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A SmallSat mission concept is formulated here to carry out Time-varying Optical Measurements of Clouds and Aerosol Transport (TOMCAT) from space while embracing low-cost opportunities enabled by the revolution in Earth science observation technologies. TOMCAT’s “around-the-clock” measurements will provide needed insights and strong synergy with existing Earth observation satellites to 1) statistically resolve diurnal and vertical variation of cirrus cloud properties (key to Earth’s radiation budget), 2) determine the impacts of regional and seasonal planetary boundary layer (PBL) diurnal variation on surface air quality and low-level cloud distributions, and 3) characterize smoke and dust emission processes impacting their long-range transport on the subseasonal to seasonal time scales. Clouds, aerosol particles, and the PBL play critical roles in Earth’s climate system at multiple spatiotemporal scales. Yet their vertical variations as a function of local time are poorly measured from space. Active sensors for profiling the atmosphere typically utilize sun-synchronous low-Earth orbits (LEO) with rather limited temporal and spatial coverage, inhibiting the characterization of spatiotemporal variability. Pairing compact active lidar and passive multiangle remote sensing technologies from an inclined LEO platform enables measurements of the diurnal and vertical variability of aerosols, clouds, and aerosol-mixing-layer (or PBL) height in tropical-to-midlatitude regions where most of the world’s population resides. TOMCAT is conceived to bring potential societal benefits by delivering its data products in near–real time and offering on-demand hazard-monitoring capabilities to profile fire injection of smoke particles, the frontal lofting of dust particles, and the eruptive rise of volcanic plumes.

Nitrogen oxides (NOx≡ NO + NO2) are of central importance for air quality, climate forcing, and nitrogen deposition to ecosystems. The Geostationary Environment Monitoring Spectrometer (GEMS) is now providing hourly NO2 satellite observations over East Asia, offering the first direct measurements of NO2 diurnal variation from space to guide understanding of NOx emissions and chemistry. The NO2 retrieval requires independent vertical profile information from a chemical transport model (CTM) to compute the air mass factor (AMF) that relates the NO2 column measured along the line of sight to the NO2 vertical column. Here, we use aircraft observations from the Korea-United States Air Quality (KORUS-AQ) campaign over the Seoul metropolitan area (SMA) and around the Korean Peninsula in May–June 2016 to better understand the factors controlling the NO2 vertical profile, its diurnal variation, the implications for the AMFs, and the ability of the GEOS-Chem CTM to compute the NO2 vertical profiles used for AMFs. Proper representation of oxidant chemistry is critical for the CTM simulation of NO2 vertical profiles and is achieved in GEOS-Chem through new model developments, including aerosol nitrate photolysis, reduced uptake of hydroperoxy (HO2) radicals by aerosols, and accounting for atmospheric oxidation of volatile chemical products (VCPs). We find that the tropospheric NO2 columns measured from space in the SMA are mainly contributed by the planetary boundary layer (PBL) below 2 km altitude, reflecting the highly polluted conditions. Repeated measurements of NO2 vertical profiles over the SMA at different times of day show that diurnal change in mixing depth affecting the NO2 vertical profile induces a diurnal variation in AMFs of comparable magnitude to the diurnal variation in the NO2 column. GEOS-Chem captures this diurnal variation in AMFs and more generally the variability in the AMFs for the KORUS-AQ NO2 vertical profiles (2.7 % mean bias, 7.6 % precision), with some outliers in the morning due to errors in the timing of mixed-layer growth.

With the advent of spaceborne spectroradiometers in a geostationary constellation, measuring high spectral resolution ultraviolet–visible (UV-VIS) and selected near-/shortwave-infrared (NIR/SWIR) radiances can enable the probing of the life cycle of key atmospheric trace gases and aerosols at higher temporal resolutions over the globe. The UV-VIS measurements are important for retrieving several key trace gases (e.g., O3, SO2, NO2, and HCHO) and particularly for deriving aerosol characteristics (e.g., aerosol absorption and vertical profile). This study examines the merit of simultaneous retrievals of trace gases and aerosols using a ground-based spectroradiometer covering the UV-NIR to monitor their physicochemical processes and to obtain reliable aerosol information for various applications. During the 2019 pre-monsoon season over northern Thailand, we deployed a ground-based SMART–s (Spectral Measurements for Atmospheric Radiative Transfer–spectroradiometer) instrument, which is an extended-range Pandora with reliable radiometric calibration in the 330–820 nm range, to retrieve remotely sensed chemical and aerosol properties for the first time near biomass burning sources. The high spectral resolution (∼ 1.0 nm full width half maximum with ∼ 3.7 × oversampling) of sun and sky measurements from SMART–s provides several key trace gases (e.g., O3, NO2, and H2O) and aerosol properties covering the UV where significant light absorption occurs by the carbonaceous particles. During the measurement period, highly correlated total column amounts of NO2 and aerosol optical thickness (τaer) retrieved from SMART–s (correlation coefficient, R=0.74) indicated their common emissions from biomass burning events. The SMART–s retrievals of the spectral single scattering albedo (ω0) of smoke aerosols showed an abrupt decrease in the UV, which is an important parameter dictating photochemical processes in the atmosphere. The values of ω0 and column precipitable water vapor (H2O) gradually increase with the mixing of biomass burning smoke particles and higher water vapor concentrations when approaching the monsoon season. The retrieved ω0 and weighted mean radius of fine-mode aerosols from SMART–s showed positive correlations with the H2O (R=0.81 for ω0 at 330 nm and 0.56 for the volume-weighted mean radius), whereas the real part of the refractive index of fine-mode aerosol (nf) showed negative correlations (R=-0.61 at 330 nm), which suggest that aerosol aging processes including hygroscopic growth (e.g., humidification and cloud processing) can be a major factor affecting the temporal trends of aerosol optical properties. Retrieved nf and ω0 were closer to those of the water droplet (i.e., nf of about 1.33 and ω0 of about 1.0) under lower amounts of NO2 during the measurement period; considering that the NO2 amounts in the smoke may indicate the aging of the plume after emission due to its short lifetime, the tendency is also consistent with active hygroscopic processes of the aerosols over this area. Retrieved UV aerosol properties from SMART–s generally support the assumed smoke aerosol models (i.e., the spectral shape of aerosol absorption) used in NASA's current satellite algorithms, and their spectral ω0 retrievals from ground and satellites showed good agreements (R = 0.73–0.79). However, temporal and spectral variabilities in the aerosol absorption properties in the UV emphasize the importance of a realistic optical model of aerosols for further improvements in satellite retrievals.

Aerosol particles can be emitted, transported, removed, or transformed, leading to aerosol variability at scales impacting the climate (days to years and over hundreds of kilometers) or the air quality (hours to days and from meters to hundreds of kilometers). We present the temporal and spatial scales of changes in AOD (aerosol optical depth) and aerosol size (using Ångström exponent – AE; fine-mode fraction – FMF) over Korea during the 2016 KORUS-AQ (KORea–US Air Quality) atmospheric experiment. We use measurements and retrievals of aerosol optical properties from airborne instruments for remote sensing (4STAR; Spectrometers for Sky-Scanning Sun-Tracking Atmospheric Research) and in situ (LARGE; NASA Langley Aerosol Research Group Experiment) on board the NASA DC-8 and geostationary satellites (GOCI; Geostationary Ocean Color Imager; Yonsei aerosol retrieval – YAER, version 2) as well as from reanalysis (MERRA-2; Modern-Era Retrospective Analysis for Research and Applications, version 2). Measurements from 4STAR when flying below 1000 m show an average AOD at 501 nm of 0.36 and an average AE of 1.11 with large standard deviation (0.12 and 0.15 for AOD and AE, respectively), likely due to mixing of different aerosol types (fine and coarse mode). The majority of AOD due to fine-mode aerosol is observed at altitudes lower than 2 km. Even though there are large variations, for 18 out of the 20 flight days, the column AOD measurements by 4STAR along the NASA DC-8 flight trajectories match the South Korean regional average derived from GOCI. GOCI-derived FMF, which was found to be slightly low compared to AErosol RObotic NETwork (AERONET) sites (Choi et al., 2018), is lower than 4STAR's observations during KORUS-AQ. Understanding the variability of aerosols helps reduce uncertainties in the aerosol direct radiative effect by quantifying the errors due to interpolating between sparse aerosol observation sites or modeled pixels, potentially reducing uncertainties in the upcoming observational capabilities. We observed that, contrary to the prevalent understanding, AE and FMF are more spatially variable than AOD during KORUS-AQ, even when accounting for potential sampling biases by using Monte Carlo resampling. Averaging between measurements and models for the entire KORUS-AQ period, the reduction in correlation by 15 % is 65.0 km for AOD and shorter at 22.7 km for AE. While there are observational and model differences, the predominant factor influencing spatial–temporal homogeneity is the meteorological period. High spatiotemporal variability occurs during the dynamic period (25–31 May), and low spatiotemporal variability occurs during the blocking pattern (1–7 June). While AOD and FMF / AE are interrelated, the spatial variability and relative variability of these parameters in this study indicate that microphysical processes vary at scales shorter than aerosol concentration processes at which microphysical processes such as aerosol particle formation, growth, and coagulation mostly impact the dominant aerosol size (characterized by, e.g., FMF / AE) and to some degree AOD. In addition to impacting aerosol size, aerosol concentration processes such as aerosol emission, transport, and removal mostly impact the AOD.

