In this paper, we introduce a cloud top-height (CTH) retrieval algorithm using simultaneous observations from the Himawari-8 and FengYun (FY)-2E geostationary (GEO) satellites (hereafter, dual-GEO CTH algorithm). The dual-GEO CTH algorithm estimates CTH based on the parallax, which is the difference in the apparent position of clouds observed from two GEO satellites simultaneously. The dual-GEO CTH algorithm consists of four major procedures: (1) image remapping, (2) image matching, (3) CTH calculation, and (4) quality control. The retrieved CTHs were compared with other satellite CTHs from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and the Cloud-Profiling Radar (CPR), on three occasions. Considering the geometric configuration and footprint sizes of the two GEO satellites, the theoretical accuracy of the dual-GEO CTH algorithm is estimated as ±0.93 km. The comparisons show that the retrieval accuracy generally tends to fall within the theoretical accuracy range. As the dual-GEO CTH algorithm is based on parallax, it could be easily applied for the estimation of the height of any elevated feature in various fields.

Despite the importance of understanding the urban emission characteristics of greenhouse gases (GHGs) and air pollutants, few studies have conducted integrated assessments across diverse urban environments. Herein, we conducted a comprehensive evaluation of the emission characteristics of GHGs and air pollutants in seven cities in the Northern Hemisphere using ground‐based Fourier transform spectrometers. Our analysis primarily focused on emission ratios of excess column‐averaged dry‐air mole fractions of carbon monoxide (CO) to carbon dioxide (CO₂) (∆XCO:∆XCO₂) and those of methane (CH₄) to CO₂ (∆XCH₄:∆XCO₂). We found that the emission ratios varied significantly across cities. Xianghe (China) and Pasadena (USA), known for severe air pollution, showed the highest emission ratios. Notably, Seoul (South Korea) showed lower ∆XCO:∆XCO₂ (3.32 ± 0.10 ppb/ppm) but relatively higher ∆XCH₄:∆XCO₂ (4.85 ± 0.04 ppb/ppm), which was comparable to the ∆XCH₄:∆XCO₂ value of Xianghe (5.15 ± 0.10 ppb/ppm), suggesting that targeted CH₄ reduction strategies may be required for climate change mitigation in Seoul.

Satellites provide global coverage for monitoring atmospheric greenhouse gases, crucial for understanding global climate dynamics. However, their temporal and spatial resolutions fall short in detecting urban‐scale variations. To enhance satellite reliability over urban areas, this study presents the first comprehensive analysis of long‐term observations of column‐averaged dry air mole fractions of CO ₂ , CH ₄ , and CO (XCO ₂ , XCH ₄ , XCO) using two ground‐based fourier transform infrared spectrometers, EM27/SUNs, in a megacity. With over 2 years of observations, our study shows that EM27/SUN measurements can effectively capture the daily and seasonal variability of XCO ₂ , XCH ₄ , and XCO over Seoul, a megacity with complex topography and various emission sources. In addition, we use the advantage of having multiple greenhouse gas satellites targeting Seoul to compare with the EM27/SUNs. Our study highlights the importance of EM27/SUN observations in Seoul to identify the need for improvements in satellites to monitor greenhouse gas behaviors and emissions in urban areas.

