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This study evaluates the Korea Meteorological Administration (KMA) Asian Dust Aerosol Model 2 (ADAM2) for Asian dust events over the dust source regions in northern China during the first half of 2017. Using the observed hourly particulate matter (PM) concentration from the China Ministry of Environmental Protection (MEP) and station weather reports, we find that a threshold value of PM10–PM2.5 = 400 μg m−3 works well in defining an Asian dust event for both the MEP-observed and the ADAM2-simulated data. In northwestern China, ADAM2 underestimates the observed dust days mainly due to underestimation of dust emissions; ADAM2 overestimates the observed Asian dust days over Manchuria due to overestimation of dust emissions. Performance of ADAM2 in estimating Asian dust emissions varies quite systematically according to dominant soil types within each region. The current formulation works well for the Gobi and sand soil types, but substantially overestimates dust emissions for the loess-type soils. This suggests that the ADAM2 model errors are likely to originate from the soil-type-dependent dust emissions formulation and that the formulation for the mixed and loess-type soils needs to be recalibrated. In addition, inability to account for the concentration of fine PMs from anthropogenic sources results in large false-alarm rates over heavily industrialized regions. Direct calculation of PM2.5 in the upcoming ADAM3 model is expected to alleviate the problems related to anthropogenic PMs in identifying Asian dust events.

Background Cities are a major source of atmospheric CO2; however, understanding the surface CO2 exchange processes that determine the net CO2 flux emitted from each city is challenging owing to the high heterogeneity of urban land use. Therefore, this study investigates the spatiotemporal variations of urban CO2 flux over the Seoul Capital Area, South Korea from 2017 to 2018, using CO2 flux measurements at nine sites with different urban land-use types (baseline, residential, old town residential, commercial, and vegetation areas). Results Annual CO2 flux significantly varied from 1.09 kg C m− 2 year− 1 at the baseline site to 16.28 kg C m− 2 year− 1 at the old town residential site in the Seoul Capital Area. Monthly CO2 flux variations were closely correlated with the vegetation activity (r = − 0.61) at all sites; however, its correlation with building energy usage differed for each land-use type (r = 0.72 at residential sites and r = 0.34 at commercial sites). Diurnal CO2 flux variations were mostly correlated with traffic volume at all sites (r = 0.8); however, its correlation with the floating population was the opposite at residential (r = − 0.44) and commercial (r = 0.80) sites. Additionally, the hourly CO2 flux was highly related to temperature. At the vegetation site, as the temperature exceeded 24 ℃, the sensitivity of CO2 absorption to temperature increased 7.44-fold than that at the previous temperature. Conversely, the CO2 flux of non-vegetation sites increased when the temperature was less than or exceeded the 18 ℃ baseline, being three-times more sensitive to cold temperatures than hot ones. On average, non-vegetation urban sites emitted 0.45 g C m− 2 h− 1 of CO2 throughout the year, regardless of the temperature. Conclusions Our results demonstrated that most urban areas acted as CO2 emission sources in all time zones; however, the CO2 flux characteristics varied extensively based on urban land-use types, even within cities. Therefore, multiple observations from various land-use types are essential for identifying the comprehensive CO2 cycle of each city to develop effective urban CO2 reduction policies.

The Korea Meteorological Administration has employed the Asian Dust Aerosol Model 2 (ADAM2) to forecast Asian dust events since 2010, where the dust emission flux is proportional to the fourth power of the friction velocity. Currently, the dust emission reduction factor (RF) is determined by the normalized difference vegetation index (NDVI). This study aims to improve the forecasting capability of ADAM2 by developing a daily dust RF using both monthly (January 2007 to December 2016) and real-time moderate resolution imaging spectroradiometer (MODIS) NDVI data. We also developed a look-up table to transform the RF using NDVI and a system to update the RF by producing MODIS NDVI data for the last 30 days. Using these data, new RFs can be produced every day. To examine the impact of RF modification, the current (CTL) and new (EXP) RFs are compared during the period from March to May 2017. The simulations are verified by ground-based PM10 observations from China and Korea. Accordingly, root mean square errors (RMSEs) are reduced by 11.58% when RF is updated using real-time NDVI data. The results suggest that recent daily NDVI data contribute positively to the forecasting ability of ADAM2, in the dust source and downwind regions.