Despite the importance of aerosol height information for events such as volcanic eruptions and long-range aerosol transport, spatial coverage of its retrieval is often limited because of a lack of appropriate instruments and algorithms. Geostationary satellite observations in particular provide constant monitoring for such events. This study assessed the application of different viewing geometries for a pair of geostationary imagers to retrieve aerosol top height (ATH) information. The stereoscopic algorithm converts a lofted aerosol layer parallax, calculated using image-matching of two visible images, to ATH. The sensitivity study provides a reliable result using a pair of Advanced Himawari Imager (AHI) and Advanced Geostationary Radiation Imager (AGRI) images at 40∘ longitudinal separation. The pair resolved aerosol layers above 1 km altitude over East Asia. In contrast, aerosol layers must be above 3 km for a pair of AHI and Advanced Meteorological Imager (AMI) images at 12.5∘ longitudinal separation to resolve their parallax. Case studies indicate that the stereoscopic ATH retrieval results are consistent with aerosol heights determined using extinction profiles from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP). Comparisons between the stereoscopic ATH and the CALIOP 90 % extinction height, defined by extinction coefficient at 532 nm data, indicated that 88.9 % of ATH estimates from the AHI and AGRI are within 2 km of CALIOP 90 % extinction heights, with a root-mean-squared difference (RMSD) of 1.66 km. Meanwhile, 24.4 % of ATH information from the AHI and AMI was within 2 km of the CALIOP 90 % extinction height, with an RMSD of 4.98 km. The ability of the stereoscopic algorithm to monitor hourly aerosol height variations is demonstrated by comparison with a Korea Aerosol Lidar Observation Network dataset.

Concentrations of ambient particulate matter (such as PM2.5 and PM10) have come to represent a serious environmental problem worldwide, causing many deaths and economic losses. Because of the detrimental effects of PM2.5 on human health, many countries and international organizations have developed and operated regional and global short-term PM2.5 prediction systems. The short-term predictability of PM2.5 (and PM10) is determined by two main factors: the performance of the air quality model and the accuracy of the initial states. While specifically focusing on the latter factor, this study attempts to demonstrate how information from ‘classical’ ground observation networks, a ‘state-of-the-art’ geostationary (GEO) satellite sensor, and an advanced air quality modeling system can be synergistically combined to improve short-term PM2.5 predictability over South Korea. Such a synergistic combination of information can effectively overcome the major obstacle of scarcity of information, which frequently occurs in PM2.5 prediction systems using low Earth orbit (LEO) satellite-borne observations. This study first presents that the scarcity of information is mainly associated with cloud masking, sun-glint effect, and ill-location of satellite-borne data, and it then demonstrates that an advanced air quality modeling system equipped with synergistically-combined information can achieve substantially improved performances, producing enhancements of approximately 10%, 17%, 49%, and 19% in the predictability of PM2.5 over South Korea in terms of IOA (index of agreement), R (correlation coefficient), MB (mean biases), and HR (hit rate), respectively, compared to PM2.5 prediction systems using only LEO satellite-derived observations.

We use 2011–2019 aerosol optical depth (AOD) observations from the Geostationary Ocean Color Imager (GOCI) instrument over East Asia to infer 24 h daily surface fine particulate matter (PM2.5) concentrations at a continuous 6 × 6 km2 resolution over eastern China, South Korea, and Japan. This is done with a random forest (RF) algorithm applied to the gap-filled GOCI AODs and other data, including information encoded in GOCI AOD retrieval failure and trained with PM2.5 observations from the three national networks. The predicted 24 h GOCI PM2.5 concentrations for sites entirely withheld from training in a 10-fold cross-validation procedure correlate highly with network observations (R2 = 0.89) with a single-value precision of 26 %–32 %, depending on the country. Prediction of the annual mean values has R2 = 0.96 and a single-value precision of 12 %. GOCI PM2.5 is only moderately successful for diagnosing local exceedances of the National Ambient Air Quality Standard (NAAQS) because these exceedances are typically within the single-value precisions of the RF and also because of RF smoothing of extreme PM2.5 concentrations. The area-weighted and population-weighted trends of GOCI PM2.5 concentrations for eastern China, South Korea, and Japan show steady 2015–2019 declines consistent with surface networks, but the surface networks in eastern China and South Korea underestimate population exposure. Further examination of GOCI PM2.5 fields for South Korea identifies hot spots where surface network sites were initially lacking and shows 2015–2019 PM2.5 decreases across the country, except for flat concentrations in the Seoul metropolitan area. Inspection of the monthly PM2.5 time series in Beijing, Seoul, and Tokyo shows that the RF algorithm successfully captures observed seasonal variations in PM2.5, even though AOD and PM2.5 often have opposite seasonalities. The application of the RF algorithm to urban pollution episodes in Seoul and Beijing demonstrates high skill in reproducing the observed day-to-day variations in air quality and spatial patterns on the 6 km scale. A comparison to a Community Multiscale Air Quality (CMAQ) simulation for the Korean peninsula demonstrates the value of the continuous GOCI PM2.5 fields for testing air quality models, including over North Korea, where they offer a unique resource.

This paper is an overview of the progress in sky radiometer technology and the development of the network called SKYNET. It is found that the technology has produced useful on-site calibration methods, retrieval algorithms, and data analyses from sky radiometer observations of aerosol, cloud, water vapor, and ozone. A formula was proposed for estimating the accuracy of the sky radiometer calibration constant F0 using the improved Langley (IL) method, which was found to be a good approximation to observed monthly mean uncertainty in F0, around 0.5 % to 2.4 % at the Tokyo and Rome sites and smaller values of around 0.3 % to 0.5 % at the mountain sites at Mt. Saraswati and Davos. A new cross IL (XIL) method was also developed to correct an underestimation by the IL method in cases with large aerosol retrieval errors. The root-mean-square difference (RMSD) in aerosol optical thickness (AOT) comparisons with other networks took values of less than 0.02 for λ≥500 nm and a larger value of about 0.03 for shorter wavelengths in city areas and smaller values of less than 0.01 in mountain comparisons. Accuracies of single-scattering albedo (SSA) and size distribution retrievals are affected by the propagation of errors in measurement, calibrations for direct solar and diffuse sky radiation, ground albedo, cloud screening, and the version of the analysis software called the Skyrad pack. SSA values from SKYNET were up to 0.07 larger than those from AERONET, and the major error sources were identified as an underestimation of solid viewing angle (SVA) and cloud contamination. Correction of these known error factors reduced the SSA difference to less than 0.03. Retrievals of other atmospheric constituents by the sky radiometer were also reviewed. Retrieval accuracies were found to be about 0.2 cm for precipitable water vapor amount and 13 DU (Dobson Unit) for column ozone amount. Retrieved cloud optical properties still showed large deviations from validation data, suggesting a need to study the causes of the differences. It is important that these recent studies on improvements presented in the present paper are introduced into the existing operational systems and future systems of the International SKYNET Data Center.

We estimated the Korea Shortwave Radiation Budget (KSRB) providing new insights into the Korea climate system. Monthly averaged clear-sky, overcast-sky, and all-sky flux measurements with cloud amounts from 2000 to 2015 were used to assess the impacts of cloud on the KSRB. A deterministic model for shortwave radiation transfer was utilized with climatological data to compute the monthly mean of KSRB at the surface, in the atmosphere and at the top-of-atmosphere (TOA). For 10 Korean stations ranging from 33.5°N to 37.7°N, the daily mean of incoming solar flux at TOA is 345.2 Wm−2. 45.5% of that is transmitted to the surface, 25.0% is absorbed in the atmosphere and 29.5% is reflected directly back to space by the cloud, atmosphere and ground surface under all-sky conditions. Under clear- and overcast-sky conditions, 63.2% and 24.9% are transmitted to the surface, 17.8% and 37.1% are absorbed in the atmosphere, whereas 19.0% and 38.0% are reflected to space, respectively, showing a remarkably invariant value with respect to locations. Clouds in all- (or overcast-) sky atmosphere diminish surface solar irradiances (SSI) from 218.1 Wm−2 to 156.9 Wm−2 (or 85.8 Wm−2) and enhance atmospheric absorptions (AA) from 61.5 Wm−2 to 86.3 Wm−2 (or 128.2 Wm−2). Clouds also enhance the reflected irradiances (RI) at the TOA from 65.6 Wm−2 to 102.0 Wm−2 (or 131.2 Wm−2) for all- (or overcast-) skies. As a result, the all- (or overcast-) sky shortwave (SW) cloud forcing (CF) is −61.2 Wm−2 (or −132.3 Wm−2) at the surface, AA is 24.8 Wm−2 (or 66.7 Wm−2) in the atmosphere and RI is 36.4 Wm−2 (or 65.6 Wm−2) at the TOA, respectively. Consequently, it is found that the values for SW-CF at the surface is 1.7 times and 2.0 times greater than that at the TOA in the KSRB under all- and overcast-sky conditions, respectively. We have also compared the KSRB with the global shortwave radiation budget (GSRB) and discussed on the shortwave radiation budget.

The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and temporal resolution from a geostationary Earth orbit (GEO) for the first time. With the development of UV–visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO, and aerosols) can be obtained. To date, all the UV–visible satellite missions monitoring air quality have been in low Earth orbit (LEO), allowing one to two observations per day. With UV–visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be on board the Geostationary Korea Multi-Purpose Satellite 2 (GEO-KOMPSAT-2) satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager 2 (GOCI-2). These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) and ESA’s Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS).

