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Abstract Machine learning is widely used to infer ground-level concentrations of air pollutants from satellite observations. However, a single pollutant is commonly targeted in previous explorations, which would lead to duplication of efforts and ignoration of interactions considering the interactive nature of air pollutants and their common influencing factors. We aim to build a unified model to offer a synchronized estimation of ground-level air pollution levels. We constructed a multi-output random forest (MORF) model and achieved simultaneous estimation of hourly concentrations of PM2.5, PM10, O3, NO2, CO, and SO2 in China, benefiting from the world’s first geostationary air-quality monitoring instrument Geostationary Environment Monitoring Spectrometer. MORF yielded a high accuracy with cross-validated R2 reaching 0.94. Meanwhile, model efficiency was significantly improved compared to single-output models. Based on retrieved results, the spatial distributions, seasonality, and diurnal variations of six air pollutants were analyzed and two typical pollution events were tracked.

Abstract 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 precision 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%, 19%, 29%, and 10% in the predictability of PM2.5 over South Korea in terms of index of agreement (IOA), correlation coefficient (R), mean biases (MB), and hit rate (HR), respectively, compared to PM2.5 prediction systems using only LEO satellite-derived observations.

Near-real time observations of aerosol properties could have a potential to improve the accuracy of XCO2 retrieval algorithm in operational satellite missions. In this study, we developed a retrieval algorithm of XCO2 (Yonsei Retrieval Algorithm; YCAR) based on the Optimal Estimation (OE) method that used aerosol information at the location of the Orbiting Carbon Observatory-2 (OCO-2) measurement from co-located measurement of the Afternoon constellation (A-train) such as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Observation (CALIPSO) and the MODerate-resolution Imaging Spectrometer (MODIS) onboard the Aqua. Specifically, we used optical depth, vertical profile, and optical properties of aerosol from MODIS and CALIOP data. We validated retrieval results to the Total Carbon Column Observing Network (TCCON) ground-based measurements and found general consistency. The impact of observed aerosol information and its constraint was examined by retrieval tests using different settings. The effect of using additional aerosol information was analyzed in connection with the bias correction process of the operational retrieval algorithm. YCAR using a priori aerosol loading parameters from co-located satellite measurements and less constraint of aerosol optical properties made comparable results with operational data with the bias correction process in three of the four cases subject to this study. Our work provides evidence supporting the bias correction process of operational algorithms and quantitatively presents the effectiveness of synergic use of multiple satellites (e.g. A-train) and better treatment of aerosol information.

North Korea’s air quality is poorly understood due to a lack of reliable data. Here, we analyzed urban- to national-scale air quality changes in North Korea using multi-year satellite observations. Pyongyang, Nampo, Pukchang, and Munchon were identified as pollution hotspots. On a national scale, we found that North Korea experienced 6.7, 17.8, and 20.6 times greater amounts of nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) per unit primary energy supply (PES) than South Korea from 2005 to 2018. Besides, North Korea had a 24.3 times larger aerosol optical depth (AOD) per PES than South Korea from 2011 to 2018. Severe CO and aerosol pollution is aligned with extensive biofuel combustion. High SO2 pollution corresponds with the strong coal dependence of the industry. The change rates of the national average columns for NO2, SO2, and CO were + 3.6, –4.4, and –0.4 % yr−1, respectively. The AOD change rate was –4.8 % yr−1. Overall decreasing trends, except for NO2, are likely due to a decline in coal-fired PES. Positive NO2 trends are consistent with increasing industrial activities. Each pollutant showed consistent patterns of linear trends, even after correcting the influence of transboundary pollution. Flue gas control and biofuel consumption reduction seem necessary to improve North Korea’s air quality.

This study develops an aerosol data assimilation and forecast system using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the three-dimensional variational (3D-VAR) data assimilation method. The system assimilates the aerosol optical depth (AOD) from the Geostationary Ocean Color Imager (GOCI) satellite and surface particulate matter (PM) observations. The simulation domain covers Northeast Asia at 15 km horizontal resolution, and the assimilation and forecast skill is evaluated for the Korea–US Air Quality (KORUS-AQ) intensive observing period. Observing system experiments (OSEs) are conducted to examine the changes in quality of assimilation and forecast skills sensitive to the assimilated observational input data. The baseline model simulation underestimates AOD and surface PM concentration in most regions, in which the assimilation of satellite and in-situ data improves the mean biases and spatial distribution. Moreover, it improves the forecast skill of the surface concentration of PM10 and PM2.5. The results from the OSEs indicate that the assimilation of GOCI AOD only slightly enhances the forecast skill. However, most of the skill improvement comes from the surface PM assimilation, showing a practically useful level of skill until 12 hours from the initial state. The marginal improvement in the PM10 forecasts by the GOCI AOD suggests the non-negligible difference between column-representing AOD and the surface PM concentration.

Numerous studies (hereafter GA: general approach studies) have been made to classify aerosols into desert dust (DD), biomass-burning (BB), clean continental (CC), and clean maritime (CM) types using only aerosol optical depth (AOD) and Ångström exponent (AE). However, AOD represents the amount of aerosol suspended in the atmospheric column while the AE is a qualitative indicator of the size distribution of the aerosol estimated using AOD measurements at different wavelengths. Therefore, these two parameters do not provide sufficient information to unambiguously classify aerosols into these four types. Evaluation of the performance of GA classification applied to AErosol Robotic NETwork (AERONET) data, at sites for situations with known aerosol types, provides many examples where the GA method does not provide correct results. For example, a thin layer of haze was classified as BB and DD outside the crop burning and dusty seasons respectively, a thick layer of haze was classified as BB, and aerosols from known crop residue burning events were classified as DD, CC, and CM by the GA method. The results also show that the classification varies with the season, for example, the same range of AOD and AE were observed during a dust event in the spring (20th March 2012) and a smog event in the autumn (2nd November 2017). The results suggest that only AOD and AE cannot precisely classify the exact nature (i.e., DD, BB, CC, and CM) of aerosol types without incorporating more optical and physical properties. An alternative approach, AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA), is proposed to provide aerosol amount and size information using AOD and AE, respectively, from the Terra-MODIS (MODerate resolution Imaging Spectroradiometer) Collection 6.1 Level 2 combined Dark Target and Deep Blue (DTB) product and AERONET Version 3 Level 2.0 data. Although AEROSA is also based on AOD and AE, it does not claim the nature of aerosol types, instead providing information on aerosol amount and size. The purpose is to introduce AEROSA for those researchers who are interested in the generic classification of aerosols based on AOD and AE, without claiming the exact aerosol types such as DD, BB, CC, and CM. AEROSA not only provides 9 generic aerosol classes for all observations but can also accommodate variations in location and season, which GA aerosol types do not.