Accurate monitoring of atmospheric carbon dioxide (CO₂) concentrations is essential for understanding the global carbon cycle. Satellite remote sensing enables global‐scale CO₂ monitoring, providing column‐averaged dry air mole fractions of CO₂ (XCO₂). Current missions such as the Orbiting Carbon Observatory‐2 (OCO‐2) and Greenhouse gases Observing SATellite (GOSAT) series provide high‐quality XCO₂ but have many observation gaps because of narrow swath widths or cloud interference. To address this, we first propose a machine learning (ML) approach to estimate daily XCO₂ (ML XCO₂) at a 0.25 resolution using OCO‐2, Sentinel‐5 Precursor TROPOspheric Monitoring Instrument, and ERA5 reanalysis data from May 2018 to December 2023. Validation against the Total Carbon Column Observing Network measurements shows high agreement, with an R² of 0.95, a root mean square error of 1.05 ppm, and a mean absolute error of 0.78 ppm. The spatiotemporal variations in ML XCO₂ are generally consistent with OCO‐2, GOSAT, and the Copernicus Atmospheric Monitoring Service (CAMS) XCO₂. Notably, during the Coronavirus disease 2019 (COVID‐19) pandemic in 2020, ML XCO₂ estimates were more consistent with OCO‐2 and GOSAT XCO₂ than CAMS XCO₂, which was overestimated owing to unadjusted COVID‐19‐related CO₂ emission reductions. The annual mean CO₂ growth rates from ML XCO₂ (2.065–2.735 ppm/year, 2019–2023) also agree with estimates from OCO‐2 and the National Oceanic and Atmospheric Administration surface measurements, indicating the robustness of our approach. Our study demonstrates that the synergy between multiple‐satellite measurements enhances the spatial coverage of XCO₂ and improves our understanding of the global carbon cycle.

Stereoscopic cloud-top height (CTH) retrieval from two geostationary (GEO) satellites is usually realized through a visible (VIS) band with a high horizontal resolution. A stereoscopic-based CTH retrieval algorithm (prototype dual-GEO CTH algorithm) proposed in our previous study also adopts this approach. Although this approach can retrieve accurate stereoscopic CTHs, the heights of optically thin upper clouds overlying the lower clouds are challenging to retrieve because the parallax difference between two GEOs is determined by the lower clouds owing to the low reflectance from the upper clouds. To address this problem, this paper proposes an improved stereoscopic CTH retrieval algorithm, named the improved dual-GEO CTH algorithm, for Himawari-8 and FengYun (FY)-4A GEOs. The proposed algorithm employs an infrared (IR) band in addition to a VIS band. A seamless image cloning technique is adopted to blend the VIS and IR images, which are then used to retrieve the stereoscopic CTHs. The retrieved CTHs are compared with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) CTHs for three occasions involving upper clouds overlying lower clouds. Results show that the proposed algorithm outperforms the prototype dual-GEO CTH algorithm in the case of upper clouds overlying lower clouds. Notably, although the proposed algorithm is intended for Himawari-8 and FY-4A GEOs, it can be easily extended to any combination of two GEOs.

Quantifying greenhouse gas (GHG) emissions from large industrial complexes remains challenging due to the coexistence of diverse source types, highly variable operational conditions, fugitive releases, and limited representativeness of bottom-up inventories. These complexities require observational approaches resolving emissions across multiple spatial and temporal scales. To address this gap, this study conducted a coordinated multi-platform observation campaign from 6–14 January 2024 at the Daesan Petrochemical Industrial Complex (DPIC), one of South Korea’s largest petrochemical hubs, which integrated in situ and ground-based column observations, mobile surveys, aircraft measurements, and satellite data across complementary spatial and temporal scales. Ground-based measurements confirmed sustained CO₂ enhancements of 27.72 ppm above background concentrations within the DPIC and observed frequent downwind transport of emissions beyond the complex boundary. Mobile measurements identified combined signatures of combustion-linked emissions and fugitive methane releases at the two largest-emitting facilities, while aircraft emission ratio characteristics corroborated these findings, revealing methane leakage concentrated in the DPIC center alongside increased combustion activity. An annual GHG emission rate of 2,595.45 ± 333.09 GgCO₂eqyr^–1 derived from the two largest-emitting facility hotspots alone accounted for approximately 37.1 % of the nationally reported inventory values, suggesting systematic underestimation of bottom-up accounting frameworks, especially of fugitive methane. Satellite observations further identified methane enhancements in access-restricted areas consistent with ground-based measurements. This study demonstrates that coordinated multi-platform observation strategies provide critical constraints for atmospheric emission verification and a scalable framework for improving the accuracy of the quantification of greenhouse gas emissions in complex industrial environments.