We use Aerosol Robotic Network (AERONET) observation data to empirically determine how natural and anthropogenic aerosol categories (i.e. mineral dust, biomass burning, and urban-industrial aerosols) affect light extinction, showing that their radiative forcing varies strongly with the surface albedo. Generally, the radiative forcing depends on the aerosol loading, but the efficiency varies with the aerosol type and aerosol-radiation-surface interactions. Desert dust, biomass burning and urban-industrial aerosols can exhibit dramatic shifts in radiative forcing at the top of the atmosphere, from cooling to warming, at surface albedos from below 0.5 to above 0.75. Based on the linear relationship between the radiative forcing efficiency and surface albedo for aeolian aerosols, using Moderate Resolution Imaging Spectroradiometer (MODIS) AOT (Aerosol Optical Thickness) and surface albedo data, we characterized a large Asian dust event during the spring of 2001, and demonstrate its immense spatially varying radiative forcing, ranging from about −84.0 to +69.3 W/m2. For extensive Russian wildfires during the summer of 2010, strong radiative cooling forcing variability of biomass combustion aerosols is found, ranging from about −86.3 to +3.1 W/m2. For a thick urban-industrial aerosol haze over northern India during the winter of 2017, a large range of about −85.0 to −0.3 W/m2 is found. These wide ranges underscore the need to accurately define aerosol-radiation-surface interactions.

Retrievals of total column NO2 (TCNO2) are compared for 14 sites from the Ozone Measuring Instrument (OMI using OMNO2-NASA v3.1) on the AURA satellite and from multiple ground-based PANDORA spectrometer instruments making direct-sun measurements. While OMI accurately provides the daily global distribution of retrieved TCNO2, OMI almost always underestimates the local amount of TCNO2 by 50 % to 100 % in polluted areas, while occasionally the daily OMI value exceeds that measured by PANDORA at very clean sites. Compared to local ground-based or aircraft measurements, OMI cannot resolve spatially variable TCNO2 pollution within a city or urban areas, which makes it less suitable for air quality assessments related to human health. In addition to systematic underestimates in polluted areas, OMI's selected 13:30 Equator crossing time polar orbit causes it to miss the frequently much higher values of TCNO2 that occur before or after the OMI overpass time. Six discussed Northern Hemisphere PANDORA sites have multi-year data records (Busan, Seoul, Washington DC, Waterflow, New Mexico, Boulder, Colorado, and Mauna Loa), and one site in the Southern Hemisphere (Buenos Aires, Argentina). The first four of these sites and Buenos Aires frequently have high TCNO2 (TCNO2 > 0.5 DU). Eight additional sites have shorter-term data records in the US and South Korea. One of these is a 1-year data record from a highly polluted site at City College in New York City with pollution levels comparable to Seoul, South Korea. OMI-estimated air mass factor, surface reflectivity, and the OMI 24 km × 13 km FOV (field of view) are three factors that can cause OMI to underestimate TCNO2. Because of the local inhomogeneity of NOx emissions, the large OMI FOV is the most likely factor for consistent underestimates when comparing OMI TCNO2 to retrievals from the small PANDORA effective FOV (measured in m2) calculated from the solar diameter of 0.5∘.

The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled to be launched in 2019–2020 on board the GEO-KOMPSAT (GEOstationary KOrea Multi-Purpose SATellite)-2B, contributing as the Asian partner of the global geostationary constellation of air quality monitoring. To support this air quality satellite mission, we perform a cross-evaluation of simulated GEMS ozone profile retrievals from OMI (Ozone Monitoring Instrument) data based on the optimal estimation and ozonesonde measurements within the GEMS domain, covering from 5∘ S (Indonesia) to 45∘ N (south of the Russian border) and from 75 to 145∘ E. The comparison between ozonesonde and GEMS shows a significant dependence on ozonesonde types. Ozonesonde data measured by modified Brewer–Mast (MBM) at Trivandrum and New Delhi show inconsistent seasonal variabilities in tropospheric ozone compared to carbon–iodine (CI) and electrochemical condensation cell (ECC) ozonesondes at other stations in a similar latitude regime. CI ozonesonde measurements are negatively biased relative to ECC measurements by 2–4 DU; better agreement is achieved when simulated GEMS ozone retrievals are compared to ECC measurements. ECC ozone data at Hanoi, Kuala Lumpur, and Singapore show abnormally worse agreements with simulated GEMS retrievals than other ECC measurements. Therefore, ECC ozonesonde measurements at Hong Kong, Pohang, Naha, Sapporo, and Tsukuba are finally identified as an optimal reference dataset. The accuracy of simulated GEMS retrievals is estimated to be ∼5.0 % for both tropospheric and stratospheric column ozone with the precision of 15 % and 5 %, which meets the GEMS ozone requirements.

To quantify the impact of the direct aerosol effect accurately, this study incorporated the Geostationary Ocean Color Imager (GOCI) aerosol optical depth (AOD) into a coupled meteorology-chemistry model. We designed three model simulations to observe the impact of AOD assimilation and aerosol feedback during the KORUS-AQ campaign (May – June 2016). By assimilating the GOCI AOD with high temporal and spatial resolutions, we improve the statistics from the comparison AOD and AERONET data (RMSE: 0.12, R: 0.77, IOA: 0.69, MAE: 0.08). The inclusion of the direct effect of aerosols produces the best model performance (RMSE: 0.10, R: 0.86, IOA: 0.72, MAE: 0.07). AOD values were increased as much as 0.15, which is associated with an average reduction in solar radiation of −31.39 W/m(2), a planetary boundary layer height (−104.70 m), an air temperature (−0.58 °C), and a surface wind speed (−0.07 m/s) over land. In addition, concentrations of major gaseous and particulate pollutants at the surface (SO(2), NO(2), NH(3), [Formula: see text] , [Formula: see text] , [Formula: see text] , PM(2.5)) increase by 7.87 – 34% while OH concentration decreases by −4.58 %. Changes in meteorology and air quality appear to be more significant in high-aerosol loading areas. The integrated process rate analysis shows decelerated vertical transport, resulting in an accumulation of air pollutants near the surface and the amount of nitrate, which is higher than that of sulfate because of its response to reduced temperature. We conclude that constraining aerosol concentrations using geostationary satellite data is a prerequisite for quantifying the impact of aerosols on meteorology and air quality.

We investigated the influence of long-range transboundary transport (LRTT) on the aerosol concentrations in the Korean peninsula using the ground- and satellite-based remote sensing with back-trajectory calculations during the Korea-United States Air Quality (KORUS-AQ) campaign. Specifically, aerosol optical depth (AOD) observations from a geostationary satellite can directly provide the progression and evolution of aerosol plume transport. During high pollution cases in western Korea, we found the AOD enhancement over the Yellow Sea and east-central China, at maximum >200% over the pathway of LRTT compared to the mean condition. Particularly, high AOD in the Shandong peninsula appears coincidentally with the high AOD over South Korea in a day, revealing the strong influence of the east-central Chinese emission. Back-trajectory patterns remarkably capture the movement of high AOD bands detected by the geostationary satellite monitoring. LRTT cases through the inside of boundary layer at east-central China usually contribute to the high AOD in Korea, while air-masses above the boundary layer in north China and Mongolia do not much relate to the Korean pollution, showing the importance for both the direction and height of the air-mass movement. Travel speed is another significant factor to describe the LRTT effect. Despite the large effect of LRTT to both urban and rural sites in Korea, sometimes urban sites are more affected by the domestic emission when the air-mass travels shorter than ∼250 km per day, notifying that the effect of Korean domestic emission cannot be negligible as well. Our findings reveal that usage of geostationary satellite observations enables us to better evaluate the influence of LRTT on the local air pollution.

This study newly applies measurements from two geostationary satellites, the Advanced Himawari Imager (AHI) onboard the geostationary satellite Himawari-8 and the Geostationary Ocean Color imager (GOCI) onboard the geostationary satellite COMS, to evaluate a unique regional aerosol-transport model coupled to a non-hydrostatic icosahedral atmospheric model (NICAM) at a high resolution without any nesting technique and boundary conditions of the aerosols. Taking advantage of the unique capability of these geostationary satellites to measure aerosols with unprecedentedly high temporal resolution, we focus on a target area (115°E-155°E, 20°N-50°N) in East Asia in May 2016, which featured the periodic transport of industrial aerosols and a very heavy aerosol plume from Siberian wildfires. The aerosol optical thickness (AOT) fields are compared among the AHI, GOCI, MODIS, AERONET and NICAM data. The results show that both AHI- and GOCI-retrieved AOTs were generally comparable to the AERONET-retrieved ones, with high correlation coefficients of approximately 0.7 in May 2016. They also show that NICAM successfully captured the detailed horizontal distribution of AOT transported from Siberia to Japan on the most polluted day (18 May 2016). The monthly statistical metrics, including correlation between the model and either AHI or GOCI, are estimated to be >0.4 in 42–49% of the target area. With the aid of sensitivity model experiments with and without Siberian wildfires, it was found that a long-range transport of aerosols from Siberian wildfires (from as far as 3000 km) to Japan influenced the monthly mean aerosol levels, accounting for 7–35% of the AOT, 26–49% of the surface PM2.5 concentrations, and 25–66% of the aerosol extinction above 3 km in height over Japan. Therefore, the air pollutants from Siberian wildfire cannot be ignored for the spring over Japan.