Abstract By using multiple satellite measurements, the changes of the aerosol optical depth (AOD) and nitrogen dioxide (NO2) over South Korea were investigated from January to March 2020 to evaluate the COVID-19 effect on the regional air quality. The NO2 decrease in South Korea was found but not significant, which indicates the effects of spontaneous social distancing under the maintenance of ordinary life. The AODs in 2020 were normally high in January, but they became lower starting from February. Since the atmosphere over Eastern Asia was unusually stagnant in January and February 2020, the AOD decrease in February 2020 clearly reveals the positive effect of the COVID-19. Considering the insignificant NO2 decrease in South Korea and the relatively long lifetime of aerosols, the AOD decrease in South Korea may be more attributed to the improvement of the air quality in neighboring countries. In March, regional atmosphere became well mixed and ventilated over South Korea, contributing to large enhancement of air quality. While the social activity was reduced after the COVID-19 outbreak, the regional meteorology should be also examined significantly to avoid the biased evaluation of the social impact on the change of the regional air quality.

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015–2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O3 and the total gaseous oxidant (OX = NO2 + O3) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015–2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO2 and NOx concentrations and peoples’ mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality.As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO2 and between 30% and 40% in mean PM2.5 concentrations over 2020 full lockdown compared to the same period in 2015–2019. However, PM2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO2/CO ratio). Some Chinese cities showed similar increases in PM2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O3 concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO2 anomalies were negative for 2020 compared to 2015–2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO2/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO2 decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO2 + O3) showed that primary NO2 emissions at urban locations were greater than the O3 production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO2 and O3 concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.

Abstract To assess air pollution emitted in Southern Hemisphere mid-latitudes and transported to Antarctica, we investigate the climatological mean and temporal trends in aerosol optical depth (AOD), carbon monoxide (CO), nitrogen dioxide (NO2), and formaldehyde (HCHO) columns using satellite observations. Generally, all these measurements exhibit sharp peaks over and near the three nearby inhabited continents: South America, Africa, and Australia. This pattern indicates the large emission effect of anthropogenic activities and biomass burning processes. High AOD is also found over the Southern Atlantic Ocean, probably because of the sea salt production driven by strong winds. Since the pristine Antarctic atmosphere can be polluted by transport of air pollutants from the mid-latitudes, we analyze the 10-day back trajectories that arrive at Antarctic ground stations in consideration of the spatial distribution of mid-latitudinal AOD, CO, NO2, and HCHO. We find that the influence of mid-latitudinal emission differs across Antarctic regions: western Antarctic regions show relatively more back trajectories from the mid-latitudes, while the eastern Antarctic regions do not show large intrusions of mid-latitudinal air masses. Finally, we estimate the long-term trends in AOD, CO, NO2, and HCHO during the past decade (2005–2016). While CO shows a significant negative trend, the others show overall positive trends. Seasonal and regional differences in trends are also discussed.

Based on multiple linear regression (MLR) models, we estimated the PM2.5 at Seoul using a number of aerosol optical depth (AOD) values obtained from ground-based and satellite remote sensing observations. To construct the MLR model, we consider various parameters related to the ambient meteorology and air quality. In general, all AOD values resulted in the high quality of PM2.5 estimation through the MLR method: mostly correlation coefficients >~0.8. Among various polar-orbit satellite AODs, AOD values from the MODIS measurement contribute to better PM2.5 estimation. We also found that the quality of estimated PM2.5 shows some seasonal variation; the estimated PM2.5 values consistently have the highest correlation with in situ PM2.5 in autumn, but are not well established in winter, probably due to the difficulty of AOD retrieval in the winter condition. MLR modeling using spectral AOD values from the ground-based measurements revealed that the accuracy of PM2.5 estimation does not depend on the selected wavelength. Although all AOD values used in this study resulted in a reasonable accuracy range of PM2.5 estimation, our analyses of the difference in estimated PM2.5 reveal the importance of utilizing the proper AOD for the best quality of PM2.5 estimation.

Atmospheric broadband transmissions of clouds, aerosols, and ozone in the erythemally weighted ultraviolet (EUV, 290–320 nm), total (spectrally integrated) ultraviolet (TUV, 290–363 nm), and total global solar (GS, 305–2800 nm) spectral regions were analysed with ground-based measurements in Seoul, Korea (37.57°N, 128.98°E) from March 2004 to February 2013. The annual average total transmission expressed as a fraction of the clear-sky irradiance was 77.6% in the EUV, 73.6% in the TUV, and 72.0% in the GS spectral regions. The corresponding values for cloud transmission were 78.4%, 73.9%, and 71.7%. In overcast cloudy conditions, atmospheric transmission was reduced by 45.9%, 50.2%, and 56.6% in the three spectral regions, respectively, indicating the dominant effect of clouds. Aerosol and ozone transmissions had almost the same annual average. Annual average atmospheric transmission effectively decreases with increasing wavelength from EUV to GS regions. However, we found that there was a difference in wavelength dependence of atmospheric transmission for monthly averages, which seems related to the monthly variation of total column ozone (TCO), aerosol, and cloud amount. It is also found that there is a critical value of TCO (TCO =370 DU) for the wavelength dependence of transmission. Higher ozone amount than this turnaround value can cause an increase in transmission from the EUV to GS regions. The monthly wavelength-dependent effects may be attributable more to the different climatological characteristics of the TCO rather than aerosols and clouds.

