• Journal of Korean Society for Atmospheric Environment
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• v.29 no.2
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• pp.199-210
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• 2013

Detailed 3 dimensional structure of Asian dust plume has been analyzed from the retrieved aerosol data from two different satellites which are the Korea's $1^{st}$ geostationary satellite, namely the Communication, Ocean, Meteorological Satellite (COMS) spacecraft launched in 2010, and the NASA's Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). COMS spacecraft provides the first time resolved aerial aerosol maps by the systematically well-calibrated multispectral measurements from the Geostationary Ocean Color Imager (GOCI) instrument. GOCI data are used here to evaluate intensity, spatial distribution, and long-range transport of Asian dust plume during 1~2 May 2011. We found that the strong Asian dust plume showing AOT of 2~5 was lofted to the altitude around 2~4 km above the Earth's surface and transported over Yellow Sea with a speed of about 25 km/hr. The CALIPSO extinction coefficient and particulate depolarization ratio (PDR) profiles confirmed that nonspherical dust particles were enriched in the dust plume. This study is a first example of quantitative integration of GOCI and CALIOP measurements for clarifying the overall structure of an Asian dust event.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.559-570
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• 2014

Backscatter signal observed from the space-borne Light Detection And Ranging (LIDAR) system is providing unique 3-dimensional spatial distribution as well as temporal variations for atmospheric aerosols. In this study, the continuous observations for aerosol profiles were analyzed during a years of 2012 by using a Cloud-Aerosol LIDAR with Orthogonal Polarization (CALIOP), carried on the Cloud-Aerosol LIDAR and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. The statistical analysis on the particulate extinction coefficient and depolarization ratio for each altitude was conducted according to time and space in order to estimate the variation of optical properties of aerosols over Northeast Asia ($E110^{\circ}-140^{\circ}$, $N20^{\circ}$ $-50^{\circ}$). The most frequent altitudes of aerosols are clearly identified and seasonal mean aerosol profiles vary with season. Since relatively high particle depolarization ratios (>0.5) are found during all seasons, it is considered that the non-spherical aerosols mixed with pollution are mainly exists over study area. This study forms initial regional 3-dimensional aerosol information, which will be extended and improved over time for estimation of aerosol climatology and event cases.

• Atmosphere
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• v.23 no.4
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• pp.471-483
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• 2013

Nighttime sea fog detection from satellite is very hard due to limitation in using visible channels. Currently, most widely used method for the detection is the Dual Channel Difference (DCD) method based on Brightness Temperature Difference between 3.7 and 11 ${\mu}m$ channel (BTD). However, this method have difficulty in distinguishing between fog and low cloud, and sometimes misjudges middle/high cloud as well as clear scene as fog. Using CALIPSO Lidar Profile measurements, we have analyzed the intrinsic problems in detecting nighttime sea fog from various satellite remote sensing algorithms and suggested the direction for the improvement of the algorithm. From the comparison with CALIPSO measurements for May-July in 2011, the DCD method excessively overestimates foggy pixels (2542 pixels). Among them, only 524 pixel are real foggy pixels, but 331 pixels and 1687 pixels are clear and other type of clouds, respectively. The 514 of real foggy pixels accounts for 70% of 749 foggy pixels identified by CALIPSO. Our proposed new algorithm detects foggy pixels by comparing the difference between cloud top temperature and underneath sea surface temperature from assimilated data along with the DCD method. We have used two types of cloud top temperature, which obtained from 11 ${\mu}m$ brightness temperature (B_S1) and operational COMS algorithm (B_S2). The detected foggy 1794 pixels from B_S1 and 1490 pixel from B_S2 are significantly reduced the overestimation detected by the DCD method. However, 477 and 446 pixels have been found to be real foggy pixels, 329 and 264 pixels be clear, and 989 and 780 pixels be other type of clouds, detected by B_S1 and B_S2 respectively. The analysis of the operational COMS fog detection algorithm reveals that the cloud screening process was strictly enforced, which resulted in underestimation of foggy pixel. The 538 of total detected foggy pixels obtain only 187 of real foggy pixels, but 61 of clear pixels and 290 of other type clouds. Our analysis suggests that there is no winner for nighttime sea fog detection algorithms, but loser because real foggy pixels are less than 30% among the foggy pixels declared by all algorithms. This overwhelming evidence reveals that current nighttime sea fog algorithms have provided a lot of misjudged information, which are mostly originated from difficulty in distinguishing between clear and cloudy scene as well as fog and other type clouds. Therefore, in-depth researches are urgently required to reduce the enormous error in nighttime sea fog detection from satellite.

• Korean Journal of Remote Sensing
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• v.33 no.3
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• pp.257-266
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• 2017

In this study, the dust/smoke detection algorithm was developed with a multi-spectral satellite remote sensing method using Moderate resolution Imaging Spectroradiometer (MODIS) Level 1B (L1B) data and the results were validated as RGB composite images of red(R; band 1), green(G; band 4), blue(B; band 3) channels using MODIS L1B data and Cloud-Aerosol Lidar with Orthogonal Polarization Satellite Observations(CALIPSO) Vertical Feature Mask (VFM) product. In the daytime on March 30, 2007 and April 27, 2012, the consistencies between the dust/smoke detected by this algorithm and verification data were approximately 56.4 %, 72.0 %, respectively. During the nighttime, the similar consistency was 40.5 % on April 27, 2012. Although these results were analyzed for limited cases due to the spatiotemporal matching for the MODIS and CALIPSO satellites, they could be used to utilize the aerosol detection of geostationary satellites for the next generations in Korea through further research.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.3
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• pp.326-336
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• 2011

