Ambient measurements of aerosol light absorption (${\sigma}_a$) and scattering coefficients (${\sigma}_s$) were done at Gosan climate observatory during summer 2008 using a 3-wavelength photoacoustic soot spectrometer (PASS). PASS was deployed photoacoustic method for light absorption and integrated nephelometry for light scattering measurements. The ${\sigma}_a$ and ${\sigma}_s$ from PASS were compared with those from co-located aethalometer and nephelometer measurements. The aethalometer measurements of ${\sigma}_a$ correlated reasonably well with photoacoustic measurements, but the slope of the linear fitting line indicated the PASS measurement values of ${\sigma}_a$ were larger by a factor of 1.53. The nephelometer measurement values of ${\sigma}_s$ correlated very well with PASS measurements of ${\sigma}_s$, with a slope of 1.12 and a small offset. Comparing to the aethalometer measurements, the photoacoustic measurements of ${\sigma}_a$ didn't exhibit a significant (i.e., the ratio between aethalometer and PASS increased) change with increasing relative humidity (RH). The ratio of ${\sigma}_s$ between nephelometer and PASS increased with increasing RH, especially when the RH increased beyond 80%. This apparent increase in ${\sigma}_s$ with RH may be due to the contribution of hygroscopic growth of aerosols.
Journal of the Korean association of regional geographers
/
v.1
no.1
/
pp.45-60
/
1995
The purpose of this study is to classify the Korean precipitation regions on the basis of the characteristics of extratropical cyclonic precipitation. From now on, extratropical cyclone is called cyclone in short. By using factor analysis and Ward method in cluster analysis, precipitation regions on the basis of the characteristics of cyclonic precipitation are classified The principal data used in this study are daily precipitation records obtained from 60 weather stations of the Korea Meteorological Service during the ten years($1981{\sim}1990$), and weather charts published by the Japan Meteorological Agency. The results obtained in this study are summarized as follows: (1) In the factor analysis using 43 variables which have relation to the extratropical cyclonic precipitations, They are seven factors whose eigenvalues are above 1.0. This explains 86 percent of total amount. The first factor explains the characteristics of precipitation in the middle-west area and its contribution degree has the highest 10.9 percent. (2) According to the cluster analysis method of Ward, extratropical cyclonic precipitation regions are classified seven macro regions(such as Kyungki and North Youngseo, Youngdong and Ullungdo, Hoseo and South Youngseo, Honam and Northwest Chejudo, Southeast Chejudo, North Youngnam, and South Youngnam), 22 meso regions. (3) The characteristics of precipitation regions have relations to the path of cyclone, the direction of air inflow and the strike of mountain ranges. As the conclusion, the Central China Low brings much precipitation in the southern coast and southern area of Korea as moving to the northeastward. The North China Low moves eastward and brings much precipitation in the western area of the Taeback mountain ranges. The probability of extratropical cyclonic precipitation is the lowest in the inland of Yeongnam and the eastern coastal areas which belong to the rain shadow region. Namely, The seasonal and spatial characteristics of precipitation are closely associated with the path of cyclone and the direction of air inflow according to its passage, and the strike of mountain ranges.