In 2019, an unusually strong positive Indian Ocean Dipole spawned hot and dry weather in southeastern Australia, which promoted devastating wildfires in the period from September 2019 to February 2020. The fires produced large plumes of biomass burning aerosols that prevented sunlight from reaching the Earth’s surface, and in this way elicited regional radiative cooling. We estimated the direct aerosol radiative forcing (ARF) resulting from these wildfires, based on Moderate Resolution Imaging Spectroradiometer space-based data and an empirical relationship from AErosol RObotic NETwork ground-based data collected in biomass-burning regions. The wildfire-derived air pollution was associated with an aerosol optical thickness of >0.3 in Victoria and a strongly negative ARF of between −14.8 and −17.7 W m ^−2 , which decreased the surface air temperature by about 3.7 °C–4.4 °C. This is of the same order of magnitude as the radiative cooling from volcanic eruptions. Although the atmospheric lifetime of biomass-burning aerosols is relatively short (about a week), the Australian wildfire pollution plumes extended across the Pacific Ocean to South America. Since climate change is expected to lead to more frequent and increasingly intense fires in many regions worldwide, the consequent biomass burning aerosols may become a significant radiative forcing factor, which will need to be accounted for in climate model projections for the future.

Clouds act as a major reflector that changes the amount of sunlight reflected to space. Change in radiance intensity due to the presence of clouds interrupts the retrieval of trace gas or aerosol properties from satellite data. In this paper, we developed a fast and robust algorithm, named the fast cloud retrieval algorithm, using a triplet of wavelengths (469, 477, and 485 nm) of the O2–O2 absorption band around 477 nm (CLDTO4) to derive the cloud information such as cloud top pressure (CTP) and cloud fraction (CF) for the Geostationary Environment Monitoring Spectrometer (GEMS). The novel algorithm is based on the fact that the difference in the optical path through which light passes with regard to the altitude of clouds causes a change in radiance due to the absorption of O2–O2 at the three selected wavelengths. To reduce the time required for algorithm calculations, the look-up table (LUT) method was applied. The LUT was pre-constructed for various conditions of geometry using Vectorized Linearized Discrete Ordinate Radiative Transfer (VLIDORT) to consider the polarization of the scattered light. The GEMS was launched in February 2020, but the observed data of GEMS have not yet been widely released. To evaluate the performance of the algorithm, the retrieved CTP and CF using observational data from the Global Ozone Monitoring Experiment-2 (GOME-2), which cover the spectral range of GEMS, were compared with the results of the Fast Retrieval Scheme for Clouds from the Oxygen A band (FRESCO) algorithm, which is based on the O2 A-band. There was good agreement between the results, despite small discrepancies for low clouds.

The retrieval of optimal aerosol datasets by the synergistic use of hyperspectral ultraviolet (UV)–visible and broadband meteorological imager (MI) techniques was investigated. The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) was used as a proxy for hyperspectral UV–visible instrument data to which the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol algorithm was applied. Moderate-Resolution Imaging Spectroradiometer (MODIS) L1B and dark target aerosol Level 2 (L2) data were used with a broadband MI to take advantage of the consistent time gap between the MODIS and the OMI. First, the use of cloud mask information from the MI infrared (IR) channel was tested for synergy. High-spatial-resolution and IR channels of the MI helped mask cirrus and sub-pixel cloud contamination of GEMS aerosol, as clearly seen in aerosol optical depth (AOD) validation with Aerosol Robotic Network (AERONET) data. Second, dust aerosols were distinguished in the GEMS aerosol-type classification algorithm by calculating the total dust confidence index (TDCI) from MODIS L1B IR channels. Statistical analysis indicates that the Probability of Correct Detection (POCD) between the forward and inversion aerosol dust models (DS) was increased from 72% to 94% by use of the TDCI for GEMS aerosol-type classification, and updated aerosol types were then applied to the GEMS algorithm. Use of the TDCI for DS type classification in the GEMS retrieval procedure gave improved single-scattering albedo (SSA) values for absorbing fine pollution particles (BC) and DS aerosols. Aerosol layer height (ALH) retrieved from GEMS was compared with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data, which provides high-resolution vertical aerosol profile information. The CALIOP ALH was calculated from total attenuated backscatter data at 1064 nm, which is identical to the definition of GEMS ALH. Application of the TDCI value reduced the median bias of GEMS ALH data slightly. The GEMS ALH bias approximates zero, especially for GEMS AOD values of >~0.4 and GEMS SSA values of

The Geostationary Environment Monitoring Spectrometer (GEMS) onboard the Geostationary Korean Multi-Purpose Satellite 2B was successfully launched in February 2020. GEMS is a hyperspectral spectrometer measuring solar irradiance and Earth radiance in the wavelength range of 300 to 500 nm. This paper introduces the spectral calibration algorithm for GEMS, which uses a nonlinear least-squares approach. Sensitivity tests for a series of unknown algorithm parameters such as spectral range for fitting, spectral response function (SRF), and reference spectrum were conducted using the synthetic GEMS spectrum prepared with the ground-measured GEMS SRF. The test results show that the required accuracy of 0.002 nm is achievable provided the SRF and the high-resolution reference spectrum are properly prepared. Such a satisfactory performance is possible mainly due to the inclusion of additional fitting parameters of spectral scales (shift, squeeze, and high order shifts) and SRF (width, shape and asymmetry). For the application to the actual GEMS data, in-orbit SRF is to be monitored using an analytic SRF function and the measured GEMS solar irradiance, while a reference spectrum is going to be selected during the instrument in-orbit test. The calibrated GEMS data is expected to be released by the end of 2020.

