• Atmosphere
• /
• v.18 no.2
• /
• pp.97-109
• /
• 2008

The aerosol characteristics in terms of 4 different cases (Asian dust, fog, clear sky and cloud) which had happened at Anmyeon Island in April 2006 were studied using various measurements such as the Micro Pulse Lidar (MPL), sunphotometer, $\beta$-ray $PM_{10}$ Analyzer, anemoscope and anemometer. In addition, synoptic charts, back trajectory analyses and satellite images were also used to help characterize the aerosol events. The aerosol optical properties were featured by the Aerosol Optical Depth (AOD) and ${\AA}ngstr\ddot{o}m$ exponent which were estimated by the sunphotometer. When Anmyeon Island was dominated by the Asian dust, the AOD was sharply increased as seven times as a yearly average of it (0.35). As compared with a yearly average of the ${\AA}ngstr\ddot{o}m$ exponent of 0.97, the ${\AA}ngstr\ddot{o}m$ exponent of a dust day was significantly low (0.099). In addition, $PM_{10}$ mass concentration showed an extremely high record. The maximum concentration reached $1790.5{\mu}gm^{-3}$ on 8 April 2006. The maximum mass concentration was shown with delay when the wind speed of $0ms^{-1}$ was observed. It was also found that a satellite image of the MODIS-RGB had a good agreement with the results of those measurements. It was shown that the MPL was able to describe effectively the vertical distribution of aerosol for all the cases. In particular, the MPL evidently captured the aerosol layer before the cloud observation. The aerosol layer was similarly described by the AOD. On a clear sky day, the AOD had not only a very low value (0.054) but also a feature of homogeneity.

• Proceedings of the KSRS Conference
• /
• 2009.03a
• /
• pp.254-259
• /
• 2009

In this study, Simplified Method for the Atmospheric Correction (SMAC) radiative transfer model (RTM) used to retrieve surface reflectance from MODIS Top Of Atmosphere (TOA) reflectance (MOD02). SMAC code provides coefficients which were previously yielded by Second Simulation of the Satellite Signal in the Solar Spectrum (6S) for each satellite sensor. We conducted error analysis of SMAC RTM using MOD02 over comparison with MODIS surface reflectance (MOD09) which was provided from 6S. It showed that low accuracy values such as, $R^2$ : 0.6196, Root Means Square Error (RMSE) : 0.00031, bias : - 0.0859. Thus sensitivity analysis of input parameters and coefficients was conducted to searching error sources. Coefficients about $\tau_p$ (average AOD) are more influence than any other coefficients of $\tau_{a550}$ (Aerosol Optical Depth at 550nm) from sensitivity test. Calibrated coefficients of $\tau_p$ from regression analysis were used to surface reflectance which showed that improve accuracy of surface reflectance ($R^2$ : 0.827, RMSE : 0.00672, bias : - 0.000762).

• Journal of Korean Society for Atmospheric Environment
• /
• v.33 no.1
• /
• pp.19-30
• /
• 2017

The altitudinal potential source contribution function (PSCFa) method was developed by considering topography and height of back trajectories. The PSCFa calculated on the contributions of trans-boundary transport to the hourly mean concentrations of aerosol optical depth (AOD) of the Aerosol Robotic Network (AERONET) in the Distributed Regional Aerosol Gridded Observation Networks (DRAGON) KORea-US Air Quality (KORUS-AQ) campaign from March 31 to July 1 in 2016. Eastern China ($33^{\circ}N{\sim}35^{\circ}N$ and $119^{\circ}E{\sim}121^{\circ}E$) can be the major source of trans-boundary pollution to the western area in South Korea resulted from PSCFa (0~700 m). In this study, AOD by Moderate Resolution Imaging Spectroradiometer (MODIS) was compared to verify the source regions. Regionally, the effects of the long-range transport of pollutants from the eastern China on air quality in south Korea have become more significant over this period.

• Journal of the Korean earth science society
• /
• v.31 no.6
• /
• pp.600-607
• /
• 2010

In 2008, multiple episodes of large-scale transport of natural airborne particles and anthropogenically affected particles from different sources in the East Asian continent were identified in the National Oceanic and Atmospheric Administration (NOAA) satellite RGB-composite images and the mass concentrations of ground level particulate matters. To analyze the aerosol size distribution during the large-scale transport of atmospheric aerosols, both aerosol optical depth (AOD; proportional to the aerosol total loading in the vertical column) and fine aerosol weighting (FW; fractional contribution of fine aerosol to the total AOD) of Moderate resolution Imaging Spectroradiometer (MODIS) aerosol products were used over the East Asian region. The six episodes of massive natural airborne particles were observed at Cheongwon, originating from sandstorms in northern China, Mongolia and the loess plateau of China. The $PM_{10}$ and $PM_{2.5}$ stood at 70% and 16% of the total mass concentration of TSP, respectively. However, the mass concentration of $PM_{2.5}$ among TSP increased as high as 23% in the episode in which they were flowing in by way f the industrial area in east China. In the other five episodes of anthropogenically affected particles that flowed into the Korean Peninsula from east China, the mass concentrations of $PM_{10}$ and $PM_{2.5}$ among TSP reached 82% and 65%, respectively. The average AOD for the large-scale transport of anthropogenically affected particle episodes in the East Asian region was measured at $0.42{\pm}0.17$ compared with AOD ($0.36{\pm}0.13$) for the natural airborne particle episodes. Particularly, the regions covering east China, the Yellow Sea, the Korean Peninsula, and the east Korean sea were characterized by high levels of AOD. The average FW values observed during the event of anthropogenically affected aerosols ($0.63{\pm}0.16$) were moderately higher than those of natural airborne particles ($0.52{\pm}0.13$). This observation suggests that anthropogenically affected particles contribute greatly to the atmospheric aerosols in East Asia.