Park, Il-Soo;Woon, Yu;Chung, Kyung-Won;Lee, Gangwoong;Owen, Jeffrey S.;Kwon, Won-Tae;Yun, Won-Tae
Journal of Korean Society for Atmospheric Environment
/
v.30
no.2
/
pp.188-200
/
2014
The IPCC 5th Assessment Report (Climate Change 2013: The Physical Science Basis) was accepted at the 36th Session of the IPCC on 26 September 2013 in Stockholm, Sweden. It consists of the full scientific and technical assessment undertaken by Working Group I. This comprehensive assessment of the physical aspects of climate change puts a focus on those elements that are relevant to understand past, document current, and project future of climate change. The assessment builds on the IPCC Fourth Assessment Report and the recent Special Report on Managing the Risk of Extreme Events and Disasters to Advance Climate Change Adaptation. The assessment covers the current knowledge of various processes within, and interactions among, climate system components, which determine the sensitivity and response of the system to changes in forcing, and they quantify the link between the changes in atmospheric constituents, and hence radiative forcing, and the consequent detection and attribution of climate change. Projections of changes in all climate system components are based on model simulations forced by a new set of scenarios. The report also provides a comprehensive assessment of past and future sea level change in a dedicated chapter. The primary purpose of this Technical Summary is to provide the link between the complete assessment of the multiple lines of independent evidence presented in the main report and the highly condensed summary prepared as Policy makers Summary. The Technical Summary thus serves as a starting point for those readers who seek the full information on more specific topics covered by this assessment. Warming of the climate system is unequivocal, and since the 1950s, many of the observed changes are unprecedented over decades to millennia. The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, sea level has risen, and the concentrations of greenhouse gases have increased. Total radiative forcing is positive, and has led to an uptake of energy by the climate system. The largest contribution to total radiative forcing is caused by the increase in the atmospheric concentration of $CO_2$ since 1750. Human influence on the climate system is clear. This is evident from the increasing greenhouse gas concentrations in the atmosphere, positive radiative forcing, observed warming, and understanding of the climate system. Continued emissions of greenhouse gases will cause further warming and changes in all components of the climate system. Limiting climate change will require substantial and sustained reductions of greenhouse gas emissions. The in-depth review for past, present and future of climate change is carried out on the basis of the IPCC 5th Assessment Report.
Seong Soo Yum;Kyu-Tae Lee;Jong-Jin Baik;Gyuwon Lee;Sang-Woo Kim;Junshik Um
Atmosphere
/
v.33
no.2
/
pp.105-124
/
2023
Research papers published in the Korean Meteorological Society (KMS) journals by the members of KMS since the establishment of KMS in 1963 in the field of atmospheric physics are summarized. A significant number of research papers published in other international journals are also cited in this paper to highlight the achievement of the KMS members in international academic community. The aim is to illustrate the historical development of research activities of the KMS members in the field of atmospheric physics, and indeed it is found that the KMS members have made enormous progress in research publications quantitatively and qualitatively in the field of atmospheric physics. In detail, however, observational studies of aerosol physical properties and cloud and precipitation physics were very active, and studies on cloud physics parameterization for cloud modeling were highly recognized in the world, but observational and theoretical studies of atmospheric radiation were relatively lacking and solicit more contribution from the KMS members.
Park, Geun-Ae;Lee, Yong-Jun;Shin, Hyung-Jin;Kim, Seong-Joon
KSCE Journal of Civil and Environmental Engineering Research
/
v.30
no.2B
/
pp.107-120
/
2010
The effect of potential future climate change on the inflow of agricultural reservoir and its impact to downstream streamflow by reservoir operation for paddy irrigation water was assessed using the SLURP (semi-distributed land use-based runoff process), a physically based hydrological model. The fundamental input data (elevation, meteorological data, land use, soil, vegetation) was collected to calibrate and validate of the SLURP model for a 366.5 $km^2$ watershed including two agricultural reservoirs (Geumgwang and Gosam) located in Anseongcheon watershed. Then, the CCCma CGCM2 data by SRES (special report on emissions scenarios) A2 and B2 scenarios of the IPCC (intergovernmental panel on climate change) was used to assess the future potential climate change. The future weather data for the year, m ms, m5ms and 2amms was downscaled by Change Factor method through bias-correction using 3m years (1977-2006) weather data of 3 meteorological stations of the watershed. In addition, the future land uses were predicted by modified CA (cellular automata)-Markov technique using the time series land use data fromFactosat images. Also the future vegetation cover information was predicted and considered by the linear regression between monthly NDVI (normalized difference vegetation index) from NOAA AVHRR images and monthly mean temperature using eight years (1998-2006) data.