Global atmospheric CO2 distributions were simulated with a chemical transport model (GEOS-Chem) and compared with space-borne observations of CO2 column density by GOSAT from April 2009 to January 2010. The GEOS-Chem model simulated 3-D global atmospheric CO2 at 2°×2.5° horizontal resolution using global CO2 surface sources/sinks as well as 3-D emissions from aviation and the atmospheric oxidation of other carbon species. The seasonal cycle and spatial distribution of GEOS-Chem CO2 columns were generally comparable with GOSAT columns over each continent with a systematic positive bias of ~1.0%. Data from the World Data Center for Greenhouse Gases (WDCGG) from twelve ground stations spanning 90°S-82°N were also compared with the modeled data for the period of 2004-2009 inclusive. The ground-based data show high correlations with the GEOS-Chem simulation (0.66≤R2≤0.99) but the model data have a negative bias of ~1.0%, which is primarily due to the model initial conditions. Together these two comparisons can be used to infer that GOSAT CO2 retrievals underestimate CO2 column concentration by ~2.0%, as demonstrated in recent validation work using other methods. We further estimated individual source/sink contributions to the global atmospheric CO2 budget and trends through 7 tagged CO2 tracers (fossil fuels, ocean exchanges, biomass burning, biofuel burning, net terrestrial exchange, shipping, aviation, and CO oxidation) over 2004-2009. The global CO2 trend over this period (2.1 ppmv/year) has been mainly driven by fossil fuel combustion and cement production (3.2 ppmv/year), reinforcing the fact that rigorous CO2 reductions from human activities are necessary in order to stabilize atmospheric CO2 levels.

Himawari-8, a next-generation geostationary meteorological satellite, was successfully launched by the Japanese Meteorological Agency (JMA) on 7 October 2014 and has been in official operation since 7 July 2015. The Advanced Himawari Imager (AHI) onboard Himawari-8 has 16 channels from 0.47 to 13.3 μm and performs full-disk observations every 10 min. This study describes AHI aerosol optical property (AOP) retrieval based on a multi-channel algorithm using three visible and one near-infrared channels (470, 510, 640, and 860 nm). AOPs were retrieved by obtaining the visible surface reflectance using shortwave infrared (SWIR) data along with normalized difference vegetation index shortwave infrared (NDVISWIR) categories and the minimum reflectance method (MRM). Estimated surface reflectance from SWIR (ESR) tends to be overestimated in urban and cropland areas. Thus, the visible surface reflectance was improved by considering urbanization effects. Ocean surface reflectance is obtained using MRM, while it is from the Cox and Munk method in ESR with the consideration of chlorophyll-a concentration. Based on validation with ground-based sun-photometer measurements from Aerosol Robotic Network (AERONET) data, the error pattern tends to the opposition between MRMver (using MRM reflectance) AOD and ESRver (Using ESR reflectance) AOD over land. To estimate optimal AOD products, two methods were used to merge the data. The final aerosol products and the two surface reflectances were merged, which resulted in higher accuracy AOD values than those retrieved by either individual method. All four AODs shown in this study show accurate diurnal variation compared with AERONET, but the optimum AOD changes depending on observation time.

In the present study, we investigate the effects of signal to noise (SNR), slit function (FWHM), and aerosol optical depth (AOD) on the accuracy of formaldehyde (HCHO) vertical column density (HCHOVCD) using the ground-based direct-sun synthetic radiance based on differential optical absorption spectroscopy (DOAS). We found that the effect of SNR on HCHO retrieval accuracy is larger than those of FWHM and AOD. When SNR = 650 (1300), FWHM = 0.6, and AOD = 0.2, the absolute percentage difference (APD) between the true HCHOVCD values and those retrieved ranges from 54 (30%) to 5% (1%) for the HCHOVCD of 5.0 × 1015 and 1.1 × 1017 molecules cm−2, respectively. Interestingly, the maximum AOD effect on the HCHO accuracy was found for the HCHOVCD of 3.0 × 1016 molecules cm−2. In addition, we carried out the first ground-based direct-sun measurements in the ultraviolet (UV) wavelength range to retrieve the HCHOVCD using Pandora in Seoul. The HCHOVCD was low at 12:00 p.m. local time (LT) in all seasons, whereas it was high in the morning (10:00 a.m. LT) and late afternoon (4:00 p.m. LT), except in winter. The maximum HCHOVCD values were 2.68 × 1016, 3.19 × 1016, 2.00 × 1016, and 1.63 × 1016 molecules cm−2 at 10:00 a.m. LT in spring, 10:00 a.m. LT in summer, 1:00 p.m. LT in autumn, and 9:00 a.m. LT in winter, respectively. The minimum values of Pandora HCHOVCD were 1.63 × 1016, 2.23 × 1016, 1.26 × 1016, and 0.82 × 1016 molecules cm−2 at around 1:45 p.m. LT in spring, summer, autumn, and winter, respectively. This seasonal pattern of high values in summer and low values in winter implies that photo-oxidation plays an important role in HCHO production. The correlation coefficient (R) between the monthly HCHOVCD values from Pandora and those from the Ozone Monitoring Instrument (OMI) is 0.61, and the slope is 1.25.