Long-term exposure to particulate matter (PM) with aerodynamic diameters 10) and 2.5 µm (PM2.5) has negative effects on human health. Although station-based PM monitoring has been conducted around the world, it is still challenging to provide spatially continuous PM information for vast areas at high spatial resolution. Satellite-derived aerosol information such as aerosol optical depth (AOD) has been frequently used to investigate ground-level PM concentrations. In this study, we combined multiple satellite-derived products including AOD with model-based meteorological parameters (i.e., dew-point temperature, wind speed, surface pressure, planetary boundary layer height, and relative humidity) and emission parameters (i.e., NO, NH3, SO2, primary organic aerosol (POA), and HCHO) to estimate surface PM concentrations over South Korea. Random forest (RF) machine learning was used to estimate both PM10 and PM2.5 concentrations with a total of 32 parameters for 2015–2016. The results show that the RF-based models produced good performance resulting in R2 values of 0.78 and 0.73 and root mean square errors (RMSEs) of 17.08 and 8.25 µg m−3 for PM10 and PM2.5, respectively. In particular, the proposed models successfully estimated high PM concentrations. AOD was identified as the most significant for estimating ground-level PM concentrations, followed by wind speed, solar radiation, and dew-point temperature. The use of aerosol information derived from a geostationary satellite sensor (i.e., Geostationary Ocean Color Imager, GOCI) resulted in slightly higher accuracy for estimating PM concentrations than that from a polar-orbiting sensor system (i.e., the Moderate Resolution Imaging Spectroradiometer, MODIS). The proposed RF models yielded better performance than the process-based approaches, particularly in improving on the underestimation of the process-based models (i.e., GEOS-Chem and the Community Multiscale Air Quality Modeling System, CMAQ).

The Korea–United States Air Quality (KORUS-AQ) field study was conducted during May–June 2016. The effort was jointly sponsored by the National Institute of Environmental Research of South Korea and the National Aeronautics and Space Administration of the United States. KORUS-AQ offered an unprecedented, multi-perspective view of air quality conditions in South Korea by employing observations from three aircraft, an extensive ground-based network, and three ships along with an array of air quality forecast models. Information gathered during the study is contributing to an improved understanding of the factors controlling air quality in South Korea. The study also provided a valuable test bed for future air quality–observing strategies involving geostationary satellite instruments being launched by both countries to examine air quality throughout the day over Asia and North America. This article presents details on the KORUS-AQ observational assets, study execution, data products, and air quality conditions observed during the study. High-level findings from companion papers in this special issue are also summarized and discussed in relation to the factors controlling fine particle and ozone pollution, current emissions and source apportionment, and expectations for the role of satellite observations in the future. Resulting policy recommendations and advice regarding plans going forward are summarized. These results provide an important update to early feedback previously provided in a Rapid Science Synthesis Report produced for South Korean policy makers in 2017 and form the basis for the Final Science Synthesis Report delivered in 2020.

We investigated the characteristics of the total ozone column (TOC) around West Antarctica (near the Weddell Sea) compared with ambient meteorological factors. For this analysis, we used ground-based and satellite TOC measurements as well as meteorology (air temperature, potential vorticity and wind field) from reanalysis data. Long-term patterns of TOC show the large year-to-year variation (e.g., maximumly ∼200 DU at King Sejong) but a steady recovering trend recently. Despite a generally consistent pattern, the TOC around West Antarctica did not correlate well between high- and low-latitude regions during austral spring; this result implies that the ozone hole area had a spatial variation over West Antarctica. The TOC pattern around West Antarctica correlated well with air temperature but showed a vertical difference; high positive correlations appeared in the lower stratosphere (maximumly R > 0.9 at ∼50–100 hPa height) showing enhanced ozone depletion in colder conditions, but negative correlations appeared in the upper stratosphere (minimum R 0.9 at ∼500–600 K height) during the austral spring but a moderately negative correlation in the lower stratosphere (minimum R

This study examines the semi-empirical models to evaluate the atmospheric broadband transmission of erythemally weighted ultraviolet (EUV), total ultraviolet (TUV), and global solar radiation (GS) by clouds, aerosols, and ozone in Seoul, Korea (37.57°N, 128.98°E). Climatological values of surface solar irradiance (SSI) for Seoul are briefly summarized, and atmospheric transmission is defined as the ratio of measured SSI to clear-sky irradiance calculated using harmonic analysis. Three multiple linear regression models are developed for the EUV, TUV, and GS spectral bands for all-sky (clear and cloudy conditions) transmission using three independent variables: cloud-cover amount, aerosol optical depth, and total ozone. The modeled total transmissions are 75%, 71%, and 67% for the EUV, TUV, and GS spectral bands, respectively, which are slightly overestimated than the measured values. The modeled annual mean clearness index, KT, is 1%, 45%, and 48% for the three spectral bands, respectively. Four semi-empirical models were developed to calculate transmission and were evaluated. Among the various empirical models, estimates from the exponential models were found to be the closest to measured values, having the lowest mean bias error (−0.3% to −2.4%) and highest explained variance (R2 = 0.17 to 0.50; p = 0.000), as was expected from their theoretical bases. The annual average transmissions of cloud, aerosol, ozone, and all three combined show decrease from the EUV to the TUV and GS bands.

Spatial distribution of diurnal variations of aerosol properties in South Korea, both long term and short term, is studied by using 9 AERONET (AErosol RObotic NETwork) sites from 1999 to 2017 and an additional 10 sites during the KORUS-AQ (Korea–United States Air Quality) field campaign in May and June of 2016. The extent to which the WRF-Chem (Weather Research and Forecasting coupled with Chemistry) model and the GOCI (Geostationary Ocean Color Imager) satellite retrieval can describe these variations is also analyzed. On a daily average, aerosol optical depth (AOD) at 550 nm is 0.386 and shows a diurnal variation of 20 to −30 % in inland sites, which is larger than the AOD of 0.308 and diurnal variation of ±20 % seen in coastal sites. For all the inland and coastal sites, AERONET, GOCI, and WRF-Chem, and observed PM2.5 (particulate matter with aerodynamic diameter less than 2.5 µm) data generally show dual peaks for both AOD and PM2.5, one in the morning (often at ∼08:00–10:00 KST, Korea Standard Time, especially for PM2.5) and another in the early afternoon (∼14:00 KST, albeit for PM2.5 this peak is smaller and sometimes insignificant). In contrast, Ångström exponent values in all sites are between 1.2 and 1.4 with the exception of the inland rural sites having smaller values near 1.0 during the early morning hours. All inland sites experience a pronounced increase in the Ångström exponent from morning to evening, reflecting an overall decrease in particle size in daytime. To statistically obtain the climatology of diurnal variation of AOD, a minimum requirement of ∼2 years of observation is needed in coastal rural sites, twice as long as that required for the urban sites, which suggests that the diurnal variation of AOD in an urban setting is more distinct and persistent. While Korean GOCI satellite retrievals are able to consistently capture the diurnal variation of AOD (although it has a systematically low bias of 0.04 on average and up to 0.09 in later afternoon hours), WRF-Chem clearly has a deficiency in describing the relative change of peaks and variations between the morning and afternoon, suggesting further studies for the diurnal profile of emissions. Furthermore, the ratio between PM2.5 and AOD in WRF-Chem is persistently larger than the observed counterparts by 30 %–50 % in different sites, but spatially no consistent diurnal variation pattern of this ratio can be found. Overall, the relatively small diurnal variation of PM2.5 is in high contrast with large AOD diurnal variation, which suggests the large diurnal variation of AOD–PM2.5 relationships (with the PM2.5 ∕ AOD ratio being largest in the early morning, decreasing around noon, and increasing in late afternoon) and, therefore, the need to use AOD from geostationary satellites to constrain either modeling or estimate of surface PM2.5 for air quality application.

Formaldehyde (HCHO) is either directly emitted from sources or produced during the oxidation of volatile organic compounds (VOCs) in the troposphere. It is possible to infer atmospheric HCHO concentrations using space-based observations, which may be useful for studying emissions and tropospheric chemistry at urban to global scales depending on the quality of the retrievals. In the near future, an unprecedented volume of satellite-based HCHO measurement data will be available from both geostationary and polar-orbiting platforms. Therefore, it is essential to develop retrieval methods appropriate for the next-generation satellites that measure at higher spatial and temporal resolution than the current ones. In this study, we examine the importance of fine spatial and temporal resolution a priori profile information on the retrieval by conducting approximately 45 000 radiative transfer (RT) model calculations in the Los Angeles Basin (LA Basin) megacity. Our analyses suggest that an air mass factor (AMF, a factor converting observed slant columns to vertical columns) based on fine spatial and temporal resolution a priori profiles can better capture the spatial distributions of the enhanced HCHO plumes in an urban area than the nearly constant AMFs used for current operational products by increasing the columns by ∼ 50 % in the domain average and up to 100 % at a finer scale. For this urban area, the AMF values are inversely proportional to the magnitude of the HCHO mixing ratios in the boundary layer. Using our optimized model HCHO results in the Los Angeles Basin that mimic the HCHO retrievals from future geostationary satellites, we illustrate the effectiveness of HCHO data from geostationary measurements for understanding and predicting tropospheric ozone and its precursors.