Journal of Korean Society for Atmospheric Environment
/
v.26
no.1
/
pp.34-47
/
2010
Long-range transport of air pollutants was simulated using Comprehensive Acid Deposition Model (CADM) and Yonsei University-Sulfuric Acid Deposition Model (YU-SADM). For the simulation, weather patterns that represent the four seasons were derived through a clustering analysis with 5-years of meteorological data. The simulation result showed that in spring, influenced by strong low pressure from China, air pollutants of moved to the Korean Peninsula. In summer, humid air moved into the Korean Peninsula across the Yellow Sea while the north pacific high pressure extended, making the concentration of air pollutants lower than that in the other seasons. In autumn, air pollutants were transported by the northwest wind caused by the movement of high pressure over the Yellow Sea, while in winter air pollutants were influenced by northwest winds from continental highs. The amount of air pollutants in each season showed that high amount of pollutants were transported in winter due to the strong northwest wind. The in-flows were 3 to 8 times higher than those of the other seasons, and out-flows were about as twice as high. The amount of wet deposition in summer and autumn increased significantly compared to the amount in the other seasons due to the increase of rainfall. Source-receptor relationship analysis for sulfur showed that 70 to 91 precent of the total deposition came from the self-contribution by the Korean Peninsula. In winter, contribution from China was about 25 percent of the total deposition which was higher amount than any other season.
Kim, Soontae;Kim, Okgil;Kim, Byeong-Uk;Kim, Hyun Cheol
Journal of Korean Society for Atmospheric Environment
/
v.33
no.2
/
pp.159-173
/
2017
The Weather Research and Forecast (WRF) - Community Multiscale Air Quality (CMAQ) system was applied to investigate the influence of major point sources located in Chungcheongnam-do (CN) on surface $PM_{2.5}$ (Particulate Matter of which diameter is $2.5{\mu}m$ or less) concentrations in its surrounding areas. Uncertainties associated with contribution estimations were examined through cross-comparison of modeling results using various combinations of model inputs and setups; two meteorological datasets developed with WRF for 2010 and 2014, and two domestic emission inventories for 2010 and 2013 were used to estimate contributions of major point sources in CN. The results show that contributions of major point sources in CN to annual $PM_{2.5}$ concentrations over Seoul, Incheon, Gyeonggi, and CN ranged $0.51{\sim}1.63{\mu}g/m^3$, $0.71{\sim}1.62{\mu}g/m^3$, $0.63{\sim}1.66{\mu}g/m^3$, and $1.04{\sim}1.86{\mu}g/m^3$, respectively, depending on meteorology and emission inventory choice. It indicates that the contributions over the surrounding areas can be affected by model inputs significantly. Nitrate was the most dominant $PM_{2.5}$ component that was increased by major point sources in CN followed by sulfate, ammonium, and others. Based on the model simulations, it was estimated that primary $PM_{2.5}$$(PPM)-to-PM_{2.5}$ conversion rates were 41.3~50.7 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 12.4~18.3 ($10^{-6}{\mu}g/m^3/TPY$) for Seoul, Incheon, and Gyeonggi, respectively. In addition, spatial gradients of PPM contributions show very steep trends. $NO_X$-to-nitrate conversion rates were 7.61~12.3 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 3.94~11.3 ($10^{-6}{\mu}g/m^3/TPY$) for the sub-regions in the SMA. $SO_2$-to-sulfate conversion rates were 4.04~5.28 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 3.73~4.43 ($10^{-6}{\mu}g/m^3/TPY$) for the SMA, respectively.