The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled to be in orbit in 2019 onboard the GEO-KOMPSAT 2B satellite and will continuously monitor air quality over Asia. The GEMS will make measurements in the UV spectrum (300–500 nm) with 0.6 nm resolution. In this study, an algorithm is developed to retrieve aerosol optical properties from UV-visible measurements for the future satellite instrument and is tested using 3 years of existing OMI L1B data. This algorithm provides aerosol optical depth (AOD), single scattering albedo (SSA) and aerosol layer height (ALH) using an optimized estimation method. The retrieved AOD shows good correlation with Aerosol Robotic Network (AERONET) AOD with correlation coefficients of 0.83, 0.73 and 0.80 for heavy-absorbing fine (HAF) particles, dust and non-absorbing (NA) particles, respectively. However, regression tests indicate underestimation and overestimation of HAF and NA AOD, respectively. In comparison with AOD from the OMI/Aura Near-UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13 km × 24 km V003 (OMAERUV) algorithm, the retrieved AOD has a correlation coefficient of 0.86 and linear regression equation, AODGEMS = 1.18AODOMAERUV + 0.09. An uncertainty test based on a reference method, which estimates retrieval error by applying the algorithm to simulated radiance data, revealed that assumptions in the spectral dependency of aerosol absorptivity in the UV cause significant errors in aerosol property retrieval, particularly the SSA retrieval. Consequently, retrieved SSAs did not show good correlation with AERONET values. The ALH results were qualitatively compared with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products and were found to be well correlated for highly absorbing aerosols. The difference between the attenuated-backscatter-weighted height from CALIOP and retrieved ALH were mostly closed to zero when the retrieved AOD is higher than 0.8 and SSA is lower than 0.93. Although retrieval accuracy was not significantly improved, the simultaneous consistent retrieval of AOD, SSA and ALH alone demonstrates the value of this stand-alone algorithm, given their nature for error using other methods. The use of these properties as input parameters for the air mass factor calculation is expected to improve the retrieval of other trace gases over Asia.

We investigate the effects of aerosol optical depth (AOD), single scattering albedo (SSA), aerosol peak height (APH), measurement geometry (solar zenith angle (SZA) and viewing zenith angle (VZA)), relative azimuth angle, and surface reflectance on the accuracy of NO2 slant column density using synthetic radiance. High AOD and APH are found to decrease NO2 SCD retrieval accuracy. In moderately polluted (5 × 1015 molecules cm−2 < NO2 vertical column density (VCD) < 2 × 1016 molecules cm−2) and clean regions (NO2 VCD < 5 × 1015 molecules cm−2), the correlation coefficient (R) between true NO2 SCDs and those retrieved is 0.88 and 0.79, respectively, and AOD and APH are about 0.1 and is 0 km, respectively. However, when AOD and APH are about 1.0 and 4 km, respectively, the R decreases to 0.84 and 0.53 in moderately polluted and clean regions, respectively. On the other hand, in heavily polluted regions (NO2 VCD > 2 × 1016 molecules cm−2), even high AOD and APH values are found to have a negligible effect on NO2 SCD precision. In high AOD and APH conditions in clean NO2 regions, the R between true NO2 SCDs and those retrieved increases from 0.53 to 0.58 via co-adding four pixels spatially, showing the improvement in accuracy of NO2 SCD retrieval. In addition, the high SZA and VZA are also found to decrease the accuracy of the NO2 SCD retrieval.

The presence of aerosol has resulted in serious limitations in the data coverage and large uncertainties in retrieving carbon dioxide (CO2) amounts from satellite measurements. For this reason, an aerosol retrieval algorithm was developed for the Thermal and Near-infrared Sensor for carbon Observation-Cloud and Aerosol Imager (TANSO-CAI) launched in January 2009 on board the Greenhouse Gases Observing Satellite (GOSAT). The algorithm retrieves aerosol optical depth (AOD), aerosol size information, and aerosol type in 0.1° grid resolution by look-up tables constructed using inversion products from Aerosol Robotic NETwork (AERONET) sun-photometer observation over Northeast Asia as a priori information. To improve the accuracy of the TANSO-CAI aerosol algorithm, we consider both seasonal and annual estimated radiometric degradation factors of TANSO-CAI in this study. Surface reflectance is determined by the same 23-path composite method of Rayleigh and gas corrected reflectance to avoid the stripes of each band. To distinguish aerosol absorptivity, reflectance difference test between ultraviolet (band 1) and visible (band 2) wavelengths depending on AODs was used. To remove clouds in aerosol retrieval, the normalized difference vegetation index and ratio of reflectance between band 2 (0.674 μm) and band 3 (0.870 μm) threshold tests have been applied. To mask turbid water over ocean, a threshold test for the estimated surface reflectance at band 2 was also introduced. The TANSO-CAI aerosol algorithm provides aerosol properties such as AOD, size information and aerosol types from June 2009 to December 2013 in this study. Here, we focused on the algorithm improvement for AOD retrievals and their validation in this study. The retrieved AODs were compared with those from AERONET and the Aqua/MODerate resolution Imaging Sensor (MODIS) Collection 6 Level 2 dataset over land and ocean. Comparisons of AODs between AERONET and TANSO-CAI over Northeast Asia showed good agreement with correlation coefficient (R) 0.739 ± 0.046, root mean square error (RMSE) 0.232 ± 0.047, and linear regression line slope 0.960 ± 0.083 for the entire period. Over ocean, the comparisons between Aqua/MODIS and TANSO-CAI for the same period over Northeast Asia showed improved consistency, with correlation coefficient 0.830 ± 0.047, RMSE 0.140 ± 0.019, and linear regression line slope 1.226 ± 0.063 for the entire period. Over land, however, the comparisons between Aqua/MODIS and TANSO-CAI show relatively lower correlation (approximate R = 0.67, RMSE = 0.40, slope = 0.77) than those over ocean. In order to improve accuracy in retrieving CO2 amounts, the retrieved aerosol properties in this study have been provided as input for CO2 retrieval with GOSAT TANSO-Fourier Transform Spectrometer measurements.