• Korean Journal of Remote Sensing
• /
• v.20 no.2
• /
• pp.77-89
• /
• 2004

The spatial and temporal variations of aerosol optical depth (AOD) over Northeast Asia regions have special importance in the aerosol research for estimation of aerosol radiative forcing parameters and climate change. Aerosol optical and physical properties (AOD and ${\AA}$ngstrom parameter) have been investigated by using Moderate Resolution Imaging Spectroradiometer (MODIS) and Total Ozone Mapping Spectrometer (TOMS) Aerosol Index (AI) to estimate aerosol characteristics over the study region during 2001. Additionally, aerosol characteristics over the Korean peninsular during Aerosol Characteristic Experiment in Asia (ACE-Asia) Intensive Observation Period (IOP) have been investigated by using satellite observations. The results showed that the daily-observed aerosol data indicate seasonal variations with relatively higher aerosol loading in the spring and very low during the winter. The typical Asian dust case showed higher AOD (>0.7) with lower Angstrom exponent (<0.5) and higher AI (>0.5) that is mainly due to the composition of coarse particles in the springtime. Mean AOD for 2001 at 4 different places showed 0.65$\pm$0.37 at Beijing, 0.31$\pm$0.19 at Gosan, 0.54$\pm$0.26 at Seoul, and 0.38$\pm$0.19 at Kwangju, respectively. An interesting result was found in the present study that polluted aerosol events with small size dominated-aerosol loading around the Korean peninsular are sometimes observed. The origin of these polluted aerosols was thought to East China. Aerosol distribution from satellite images and trajectory results shows the proof of aerosol transport. Therefore, aerosol monitoring using satellite data is very useful.

• 한국지구과학회:학술대회논문집
• /
• 2010.04a
• /
• pp.37-37
• /
• 2010

2008년 동아시아 대륙에서 발생기원이 다른 황사와 인위적 오염입자의 광역적 이동 사례를 NOAA위성 RGB 합성영상과 지상 TSP, PM10, PM2.5 질량농도 관측으로 구별하였다. 또한 Terra/Aqua 위성MODIS (MODerate Imaging Spectroradiometer) 센서의AOD (Aerosol Optical Depth)와 FW (Fine aerosol Weighting)를 통해 동아시아 지역에서 발생기원이 다른 대기 에어로졸의 분포와 입자 크기 특성을 분석하였다. 중국 북부와 몽골, 그리고 중국 황토고원에서 모래폭풍이 발생하여 광역적으로 이동하여 청원에 먼지입자(황사)로 영향을 주는 6 사례를 분석했다. 질량농도 TSP중 PM10 은 70%, PM2.5 는 16% 로 조대입자 (> $2.5{\mu}m$)의 비율이 큰 것은 사막과 반사막의 자연적 발생원에서 생성되었기 때문이다. 그러나, 모래 폭풍이 이동 과정에서 중국 동부의 산업 지역을 거쳐 유입 되는 사례에서는 TSP 중 PM2.5 가 23% 까지 증가하기도 했다. 중국 동부로부터 황해를 거쳐 한반도로 유입하고 있는 다른5사례는 TSP 중 PM10, PM2.5가 각각 82%, 65% 로 PM2.5 의 비율이 높았는데 인위적 오염입자의 영향 때문이다. 동아시아 지역에서 인위적 오염입자의 광역적 이동 사례에 대한 평균 AOD는 $0.42{\pm}0.17$로 황사에 의한 AOD ($0.36{\pm}0.13$)와 비교하여 대기 에어로졸에 대한 비율이 높게 나타났다. 특히, 중국 동부에서 황해, 한반도, 동해에 이르는 광역적 지역에 높은 AOD값이 분포했다. 인위적 오염입자의 사례는 FW가 평균 $0.63{\pm}0.16$로 모래폭풍의 이동 사례의 $0.52{\pm}0.13$ 보다 높은 값을 보이고 있어, 대기 에어로졸에 대한 인위적 미세 오염입자의 기여가 크게 나타나고 있었다.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.370-373
• /
• 2005

