Using machine learning skills, this study evaluated the performance of surface PM2.5 estimation based on the consideration of geostationary satellite AOD having two different spatial resolution. This study targeted two megacities in Korea: Seoul and Busan. We used PM2.5 data from hundreds of low-cost sensors installed in these megacities over a 19-month period (June 2018 to December 2019), combined with 6 & times; 6 km2 (officially released version) and 0.5 & times; 0.5 km2 (research-purpose product) AOD data from the Geostationary Ocean Color Imager (GOCI). Both cross-validation and independent validation against national surface PM2.5 observations (AirKorea) showed improved performance at finer spatial resolution. When our machine-learning PM2.5 estimation using low-cost sensor data was compare to AirKorea data, the 0.5 & times; 0.5 km2 resolution yielded higher R2 and lower RMSE compared to the 6 & times; 6 km2 resolution in both cities; R2 slightly increased from 0.32 (0.44) to 0.38 (0.45) and RMSE decreased from 11.36 (10.18) to 10.82 (10.13) mu g/m3 in Seoul (Busan) at 0.5 & times; 0.5 km2 resolution. When the variable importance is evaluated, 6 & times; 6 km2 AOD contribution is large, but 0.5 & times; 0.5km2 AOD contribution is not significant in the machine learning process. This finding shows that the usage of satellite data having higher spatial resolution results in better performance of PM2.5 estimation in spite of larger uncertainty. In other words, it is expected to achieve better results when more qualified AOD is ready in the future with higher spatial resolution. We also provided estimated PM2.5 in a hourly scale, which is another advantage to use the geostationary satellite AOD.

Based on the combination of multiple satellite and reanalysis data, we conducted the evaluation of unprecedented large wildfires in South Korea during March 2025. This wildfire event was quite enormous for changing the climatology of Korean wildfire properties in recent 20 years. In spite of moderate performance, the Moderate Resolution Imaging Spectroradiometer (MODIS), the sensor of low-earth orbit satellite, did not perfectly capture the smoke plume from this event. When we utilized the Geostationary Environment Monitoring Spectrometer (GEMS), however, we clearly detected the plume generation having high aerosol density, revealing that the geostationary satellite sensor enables to play a role of the biomass burning monitoring better. Additionally, we could see the enhancement of carbon monoxide even around ~ 10 km altitude over the wildfire area using the measurement of limb-viewing satellite (Microwave Limb Sounder, MLS), implying the potential impact of burning event to the upper tropospheric chemistry. We also found that multiple reanalysis data can detect the region that smoke plume affects during this wildfire event of March 2025, but also confirmed the necessity to improve reanalysis data quality especially for the spatial resolution. This study can be a useful reference for showing how to use the existed satellite and reanalysis dataset when the urgent diagnosis of atmospheric change is needed related to the occurrence of wildfire events.

Abstract Accurate retrieval of aerosol and cloud properties is essential for improving both climate data records and trace gas observations by satellites. The Global Observing Satellite for Greenhouse gases and Water cycle (GOSAT-GW), launched on 29 June 2025, carries the Total Anthropogenic and Natural emissions mapping SpectrOmeter-3 (TANSO-3), which is designed to provide high-spectral-resolution measurements in the visible (~ 450 nm), O2 A band, and near-infrared (~ 1600 nm) regions. To prepare for operation, an aerosol and cloud retrieval algorithm was developed using Tropospheric Monitoring Instrument (TROPOMI) Level 1B data as a proxy. The algorithm retrieves the aerosol optical depth (AOD) and cloud optical depth (COD) from band 1 only from TANSO-3, and estimates the aerosol layer height (ALH) and cloud layer height (CLH) using O2–O2 absorption at 477 nm. Lookup tables were generated with the linearized pseudo-spherical vector Discrete Ordinate Radiative Transfer, and additional corrections were applied to account for relative humidity, temperature dependence, and systematic offsets in the O2–O2 absorption band. During the airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) 2024 campaign, the AODs showed good agreement with AERONET (correlation coefficient (R) = 0.781, root mean square error (RMSE) = 0.265), while comparison with Pandora SMART-s retrievals showed more moderate agreement (R = 0.444, RMSE = 0.378). ALH retrievals agreed with High Spectral Resolution Lidar-2 (HSRL-2) data within ± 1 km for AODs greater than 0.5, while under low-aerosol loading conditions the retrieved ALH became less sensitive and tended to converge toward near-surface values. COD retrievals were consistent with TROPOMI operational products, while CLH retrievals based on the O2–O2 absorption band were on average about 1 km lower than O2 A band-based results, likely due to differences in the vertical sensitivity of the two absorption bands. With further refinement in surface reflectance treatment, aerosol classification, and cloud phase detection, the algorithm will contribute to the effective use of GOSAT-GW observations for atmospheric research and air quality applications.

