Search

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 (NOx) and primary 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 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 extent 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 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 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 extent of PM2.5 underestimation to 19.58 % and 6.81 %, respectively. These findings indicate that the series of emissions adjustments 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 multi-source top-down information for capturing highly varying anthropogenic emissions. [ABSTRACT FROM AUTHOR]

Geostationary satellite sensors over East Asia (GOCI and AHI) are now providing continuous mapping of aerosol optical depth (AOD) at 550 nm to improve monitoring of fine particulate matter (PM2.5) air quality. Here we evaluate our understanding of the physical relationships between AOD and PM2.5 over East Asia by using the GEOS-Chem atmospheric chemistry model to simulate observations from multiple sources: 1) the joint NASA-NIER Korea - United States Air Quality aircraft campaign over South Korea (KORUS-AQ; May-June 2016); 2) AODs from the AERONET ground-based network; 3) AOD from a new GOCI/AHI fused product; and 4) surface PM2.5 networks in South Korea and China. The KORUS-AQ data show that 550 nm AOD is mainly contributed by sulfate-nitrate-ammonium (SNA) and organic aerosols in the planetary boundary layer (PBL), despite large dust concentrations in the free troposphere, reflecting the optically effective size and the high hygroscopicity of the PBL aerosols. Although GEOS-Chem is successful in reproducing the KORUS-AQ vertical profiles of aerosol mass, its ability to link AOD to PM2.5 is limited by under-accounting of coarse PM and by a large overestimate of nighttime PM2.5 nitrate. A broader analysis of the GOCI/AHI AOD data over East Asia in different seasons shows agreement with AERONET AODs and a spatial distribution consistent with surface PM2.5 network data. The AOD observations over North China show a summer maximum and winter minimum, opposite in phase to surface PM2.5. This is due to low PBL depths compounded by high residential coal emissions in winter, and high relative humidity (RH) in summer. Seasonality of AOD and PM2.5 over South Korea is much weaker, reflecting weaker variation of PBL depth and lack of residential coal emissions. Physical interpretation of the satellite AOD data in terms of surface PM2.5 is sensitive to accurate information on aerosol size distributions, PBL depths, RH, the role of coarse particles, and diurnal variation of PM2.5. [ABSTRACT FROM AUTHOR]