In an environment with many local, remote, persistent, and episodic sources of pollution, meteorology is the primary factor that drives periods of unhealthy air quality and reduced visibility. The 2016 Korea-UnitedStatesAirQuality(KORUS-AQ)fieldstudyprovidesauniqueopportunitytoexaminethe impactofmeteorologyontherelativeinfluenceoflocalandtransboundarypollution.MuchoftheKORUS-AQ campaign can be grouped into four distinct research periods based on observed synoptic meteorology, includingaperiodofcomplexaerosolverticalprofilesdrivenbydynamicmeteorology,stagnationundera persistent anticyclone, low-level transport and haze development, and a blocking pattern. These episodes areexaminedusingadiversearchiveofground,airborne,andsatellitedata.Whilefrontalboundaries are recognized as the primary mechanism driving pollution transport in eastern Asia, results show that they are not always related to sustained periods of hazardous air quality and reduced visibility at the surface.Significantlong-rangetransportofpollutionanddustwasconstrainedtoafewshortevents, suggesting that the majority of pollutants sampled during KORUS-AQ originated from local sources. A severeregionalpollutionepisodeisexaminedindetail,featuringdensehazeandsignificantsecondary particle formation within a shallow moist boundary layer. Observations during KORUS-AQ also highlight a rapid,40ppbvincreaseinozonepollutionasastrongseabreezefronttraversedtheSeoulMetropolitan Area. Representativeness of meteorology and pollution conditions measured by KORUS-AQ is considered by comparison with climatology. This analysis is an essential step toward improved local and regional forecasting of air quality and visibility.

To understand the carbon cycle at policy-relevant spatial scales, a high density of high-quality CO 2 measurement sites is needed. In 2012, the Korea Meteorological Administration (KMA) installed CO 2 monitoring systems at Anmyeondo (AMY) in the west, Jejudo Gosan Suwolbong (JGS) in the southwest, and Ulleungdo (ULD) in the east of South Korea. Three stations were instrumented with identical greenhouse gas measurement systems based on cavity ring-down spectroscopy (CRDS) and a new drying system developed by KMA and the Korea Research Institute of Standards and Science (KRISS). This drying system is suitable in humid areas; water vapor measured using CRDS in ambient air was 0.001 % to 0.004 % across the stations. Measurement uncertainties expressed by the quadrature sum of the uncertainties from the drying system, scale propagations, repeatability, and reproducibility were ∼ 0.11 ppm from all KMA stations in the 68 % confidence interval. Average monthly CO 2 enhancements above the local background at each station were 4.3 ± 3.3 ppm at AMY, 1.7 ± 1.3 ppm at JGS, and 1 ± 1.9 ppm (1σ ) at ULD, respectively, during 2012 to 2016. At AMY station, located between China and South Korea, CO 2 annual means and seasonal variations are also greater than the other KMA stations, indicating that it is affected not only by local vegetation, but also added anthropogenic sources. Selected baseline CO 2 at AMY and at JGS in the west of South Korea is more sensitive to East Asia (e.g., China) according to wind direction and speed. Through the comparison of long-term trends and growth rates at AMY with other East Asian stations over 15 years, it was suggested that they could be affected not only by local vegetation but also by measurement quality.Published by Copernicus Publications on behalf of the European Geosciences Union.

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This study investigated the temporal and spatial characteristics of summertime (June-August) precipitation over Korean peninsula, using Korea Meteorological Administration (KMA)ís Automated Synoptic Observing System (ASOS) data for the period of 1973-2010 and Automatic Weather System (AWS) data for the period of 1998-2010.The authors looked through climatological features of the summertime precipitation, then examined the degree of locality of the precipitation, and probable precipitation amount and its return period of 100 years (i.e., an extreme precipitation event). The amount of monthly total precipitation showed increasing trends for all the summer months during the investigated 38-year period. In particular, the increasing trends were more significant for the months of July and August. The increasing trend of July was seen to be more attributable to the increase of precipitation intensity than that of frequency, while the increasing trend of August was seen to be played more importantly by the increase of the precipitation frequency. The e-folding distance, which is calculated using the correlation of the precipitation at the reference station with those at all other stations, revealed that it is August that has the highest locality of hourly precipitation, indicating higher potential of localized heavy rainfall in August compared to other summer months. More localized precipitation was observed over the western parts of the Korean peninsula where terrain is relatively smooth. Using the 38-years long series of maximum daily and hourly precipitation as input for FARD2006 (Frequency Analysis of Rainfall Data Program 2006), it was revealed that precipitation events with either 360 mm day −1 or 80 mm h −1 can occur with the return period of 100 years over the Korean Peninsula.

[1] Cloud resolving model outputs are often used to build databases for satellite microwave remote sensing of precipitating clouds. A known problem of this approach is that cloud resolving models tend to systematically produce excessive amount of high density frozen hydrometeors, causing the cloud/radiation model database to have stronger scattering signatures at high microwave frequencies than those observed by satellite or airborne sensors. Consequently, it lowers the performance of the cloud and precipitation retrieval algorithms that utilize the model database. Since multi-frequency satellite observations contain information on hydrometeors' properties, measured brightness temperatures can give guidance as to how the modeled cloud database may be modified to better mimic natural clouds. Following this philosophy, in this study, we propose a method to adapt the modeled database toward observations. The newly constructed database results in a better match to the characteristics of the satellite observed brightness temperatures. Citation: Seo, E.-K., G. Liu, W.-K. Tao, and S.-O. Han (2007), Adaptation of a model-generated cloud database to satellite observations, Geophys. Res. Lett., 34, L03805,

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