Computational advances have enabled the evolution and refinement of satellite aerosol retrieval algorithms to mitigate issues in previous versions and improve their results. This study addressed a fundamental limitation in operational satellite remote sensing which is the trade-off between computational efficiency and retrieval accuracy in conventional look-up table (LUT)-based algorithms. The hybrid-Yonsei Aerosol Retrieval (hybrid-YAER) algorithm for the Advanced Meteorological Imager (AMI) incorporates a flexible aerosol model input, which became available by using a deep learning (DL) radiative transfer model (RTM) that replaces the interpolation-based inversion with LUTs. The DLRTM, trained with 100,000 Vector LInearized Discrete Ordinate Radiative Transfer (VLIDORT) code simulations using a Set Transformer architecture, accurately reproduces top-of-atmosphere reflectance (the root-mean-square error is about 0.002 for Mie calculations) at computation times about 0.1% of those of a conventional RTM. Additionally, a dust call-back procedure and an extended surface reflectance database based on multi-year minimum reflectance are introduced in the hybrid-YAER algorithm. Validation against AERONET and comparison with Visible Infrared Imaging Radiometer Suite (VIIRS) Dark Target (DT) and Deep Blue (DB) AOD products demonstrate that the new algorithm mitigates underestimation of low values of aerosol optical depth (AOD) and discontinuities of AOD values between adjacent land and ocean, while improving overall correlation (R increased from 0.757 to 0.767 for land pixels and from 0.856 to 0.881 for ocean pixels) and reducing bias. Over ocean, bias patterns become more linear, and the retrieval stability is improved (R2 increased from 0.88 to 0.94) compared to the original version. Uncertainty analysis shows that retrieval uncertainty scales linearly with AOD, confirming the robustness of the hybrid algorithm. The concept of advancement in aerosol retrieval via hybrid algorithm can be applied to any other aerosol remote sensing algorithms.

We assessed nitrogen dioxide (NO2) air-mass factors (AMF) by replacing the traditional Lambertian cloud approximation with a radiatively consistent scattering-cloud scheme based on Mie phase functions in the linearized pseudo-spherical vector Discrete Ordinate Radiative Transfer v2.8.3. Using TROPOspheric Monitoring Instrument (TROPOMI) observations, we contrasted the two cloud models via targeted sensitivity tests and validated the impacts against the vertical column density (VCD) obtained from the Pandora network. Differences peaked at low cloud optical depth and diminished as the paths saturated; lower cloud-top height within the troposphere amplified the tropospheric sensitivity. In real scenes, scattering-cloud AMF are slightly larger than Lambertian AMF as cloud influence increases, yielding modestly smaller VCDs. Over Seoul, the operational Lambertian product overestimates the Pandora data by similar to 10-12%, and the scattering-cloud treatment reduces this bias without tuning via radiance closure. Across collocations, Global Climate Observing System compliance fractions are similar (similar to 54-56%), while the spread is dominated by cloud edges, sub-pixel heterogeneity, and aerosol - cloud configurations. We also showed that the operational radiance-closure adjustment in the NO2 window can shift AMF by similar to 10-15% in polluted, partly cloudy scenes. The scattering-cloud approach improves physical consistency and clarifies remaining error sources, informing practical steps towards operational implementation.

