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In this study, we carried out an independent validation of two methane retrieval algorithms using spectra from the Infrared Atmospheric Sounding Interferometer (IASI) that has been aboard the Meteorological Operational Satellite A (MetOp-A) since 2006. Both algorithms, one developed by the Laboratoire de Météorologie Dynamique (LMD), called the non-linear inference scheme (NLISv8.3), and the other by the Rutherford Appleton Laboratory (RAL), referred to as RALv2.0, provide long-term global CH4 concentrations using distinctively different retrieval approaches (neural network vs. optimal estimation, respectively). They also differ with respect to the vertical range covered, where LMD provides mid-tropospheric dry-air mole fractions (mt CH4), and RAL provides mixing ratio profiles from which we can derive total column-averaged dry-air mole fractions (X CH4) and potentially two partial column layers (q CH4). We compared both CH4 products using the Copernicus Atmospheric Monitoring Service (CAMS) model, in situ profiles (range extended using CAMS model data), and ground-based Fourier transform infrared (FTIR) remote-sensing measurements. The average difference (in mt CH4) with respect to in situ profiles for LMD ranges between -0.3 and 10.9 ppb, while for RAL the X CH4 difference ranges between -4.6 and -1.6 ppb. The standard deviation (SD) of the observed differences between in situ measurements and RAL retrievals is 14.1–21.9 ppb, which is consistently smaller than that between LMD retrievals and in situ measurements (15.2–30.6 ppb). By comparing with ground-based FTIR sites, the mean differences are within ±10 ppb for both RAL and LMD retrievals. However, the SD of the differences at the ground-based FTIR stations shows significantly lower values for RAL (11–15 ppb) than for LMD (about 25 ppb). The long-term trend and seasonal cycles of CH4 derived from the LMD and RAL products are further investigated and discussed. The seasonal variation in X CH4 derived from RAL is consistent with the seasonal variation observed by the ground-based FTIR measurements. However, the overall 2007–2015 X CH4 trend derived from RAL measurements is underestimated, if not adjusted, for an anomaly occurring on 16 May 2013 due to a L1 calibration change. For LMD, we see very good agreement at the (sub)tropics (<35 ° N– 35° S) but notice deviations in the seasonal cycle (both in the amplitude and phase) and an underestimation of the long-term trend with respect to the RAL and reference data at higher-latitude sites. [ABSTRACT FROM AUTHOR]

Ethane is the most abundant non-methane hydrocarbon in the troposphere, where it impacts ozone and reactive nitrogen and is a key tracer used for partitioning emitted methane between anthropogenic and natural sources. However, quantification has been challenged by sparse observations. Here, we present a satellite-based measurement of tropospheric ethane and demonstrate its utility for fossil-fuel source quantification. An ethane spectral signal is detectable from space in Cross-track Infrared Sounder (CrIS) radiances, revealing ethane signatures associated with fires and fossil fuel production. We use machine-learning to convert these signals to ethane abundances and validate the results against surface observations (R2 = 0.66, mean CrIS/surface ratio: 0.65). The CrIS data show that the Permian Basin in Texas and New Mexico exhibits the largest persistent ethane enhancements on the planet, with regional emissions underestimated by seven-fold. Correcting this underestimate reveals Permian ethane emissions that represent at least 4-7% of the global fossil-fuel ethane source. The authors have developed a satellite-based method that detects tropospheric ethane, revealing major sources like fires and fossil fuels. It agrees with ground-based observations and shows Permian Basin emissions of ethane are higher than estimated. [ABSTRACT FROM AUTHOR]

