• Atmosphere
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• v.26 no.3
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• pp.357-372
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• 2016

Northeast Asian regions have recently become the main source of anthropogenic and natural aerosols. Measurement of aerosols on the sea in these regions have been rarely conducted since the experimental campaigns such as ACE-ASIA (Asian Pacific Regional Aerosol Characterization Experiment) in 2001. Research vessel observations of aerosol mass and chemical composition were performed on the Yellow and south sea around the Korean peninsula. The ship measurements showed six representative cases such as aerosol event and non-event cases during the study periods. On non-event cases, the anthropogenic chemical and natural soil composition on the Yellow sea were greater than those on the south sea. On aerosol event cases such as haze, haze with dust, and dust, the measured mass concentrations of anthropogenic chemical and element compositions were clearly changed by the events. In particular, methanesulfonate ($MSA^-$, $CH_3SO_3^-$), a main component of natural oceanic aerosol important for sulfur circulation on Earth, was first observed by the vessel in Korea, and its concentration on the Yellow sea was three times that on the south sea during the study period. Sea salt concentration important to chemical composition on the sea is related to wind speed. Coefficients of determination ($R^2$) between wind speed and sea salt concentration were 0.68 in $PM_{10}$ and 0.82 in $PM_{2.5}$. Maximum wave height was not found to be correlated to the sea salt concentration. When sea-salt comes into contact with pollutants, the total sea-salt mass is reduced, i.e., a loss of $Cl^-$ concentration from NaCl, the main chemical composing sea salt, is estimated by reaction with $HNO_3$(gas) and $H_2SO_4$(gas). The $Cl^-$ concentration loss by $SO_4^{2-}$ and $NO_3^-$ more easily increased for $PM_{10}$ compared to $PM_{2.5}$. The results of this study will be applied to verifying a dust-haze forecasting model. In addition, continued vessel measurements of aerosol data will become important to research for climate change studies in the future.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.25-32
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• 2021

In line with future changes in the marine environment, Aids to Navigation has been used in various fields and their use is increasing. The term "Aids to Navigation" means an aid to navigation prescribed by Ordinance of the Ministry of Oceans and Fisheries which shows navigating ships the position and direction of the ships, position of obstacles, etc. through lights, shapes, colors, sound, radio waves, etc. Also now the use of Aids to Navigation is transforming into a means of identifying and recording the marine weather environment by mounting various sensors and cameras. However, Aids to Navigation are mainly lost due to collisions with ships, and in particular, safety accidents occur because of poor observation visibility due to sea fog. The inflow of sea fog poses risks to ports and sea transportation, and it is not easy to predict sea fog because of the large difference in the possibility of occurrence depending on time and region. In addition, it is difficult to manage individually due to the features of Aids to Navigation distributed throughout the sea. To solve this problem, this paper aims to identify the marine weather environment by estimating sea fog level approximately with images taken by cameras mounted on Aids to Navigation and to resolve safety accidents caused by weather. Instead of optical and temperature sensors that are difficult to install and expensive to measure sea fog level, sea fog level is measured through the use of general images of cameras mounted on Aids to Navigation. Furthermore, as a prior study for real-time sea fog level estimation in various seas, the sea fog level criteria are presented using the Haze Model and Dark Channel Prior. A specific threshold value is set in the image through Dark Channel Prior(DCP), and based on this, the number of pixels without sea fog is found in the entire image to estimate the sea fog level. Experimental results demonstrate the possibility of estimating the sea fog level using synthetic haze image dataset and real haze image dataset.