Yu, Geun-Hye;Park, Seung-Shik;Jung, Sun A;Jo, Mi Ra;Jang, Yu Woon;Lim, Yong Jae;Ghim, Young Sung
Particle and aerosol research
/
v.15
no.3
/
pp.91-104
/
2019
In this study, hourly measurements of $PM_{2.5}$ and its major chemical constituents such as organic and elemental carbon (OC and EC), and ionic species were made between January 15 and February 10, 2018 at the air pollution intensive monitering station in Gwangju. In addition, 24-hr integrated $PM_{2.5}$ samples were collected at the same site and analyzed for OC, EC, water-soluble OC (WSOC), humic-like substance (HULIS), and ionic species. Over the whole study period, the organic aerosols (=$1.6{\times}OC$) and $NO_3{^-}$ concentrations contributed 26.6% and 21.0% to $PM_{2.5}$, respectively. OC and EC concentrations were mainly attributed to traffic emissions with some contribution from biomass burning emissions. Moreover, strong correlations of OC with WSOC, HULIS, and $NO_3{^-}$ suggest that some of the organic aerosols were likely formed through atmospheric oxidation processes of hydrocarbon compounds from traffic emissions. For the period between January 18 and 22 when $PM_{2.5}$ pollution episode occurred, concentrations of three secondary ionic species ($=SO{_4}^{2-}+NO_3{^-}+NH_4{^+}$) and organic matter contributed on average 50.8 and 20.1% of $PM_{2.5}$, respectively, with the highest contribution from $NO_3{^-}$. Synoptic charts, air mass backward trajectories, and local meteorological conditions supported that high $PM_{2.5}$ pollution was resulted from long-range transport of haze particles lingering over northeastern China, accumulation of local emissions, and local production of secondary aerosols. During the $PM_{2.5}$ pollution episode, enhanced $SO{_4}^{2-}$ was more due to the long-range transport of aerosol particles from China rather than local secondary production from $SO_2$. Increasing rate in $NO_3{^-}$ was substantially greater than $NO_2$ and $SO{_4}^{2-}$ increasing rates, suggesting that the increased concentration of $NO_3{^-}$ during the pollution episode was attributed to enhanced formation of local $NO_3{^-}$ through heterogenous reactions of $NO_2$, rather than impact by long-range transportation from China.
Oh, Mi Ju;Kim, Dongwook;Lee, Joo-Heon;Kim, Tae-Woong
Journal of Korea Water Resources Association
/
v.56
no.6
/
pp.381-392
/
2023
Precipitation, runoff, and evapotranspiration are changing worldwide due to climate change and human activities. Because watershed runoff is an important component of the hydrological cycle, it is important to investigate the changes in watershed runoff for water resources management. This study collected observed data of runoff, precipitation, temperature, and evapotranspiration in the Geum River basin as well as their synthetic data according to Representative Concentration Pathways (RCP) scenarios, investigated the trend of hydro-meteorological variables using the Mann-Kendall test, and quantitatively evaluated the effects of climate change and human activities on the watershed runoff using the climate elasticity approach and the Budyko framework. The results indicated that the relative contribution of climate change and human activity to changes in runoff varies from region to region. For example, the watershed with the greatest contribution from climate change and human activity were the Yongdam Dam (#3001) basin and the Daecheong Dam (#3008) basin, respectively. Future climate change showed an increase in precipitation and temperature in both RCP 4.5 and 8.5 scenarios, resulting in changes in runoff in the Geum River basin from 44.8% to 65.5%, respectively. We concluded that the effect on watershed runoff can be separated into climate change and human activities, which will be important information in establishing sustainable water resource management plans.
Shin, Ji Yae;Lee, Baesung;Yoon, Hyeon-Cheol;Kwon, Hyun-Han;Kim, Tae-Woong
Journal of Korea Water Resources Association
/
v.56
no.8
/
pp.509-520
/
2023
Various drought indices are widely used for assessing drought conditions which are affected by many factors such as precipitation, soil moisture, and runoff. The values of drought indices varies depending on hydro-meteorological data and calculation formulas, and the judgment of the drought condition may also vary. This study selected four calculation components such as precipitation data length, accumulation period, probability distribution function, and parameter estimation method as the sources of uncertainty in the calculation of standardized precipitation index (SPI), and evaluated their contributions to the uncertainty using root mean square error (RMSE) and linear mixed model (LMM). The RMSE estimated the overall errors in the SPI calculation, and the LMM was used to quantify the uncertainty contribution of each factor. The results showed that as the accumulation period increased and the data period extended, the RMSEs decreased. The comparison of relative uncertainty using LMM indicated that the sample size had the greatest impact on the SPI calculation. In addition, as sample size increased, the relative uncertainty related to the sample size used for SPI calculation decreased and the relative uncertainty associated with accumulation period and parameter estimation increased. In conclusion, to reduce the uncertainty in the SPI calculation, it is essential to collect long-term data first, followed by the appropriate selection of probability distribution models and parameter estimation methods that represent well the data characteristics.
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