The increase in greenhouse gas concentrations, particularly CO2 , has significant implications for global climate patterns and various aspects of human life. Spaceborne remote sensing satellites play a crucial role in high-resolution monitoring of atmospheric CO2. However, the next generation of greenhouse gas monitoring satellites is expected to face challenges, particularly in terms of computational efficiency in atmospheric CO2 retrieval and analysis. To address these challenges, this study focuses on improving the speed of retrieving the column-averaged dry-air mole fraction of carbon dioxide (XCO2) using spectral data from the Orbiting Carbon Observatory-2 (OCO-2) satellite while still maintaining retrieval accuracy. A novel approach based on neural network (NN) models is proposed to tackle the nonlinear inversion problems associated with XCO2 retrievals. The study employs a data-driven supervised learning method and explores two distinct training strategies. Firstly, training is conducted using experimental data obtained from the inversion of the operational optimization model, which is released as the OCO-2 satellite products. Secondly, training is performed using a simulated dataset generated by an accurate forward calculation model. The inversion performance and prediction performance of the machine learning model for XCO2 are compared, analyzed, and discussed for the observed region over east Asia. The results demonstrate that the model trained on simulated data accurately predicts XCO2 in the target area. Furthermore, when compared to OCO-2 satellite product data, the developed XCO2 retrieval model not only achieves rapid predictions (<1 ms) with good accuracy (1.8 ppm or approximately 0.45 %) but also effectively captures sudden increases in XCO2 plumes near industrial emission sources. The accuracy of the machine learning model retrieval results is validated against reliable data from Total Carbon Column Observing Network (TCCON) sites, demonstrating its ability to effectively capture CO2 seasonal variations and annual growth trends. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Many cities experience significant greenhouse gas and air pollutant emissions. We comprehensively analyzed the characteristics of these emissions using ground‐based solar‐viewing Fourier transform spectrometers (FTSs) in Northern Hemisphere cities including Seoul (South Korea), Thessaloniki (Greece), Tsukuba (Japan), Karlsruhe (Germany), Xianghe (China), Pasadena (USA), and Paris (France). First, we focused on variations in carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO). Based on these variations, their emission ratios were estimated in terms of excess column‐averaged dry‐air mole fractions of CO to CO2 (∆XCO:∆XCO2) and CH4 to CO2 (∆XCH4:∆XCO2) in urban sites. We found that the emission ratios differed across cities. For example, Xianghe and Pasadena, which generally exhibit high levels of air pollution, had the highest emission ratios. However, Seoul had lower emission ratios of ∆XCO:∆XCO2, but higher levels of ∆XCH4:∆XCO2. This study suggests that ground‐based Fourier transform spectrometer measurements can be used as a tool for comprehensively evaluating the emission characteristics of greenhouse gases and air pollutants in urban environments. Furthermore, extending our study to longer periods and more sites could further aid in formulating effective mitigation strategies for climate change. Key Points: We comprehensively assessed the emission characteristics of greenhouse gases and air pollutants in seven cities in the Northern HemisphereGround‐based Fourier transform spectrometers offers a comprehensive analysis of the urban emission characteristics of greenhouse gases and air pollutantsOur results can further support effective strategies for mitigating climate change [ABSTRACT FROM AUTHOR]

Quantifying atmospheric CO2 over long periods from space is crucial in understanding the carbon cycle's response to climate change. However, a single satellite offers limited spatiotemporal coverage, making comprehensive monitoring challenging. Moreover, biases among various satellite retrievals hinder their direct integration. This study proposed a machine learning framework for fusing the column‐averaged dry‐air mole fraction of CO2 (XCO2) retrievals from Greenhouse Gases Observing Satellite (GOSAT) and OCO‐2 satellites. The best model (R2 = 0.85) presented improved consistency of GOSAT retrievals by reducing 71.5% of the average monthly bias while using OCO‐2 retrievals as a benchmark, indicating the fusion data set's potential to enhance observation coverage. Incorporating the adjusted GOSAT XCO2 retrievals into the OCO‐2 data set added an average of 84.7 thousand observations annually, enhancing the yearly temporal coverage by 53.6% (from 14 to 21.5 days per grid). This method can be adapted to other satellites, maximizing satellite resources for a more robust carbon flux inversion. Plain Language Summary: CO2 sources and sinks are primarily regulated by anthropogenic emissions, photosynthesis and respiration on land and in the ocean, as well as by physical dissolution and carbonate chemistry with ocean circulation. The consistent long‐term quantification of atmospheric CO2 concentrations using satellite observations plays a pivotal role in understanding the response of global and regional carbon cycles to climate change. However, satellites have a revisit period, and factors like cloud and aerosol scattering impact the quality and quantity of their observations. A single satellite currently falls short of the demand to monitor global carbon sources and sinks, necessitating the integration of observations from various satellites to conduct carbon flux inversions. Different satellites come with distinct sampling patterns, instrument parameters, and retrieval algorithms, which leads to biases in their retrieval products. Our study, focusing on OCO‐2 and GOSAT, employs machine learning models to improve consistency between retrievals derived from these two satellites, thus generating a harmonized data set. The bias‐corrected GOSAT XCO2 retrievals exhibit high spatiotemporal consistency with OCO‐2 XCO2 retrievals, immensely enhancing the observational constraints for carbon flux inversions. This method holds promise for application to recently launched and future satellites, aiming to offer carbon flux inversions with amplified spatiotemporal observational constraints. Key Points: This study employs machine learning (ML) models to enhance XCO2 consistency between OCO‐2 and Greenhouse Gases Observing Satellite (GOSAT), reducing monthly inconsistency by 71.5%Integrating the OCO‐2 data set with GOSAT retrievals increased yearly observations by 56.2%Fusing satellite data through ML models can pave the way for improved carbon flux inversions in the future [ABSTRACT FROM AUTHOR]