In this study, Geostationary Ocean Color Imager (GOCI) AOD and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD data were assimilated to forecast surface PM2.5 concentrations over Eastern China, by using the three–dimensional variational (3DAVR) data assimilation (DA) system, to compare DA impacts by assimilating AOD retrievals from these two types of satellites. Three experiments were conducted, including a CONTROL without the AOD assimilation, and GOCIDA and VIIRSDA with the assimilation of AOD retrievals from GOCI and VIIRS, respectively. By utilizing the Weather Research and Forecasting with Chemistry (WRF/Chem) model, 48-h forecasts were initialized at each 06 UTC from 19 November to 06 December 2013. These forecasts were evaluated with 248 ground-based measurements from the air quality monitoring network across 67 China cities. The results show that overall the CONTROL underestimated surface PM2.5 concentrations, especially over Jing–Jin–Ji (JJJ) region and Yangtze River Delta (YRD) region. Both the GOCIDA and VIIRSDA produced higher surface PM2.5 concentrations mainly over Eastern China, which fits well with the PM2.5 measurements at these eastern sites, with more than 8% error reductions (ER). Moreover, compared to CONTROL, GOCIDA reduced 14.0% and 6.4% error on JJJ region and YRD region, respectively, while VIIRSDA reduced respectively 2.0% and 13.4% error over the corresponding areas. During the heavy polluted period, VIIRSDA improved all sites within YRD region, and GOCIDA enhanced 84% sites. Meanwhile, GOCIDA improved 84% sites on JJJ region, while VIIRSDA did not affect that region. These geographic distinctions might result from spatial dissimilarity between GOCI AOD and VIIRS AOD at time intervals. Moreover, the larger increment produced by AOD DA under stable meteorological conditions could lead to a longer duration (e.g., 1–2 days, > 2 days) of AOD DA impacts. Even though with AOD DA, surface PM2.5 concentrations were still underestimated clearly over heavy polluted periods. And 3% sites performed worse, where low PM2.5 values were observed and CONTROL performed well. With this study, the results indicate that AOD DA can partially improve the accuracy of PM2.5 forecasts. And the obvious geographic differences on forecasts emphasize the potential and importance of combining AOD retrievals from GOCI and VIIRS into data assimilation.

Clouds are an important component of the atmosphere that affects both climate and weather, however, their contributions can be very difficult to determine. Ceilometer measurements can provide high resolution information on atmospheric conditions such as cloud base height (CBH) and vertical frequency of cloud occurrence (CVF). This study presents the first comprehensive analysis of CBH and CVF derived using Vaisala CL51 ceilometers at two urban stations in Seoul, Korea, during a three-year period from January 2014 to December 2016. The average frequency of cloud occurrence detected by the ceilometers is 54.3%. It is found that the CL51 is better able to capture CBH as compared to another ceilometer CL31 at a nearby meteorological station because it could detect high clouds more accurately. Frequency distributions for CBH up to 13,000 m providing detailed vertical features with 500-m interval show 55% of CBHs below 2 km for aggregated CBHs. A bimodal frequency distribution was observed for three-layers CBHs. A monthly variation of CVF reveals that frequency concentration of lower clouds is found in summer and winter, and higher clouds more often detected in spring and autumn. Monthly distribution features of cloud occurrence and precipitation are depending on seasons and it might be easy to define their relationship due to higher degree of variability of precipitation than cloud occurrence. However, a fluctuation of cloud occurrence frequency in summer is similar to precipitation in trend, whereas clouds in winter are relatively frequent but precipitation is not accompanied. In addition, recent decrease of summer precipitation could be mostly explained by a decrease of cloud occurrence. Anomalous precipitation recorded sometimes is considerably related to corresponding cloud occurrence. The diurnal and daily variations of CBH and CVF from ceilometer observations and the analysis of microwave radiometer measurements for two typical cloudiness cases are also reviewed in parallel. This analysis in finer temporal scale exhibits that utilization of ground-based observations together could help to analyze the cloud behaviors.

In this study, we present and compare regional correlations between CO total column density (TCD CO ) from the data set of Measurement of Pollution in the Troposphere (MOPITT), and high-absorbing BC dominant aerosol optical depth (AOD BC ) from the retrieval algorithm using Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI) (MODIS-OMI algorithm, MOA). TCD CO shows positive relationship to both fine-mode AOD (AOD FM ) and AOD BC in general, but TCD CO better correlates with AOD BC than AOD FM . This enhanced correlation between TCD CO and AOD BC appears more clearly during spring and summer. Correlation between TCD CO and AOD BC is exceptionally poor in Northern Africa where the BC-dominated aerosols are frequently mixed with mineral dust particles from the Sahara. Another issue is also found in Southern Africa; the correlation between AOD BC and TCD CO in this region is not much higher than that between the AOD FM and TCD CO in spite of large occurrence of biomass burning and wildfire. This can be explained by the cloud perturbation near the source regions and dispersion effect due to the typical wind pattern. Correlations between AOD BC and TCD CO increase further when fire detected areas are only considered, but does not change much over the urban area. This difference clarifies the large contribution of burning events to the positive relationship between BC and CO. All findings in this study demonstrate a possible use of satellite CO product in evaluating the BC-dominated aerosol product from satellite remote sensing over the globe.

Korea will launch the Geostationary Environment Monitoring Spectrometer (GEMS) instrument in 2018 onboard the Geostationary Korean Multi-Purpose Satellite to monitor tropospheric gas concentrations with high temporal and spatial resolutions. The purpose of this study is to examine the performance of total column ozone (TCO) measurements from ground-based Pandora and Brewer instruments that will be used for validation of the GEMS ozone product. Satellite measurements can be used to detect erroneous outliers at a particular ground station, which deviate significantly from co-located satellite measurements relative to other stations. This is possible because a single satellite retrieval algorithm is used to process the entire satellite dataset, and instrument characteristics typically change slowly over the life of the satellite. Thus, the short-term stability (months) of satellite measurements can be used to estimate the performance of the ground-based measurement network as well as to identify potential problems at individual stations. As a reference for satellite ozone measurements, we have selected TCO data derived from OMI-TOMS V8.5 algorithm, because it is a robust algorithm that has been well studied to identify its various error sources. We validated ground-based Brewer and Pandora TCO measurements using OMI-TOMS TCO data collected over South Korea from March 2012 to December 2014. The Brewer TCO measurements at Pohang showed significant deviation from overall seasonal variation during the study period. In addition, in the presence of clouds, Pandora TCO measurements are unusually ∼7% higher than OMI-TOMS TCO data. To filter out these cloud-contaminated data, we applied a Kalman filter to the Pandora measurements. The diurnal variation in the Kalman-filtered Pandora data agrees well with the Brewer data, and the correlation of Kalman-filtered Pandora data with OMI-TOMS TCO is significantly improved from 0.89 to 0.99 at Seoul and from 0.93 to 0.99 at Busan.

The sensitivities of oxygen dimer (O4) slant column densities (SCDs) were examined by applying temperature-dependent O4 cross sections using the radiative transfer model (RTM) calculation with the linearized pseudo-spherical vector discrete ordinate radiative transfer model. For the sensitivity study, we used a newly developed cross section database in place of the database used in the operational algorithm. Newly investigated O4 cross section databases for 203 K and 293 K were used for the radiance simulation by interpolating temperature for each atmospheric layer based on the vertical profile of standard atmosphere in the RTM. The effect of the temperature-dependent cross sections was a significant O4 SCD increase of 8.3% with dependence on satellite and solar viewing geometries. Furthermore, the O4 SCD generally increased by an estimated 3.9% based on the observation geometries of the Ozone Monitoring Instrument. For the long-term comparison, the O4 SCD estimated from the temperature-dependent cross sections corrects 20% of the total underestimation of O4 SCD between the observation and simulation. Although the surface pressure variation and background aerosol effect also correct the O4 SCD discrepancy, the effect of temperature-dependent cross sections was more important than the effects of surface pressure variation and background aerosols. Therefore, temperature dependence of the cross section in the RTM calculation is essential for the accurate simulation of O4 SCD.

It is important to estimate the effects of the long-range transport of atmospheric pollutants for efficient and effective strategies to control air quality. In this study, the contributions of trans-boundary transport to the mean concentrations of SO 2 , NO 2 , CO, and PM 10 in Seoul, Korea from 2001 to 2014 were estimated based on the conditional potential source contribution function (CPSCF) method. Eastern China was found to be the major source of trans-boundary pollution in Seoul, but moderate sources were also located in northeastern China. The contribution of long-range transport from Japan was negligible. The spatial distributions of the potential source contribution function (PSCF) values of each pollutant showed reasonable consistency with their emission inventory and satellite products. The PSCF values of SO 2 and PM 10 from eastern China were higher than those of NO 2 and CO. The mean concentrations of SO 2 , NO 2 , CO, and PM 10 in Seoul for the period from 2001 to 2014 were 5.34, 37.0, and 619.1 ppb, and 57.4 4 μg/m 3 , respectively. The contributions of long-range transport to the mean concentrations of SO 2 , NO 2 , CO, and PM 10 in Seoul were 0.74, 3.4, and 39.0 ppb, and 12.1 μg/m 3 , respectively, which are 14%, 9%, 6%, and 21% of the mean concentrations, respectively. The annual mean concentrations of SO 2 and NO 2 followed statistically significant increasing linear trends (0.5 and 1.6 ppb per decade, respectively), whereas the trends in the annual mean concentrations of CO and PM 10 were statistically insignificant. The trends in the ratio of the increased concentrations associated with long-range transport to the annual mean concentrations of the pollutants were statistically insignificant. However, the results indicate that the trans-boundary transport of SO 2 , NO 2 , CO, and PM 10 from eastern China consistently affected air quality in Seoul over the study period (2001–2014). Regionally, the effects of the long-range transport of pollutants from Beijing and Harbin-Changchun on air quality in Seoul have become more significant over this period.