The Japanese Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW) will be an Earth-observing satellite to conduct global observations of atmospheric carbon dioxide (CO2), methane (CH4), and nitrogen dioxide (NO2) simultaneously from a single platform. GOSAT-GW is the third satellite in the series of the currently operating Greenhouse gases Observing SATellite (GOSAT) and GOSAT-2. It will carry two sensors, the Total Anthropogenic and Natural emissions mapping SpectrOmeter-3 (TANSO-3), and the Advanced Microwave Scanning Radiometer 3 (AMSR3), with the latter dedicated to the observation of physical parameters related to the water cycle. TANSO-3 is a high-resolution grating spectrometer designed to measure reflected sunlight in the visible to short-wave infrared spectral ranges. It aims to retrieve the column-averaged dry-air mole fractions of CO2 and CH4 (denoted as XCO2 and XCH4, respectively), as well as the vertical column density of tropospheric NO2. The TANSO-3 sensor onboard GOSAT-GW will utilize the wavelength bands of 0.45, 0.76, and 1.61 µm for NO2, O2, and CO2 and CH4 retrievals, respectively. GOSAT-GW will fly in a sun-synchronous orbit with a local overpass time of approximately 13:30 and a 3-day ground-track repeat cycle. The TANSO-3 sensor has two observation modes in the push-broom operation: Wide Mode, which provides globally covered maps with a 10-km spatial resolution within 3 days, and Focus Mode, which provides snapshot maps over targeted areas with a high spatial resolution of 1–3 km. The objectives of the GOSAT-GW mission include (1) monitoring atmospheric global-mean concentrations of greenhouse gasses (GHGs), (2) verifying national anthropogenic GHG emissions inventories, and (3) detecting GHG emissions from large sources, such as megacities and power plants. A comprehensive validation exercise will be conducted to ensure that the sensor products’ quality meets the required precision to achieve the above objectives. With a projected operational lifetime of seven years, GOSAT-GW will provide vital space-based constraints on both anthropogenic and natural GHG emissions. These measurements will contribute significantly to climate change mitigation efforts, particularly by supporting the Global Stocktake (GST) mechanism, a key element of the Paris Agreement.

Aerosol size information is important to the understanding of aerosol dynamics, which change rapidly during wildfire, dust transport, and volcanic eruption events over Asia. In this study, a deep neural network (DNN) model was trained using Advanced Meteorological Imager (AMI) Level 1B observations, AMI Yonsei aerosol retrieval (YAER) aerosol products, and observation geometries to retrieve the aerosol optical depth (AOD),& Aring;ngstr & ouml;m exponent (AE), and spectral derivatives of AE (AE ' ). The fine-mode fraction (FMF) was calculated with a spectral deconvolution algorithm (SDA) using retrieved AE and AE ' when AOD >0.2. The retrieved aerosol products were validated using Aerosol RObotic NETwork (AERONET) (AOD at 550 nm: R=0.837 , root-mean-square error (RMSE) =0.219, and mean bias error (MBE) =-0.066 ; AE: R=0.726 ; RMSE =0.231; MBE =-0.007 ; FMF: R =0.875; RMSE =0.072; and MBE =0.007). Case studies of dust transport, wildfire, and haze events in Asia revealed that the retrieved aerosol size products may be used for analysis of sudden pollution events. Results of this study indicate the potential for a comprehensive analysis of aerosol properties in Asia using continuous aerosol size data from geostationary Earth orbit (GEO) satellite observations.

Aerosol optical depth (AOD) data fusion for aerosol datasets obtained from the Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT; GK) series was conducted through the application of both statistical and deep neural network (DNN)-based methodologies. The GK mission incorporates the Advanced Meteorological Imager (AMI) on GK-2A, as well as the Geostationary Environment Monitoring Spectrometer (GEMS) and Geostationary Ocean Color Imager-II (GOCI-II) on GK-2B. The statistical fusion approach rectified biases in each aerosol product by assuming a Gaussian error distribution. Utilizing Maximum Likelihood Estimation (MLE) fusion, the technique accounted for pixel-level uncertainties by weighting the root-mean-square error of each AOD product for individual pixels. A DNN-based fusion model was trained to align with Aerosol Robotic Network AOD values through fully connected hidden layers. The results of both statistical and DNN-based fusion generally surpassed the performance of individual GEMS and AMI AOD datasets in East Asia (R = 0.888; RMSE = −0.188; MBE = −0.076; 60.6% within EE for MLE AOD; R = 0.905; RMSE = 0.161; MBE = −0.060; 65.6% within EE for DNN AOD). Particularly, focusing on AOD around the Korean peninsula, encompassing all aerosol products, yielded significantly improved outcomes (R = 0.911; RMSE = 0.113; MBE = −0.047; 73.3% within EE for MLE AOD; R = 0.912; RMSE = 0.102; MBE = −0.028; 78.2% within EE for DNN AOD). The DNN AOD demonstrated effective handling of the rapid increase in uncertainty at higher aerosol loadings. Overall, the fusion AOD, particularly DNN AOD, closely matched with the performance of the Moderate Resolution Imaging Spectroradiometer Dark Target algorithm, exhibiting slightly less variance and a negative bias. Both fusion algorithms stabilized diurnal error variations and provided additional insights into hourly aerosol evolution.