Abstract. To understand the Korean Peninsula's carbon dioxide (CO2) emissions and sinks as well as those of the surrounding region, we used 70 flask-air samples collected during May 2014 to August 2016 at Anmyeondo (AMY; 36.53∘ N, 126.32∘ E; 46 m a.s.l.) World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) station, located on the west coast of South Korea, for analysis of observed 14C in atmospheric CO2 as a tracer of fossil fuel CO2 contribution (Cff). Observed 14C ∕ C ratios in CO2 (reported as Δ values) at AMY varied from −59.5 ‰ to 23.1 ‰, with a measurement uncertainty of ±1.8 ‰. The derived mean value Cff of (9.7±7.8) µmol mol−1 (1σ) is greater than that found in earlier observations from Tae-Ahn Peninsula (TAP; 36.73∘ N, 126.13∘ E; 20 m a.s.l., 28 km away from AMY) of (4.4±5.7) µmol mol−1 from 2004 to 2010. The enhancement above background mole fractions of sulfur hexafluoride (Δx(SF6)) and carbon monoxide (Δx(CO)) correlate strongly with Cff (r>0.7) and appear to be good proxies for fossil fuel CO2 at regional and continental scales. Samples originating from the Asian continent had greater Δx(CO) : Cff(RCO) values, (29±8) to (36±2) nmol µmol−1, than in Korean Peninsula local air ((8±2) nmol µmol−1). Air masses originating in China showed (1.6±0.4) to (2.0±0.1) times greater RCO than a bottom-up inventory, suggesting that China's CO emissions are underestimated in the inventory, while observed RSF6 values are 2–3 times greater than inventories for both China and South Korea. However, RCO values derived from both inventories and observations have decreased relative to previous studies, indicating that combustion efficiency is increasing in both China and South Korea.

In order to estimate MODIS-equivalent aerosol optical thicknesses (AOTs) for dust particles during the nighttime over East Asia, an Artificial Neural Network (ANN) model approach was used to combine MODIS-measured infrared (IR) brightness temperatures (BTs) and visible (VIS) AOTs. For training the ANN model, IR BTs were used together with surface type and geometrical information as inputs to predict MODIS-derived AOTs as target data during the daytime when VIS-based AOTs are available. The training was done exclusively over dust-laid pixels during the spring (MarchMay) of 2006 over the East Asian domain (20It should be noted that the obtained daytime AOTs from the ANN model are in good agreement with MODIS-derived AOTs, with a correlation coefficient of 0.77 over the analysis domain. Although a weaker correlation is found during the nighttime when derived AOTs are compared against AOTs from CALIPSO, the case study indicates that the developed ANN method appears to effectively depict both the evolutionary features and intensity of the Asian dust plume during the nighttime. Results further indicate that nighttime VIS-like AOTs can be readily used for monitoring dust movement and its intensity during the nighttime, filling the gap between consecutive daytime AOT distributions.

A data assimilation (DA) system employing day-and nighttime aerosol optical thickness (AOT) was developed for the Asian Dust Aerosol Model 2 (ADAM2), using the optimal interpolation (OI) method. The DA system assimilated nighttime AOT for dust retrieved from MODIS infrared (IR) measurements with an artificial neural network (ANN) approach. An Asian dust case that occurred during 14−18 March 2009 was simulated using ADAM2.To examine the impact of the inclusion of nighttime AOT on forecasts of the data assimilation system, experiments were performed with different assimilation cycles (i.e., DA1: 24-hour cycle with daytime MODIS AOT only, DA2: 12-hour cycle with additional nighttime AOT). A control simulation was also performed without data assimilation (CTL). Forecasts were assessed using MODIS-derived AOT distributions as well as ground-based skyradiometer, PM 10 , and lidar observations. The model-estimated vertical distribution of the dust extinction coefficient was also compared with lidar measurements. Both experiments (DA1, DA2) were found to have improved forecasting, but DA2 outperformed DA1. Results suggest that the ANN-based nighttime AOT contributes more positively to the forecasting through better temporal coverage for data assimilation.(Citation: Lee, S.-S., E.-H. Lee, B.-J. Sohn, H. C. Lee, J. H. Cho, and S.-B. Ryoo, 2017: Improved dust forecast by assimilating MODIS IR-based nighttime AOT in the ADAM2 model. SOLA, 13,[192][193][194][195][196][197][198]

A cloud screening method employing two successive procedures of variability test and coarse mode test was developed, aiming at better elimination of cloud-contaminated data in the sky radiometer retrievals. The performance of the new cloud screening method was evaluated by examining statistical features with cloud coverage observations and lidar measurements. The variability test appeared to effectively eliminate data contaminated by relatively thick low-level clouds, whereas the coarse mode test appeared to eliminate data likely contaminated by thin cirrus-type clouds. Overall, the new method was considered to improve the current Sky Radiometer Network (SKYNET) data. The cloud screening method was then applied to dust detection from sky radiometer measurements. The detection performance was evaluated using surface synoptic observations (SYNOP) dust reports and the yellow sand index from NIES lidar measurements. It was shown that the new method helped to detect dust, effectively eliminating cloud-contaminated signals that were similar to those of the dust.