• Atmosphere
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• v.26 no.2
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• pp.257-275
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• 2016

Asian dust is a seasonal meteorological phenomenon influencing most East Asia, irregularly occurring during spring. Unusual heavy Asian dust event in winter was observed in Seoul, Korea, with up to $1,044{\mu}g\;m^{-3}$ of hourly mean $PM_{10}$, in 22~23 February 2015. Causes of such infrequent event has been studied using both ground based and spaceborne observations, as well as numerical simulations including ECMWF ERA Interim reanalysis, NOAA HYSPLIT backward trajectory analysis, and ADAM2-Haze simulation. Analysis showed that southern Mongolia and northern China, one of the areas for dust origins, had been warm and dry condition, i.e. no snow depth, soil temperature of ${\sim}0^{\circ}C$, and cumulative rainfall of 1 mm in February, along with strong surface winds higher than critical wind speed of $6{\sim}7.5m\;s^{-1}$ during 20~21 February. While Jurihe, China, ($42^{\circ}23^{\prime}56^{{\prime}{\prime}}N$, $112^{\circ}53^{\prime}58^{{\prime}{\prime}}E$) experienced $9,308{\mu}g\;m^{-3}$ of hourly mean surface $PM_{10}$ during the period, the Asian dust had affected the Korean Peninsula within 24 hours traveling through strong north-westerly wind at ~2 km altitude. KMA issued Asian dust alert from 1100 KST on 22nd to 2200 KST on 23rd since above $400{\mu}g\;m^{-3}$ of hourly mean surface $PM_{10}$. It is also important to note that, previously to arrival of the Asian dust, the Korean Peninsula was affected by anthropogenic air pollutants ($NO_3^-$, $SO_4^{2-}$, and $NH_4^+$) originated from the megacities and large industrial areas in northeast China. In addition, this study suggests using various data sets from modeling and observations as well as improving predictability of the ADAM2-Haze model itself, in order to more accurately predict the occurrence and impacts of the Asian dust over the Korean peninsula.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1182-1194
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• 2016

The single image dehazing algorithm in existence can satisfy the demand only for improving either the effectiveness or efficiency. In order to solve the problem, a novel variational framework for single image dehazing based on restoration is proposed. Firstly, the initial atmospheric scattering model is transformed to meet the kimmel's Retinex variational model. Then, the green light component of image is considered as an input of the variational framework, which is generated by the sensitivity of green wavelength. Finally, the atmospheric transmission map is achieved by multi-resolution pyramid reduction to improve the visual effect of the results. Experimental results demonstrate that the proposed method can remove haze effectively with less memory consumption.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.60-63
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• 2020

With the advent of deep learning, a lot of attempts have been made in computer vision to substitute deep learning models for conventional algorithms. Among them, image classification, object detection, and image restoration have received a lot of attention from researchers. However, most of the contributions were refined in one of the fields only. We propose a new paradigm of model structure. End-to-end model which we will introduce classifies noise of an image and restores accordingly. Through this, the model enhances universality and efficiency. Our proposed model is an 'One-For-All' model which classifies weather condition in an image and returns clean image accordingly. By separating weather conditions, restoration model became more compact as well as effective in reducing raindrops, snowflakes, or haze in an image which degrade the quality of the image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• Journal of IKEEE
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• v.1 no.1 s.1
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• pp.120-137
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• 1997

Han's model, which is able to analyze optical communication between earth station and geo-satellite as a function of atmospheric conditions and elevation angles. is presented. In Han's model, atmospheric conditions are roughly classified into six basic types; clear sky, cloud, haze, fog, rain and snow. Data rate satisfying for the BER below $10^{-7}$ is analyzed by Han's model in case of up-link and down-link, respectively. Data rate is more limited by up-link than by down-link because the pointing loss caused by atmosphere on the up-link is greater than the spatial coherence degradation caused by atmosphere on the down-link.

