실측 위성 사진들과 매우 유사한 것으로 나타났다. 또한, 생성된 이미지들의 교차상관으로 얻어진 바람장 벡터들의 풍향, 풍속, 벡터 일관성 및 수치 모델과의 비교를 통해 각각의 벡터들의 품질 계수를 구하고 정확도가 높은 벡터들만 결과에 포함하였다. 마지막으로 국내 6개의 라디오존데 관측점에서의 실측 벡터와의 비교를 통해 본 연구 결과의 실효성을 검증하였다. 본 연구에서 확장하여, 이와 같이 AI 기법과 이미지 교차상관 기법을 사용하여 얻어진 바람장으로부터 태풍 강도예측에 필요한 요소인 태풍의 눈의 위치, 최고 속도와 태풍 반경을 직접적으로 산출할 수 있고. 이러한 위성 사진을 기반으로 한 바람장은 단순화된 해석적 바람장을 대체하여 폭풍 해일 모델링의 예측 성능 개선에 기여할 것으로 보여진다.

• Atmosphere
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• v.12 no.4
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• pp.20-42
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• 2002

Based on the "Mid to Long Term Plan for Space Development", a project to launch COMeS (Communication, Oceanography, and Meteorological Satellite) into the geostationary orbit is undergoing. Accordingly, KMA (Korea Meteorological Administration) has defined the meteorological missions and prepared the user requirements to fulfill the missions. To make a realistic user requirements, we prepared a first draft based on the ideal meteorological products derivable from a geostationary platform and sent the RFI (request for information) to the sensor manufacturers. Based on the responses to the RFI and other considerations, we revised the user requirement to be a realistic plan for the 2008 launch of the satellite. This manuscript introduces the revised user requirements briefly. The major mission defined in the revised user requirement is the augmentation of the detection and prediction ability of the severe weather phenomena, especially around the Korean Peninsula. The required payload is an enhanced Imager, which includes the major observation channels of the current geostationary sounder. To derive the required meteorological products from the Imager, at least 12 channels are required with the optimum of 16 channels. The minimum 12 channels are 6 wavelength bands used for current geostationary satellite, and additional channels in two visible bands, a near infrared band, two water vapor bands and one ozone absorption band. From these enhanced channel observation, we are going to derive and utilize the information of water vapor, stability index, wind field, and analysis of special weather phenomena such as the yellow sand event in addition to the standard derived products from the current geostationary Imager data. For a better temporal coverage, the Imager is required to acquire the full disk data within 15 minutes and to have the rapid scan mode for the limited area coverage. The required thresholds of spatial resolutions are 1 km and 2 km for visible and infrared channels, respectively, while the target resolutions are 0.5 km and 1 km.

• Journal of the Korean earth science society
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• v.30 no.6
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• pp.744-758
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• 2009

The characteristics of brightness temperature (BT) of infrared and water vapor channels from MTSAT-1R have been investigated using 12 persistent and frequent lightning cases selected from the summer lightnings of 2006-2008. The infrared (IR1, 10.3-11.3 ${\mu}M$) and water vapor (WV, 6.5-7.0 ${\mu}M$) channels from the MTSAT-1R and the lightning observation data from Korea Meteorological Administration are used. When there is no lightning, the BTs of the IR1 and WV channels show the largest frequency at around 290-295K and 245K, respectively. On the other hand, the BTs of two channels show the largest frequency at 215K caused by strong convection when there is lightning. As a result, the WV-IR1 difference (BTDWI) sharply increases from -50K to 0K. Although it depends on the evolution stage of thunderstorms, the lightning mainly occurs at the core of circular convection in the mesoscale convective complex (MCC), whereas the lightning occurs by concentrated line-shape in the squall line. A strong positive correlation exists between the lightning frequency and the BT in the MCC regardless of the BT, but only at the very cold BT in the squall line. In general, the characteristics of BT are well defined for the lightning occurring in the concentrated line, but they are not well defined in the MCC, especially during the decaying stage of MCC. When they are defined well, the lightning occurs when the BTs of IR1 and WV are lower than 215K, BTDWI is near -3 to 1K, and local standard deviation of IR1 decreases to around 1K.