Applying assumptions about the optical properties of dust, particularly the refractive index (RI), introduces significant uncertainty in thermal infrared dust-retrieval algorithms. To address this, we present a tailored RI dataset (ERML 2025) for Asian dust, derived from long-term chemical composition measurements in South Korea. An enhanced algorithm was developed using this Asian dust RI and thermal infrared channels from the GK-2 A Korean geostationary satellite. This LUT-based algorithm integrates three methods for dust layer height estimation: the Unified Model (UM), the Asian Dust Aerosol Model 3 (ADAM3), and a fixed-height approach. Operational dust detection processes and consistent assumptions were applied to minimize confounding variables in sensitivity tests. Qualitative validation using GK-2 A RGB and IASI-LMD products showed strong alignment in some regions and notable mismatches elsewhere, likely due to dust detection performance. Quantitative comparisons were conducted using MODIS data. Sensitivity analyses demonstrated that the combined use of the updated algorithm and UM model improved the operational method in most cases. Results also indicated that the updated algorithm retrieved higher AOD values, attributable to the increased absorption in the new RI dataset. Furthermore, comparisons with widely cited RI datasets revealed that while the real part of the Asian dust RI showed similar trends, its imaginary part differed markedly in magnitude and shape—reflecting the variability in dust origins. This region-specific RI dataset will help reduce inconsistencies in future studies caused by using RI values from remote sources that may not accurately represent Asian dust characteristics.

The Yonsei AErosol Retrieval Algorithm (YAER) has been developed and improved for application with geostationary satellite-based imagers such as the Geostationary Ocean Color Imager (GOCI) and Advanced Himawari Imager (AHI). In this study its application was extended to the Advanced Meteorological Imager (AMI) and Advanced Geostationary Radiation Imager (AGRI). With the 12 AMI and 11 AGRI infrared channels, the observed data from both sensors can mask bright pixels with considerable accuracy. Detection of cirrus cloud pixels is more accurate with the AMI and AGRI than with other imagers (e.g., GOCI, AHI), as the former have a 1.3 μm shortwave infrared channel. Despite there being two visible channels in AGRI and three in AMI, retrieved aerosol optical depth products are qualitatively consistent with Aerosol Robotic Network (AERONET) data. Retrieval of aerosol properties with the AMI and AGRI YAER algorithm will enhance the aerosol monitoring capability of GOCI and AHI systems, both spatially and temporally.

Data fusion of aerosol optical depth (AOD) datasets from the second generation of the Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT-2, GK-2) series was undertaken using both statistical and deep neural network (DNN)-based methods. The GK-2 mission includes an Advanced Meteorological Imager (AMI) aboard GK-2A and a Geostationary Environment Monitoring Spectrometer (GEMS) and Geostationary Ocean Color Imager II (GOCI-II) aboard GK-2B. The statistical fusion method, maximum likelihood estimation (MLE), corrected the bias of each aerosol product by assuming a Gaussian error distribution and accounted for pixel-level uncertainties by weighting the root-mean-square error of each AOD product for every pixel. A DNN-based fusion model was trained to target AErosol RObotic NETwork (AERONET) AOD values using fully connected hidden layers. The MLE and DNN AOD outperformed individual GEMS and AMI AOD datasets in East Asia (R = 0.888; RMSE = −0.188; MBE = −0.076; 60.6 % within EE for MLE AOD; R = 0.905; RMSE = 0.161; MBE = −0.060; 65.6 % within EE for DNN AOD). The selection of AOD around the Korean Peninsula, which incorporates all aerosol products including GOCI-II, resulted in much better results (R = 0.911; RMSE = 0.113; MBE = −0.047; 73.3 % within EE for MLE AOD; R = 0.912; RMSE = 0.102; MBE = −0.028; 78.2 % within EE for DNN AOD). The DNN AOD effectively addressed the rapid increase in uncertainty at higher aerosol loadings. Overall, fusion AOD (particularly DNN AOD) showed improvements with less variance and a negative bias. Both fusion algorithms stabilized diurnal error variations and provided additional insights into hourly aerosol evolution. The application of aerosol fusion techniques to future geostationary satellite projects such as Tropospheric Emissions: Monitoring of Pollution (TEMPO), Sentinel-4, and Geostationary Extended Observations (GeoXO) may facilitate the production of high-quality global aerosol data.