The Ultra-Violet Aerosol Index (UVAI) is a practical parameter for detecting aerosols that absorb UV radiation, especially where other aerosol retrievals fail, such as over bright surfaces (e.g., deserts and clouds). However, typical UVAI retrieval requires at least two UV channels, while several satellite instruments, such as the Thermal And Near infrared Sensor for carbon Observation Cloud and Aerosol Imager (TANSO-CAI) instrument onboard a Greenhouse gases Observing SATellite (GOSAT), provide single channel UV radiances. In this study, a new UVAI retrieval method was developed which uses a single UV channel. A single channel aerosol index (SAI) is defined to measure the extent to which an absorbing aerosol state differs from its state with minimized absorption by aerosol. The SAI qualitatively represents absorbing aerosols by considering a 30-day minimum composite and the variability in aerosol absorption. This study examines the feasibility of detecting absorbing aerosols using a UV-constrained satellite, focusing on those which have a single UV channel. The Vector LInearized pseudo-spherical Discrete Ordinate Radiative Transfer (VLIDORT) was used to test the sensitivity of the SAI and UVAI to aerosol optical properties. The theoretical calculations showed that highly absorbing aerosols have a meaningful correlation with SAI. The retrieved SAI from OMI and operational OMI UVAI were also in good agreement when UVAI values were greater than 0.7 (the absorption criteria of UVAI). The retrieved SAI from the TANSO-CAI data was compared with operational OMI UVAI data, demonstrating a reasonable agreement and low rate of false detection for cases of absorbing aerosols in East Asia. The SAI retrieved from TANSO-CAI was in better agreement with OMI UVAI, particularly for the values greater than the absorbing threshold value of 0.7.

We investigate the simultaneous effects of aerosol peak height (APH), aerosol properties, measurement geometry, and other factors on the air mass factor for NO2 retrieval at sites with high NO2 concentration. A comparison of the effects of high and low surface reflectance reveals that NO2 air mass factor (AMF) values over a snowy surface (surface reflectance 0.8) are generally higher than those over a deciduous forest surface (surface reflectance 0.05). Under high aerosol optical depth (AOD) conditions, the aerosol shielding effect over a high-albedo surface is revealed to reduce the path-length of light at the surface, whereas high single scattering albedo (SSA) conditions (e.g., SSA = 0.95) lead to an increase in the aerosol albedo effect, which results in an increased AMF over areas with low surface reflectance. We also conducted an in-depth study of the APH effect on AMF. For an AOD of 0.1 and half width (HW) of 5 km, NO2 AMF decreases by 29% from 1.36 to 0.96 as APH changes from 0 to 2 km. In the case of high-AOD conditions (0.9) and HW of 5 km, the NO2 AMF decreases by 240% from 1.85 to 0.54 as APH changes from 0 to 2 km. The AMF variation due to error in the model input parameters (e.g., AOD, SSA, aerosol shape, and APH) is also examined. When APH is 0 km with an AOD of 0.4, SSA of 0.88, and surface reflectance of 0.05, a 30% error in AOD induces an AMF error of between 4.85% and −3.67%, an SSA error of 0.04 leads to NO2 VCD errors of between 4.46% and −4.77%, and a 30% error in AOD induces an AMF error of between −9.53% and 8.35% with an APH of 3 km. In addition to AOD and SSA, APH is an important factor in calculating AMF, due to the 2 km error in APH under high-SZA conditions, which leads to an NO2 VCD error of over 60%. Aerosol shape is also found to have a measureable effect on AMF under high-AOD and small relative azimuth angle (RAA) conditions. The diurnal effect of the NO2 profile is also examined and discussed.

A ground Fabry-Perot interferometer has been used to measure atomic oxygen nightglow (OI 630.0 nm) from the thermosphere (about 250 km) at King Sejong station (KSS, geographic: 62.22oS, 301.25oE; geomagnetic: 50.65oS, 7.51oE), Antarctica. While numerous studies of the thermosphere have been performed on high latitude using ground-based Fabry-Perot interferometers, the thermospheric measurements in the Southern Hemisphere are relatively new and sparse. Therefore, the nightglow measurements at KSS play an important role in extending the thermospheric studies to the Southern Hemisphere. In this study, we investigated the effects of the geomagnetic and solar activities on the thermospheric neutral temperatures that have been observed at KSS in 1989 and 1997. The measured average temperatures are 1400 K in 1989 and 800 K in 1997, reflecting the influence of the solar activity. The measurements were compared with empirical models, MSIS-86 and semi-empirical model, VSH.

KARI (Korea Aerospace Research Institute) has measured the ozone density profiles over the Korean Peninsular since the launch of the Korean Sounding Rocket-I (KSR-I) in 1993. The purpose of ozone measurements is to obtain the stratospheric and mesospheric vertical ozone density profiles over the Korean Peninsular with solar UV radiometers. With the visible channel of the radiometer, the attitude variation of the rocket was corrected and compensated. Developed system is based on ozone detector designs onboard the KSR-I and KSR-II. We discuss the development of ozone detector which will be onboard the KSR-III and its circuit and vibration test results for EM Model.

Bow shock formation, in case the supersonic solar wind flow is hindered by the atmosphere of Mars, is investigated. The atoms newly ionized from the extensive neutral atmosphere of Mars are loaded to the solar wind. By the conservation of momentum, the solar wind velocity is decreased. Then the supersonic flow velocity drops to the subsonic flow velocity in front of Mars at certain region, which is called the bow shock. The location of Mars subsolar bow shock is highly varying in the range of 1.3 to 2.5 Rm. Martian bow shock location is estimated by one-dimensional flux tube equations reduced from full three-dimensional MHD equations. The variability of Mars bow shock location effected by the solar wind conditions is studied. It is evident that the solar wind dynamic pressure change is able to make the Mars bow shock location variable.