• Journal of Korean Society for Atmospheric Environment
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• v.31 no.5
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• pp.411-429
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• 2015

The Korea Ministry of Environment has established an air quality standard for $PM_{2.5}$ in 2012 and it is effective from January 2015. In this study, we review various aspects of $PM_{2.5}$ in China, including its measurement, modeling, source apportionment, and health effect, and suggest future research directions for $PM_{2.5}$ studies in Korea. Measurements studies for $PM_{2.5}$ have examined organic marker compounds and $^{14}C$ as well as inorganic aerosols for distinguishing sources. Modeling results supported that the control of $PM_{2.5}$ pollution in big city needs effective cooperation between city and its surrounding regions. The major $PM_{2.5}$ sources in China have been identified to be secondary sulfur, motor vehicle emissions, coal combustion, dust, biomass burning, and industrial sources, however, they have seasonal dependency. Especially, the severe haze pollution event during January 2013 over eastern and northern China was driven to a large extent by secondary aerosol formation. Short-term exposure to $PM_{2.5}$ is strongly associated with the increased risk of morbidity and mortality from cardiovascular and respiratory diseases, as well as total non-accidental mortality. Considered previous $PM_{2.5}$ studies in China, analysis of specific organic species using online measurement, chamber experiment for secondary aerosol formation mechanism, and development of parameterizing this process in the model are needed to elucidate factors governing the abundance and composition of $PM_{2.5}$ in Korea.

• Journal of Internet Computing and Services
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• v.19 no.5
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• pp.43-54
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• 2018

Images taken in haze weather are characteristic of low contrast and poor visibility. The process of reconstructing clear-weather image from a hazy image is called dehazing. The main challenge of image dehazing is to estimate the transmission map or depth map for an input hazy image. In this paper, we propose a single image dehazing method by utilizing the Generative Adversarial Network(GAN) for accurate depth map estimation. The proposed GAN model is trained to learn a nonlinear mapping between the input hazy image and corresponding depth map. With the trained model, first the depth map of the input hazy image is estimated and used to compute the transmission map. Then a guided filter is utilized to preserve the important edge information of the hazy image, thus obtaining a refined transmission map. Finally, the haze-free image is recovered via atmospheric scattering model. Although the proposed GAN model is trained on synthetic indoor images, it can be applied to real hazy images. The experimental results demonstrate that the proposed method achieves superior dehazing results against the state-of-the-art algorithms on both the real hazy images and the synthetic hazy images, in terms of quantitative performance and visual performance.

• International Union of Geodesy and Geophysics Korean Journal of Geophysical Research
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• v.21 no.1
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• pp.31-46
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• 1993

A radiative transfer model with two-stream/delta-Eddington approximation has been developed to calculate the vertical distributions of atmospheric heating rates and radiative fluxes. The performance of the model has been evaluated by comparison with the results of ICRCCM (Intercomparison of radiative codes in climate models). It has been demonstrated that the presented model has a capability to calculate the solar radiation not only accurately but also economically. The characteristics of ultraviolet-B radiation (UV-B; 280-320nm) are examined by comparison of relation between the flux at the top of atmosphere and that at the surface. The relation of UV-B is quadratic due to the strong ozone absorption in this band. Also, the dependence of the UV-B radiation on the stratospheric ozone depletion and stratospheric aerosol haze due to volcanic eruption on the stratospheric ozone depletion and stratospheric aerosol haze due to volcanic eruption has been tested with various solar zenith angles. The surface UV-B increases as the solar zenith angle increases. The existence of stratospheric aerosols causes an increase in the planetary albedo due to the aerosols' backscattering. The planetary albedo with aerosol's effect has been increases as the solar zenith angle is not sensitive. It may be caused by the fact that the aerosols' scattering effect becomes saturated with the relatively long path length in a large solar zenith angle. Finally, the regional impact of stratospheric aerosols due to volcanic eruption on the intensity of UV-B radiation at the surface has been estimated. A direct effect is that the flux is diminished at the low latitudes, while it is enhanced in the high latitudes by the aerosols' photon trap or twilight effect. In the high latitudes, both aerosols' scattering and ozone absorption have strong and opposite impacts to the surface UV-B radiation is located at the mid-latitudes during spring season in both hemispheres.