Aerosol layer height (ALH) has been retrieved using multi-angle observations or the O2–O2 and O2–A/B absorption bands. This study attempted to retrieve ALH using the Advanced Himawari Imager (AHI), a single passive imager onboard Himawari-8 and -9. ALH retrieval using geostationary Earth orbit (GEO) satellites is advantageous for monitoring diurnal changes in ALH and understanding long-range transport. Before retrieving the ALH, the aerosol optical properties (AOPs) are retrieved using the green-near infrared (NIR) band, which is relatively insensitive to aerosol height. The retrieved AOPs are used as input to the radiative transfer calculation to compute the top-of-atmosphere (TOA) reflectance of a highly sensitive band (the blue band in this study). Then, the ALH is retrieved using the observed and calculated TOA reflectances. Since the retrieval accuracy of the aerosol optical depth (AOD) is better over the ocean, the retrieval was performed only over the ocean during the Korea–United States Air Quality Study (KORUS-AQ) campaign period. The retrieved ALH was validated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and high-spectral-resolution Lidar (HSRL).

. In response to the need for securing a spatiotemporally more up-to-date emissions inventory and the impending release of new geostationary platform-derived observational data generated by the Geostationary Environment Monitoring Spectrometer (GEMS) and its sister instruments, this study, using a series of GEMS data fusion product and its proxy data and CTM-based inverse modeling techniques, aims to establish a top-down approach for adjusting aerosol precursor emissions over East Asia. We begin by sequentially adjusting bottom-up estimates of nitrogen oxides (NO x ) and primary 20 particulate matter (PM) emissions, both of which significantly contribute to aerosol loadings over East Asia, to reduce model biases in aerosol optical depth (AOD) simulations during the year 2019. While the model initially underestimates AOD by 50.73% on average, the sequential emissions adjustments that led to overall increases in the amounts of NO x emissions by 122.79% and of primary PM emissions by 76.68% and 114.63% (single-and multiple-instrument-derived emissions adjustments, respectively), reduce the extent of AOD underestimation

In response to the need for an up-to-date emissions inventory and the recent achievement of geostationary observations afforded by the Geostationary Environment Monitoring Spectrometer (GEMS) and its sister instruments, this study aims to establish a top-down approach for adjusting aerosol precursor emissions over East Asia. This study involves a series of the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product, the GEMS aerosol optical depth (AOD) data fusion product and its proxy product, and chemical transport model (CTM)-based inverse modeling techniques. We begin by sequentially adjusting bottom-up estimates of nitrogen oxides (NOx) and primary particulate matter (PM) emissions, both of which significantly contribute to aerosol loadings over East Asia to reduce model biases in AOD simulations during the year 2019. While the model initially underestimates AOD by 50.73 % on average, the sequential emissions adjustments that led to overall increases in the amounts of NOx emissions by 122.79 % and of primary PM emissions by 76.68 % and 114.63 % (single- and multiple-instrument-derived emissions adjustments, respectively) reduce the extents of AOD underestimation to 33.84 % and 19.60 %, respectively. We consider the outperformance of the model using the emissions constrained by the data fusion product to be the result of the improvement in the quantity of available data. Taking advantage of the data fusion product, we perform sequential emissions adjustments during the spring of 2022, the period during which the substantial reductions in anthropogenic emissions took place accompanied by the COVID-19 pandemic lockdowns over highly industrialized and urbanized regions in China. While the model initially overestimates surface PM2.5 concentrations by 47.58 % and 20.60 % in the North China Plain (NCP) region and South Korea (hereafter referred to as Korea), the sequential emissions adjustments that led to overall decreases in NOx and primary PM emissions by 7.84 % and 9.03 %, respectively, substantially reduce the extents of PM2.5 underestimation to 19.58 % and 6.81 %, respectively. These findings indicate that the series of emissions adjustments, supported by the TROPOMI and GEMS-involved data fusion products, performed in this study are generally effective at reducing model biases in simulations of aerosol loading over East Asia; in particular, the model performance tends to improve to a greater extent on the condition that spatiotemporally more continuous and frequent observational references are used to capture variations in bottom-up estimates of emissions. In addition to reconfirming the close association between aerosol precursor emissions and AOD as well as surface PM2.5 concentrations, the findings of this study could provide a useful basis for how to most effectively exploit multisource top-down information for capturing highly varying anthropogenic emissions.