A Fourier Transform Spectrometer was used to study upper mesospheric thermodynamic by observing the hydroxyl (OH) emission. Rocket-born and satellited-born photometers place the peak emission near 87 km. The instrument was installed in February 1999 at King Sejong station (62.22 °S, 301.25 °E), Antarctica and has been in routine operation since then. An intensive operational effort has resulted in a substantial data between April and June, 1999. A harmonic analysis was carried out to examine information on the tidal characteristics. The measured amplitudes of the 12-hour oscillation are in the range of 2.4-3.7 K, which are in resonable agreement with theoretical model outputs. The harmonic analysis also revealed 8-hour oscillation which is not expected from the traditional theoretical studies. In addition, the observed 8-hour oscillations are apparent and sometimes dominate the temperature variation in the upper mesosphere.

This paper reports the results obtained from the Langmuir probe (LP) and Electron Temperature Probe (ETP) experiments on the sounding rocket KSR-II (Korean Scientific Rocket - II) which was launched on Jun 11, 1998 at 10:00 KST from Tae-An peninsula (37 N, 126 E). The instruments successfully measured the electron density, electron temperature, and the floating potential at altitudes of 73km to 130km. While the electron temperature measurement is not easy in this region, since the temperature is very low and the contamination effect of the probe may give rise to a problem, we were able to obtain a reasonable electron temperature profile by employing two independent methods, the pulse modulated Langmuir Probe and Electron Temperature Probe. The preliminary results show that electron density increases sharply at about 90km, and forms a peak at 102km. The density profile is roughly consistent with IRI (International Reference Ionosphere)95-model or PIM (Parameterized Ionospheric Model) results except that the peak density appears at 110km in the model and model electron density is slightly lower than the observed one. Electron temperature obtained from ETP fluctuates between 200K and 700K, an effect presumably coming from the wakes developed by LP, and it tends to increase with the altitude, which is consistent with the LP results.

KSR-II, a two-stage sounding rocket of KARI was launched successfully at the west coast of the Korean Peninsula at 1000LST, June 11, 1998. For the ozone measurement mission, 8-channel UV and visible radiometers were onboard the rocket. The rocket measured the first in situ stratospheric and mesospheric ozone density profile over Korea during its ascending phase using the radiometer. Comparisons with Dobson spectrophotometer, ozonesonde, and HALOE onboard the UARS are shown together. Our results are in reasonable agreements with others.

We measured the terrestrial nightglow of OI 6300A in the thermosphere(~250km) using a ground-based Fabry-Perot interferometer at the King Sejong Station, Antarctic from March through September, 1997. The King Sejong Station is located at high latitude geographically (62.22 deg S, 301.25 deg E) but at mid-latitude geomagnetically (50.65 deg S, 7.51 deg E). It is therefore the strategic location to measure the temperatures of the thermosphere in the Southern Hemisphere associated with both solar and geomagnetic activities. In this study, we analyzed the observed temperatures in relation to F10.7 and Kp indices to examine the effect of the solar and the geomagnetic activities on high-latitude neutral thermosphere. During the observing period, the solar activity was at its minimum. The measured temperatures are usually in the range between about 600~1000 K with some seasonal variation and are higher than those predicted by semi-empirical model, VSH (Vector Spherical Harmonics) and empirical model, MSIS (Mass-Spectrometer-Incoherent-Scatter)-86.

Radiometers in UV and visible wavelengths were onboard the Korean Sounding Rocket(KSR)-1 and 2 which were launched on June 4th and September 1st, 1993. These radiometers were designed to capture the solar radiation during the ascending period of the rocket flight. The purpose of the instrument was to measure the vertical profiles of stratospheric ozone densities. Since the instrument measured the solar radiation from the ground to its apogee, it is possible to investigate the altitude variation of the measured intensity and to estimate the effect of atmospheric scattering by comparing the UV and visible intensity. The visible channel was a reference because the 450-nm wavelength is in the atmospheric window region, where the solar radiation is transmitted through the atmosphere without being absorbed by other atmospheric gases. The use of 450-nm channel intensity as a reference should be limited to the altitude ranges above the certain altitudes, say 20 to 25§° where the signals are not perturbed by atmospheric scattering effects.

To investigate the characteristics of Spinning Enhanced Visible and Infrared Imager (SEVIRI)-derived products from the UK Met Office algorithm, one year of cloud occurrence frequency (COF) and cloud-top height (CTH) data from May 2013 to April 2014 was analysed in comparison with Cloud Profiling Radar (CPR) and Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP) cloud products observed from the A-Train constellation. Because CPR operated in daylight-only data collection mode, daytime products were validated in this study. It is important to note that the different sensor characteristics cause differences in CTH retrievals. The CTH of active instruments, CPR and CALIOP, is derived from the return time of the backscattered radar or LiDAR signal, while the infrared sensor, SEVIRI, measures a radiatively effective CTH. Therefore, some systematic differences in comparison results are expected. However, similarities in spatial distribution and seasonal variability of COFs were noted among SEVIRI, CALIOP, and CPR products, although COF derived by the SEVIRI algorithm showed biases of 14.35% and −3.90% compared with those from CPR and CALIOP measurements, respectively. We found that the SEVIRI algorithm estimated larger COF values than the CPR product, especially over oceans, whereas smaller COF was detected by SEVIRI measurements over land and in the tropics than by CALIOP, where multi-layer clouds and thin cirrus clouds are dominant. CTHs derived from SEVIRI showed better agreement with CPR than with CALIOP. Further comparison with CPR showed that SEVIRI CTH was highly sensitive to the CO2 bias correction used in the Minimum Residual method. Compared with CPR CTHs, SEVIRI has produced stable CTHs since the bias correction update in November 2013, with a correlation coefficient of 0.93, bias of −0.27 km, and standard deviation of 1.61 km.