Propane (C3H8) is an important trace gas in the atmosphere, as it is a proxy for oil and gas production and has a significant impact on atmospheric chemical reactions related to the hydroxyl radical and tropospheric ozone formation. In this study, solar direct absorption spectra near 2967 cm−1 recorded by a ground-based Fourier Transform InfraRed spectrometer (FTIR) are applied to retrieve C3H8 total columns between June 2018 and July 2022 at Xianghe in North China. The systematic and random uncertainties of the C3H8 column retrieval are estimated to be 18.2 % and 18.1 %, respectively. The mean and standard deviation of the C3H8 columns derived from the FTIR spectra at Xianghe are 1.80±0.81(1 σ) × 1015 molecules / cm 2. Good correlations are found between C3H8 and other non-methane hydrocarbons, such as C2H6 (R=0.84) and C2H2 (R=0.79), as well as between C3H8 and CO (R=0.72). However, the correlation between C3H8 and CH4 is relatively weak (R=0.45). The FTIR C3H8 measurements are also compared against two atmospheric chemical transport model simulations (the Whole Atmosphere Community Climate Model (WACCM) and the Copernicus Atmosphere Monitoring Service (CAMS)). We find that the C3H8 columns from both models have different seasonal variations as compared to the FTIR measurements. Moreover, the mean C3H8 columns derived from the WACCM and CAMS models are about 68 % larger than the FTIR retrievals. The new FTIR measurements at Xianghe provide us an insight into the C3H8 column variations and underlying processes in North China. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) is the most important long-lived greenhouse gas and can be retrieved using solar absorption spectra recorded by a ground-based Fourier-transform infrared spectrometer (FTIR). In this study, we investigate the CO2 retrieval strategy using the Network for the Detection of Atmospheric Composition Change–Infrared Working Group (NDACC–IRWG) type spectra between August 2018 and April 2022 (~4 years) at Xianghe, China, aiming to find the optimal observed spectra, retrieval window, and spectroscopy. Two spectral regions, near 2600 and 4800 cm−1, are analyzed. The differences in column-averaged dry-air mole fraction of CO2 (XCO2) derived from spectroscopies (ATM18, ATM20, HITRAN2016, and HITRAN2020) can be up to 1.65 ± 0.95 ppm and 7.96 ± 2.02 ppm for NDACC-type 2600 cm−1 and 4800 cm−1 retrievals, respectively, which is mainly due to the CO2 differences in air-broadened Lorentzian HWHM coefficient (γair) and line intensity (S). HITRAN2020 provides the best fitting, and the retrieved CO2 columns and profiles from both 2600 and 4800 cm−1 are compared to the co-located Total Column Carbon Observing Network (TCCON) measurements and the greenhouse gas reanalysis dataset from the Copernicus Atmosphere Monitoring Service (CAMS). The amplitude of XCO2 seasonal variation derived from the NDACC-type (4800 cm−1) is closer to the TCCON measurements than that from the NDACC-type (2600 cm−1). Moreover, the NDACC-type (2600 cm−1) retrievals are strongly affected by the a priori profile. For tropospheric XCO2, the correlation coefficient between NDACC-type (4800 cm−1) and CAMS model is 0.73, which is higher than that between NDACC-type (2600 cm−1) and CAMS model (R = 0.56). [ABSTRACT FROM AUTHOR]

Atmospheric ammonia (NH3) is a chemically active trace gas that plays an important role in the atmospheric environment and climate change. Satellite remote sensing is a powerful technique to monitor NH3 concentration based on the absorption lines of NH3 in the thermal infrared region. In this study, we establish a retrieval algorithm to derive the NH3 column from the Hyperspectral Infrared Atmospheric Sounder (HIRAS) onboard the Chinese FengYun (FY)-3D satellite and present the first atmospheric NH3 column global map observed by the HIRAS instrument. The HIRAS observations can well capture NH3 hotspots around the world, e.g., India, West Africa, and East China, where large NH3 emissions exist. The HIRAS NH3 columns are also compared to the space-based Infrared Atmospheric Sounding Interferometer (IASI) measurements, and we find that the two instruments observe a consistent NH3 global distribution, with correlation coefficient (R) values of 0.28–0.73. Finally, some remaining issues about the HIRAS NH3 retrieval are discussed. [ABSTRACT FROM AUTHOR]