Hyun Ho Lim, MD, Jae Kwang Lee, MHA, Sunyoung Park, MD, Jhin Goo Chang, MD, PhD, Jooyoung Oh, MD, PhD, Jaesub Park, MD, Jungeun Song, MD. Mood Emot 2023;21:95-103. https://doi.org/10.35986/me.2023.21.3.95

Fine particulate matter with a diameter below 2.5 μm (PM2.5) is deleterious to the cardiovascular and respiratory systems. It is often difficult to assess the effects of PM2.5 on human health over regions with limited ground monitoring sites, especially in East Asia. As an alternative, we estimated near-surface PM2.5 concentrations by analyzing Advanced Himawari Imager (AHI) Yonsei Aerosol Retrieval (YAER) products. This study incorporates daytime data for East Asia covering the Korean Peninsula, China, Japan, Southeast Asia, and southern Mongolia. We collocated AHI YAER product pixels with meteorological, land-cover, and other ancillary data for the period from March 2018 to February 2019. To estimate PM2.5 concentrations over wide areas spanning many countries displaying various relationships between aerosol optical depth and PM2.5, monthly models were developed by considering both the spatial and temporal characteristics of ground-based PM2.5 measurements. Random forest machine learning model estimated ground-level mass concentrations of PM2.5; subsequent 10-fold cross validation (CV) yielded a CV R2 value of 0.81 and a CV root mean squared error (RMSE) of 12.3 μg m−3. We investigated the spatial pattern of PM2.5 concentrations over multiple countries and seasonal variation in PM2.5 concentrations. Diurnal variation of a severe PM2.5 event in the Korean Peninsula was investigated as a case study. The model captured the extremely heterogeneous spatial distribution of PM2.5 concentrations peaked around local noon. To measure the capability of the developed model to estimate PM2.5 concentrations in areas with few in-situ data, its predictive performance was evaluated using a dataset independent of the training process with an R2 of 0.60 and RMSE of 8.18 μg m−3. This study demonstrates the potential for satellite-based PM2.5 estimation for areas with insufficient measuring stations.

Temporal variation in cloud cover and surface conditions greatly affects estimation errors associated with reference surface reflectance and atmospheric composition retrievals. In this study, to determine an optimal temporal window for clear-sky composition methods that are used to identify surface reflectance for the retrieval of atmospheric properties, we analyzed temporal variation in surface reflectance and cloud fractions using long-term daily observations from the Moderate Resolution Imaing Spectroradiometer (MODIS) satellite. For the temporal variation in surface reflectance, gridded pixels with a standard deviation less than 0.025 represented 87.0%, 84.5%, 80.5%, and 77.3% of the total pixels for periods of 15, 20, 30, and 40 days, respectively. The temporal variability of surface reflectance was lowest in summer and highest in winter due to vegetation and snow cover changes over land surface in East Asia. For the temporal variation in cloud fractions, pixels with a cloud fraction <10% ranged from 91.2% (15-day) to 98.1% (40-day). Only temporal windows of 30 and 40 days satisfied the criterion of 95% cumulative distribution in the 10% cloud fraction range. Thus, and appropriate temporal window for clear-sky composition methods must be selected in consideration of the seasonal dependency of surface types and cloud cover variation. The temporal window for the clear-sky composition must be longer than 30 days considering the temporal variability of cloud cover, and shorter than 30 days considering that of surface reflectance. However, seasonal dependencies of surface reflectance and cloud fraction are also additionally considered to select the appropriated temporal window for the clear-sky composition.

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.

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 degrees 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 degrees 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 rootmean-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.