We describe a formaldehyde (HCHO) retrieval algorithm for the Geostationary Environment Monitoring Spectrometer (GEMS) that will be launched by the Korean Ministry of Environment in 2019. The algorithm comprises three steps: preprocesses, radiance fitting, and postprocesses. The preprocesses include a wavelength calibration, as well as interpolation and convolution of absorption cross sections; radiance fitting is conducted using a nonlinear fitting method referred to as basic optical absorption spectroscopy (BOAS); and postprocesses include air mass factor calculations and bias corrections. In this study, several sensitivity tests are conducted to examine the retrieval uncertainties using the GEMS HCHO algorithm. We evaluate the algorithm with the Ozone Monitoring Instrument (OMI) Level 1B irradiance/radiance data by comparing our retrieved HCHO column densities with OMI HCHO products of the Smithsonian Astrophysical Observatory (OMHCHO) and of the Quality Assurance for Essential Climate Variables project (OMI QA4ECV). Results show that OMI HCHO slant columns retrieved using the GEMS algorithm are in good agreement with OMHCHO, with correlation coefficients of 0.77–0.91 and regression slopes of 0.94–1.04 for March, June, September, and December 2005. Spatial distributions of HCHO slant columns from the GEMS algorithm are consistent with the OMI QA4ECV products, but relatively poorer correlation coefficients of 0.52–0.76 are found compared to those against the OMHCHO products. Also, we compare the satellite results with ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations. OMI GEMS HCHO vertical columns are 9 %–25 % lower than those of MAX-DOAS at Haute-Provence Observatory (OHP) in France, Bremen in Germany, and Xianghe in China. We find that the OMI GEMS retrievals have less bias than the OMHCHO and OMI QA4ECV products at OHP and Bremen in comparison with MAX-DOAS.

The Geostationary Ocean Color Imager (GOCI) Yonsei aerosol retrieval (YAER) version 1 algorithm was developed to retrieve hourly aerosol optical depth at 550 nm (AOD) and other subsidiary aerosol optical properties over East Asia. The GOCI YAER AOD had accuracy comparable to ground-based and other satellite-based observations but still had errors because of uncertainties in surface reflectance and simple cloud masking. In addition, near-real-time (NRT) processing was not possible because a monthly database for each year encompassing the day of retrieval was required for the determination of surface reflectance. This study describes the improved GOCI YAER algorithm version 2 (V2) for NRT processing with improved accuracy based on updates to the cloud-masking and surface-reflectance calculations using a multi-year Rayleigh-corrected reflectance and wind speed database, and inversion channels for surface conditions. The improved GOCI AOD τG is closer to that of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD than was the case for AOD from the YAER V1 algorithm. The V2 τG has a lower median bias and higher ratio within the MODIS expected error range (0.60 for land and 0.71 for ocean) compared with V1 (0.49 for land and 0.62 for ocean) in a validation test against Aerosol Robotic Network (AERONET) AOD τA from 2011 to 2016. A validation using the Sun-Sky Radiometer Observation Network (SONET) over China shows similar results. The bias of error (τG−τA) is within −0.1 and 0.1, and it is a function of AERONET AOD and Ångström exponent (AE), scattering angle, normalized difference vegetation index (NDVI), cloud fraction and homogeneity of retrieved AOD, and observation time, month, and year. In addition, the diagnostic and prognostic expected error (PEE) of τG are estimated. The estimated PEE of GOCI V2 AOD is well correlated with the actual error over East Asia, and the GOCI V2 AOD over South Korea has a higher ratio within PEE than that over China and Japan.

PM2.5 has been considered as a crucial issue in densely populated cities because of its adverse effects on human health. As a way to mitigate this problem on city scale, urban forest is recently suggested as a solution due to its potential to absorb and adhere PM2.5. However, limited observations in the forest prevent a full understanding in the role of forest on urban PM2.5 concentration. In this paper, we present an application of deep learning using satellite data to clarify the role of forest with respect to the PM2.5 concentration mitigation in the city. Based on PM2.5 data collected from the ground-based observation stations, two PM2.5 prediction models are constructed through the deep learning approach. Satellite-derived aerosol optical depth from the GOCI and MODIS are used as the main predictors of two models. Both models predict spatial distribution throughout Seoul, Korea (R2 of 0.61 and 0.78). Particularly, the western parts of Seoul tend to have higher PM2.5 concentrations than the other parts, whereas mid-outskirts have noticeably low concentrations. A comparison of spatial distributions of PM2.5 between observations and predictions show that the existing observation network only reflects 15%–60% of entire characteristics of Seoul. Utilizing city-wide PM2.5 estimations, a comparison of estimated PM2.5 between urban and forest regions has been performed. The results show that the estimated PM2.5 concentrations in the forest regions are less than those in urban regions by up to 16.4 μg m−3. In contrast to urban regions, the average of PM2.5 concentrations in the forest regions is likely below the daily mean WHO PM2.5 outdoor standard. Our study suggests that urban forest could be a potential way to improve urban air quality with a specific focus on PM2.5. In addition, the deep learning approach used in this study can be applied to other cities where obtaining observation measurements is difficult.

Springtime trans-Pacific transport of Asian air pollutants has been investigated in many ways to figure out its mechanism. Based on the Western Pacific (WP) pattern, one of climate variabilities in the Northern Hemisphere known to be associated with the pattern of atmospheric circulation over the North Pacific Ocean, in this study, we characterize the pattern of springtime trans-Pacific transport using long-term satellite measurements and reanalysis datasets. A positive WP pattern is characterized by intensification of the dipole structure between the northern Aleutian Low and the southern Pacific High over the North Pacific. The TOMS/OMI Aerosol Index (AI) and MOPITT CO show the enhancement of Asian pollutant transport across the Pacific during periods of positive WP pattern, particularly between 40 and 50°N. This enhancement is confirmed by high correlations of WP index with AI and CO between 40 and 50°N. To evaluate the influence of the WP pattern, we examine several cases of trans-Pacific transport reported in previous research. Interestingly, most trans-Pacific transport cases are associated with the positive WP pattern. During the period of negative WP pattern, reinforced cyclonic wave breaking is consistently found over the western North Pacific, which obstructs zonal advection across the North Pacific. However, some cases show the trans-Pacific transport of CO in the period of negative WP pattern, implying that the WP pattern is more influential on the transport of particles mostly emitted near ∼40°N. This study reveals that the WP pattern can be utilized to diagnose the strength of air pollutant transport from East Asia to North America.

Absorption and scattering characteristics of various aerosol events are investigated using 2-years of measurements from a skyradiometer at Yonsei University in Seoul, Korea. Both transported dust and anthropogenic aerosols are observed at distinct geo-location of Seoul, a megacity located a few thousand kilometers away from dust source regions in China. We focus on the wavelength dependence of Angstrom exponent (AE) and single scattering albedo (SSA), showing the characteristics of regional aerosols. The correlation between spectral SSAs and AEs calculated using different wavelength pairs generally indicates relatively weak absorption of fine-mode aerosols (urban pollution and/or biomass burning) and strong absorption of coarse-mode aerosols (desert dust) at this location. AE ratio (AER), a ratio of AEs calculated using wavelength pair between shorter (340–675 nm) and longer wavelength pair (675–1020 nm) correlates differently with SSA according to the dominant size of local aerosols. Correlations between SSA and AER show strong absorption of aerosols for AER 2.0. Based on the seasonal pattern of wavelength dependence of AER and SSA, this correlation difference looks to reveal the separated characteristics of transported dust and anthropogenic particles from urban pollution respectively. The seasonal characteristics of AER and SSAs also show that the skyradiometer measurement with multiple wavelengths may be able to detect the water soluble brown carbon, one of the important secondary organic aerosols in the summertime atmospheric composition.

The sensitivities of oxygen-dimer (O4) slant column densities (SCDs) to changes in aerosol layer height are investigated using the simulated radiances by a radiative transfer model, the linearized pseudo-spherical vector discrete ordinate radiative transfer (VLIDORT), and the differential optical absorption spectroscopy (DOAS) technique. The sensitivities of the O4 index (O4I), which is defined as dividing O4 SCD by 1040 molecules2 cm−5, to aerosol types and optical properties are also evaluated and compared. Among the O4 absorption bands at 340, 360, 380, and 477 nm, the O4 absorption band at 477 nm is found to be the most suitable to retrieve the aerosol effective height. However, the O4I at 477 nm is significantly influenced not only by the aerosol layer effective height but also by aerosol vertical profiles, optical properties including single scattering albedo (SSA), aerosol optical depth (AOD), particle size, and surface albedo. Overall, the error of the retrieved aerosol effective height is estimated to be 1276, 846, and 739 m for dust, non-absorbing, and absorbing aerosol, respectively, assuming knowledge on the aerosol vertical distribution shape. Using radiance data from the Ozone Monitoring Instrument (OMI), a new algorithm is developed to derive the aerosol effective height over East Asia after the determination of the aerosol type and AOD from the MODerate resolution Imaging Spectroradiometer (MODIS). About 80 % of retrieved aerosol effective heights are within the error range of 1 km compared to those obtained from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements on thick aerosol layer cases.