The Light Detection and Ranging (Lidar) observation provides a specific knowledge of the temporal and vertical distribution and the optical properties of the aerosols. Unlike typical Mie scattering Lidars, which can measure backscattering and depolarization, the Raman Lidar can measure the quartz signal at the ultra violet (360 nm) and the visible (546 nm) wavelengths. In this work, we developed a method for estimating mineral quartz concentration immersed in Asian dust using Raman scattering of quartz (silicon dioxide, silica). During the Asian dust period of March 15, 16, and 21 in 2010, Raman lidar measurements detected the presence of quartz, and successfully showed the vertical profile of the dust concentrations. The satellite observations such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) confirmed spatial distribution of Asian dust. This approach will be useful for characterizing the quartz dominated in the atmospheric aerosols and the investigations of mineral dust. It will be especially applicable for distinguishing the dust and non-dust aerosols in studies on the mixing state of Asian aerosols. Additionally, the presented method combined with satellite observations is enable qualitative and quantitative monitoring for Asian dust.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.2
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• pp.159-171
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• 2012

Aerosol direct radiative forcing (ADRF) retrieval method was developed by combining data from passive and active satellite sensors. Aerosol optical thickness (AOT) retrieved form the Moderate Resolution Imaging Spectroradiometer (MODIS) as a passive visible sensor and aerosol vertical profile from to the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) as an active laser sensor were investigated an application possibility. Especially, space-born Light Detection and Ranging (Lidar) observation provides a specific knowledge of the optical properties of atmospheric aerosols with spatial, temporal, vertical, and spectral resolutions. On the basis of extensive radiative transfer modeling, it is demonstrated that the use of the aerosol vertical profiles is sensitive to the estimation of ADRF. Throughout the investigation of relationship between aerosol height and ADRF, mean change rates of ADRF per increasing of 1 km aerosol height are smaller at surface than top-of-atmosphere (TOA). As a case study, satellite data for the Asian dust day of March 31, 2007 were used to estimate ADRF. Resulting ADRF values were compared with those retrieved independently from MODIS only data. The absolute difference values are 1.27% at surface level and 4.73% at top of atmosphere (TOA).

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.527-544
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• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.3
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• pp.493-507
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• 2018

Due to high spatio-temporal variability of amount and optical/microphysical properties of atmospheric aerosols, satellite-based observations have been demanded for spatiotemporal monitoring the major aerosols. Observations of the heavy aerosol episodes and determination on the dominant aerosol types from a geostationary satellite can provide a chance to prepare in advance for harmful aerosol episodes as it can repeatedly monitor the temporal evolution. A new geostationary observation sensor, namely the Advanced Himawari Imager (AHI), onboard the Himawari-8 platform, has been observing high spatial and temporal images at sixteen wavelengths from 2016. Using observed spectral visible reflectance and infrared brightness temperature (BT), the algorithm to find major aerosol type such as volcanic ash (VA), desert dust (DD), polluted aerosol (PA), and clean aerosol (CA), was developed. RGB color composite image shows dusty, hazy, and cloudy area then it can be applied for comparing aerosol detection product (ADP). The CALIPSO level 2 vertical feature mask (VFM) data and MODIS level 2 aerosol product are used to be compared with the Himawari-8/AHI ADP. The VFM products can deliver nearly coincident dataset, but not many match-ups can be returned due to presence of clouds and very narrow swath. From the case study, the percent correct (PC) values acquired from this comparisons are 0.76 for DD, 0.99 for PA, 0.87 for CA, respectively. The MODIS L2 Aerosol products can deliver nearly coincident dataset with many collocated locations over ocean and land. Increased accuracy values were acquired in Asian region as POD=0.96 over land and 0.69 over ocean, which were comparable to full disc region as POD=0.93 over land and 0.48 over ocean. The Himawari-8/AHI ADP algorithm is going to be improved continuously as well as the validation efforts will be processed by comparing the larger number of collocation data with another satellite or ground based observation data.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.6
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• pp.629-640
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• 2008

This study investigates long-term trends and characteristics of aerosol optical depth ($\tau_a$) and Angstrom exponent (${\AA}$) around Korea in order to understand aerosol effects on the regional climate change. The analysis period is mainly from 1999 to 2006, and the analysis sites are Anmyun and Gosan, the background monitoring sites in Korea, and two other sites of Xianghe in China and Shirahama in Japan. The annual variations of $\tau_a$ at Anmyun and Gosan have slightly systematic increasing and decreasing trends, respectively. $\tau_a$ at Anmyun shows more substantial variation, probably because of it's being closer and vulnerable to anthropogenic influence from China and/or domestic sources than Gosan. Both values at Gosan and Anmyun are approximately 1.5 times greater than those at Shirahama. The monthly variation of $\tau_a$ exhibits the highest values at late Spring and the lowest at late-Summer, which are thought to be associated with the accumulation of fine aerosol formed through the photochemical reaction before the Jangma period and the scavenging effect after the Jangma period, respectively. Meanwhile, the episode-average $\tau_a$ for the Yellow dust period increases 2 times greater than that for the non-Yellow dust period. A significant decrease in ${\AA}$ for the Yellow dust period is attributable to an increase in the loading of especially the coarse particles. Also we found no weekly periodicity of $\tau_a$'s, but distinct weekly cycle of $PM_{10}$ concentrations, such as an increase on weekdays and a decrease on weekends at Anmyun and Gosan. We expect these findings would help to initiate a study on aerosol-cloud interactions through the combination of surface aerosol and satellite remote sensing (MODIS, Calipso and CloudSat) in East Asia.