The Twin Anthropogenic Greenhouse Gas Observers (TANGO) mission, scheduled for launch in 2028, will observe carbon dioxide (CO2), methane (CH4), and nitrogen dioxide (NO2) emission plumes from more than 10 000 industrial facilities per year using two formation-flying CubeSats. In general, NO222-based masking of CO2−1. This yields CO22 
Ice nucleating particles (INPs) are a rare yet climatically relevant subset of aerosols that initiate ice formation in mixed-phase clouds, strongly influencing cloud microphysics, precipitation, and Earth's radiative balance. Despite their significance, ground-based measurements of INPs may not always be representative of those at cloud level, yet vertically-resolved INP measurements remain limited. Here, we introduce PUFIN (Profiling Upper altitudes For Ice Nucleation), a robust, lightweight INP sampler designed for routine deployment on the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility's tethered balloon system (TBS). PUFIN collects multiple filter samples per flight at up to three altitudes, integrating real-time monitoring of flow, power consumption, and atmospheric conditions, while remaining fully operable from the ground. Multiple deployments at two ARM observatories in Maryland and Alabama demonstrate that PUFIN achieves sufficient aerosol loading to detect INPs down to ∼ 10−3 L−1
Chemical ionization mass spectrometry (CIMS) offers high time-resolution measurements for diverse compounds, but atmospheric quantification remains challenging. Here, we combine a recently published method for determining the collision limit using a single reagent ion with a voltage scanning approach for assessing the relative sensitivities of diverse adduct ions. We used voltage scanning in a Multi-Reagent Chemical Ionization Mass Spectrometer (MR-CIMS) to assess ion–molecule adduct strength. The sensitivities to most detectable compounds were calculated based on this relationship using a collision-limit sensitivity of 13.87 ± 0.69 ncps pptv−1α-pinene using the benzene channel. Following previously published work, the collision limit sensitivity of the other reagent ions used was assumed to be equal to that of the benzene channel and was further examined using the binding energy and measured sensitivity of nitrophenol in the bromide channel. Calibration of 13 molecules, including nitric acid, formic acid, and oxygenated VOCs, was performed to obtain a universal relationship between the sensitivities and the voltage at which the adduct signal halves (dV50). Quantification uncertainties stayed below 20 % for compounds with sensitivities above 5.69 and 5.30 ncps pptv−1R2 = 0.891, slope = 1.24). Six of the organic compounds were taken as examples to show the results of this method for a daytime oxidation chamber experiment. The measurement uncertainties for these pptv-level compounds ranged from 8.9 % to 39.0 %, depending on their sensitivities and concentrations. Further theoretical and experimental investigations showed that halogen compounds can form intermolecular halogen bonds with strength comparable to their intramolecular bonds, preventing this approach from being applied to determine their sensitivities. This work highlights that voltage scanning is a useful approach to the determination ion–molecule adduct sensitivity in CIMS.
We investigated the optical and microphysical characterization of High- and sub-Arctic dust events across the Canadian Arctic Archipelago (CAA). Events from local sources (local dust) were first identified and characterized using a combination of ground-based lidar, two AERONET instruments, and passive (MODIS, Sentinel-2, MISR) imagery in the neighbourhood of the High-Arctic Polar Environment Atmospheric Research Laboratory (PEARL) at Eureka, Nunavut (on Ellesmere Island in the northernmost part of the CAA).The PEARL findings informed the identification and characterization of local dust events over other parts of the CAA using a suite of satellite instruments whose remote sensing (RS) capabilities were complementary to or an extension of the ground- and satellite-based techniques employed at Eureka. The events included plumes emanating from Axel Heiberg Island, just west of Ellesmere Island, Banks Island in the southwest corner of the CAA, Ellef Ringnes Island in the eastern part of the central CAA and Prince of Wales Island/Victoria Island in the central southern CAA. Plume identification, plume source and CM (coarse mode) aerosol optical depth (AOD) retrievals were investigated using a combination of low to high spatial resolution (MODIS to Sentinel-2) color imagery and the MODIS dark target AOD product over water. Plume thickness, height and speed for most of the events were obtained (depending on orbit availability and lack of cloud contamination) from MISR (Multi-angle Imaging Spectro Radiometer) stereoscopic products.These RS results support an argument for the ubiquitous presence of pan-Arctic, low altitude dust that is typically (away from any strong sources such as mountainous drainage basins) at the lower levels of detectability offered by ground- and satellite-based RS techniques. The ability to RS airborne, near-source, local dust events and characterize dust properties and dynamics of important regions such as the CAA is critical to understanding local dust impacts such as early snow/ice melt and the nucleation role of local dust in the formation of low-altitude clouds.