An optimal-estimation(OE)-based aerosol retrieval algorithm using the OMI (Ozone Monitoring Instrument) near-ultraviolet observation was developed in this study. The OE-based algorithm has the merit of providing useful estimates of errors simultaneously with the inversion products. Furthermore, instead of using the traditional look-up tables for inversion, it performs online radiative transfer calculations with the VLIDORT (linearized pseudo-spherical vector discrete ordinate radiative transfer code) to eliminate interpolation errors and improve stability. The measurements and inversion products of the Distributed Regional Aerosol Gridded Observation Network campaign in northeast Asia (DRAGON NE-Asia 2012) were used to validate the retrieved aerosol optical thickness (AOT) and single scattering albedo (SSA). The retrieved AOT and SSA at 388 nm have a correlation with the Aerosol Robotic Network (AERONET) products that is comparable to or better than the correlation with the operational product during the campaign. The OE-based estimated error represented the variance of actual biases of AOT at 388 nm between the retrieval and AERONET measurements better than the operational error estimates. The forward model parameter errors were analyzed separately for both AOT and SSA retrievals. The surface reflectance at 388 nm, the imaginary part of the refractive index at 354 nm, and the number fine-mode fraction (FMF) were found to be the most important parameters affecting the retrieval accuracy of AOT, while FMF was the most important parameter for the SSA retrieval. The additional information provided with the retrievals, including the estimated error and degrees of freedom, is expected to be valuable for relevant studies. Detailed advantages of using the OE method were described and discussed in this paper.

This investigation examines aerosol dynamics during major fine mode aerosol transboundary pollution events in South Korea primarily during the KORUS-AQ campaign from May 1 – June 10, 2016, particularly when cloud fraction was high and/or fog was present to quantify the change in aerosol characteristics due to near-cloud or fog interaction. We analyze the new AERONET Version 3 data that have significant changes to cloud screening algorithms, allowing many more fine-mode observations in the near vicinity of clouds or fog. Case studies for detailed investigation include May 25–26, 2016 when cloud fraction was high over much of the peninsula, associated with a weak frontal passage and advection of pollution from China. These cloud-influenced Chinese transport dates also had the highest aerosol optical depth (AOD), surface PM2.5 concentrations and fine mode particle sizes of the entire campaign. Another likewise cloud/high relative humidity (RH) case is June 9 and 10, 2016 when fog was present over the Yellow Sea that appears to have affected aerosol properties well downwind over the Korean peninsula. In comparison we also investigated aerosol properties on air stagnation days with very low cloud cover and relatively low RH (May 17 & 18, 2016), when local Korean emissions dominated. Aerosol volume size distributions show marked differences between the transport days (with high RH and cloud influences) and the local pollution stagnation days, with total column-integrated particle fine mode volume being an order of magnitude greater on the pollution transport dates. The PM2.5 over central Seoul were significantly greater than for coastal sites on the transboundary transport days yet not on stagnation days, suggesting additional particle formation from gaseous urban emissions in cloud/fog droplets and/or in the high RH humidified aerosol environment. Many days had KORUS-AQ research aircraft flights that provided observations of aerosol absorption, particle chemistry and vertical profiles of extinction. AERONET retrievals and aircraft in situ measurements both showed high single scattering albedo (weak absorption) on the cloudy or cloud influenced days, plus aircraft profile in situ measurements showed large AOD enhancements (versus dried aerosol) at ambient relative humidity (RH) on the pollution transport days, consistent with the significantly larger fine mode particle radii and weak absorption.

Analysis of sun photometer measured and satellite retrieved aerosol optical depth (AOD) data has shown that major aerosol pollution events with very high fine mode AOD (>1.0 in mid-visible) in the China/Korea/Japan region are often observed to be associated with significant cloud cover. This makes remote sensing of these events difficult even for high temporal resolution sun photometer measurements. Possible physical mechanisms for these events that have high AOD include a combination of aerosol humidification, cloud processing, and meteorological co-variation with atmospheric stability and convergence. The new development of Aerosol Robotic network (AERONET) Version 3 Level 2 AOD with improved cloud screening algorithms now allow for unprecedented ability to monitor these extreme fine mode pollution events. Further, the Spectral Deconvolution Algorithm (SDA) applied to Level 1 data (L1; no cloud screening) provides an even more comprehensive assessment of fine mode AOD than L2 in current and previous data versions. Studying the 2012 winter-summer period, comparisons of AERONET L1 SDA daily average fine mode AOD data showed that Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote sensing of AOD often did not retrieve and/or identify some of the highest fine mode AOD events in this region. Also, compared to models that include data assimilation of satellite retrieved AOD, the L1 SDA fine mode AOD was significantly higher in magnitude, particularly for the highest AOD events that were often associated with significant cloudiness.

We determine and interpret fine particulate matter (PM2.5) concentrations in eastern China for January to December 2013 at a horizontal resolution of 6 km from aerosol optical depth (AOD) retrieved from the Korean geostationary ocean color imager (GOCI) satellite instrument. We implement a set of filters to minimize cloud contamination in GOCI AOD. Evaluation of filtered GOCI AOD with AOD from the Aerosol Robotic Network (AERONET) indicates significant agreement with mean fractional bias (MFB) in Beijing of 6.7 % and northern Taiwan of −1.2 %. We use a global chemical transport model (GEOS-Chem) to relate the total column AOD to the near-surface PM2.5. The simulated PM2.5 / AOD ratio exhibits high consistency with ground-based measurements in Taiwan (MFB = −0.52 %) and Beijing (MFB = −8.0 %). We evaluate the satellite-derived PM2.5 versus the ground-level PM2.5 in 2013 measured by the China Environmental Monitoring Center. Significant agreement is found between GOCI-derived PM2.5 and in situ observations in both annual averages (r2 = 0.66, N = 494) and monthly averages (relative RMSE = 18.3 %), indicating GOCI provides valuable data for air quality studies in Northeast Asia. The GEOS-Chem simulated chemical composition of GOCI-derived PM2.5 reveals that secondary inorganics (SO42-, NO3-, NH4+) and organic matter are the most significant components. Biofuel emissions in northern China for heating increase the concentration of organic matter in winter. The population-weighted GOCI-derived PM2.5 over eastern China for 2013 is 53.8 μg m−3, with 400 million residents in regions that exceed the Interim Target-1 of the World Health Organization.

This study investigated how cloud fraction and snow cover affect the variation of surface ultraviolet (UV) radiation by using surface Erythemal UV (EUV) and Near UV (NUV) observed at the King Sejong Station, Antarctica. First the Radiative Amplification Factor (RAF), the relative change of surface EUV according to the total-column ozone amount, is compared for different cloud fractions and solar zenith angles (SZAs). Generally, all cloudy conditions show that the increase of RAF as SZA becomes larger, showing the larger effects of vertical columnar ozone. For given SZA cases, the EUV transmission through mean cloud layer gradually decreases as cloud fraction increases, but sometimes the maximum of surface EUV appears under partly cloudy conditions. The high surface EUV transmittance under broken cloud conditions seems due to the re-radiation of scattered EUV by cloud particles. NUV transmission through mean cloud layer also decreases as cloud amount increases but the sensitivity to the cloud fraction is larger than EUV. Both EUV and NUV radiations at the surface are also enhanced by the snow cover, and their enhancement becomes higher as SZA increases implying the diurnal variation of surface albedo. This effect of snow cover seems large under the overcast sky because of the stronger interaction between snow surface and cloudy sky.

This study examined the quiescence of Asian dust events in South Korea and Japan during the spring of 2012, presenting a synoptic characterization and suggesting possible causes. Synoptic observation reports from the two countries confirmed that spring 2012 had the lowest number of dust events in 2000–2012. The monthly dust frequency (DF) in March 2012 over the dust source regions, i.e., deserts in northern China and Mongolia, indicated a significant decrease compared to the 12 year (2000–2011) March climatology. The DF in April 2012 was comparable to the 12 year climatology values, but in May 2012 it was slightly lower. The daily Ozone Monitoring Instrument Aerosol Index and the Navy Aerosol Analysis and Prediction System simulations revealed stagnant dust movement in March and May 2012. Anomalous anticyclones north of the source regions decreased the dust outbreaks and enhanced the southeasterly winds, resulting in few dust events over the downwind countries (i.e., South Korea and Japan). By contrast, in April 2012, a strong anomalous cyclone east of Lake Baikal slightly increased the dust outbreaks over northeastern China. However, the major dust outbreaks were not transported downwind because of exceptional dust pathways, i.e., the southeastward pathway of dust transport was unusually blocked by the expansion of an anomalous anticyclonic circulation over the Sea of Okhotsk, with dust being transported northeast.