In East Asia, where aerosol concentrations are persistently high throughout the year, most satellite CO2 retrieval algorithms screen out many measurements during quality control in order to reduce retrieval errors. To reduce the retrieval errors associated with aerosols, we have modified YCAR (Yonsei Carbon Retrieval) algorithm to YCAR-CAI to retrieve XCO2 from GOSAT FTS measurements using aerosol retrievals from simultaneous Cloud and Aerosol Imager (CAI) measurements. The CAI aerosol algorithm provides aerosol type and optical depth information simultaneously for the same geometry and optical path as FTS. The YCAR-CAI XCO2 retrieval algorithm has been developed based on the optimal estimation method. The algorithm uses the VLIDORT V2.6 radiative transfer model to calculate radiances and Jacobian functions. The XCO2 results retrieved using the YCAR-CAI algorithm were evaluated by comparing them with ground-based TCCON measurements and current operational GOSAT XCO2 retrievals. The retrievals show a clear annual cycle, with an increasing trend of 2.02 to 2.39 ppm per year, which is higher than that measured at Mauna Loa, Hawaii. The YCAR-CAI results were validated against the Tsukuba and Saga TCCON sites and show an root mean square error of 2.25, a bias of −0.81 ppm, and a regression line closer to the linear identity function compared with other current algorithms. Even after post-screening, the YCAR-CAI algorithm provides a larger dataset of XCO2 compared with other retrieval algorithms by 21% to 67%, which could be substantially advantageous in validation and data analysis for the area of East Asia. Retrieval uncertainty indicates a 1.39 to 1.48 ppm at the TCCON sites. Using Carbon Tracker-Asia (CT-A) data, the sampling error was analyzed and was found to be between 0.32 and 0.36 ppm for each individual sounding.

Based on an optimal estimation method, an algorithm was developed to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO2) using Shortwave Infrared (SWIR) channels, referred to as the Yonsei CArbon Retrieval (YCAR) algorithm. The performance of the YCAR algorithm is here examined using simulated radiance spectra, with simulations conducted using different Aerosol Optical Depths (AODs), Solar Zenith Angles (SZAs) and aerosol types over various surface types. To characterize the XCO2 retrieval algorithm, reference tests using simulated spectra were analysed through a posteriori XCO2 retrieval errors and averaging kernels. The a posteriori XCO2 retrieval errors generally increase with increasing SZA. However, errors were found to be small (

The spatio-temporal characteristics of the clearness index (KT) were investigated using daily global solar irradiance measurements (290–2800 nm) for the period of 2000–2014 at 21 sites in Korea, a complex region in East Asia with a distinct monsoon season and heavy aerosol loading year-round. The annual mean KT value for all sites is 0.46, with values of 0.63 and 0.25 for clear and overcast skies, respectively. The seasonal variations in monthly average KT show a minimum of 0.37 in July at all sites except for Jeju, where the value was 0.29 in January. The maximum value (KT = 0.51) is observed in October, followed by a secondary peak (KT = 0.49) during February–April. The lowest KT value (KT = 0.42) was observed at both the Seoul and Jeju sites, and the highest (KT = 0.48) in the southeastern regions. Increases in average KT exceeding 4% per decade were observed in the middle and southeastern regions, with the maximum (+8% per decade) at the Daegu site. Decreasing trends (

The first sounding rocket in Korea, KSR-420S has been under the development at Korea Aerospace Research Institute(KARI), and is expected to launched on 1993 to measure the vertical ozone profile over the Korean Peninsula. The KSR-420S is expected to provide the first in situ measurement of ozone concentrations over the Korean Peninsula. An optical ozone detector has been developed at Korea Research Institute of Standards and Science(KRISS), and its calibration has been completed recently. In this paper, measurement principles of the ozone detector in KSR-420S, its calibration data, ozone measurement procedure and data reduction algorithm are presented with sample calculations.

Wildfires and agricultural burning generate seemingly increasing smoke aerosol emissions, impacting societal and natural ecosystems. To understand smoke's effects on climate and public health, we analyzed the spatiotemporal distribution of smoke aerosols, focusing on two major light-absorbing components, black carbon (BC) and brown carbon (BrC) aerosols. Using NASA's Earth Polychromatic Imaging Camera (EPIC) instrument aboard the NOAA's Deep Space Climate Observatory (DSCOVR) spacecraft, we inferred BC and BrC volume fractions and particle mass concentrations based on spectral absorption provided by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm with 1-2 hours temporal resolution and ~10 km spatial resolution over North America and Central Africa. Our analyses of regional smoke properties reveal distinct characteristics for aerosol optical depth (AOD) at 443 nm, spectral single scattering albedo (SSA), aerosol layer height (ALH), and BC and BrC amounts. Smoke cases in North America show extremely high AOD up to 6, with elevated ALH (6-7 km) and significant BrC components up to 250 mg/m2 along the transport paths, whereas the smoke aerosols in Central Africa exhibited stronger light absorption (i.e., lower SSA) and lower AOD, resulting in higher BC mass concentrations and similar BrC mass concentrations than the cases in North America. Seasonal burning source locations in Central Africa following the seasonal shift of Inter Tropical Convergence Zone and diurnal variations in smoke amounts were also captured. Comparison of retrieved AOD443, SSA443, SSA680, and ALH with collocated AERONET and CALIOP measurements shows agreement with rmse of 0.2, 0.03-0.04, 0.02-0.04, and 0.8-1.3 km, respectively. Analysis of spatiotemporally average reveals distinct geographical characteristics in smoke properties closely linked to burning types and meteorological conditions. Forest wildfires over western North America generated smoke with small BC volume fraction of 0.011 and high ALH with large variability (2.2 ± 1.2 km), whereas smoke from wildfires and agricultural burning over Mexico region shows more absorption and low ALH. Smoke from savanna fires over Central Africa has the most absorption with high BC volume fraction (0.015) and low ALH with small variation (1.8 ± 0.6 km) among the analyzed regions. Tropical forest smoke was less absorbing and had a high variance in ALH. We also quantify the estimation uncertainties related to the assumptions of BC and BrC refractive indices. The MAIAC EPIC smoke properties with BC and BrC volume and mass fractions and assessment of layer height provide observational constraints for radiative forcing modeling and air quality and health studies. [ABSTRACT FROM AUTHOR]