We have developed Hybrid algorithm for yellow sand detection. Hybrid algorithm is composed of three methods using infrared bands. The first method used the differential absorption in brightness temperature difference between $11\mu m\;and\;12\mu m$ (BID _1), through which help distinguish the yellow sand from various meteorological clouds. The second method uses the brightness temperature difference between $3.7\mu m\;and\;11\mu m$ (BID_2). The technique would be most sensitive to dust loading during the day when the BID _2 is enhanced by reflection of $3.7\mu m$ solar radiation. The third one is a newly developed algorithm from our research, the so-called surface temperature variation method (STY). We have applied the three methods to MODIS for derivation of the yellow sand dust and in conjunction with the Principle Component Analysis (PCA), a form of eigenvector statistical analysis. PCI shows better results for yellow sand detection in comparison with the results from individual method. The comparison between PCI and MODIS aerosols optical depth (AOD) shows remarkable good correlations during daytime and relatively good correlations over the land.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_1
• /
• pp.919-932
• /
• 2019

Fine particulate matter (PM2.5) is not only affected by anthropogenic emissions, but also intensifies, migrates, decreases by hydrometeorological factors. Therefore, it is essential to understand relationships between the hydrometeorological factors and PM2.5 concentration. In Korea, PM2.5 concentration is measured at the ground observatories and estimated data are given to locations where observatories are not present. In this way, the data is not suitable to represent an area, hence it is impossible to know accurate concentration at such locations. In addition, it is hard to trace migration, intensification, reduction of PM2.5. In this study, we analyzed the relationships between hydrometeorological factors, acquired from Global Land Data Assimilation System (GLDAS), and PM2.5 by means of Bayesian Model Averaging (BMA). By BMA, we also selected factors that have meaningful relationship with the variation of PM2.5 concentration. 4 PM2.5 concentration models for different seasons were developed using those selected factors, with Aerosol Optical Depth (AOD) from MODerate resolution Imaging Spectroradiometer (MODIS). Finally, we mapped the result of the model, to show spatial distribution of PM2.5. The model correlated well with the observed PM2.5 concentration (R ~0.7; IOA ~0.78; RMSE ~7.66 ㎍/㎥). When the models were compared with the observed PM2.5 concentrations at different locations, the correlation coefficients differed (R: 0.32-0.82), although there were similarities in data distribution. The developed concentration map using the models showed its capability in representing temporal, spatial variation of PM2.5 concentration. The result of this study is expected to be able to facilitate researches that aim to analyze sources and movements of PM2.5, if the study area is extended to East Asia.

• Korean Journal of Remote Sensing
• /
• v.36 no.4
• /
• pp.573-586
• /
• 2020

PM (particulate matter) is of interest to everyone because it can have adverse effects on human health by the infiltration from respiratory to internal organs. To date, many studies have made efforts for the prediction of PM10 and PM2.5 concentrations. Unlike previous studies, we conducted the prediction of tomorrow's PM10 concentration for the Air Korea stations using Chinese PM10 data in addition to the satellite AOD and weather variables. We constructed 230,639 matchups from the raw data over 3 million and built an RF (random forest) model from the matchups to cope with the complexity and nonlinearity. The validation statistics from the blind test showed excellent accuracy with the RMSE (root mean square error) of 9.905 ㎍/㎥ and the CC (correlation coefficient) of 0.918. Moreover, our prediction model showed a stable performance without the dependency on seasons or the degree of PM10 concentration. However, part of coastal areas had a relatively low accuracy, which implies that a dedicated model for coastal areas will be necessary. Additional input variables such as wind direction, precipitation, and air stability should also be incorporated into the prediction model as future work.

• Korean Journal of Remote Sensing
• /
• v.33 no.6_1
• /
• pp.1029-1040
• /
• 2017

Wildfires release a large amount of greenhouse gases (GHGs) into the atmosphere. Fire radiative power (FRP) data obtained from geostationary satellites can play an important role for tracing the GHGs. This paper describes an estimation of the Himawari-8 FRP and fire emissions for Samcheock and Gangnueng wildfire in 6 May 2017. The FRP estimated using Himawari-8 well represented the temporal variability of the fire intensity, which cannot be captured by MODIS (Moderate Resolution Imaging Spectroradiometer) because of its limited temporal resolution. Fire emissions calculated from the Himwari-8 FRP showed a very similar time-series pattern compared with the AirKorea observations, but 1 to 3 hour's time-lag existed because of the distance between the station and the wildfire location. The estimated emissions were also compared with those of a previous study which analyzed fire damages using high-resolution images. They almost coincided with 12% difference for Samcheock and 2% difference for Gangneung, demonstrating a reliability of the estimation of fire emissions using our Himawari-8 FRP without high-resolution images. This study can be a reference for estimating fire emissions using the current and forthcoming geostationary satellites in East Asia and can contribute to improving accuracy of meteorological products such as AOD (aerosol optical depth).