Dust storms are known to have adverse effects on human health and significant impact on weather, air quality, hydrological cycle, and ecosystem. Atmospheric dust loading is also one of the large uncertainties in global climate modeling, due to its significant impact on the radiation budget and atmospheric stability. Observations of dust storms in humid tropical south China (e.g. Hong Kong), are challenging due to high industrial pollution from the nearby Pearl River Delta region. This study develops a method for dust storm detection by combining ground station observations (PM10 concentration, AERONET data), geostationary satellite images (MTSAT), and numerical weather and climatic forecasting products (WRF/Chem). The method is based on a hybrid neural network (NN) retrieval model for two scales: (i) a NN model for near real-time detection of dust storms at broader regional scale; (ii) a NN model for detailed dust storm mapping for Hong Kong and Taiwan. A feed-forward multilayer perceptron (MLP) NN, trained using back propagation (BP) algorithm, was developed and validated by the k-fold cross validation approach. The accuracy of the near real-time detection MLP-BP network is 96.6%, and the accuracies for the detailed MLP-BP neural network for Hong Kong and Taiwan is 74.8%. This newly automated multi-scale hybrid method can be used to give advance near real-time mapping of dust storms for environmental authorities and the public. It is also beneficial for identifying spatial locations of adverse air quality conditions, and estimates of low visibility associated with dust events for port and airport authorities.

The performance of various empirical linear models to estimate the concentrations of surface-level particulate matter with a diameter less than 10 μm (PM10) was evaluated using Aerosol Robotic Network (AERONET) sun photometer and Moderate-Resolution Imaging Spectroradiometer (MODIS) data collected in Seoul during the Distributed Regional Aerosol Gridded Observation Network (DRAGON)-Asia campaign from March to May 2012. An observed relationship between the PM10 concentration and the aerosol optical depth (AOD) was accounted for by several parameters in the empirical models, including boundary layer height (BLH), relative humidity (RH), and effective radius of the aerosol size distribution (Reff), which was used here for the first time in empirical modeling. Among various empirical models, the model which incorporates both BLH and Reff showed the highest correlation, which indicates the strong influence of BLH and Reff on the PM10 estimations. Meanwhile, the effect of RH on the relationship between AOD and PM10 appeared to be negligible during the campaign period (spring), when RH is generally low in northeast Asia. A large spatial dependency of the empirical model performance was found by categorizing the locations of the collected data into three different site types, which varied in terms of the distances between instruments and source locations. When both AERONET and MODIS data sets were used in the PM10 estimation, the highest correlations between measured and estimated values (R = 0.76 and 0.76 using AERONET and MODIS data, respectively) were found for the residential area (RA) site type, while the poorest correlations (R = 0.61 and 0.68 using AERONET and MODIS data, respectively) were found for the near-source (NS) site type. Significant seasonal variations of empirical model performances for PM10 estimation were found using the data collected at Yonsei University (one of the DRAGON campaign sites) over a period of 17 months including the DRAGON campaign period. The best correlation between measured and estimated PM10 concentrations (R = 0.81) was found in winter, due to the presence of a stagnant air mass and low BLH conditions, which may have resulted in relatively homogeneous aerosol properties within the BLH. On the other hand, the poorest correlation between measured and estimated PM10 concentrations (R = 0.54) was found in spring, due to the influence of the long-range transport of dust to both within and above the BLH.

This study compares, for the first time, the total ozone columns derived from the Ozone Monitoring Instrument-Differential Optical Absorption Spectroscopy (OMI-DOAS algorithm) (TOCs-OMI) with those obtained from ground-based Brewer and Dobson spectrophotometers (TOCs-Ground) in Seoul, a megacity in northeast Asia, over the 3 years between 2008 and 2010. We found a seasonal mean underestimation of 2.68% (maximum 18.33% on a single day) in TOCs-OMI when compared with TOCs-Ground from Seoul, particularly during the summer seasons (June, July, and August) of our study period: 20 of the 30 days when this underestimation of TOCs-OMI was greatest occurred during the summer. The causes of such large underestimations in summer TOCs-OMI were investigated, and we found that the ghost column densities (GCDs) used in the current OMI-DOAS algorithm did not fully account for the tropospheric ozone amounts below the cloud top in Seoul, particularly during the summer season when surface ozone is enhanced due to active photochemical reactions. We propose the use of New TOCs-OMI based on New GCDs that were calculated using ozonesonde data for the limited number of days when such data were available. The mean bias errors (MBE) against the TOCs-Ground of the New TOCs-OMI and original TOCs-OMI were −0.60% and −2.16%, respectively, which demonstrates the greater accuracy of the New TOCs-OMI. To increase the amount of New TOCs-OMI data available for comparison with the TOCs-Ground data, the regression equation for the relationship between the ozonesonde data and OMI-DOAS cloud pressure data was used to increase the availability of New GCD data for each measurement date that TOCs-OMI data were available for. This procedure reduced the MBE of the original TOCs-OMI by 1.29%, 1.67%, and 1.29% in June, July, and August, respectively. The present study demonstrates that the underestimation of GCDs is one of the major causes of the underestimation of TOCs-OMI during the summer season and suggests that improvements could be achieved using the proposed correction for these GCDs.

A unified satellite algorithm is presented to simultaneously retrieve aerosol properties (aerosol optical depth; AOD and aerosol type) and clear-sky shortwave direct radiative effect (hereafter, DREA) over ocean. The algorithm is applied to Moderate Resolution Imaging spectroradiometer (MODIS) observations for a period from 2003 to 2010 to assess the DREA over the global ocean. The simultaneous retrieval utilizes lookup table (LUT) containing both spectral reflectances and solar irradiances calculated using a single radiative transfer model with the same aerosol input data. This study finds that aerosols cool the top-of-atmosphere (TOA) and bottom-of-atmosphere (BOA) by 5.2 +/- 0.5 W/sq m and 8.3 W/sq m, respectively, and correspondingly warm the atmosphere (hereafter, ATM) by 3.1 W/sq m. These quantities, solely based on the MODIS observations, are consistent with those of previous studies incorporating chemical transport model simulations and satellite observations. However, the DREAs at BOA and ATM are expected to be less accurate compared to that of TOA due to low sensitivity in retrieving aerosol type information, which is related with the atmospheric heating by aerosols, particularly in low AOD conditions; consequently, the uncertainties could not be quantified. Despite the issue in the aerosol type information, the present method allows us to confine the DREA attributed only to fine-mode dominant aerosols, which are expected to be mostly anthropogenic origin, in the range from 1.1 W/sq m to 1.3 W/sq m at TOA. Improvements in size-resolved AOD and SSA retrievals from current and upcoming satellite instruments are suggested to better assess the DREA, particularly at BOA and ATM, where aerosol absorptivity induces substantial uncertainty.

The effects of El Nino/Southern Oscillation (ENSO) under negative Arctic Oscillation (AO) phase on the Asian dust activity are investigated for springs of the period 1961–2002. The spring dust index (DI) describing the monthly frequencies of three types of dust events (e.g. dust storm, blowing dust, and floating dust) exhibits a significant increase in the years of negative AO phase (hereafter AO−) and El Nino, compared with that in the years of AO− and La Nina. Averaged over all observation stations, the spring DI (49.7) during the El Nino/AO− years is higher by 11.4% or 29.8% than that (38.3) during the La Nina/AO− years. We suggest possible physical mechanism that the anomalous large-scale environments associated with AO− and El Nino are more effective to provide favourable conditions to enhance Asian dust activity. During the El Nino/AO− years, meridional gradients of pressure and temperature over the dust source regions are significantly enhanced by decreasing the geopotential height and warming air temperature that originated from the north and south of source regions, respectively, under the influence of AO− and El Nino. These also intensify the zonal wind shear and atmospheric baroclinicity, thereby producing enhanced cyclogenesis and dust occurrences over the major source regions. At the same time, dust transport paths with the stronger westerly winds are developed by the combined constraints of anomalous cyclone over the Siberia and the Mongolia and anomalous anticyclone over the western North Pacific, and thus strengthen dust transport to the downwind regions.

The total ozone (O3) and aerosol optical depth (AOD) at 320 nm have been observed from the ultraviolet (UV) measurements made at Yonsei University in Seoul, South Korea, with Dobson and Brewer spectrophotometers, respectively, during 2004–10. The daily datasets are analyzed to show the sensitivities of UV radiation to changes in O3, AOD, and cloud cover (CC) together with global solar radiation (GS), including the long-term characteristics of surface UV irradiance in Seoul. The UV sensitivities show that 1% increases of O3 and AOD relative to their reference values under all- and clear-sky conditions similarly manifest as 1–1.2% and 0.2% decreases of both daily erythemal UV (EUV) and total UV (TUV) irradiance at the ground level except for TUV sensitivity to O3 (~0.3%). Those UV sensitivities to CC and GS changes are associated with a 0.12% decrease and 0.7% increase, respectively, in fractional UV changes. The trends show that the positive trends of O3 (+7.2% decade−1), AOD (+22.4% decade−1), and CC (+52.4% decade−1) induce negative trends in EUV (−8.4% decade−1) and TUV (−2.5% decade−1), in both UV (−4.7% decade−1), and in EUV (−6.3% decade−1) and TUV (−6.8% decade−1), respectively. On the basis of the multiple linear regression analyses, it is found that UV sensitivity to O3 is relatively high in the forcing factors, but the contributions of the UV forcing factors to the daily variability and the range of UV disturbances due to the variability of the forcing factors are affected more by AOD than by O3 and CC in both UV fractional changes.