This study investigates greenhouse gas (GHG) emissions and indoor air quality associated with residential cooking practices in São Paulo, Brazil. Measurements were conducted in 30 households, focusing on kitchens using natural gas (NG) or liquefied petroleum gas (LPG) stoves. A measurement protocol was developed to assess emissions of carbon dioxide (CO2), methane (CH4), and nitrogen oxides (NOx) under different operational conditions. Emission rates and factors were calculated using mass balance approaches, considering kitchen volume, air exchange rates, and gas concentrations. The results show different behavior for the type of fuel, especially for methane, which has a significant response to the use of NG, unlike LPG. It was also possible to observe a difference between the temporal variability cycles, as the burners responded quickly to the increase in concentration, while the oven showed a delayed increase observed in the environment. There was a high variability in the concentrations in the different residences, which may be associated with factors such as the age of the stove, model, leak and internal influence. The emission factors obtained were three times higher than the IPCC considering only the consistent values, but when considering the outliers it is up to 10 times higher for CH42
The use of a diffusion charger based AQ Urban sensors to monitor particle number concentrations was investigated in Helsinki metropolitan area. The comparisons between the AQ Urban sensors and traditional butanol CPCs were made at a heavily trafficked street canyon (Traffic Supersite) and at an urban background site (UB Supersite) in 2022. The agreement with the measured particle number concentrations within different AQ Urban units was good. Comparison of the AQ Urban sensor with the two CPCs showed that AQ Urban sensors should be suitable to measure concentration of particles approx. larger than 10 nm in highly trafficked areas. The long-term agreement between AQ Urban sensors and CPCs was also investigated in the two different environments between 1 January and 15 August 2022. Overall, the correlation between AQ Urban sensors and the CPCs was good at both sites (r
The retrieval of Precipitable Water Vapour (PWV) from Global Navigation Satellite Systems (GNSS) in thermodynamically complex environments is significantly limited by the accuracy of the weighted mean temperature (Tm). This study evaluates the efficacy of static climatological models versus dynamic ground-based microwave radiometry for TmTmr=0.98) but exhibits a systematic cold bias of −1.91 KTmTm
Transmission line icing severely threatens the safety of the power grid. Accurate prediction of the sticking efficiency (the proportion of supercooled droplets that remain on the conductor after impact, excluding bouncing and splashing) is critical for preventing and mitigating icing disasters. Traditional prediction models for sticking efficiency typically exhibit significant errors under complex conditions (e.g., varying wind speeds and precipitation intensities), thereby limiting their practical applications. To overcome this limitation, a two-stage coupled model based on coaxial digital holography was proposed. In this model, the diameters, velocities, and collision angles of supercooled droplets were accurately measured and incorporated into a two-stage framework that couples droplet impact dynamics with thermodynamic processes to calculate sticking efficiency. For the laboratory and field-validation cases examined in this study, the prediction errors of the proposed model remained below 3.5 % under various conditions, representing a significant improvement over traditional models and underscoring its enormous potential in engineering applications.
This study provides new observational evidence on the contribution of salt-dust plumes originating from the desiccated bed of Urmia Lake. The near-surface atmosphere over the lake bed was investigated using a scanning polarization lidar. Nighttime measurements at 532 nm were conducted in September 2022, with the instrument operating in azimuthal scan mode. Investigations show that the aerosol plumes above the lake contain both dust and salt particles. A modified two-step polarization-lidar photometer networking retrieval scheme was applied to lidar azimuthal scans to obtain backscatter ratios and mass concentrations of dust, salt-dust, and wet-salt aerosols. Plume regions were detected and isolated from their surroundings using a multi-scale layer detection algorithm. Averages of particle linear depolarization ratios, backscattering coefficients, and mass concentrations for each detected plume are retrieved to quantify the contributions of different particle types to the plume composition. The retrievals indicate that salty particles exhibit characteristically lower linear depolarization ratios but substantially higher backscattering than pure dust particles. The results demonstrate that even relatively low mass fractions of saline aerosols markedly enhance particle backscattering over the dried lake bed. Based on plume-averaged backscattering values, the detected aerosol plumes were classified as dust-dominant, salt-dominant, or mixed mode. Analysis of 6447 %25 %28 %
Reducing methane (CH4) emissions has become increasingly important in recent years due to its importance for radiative forcing. Fugitive emissions of CH4CH4, with some studies using ratios to secondary co-emitted compounds as a means of predicting the source of emission. This study aims to adapt existing algorithm parameters by investigating the limitations of equipment within the platform used for mobile surveys. These changes suggest that previous methods may underpredict the number of Leak Indications (LIs) by 53.5 % with 27 LIs detected with the old methodology compared to 58 LIs detected with the new methodology. The majority of these LIs were found to be emitting in a leak rate category of 0–2 L min−1. Source determination was included as a core step within the algorithm, which was shown to reduce the misassignment of LIs, suggesting when not using this step, emissions from pyrogenics and biogenics are included within LI assignments.
This study presents an independent retrieval algorithm combining wavelength pairing and the multiplicative algebraic reconstruction technique (MART) to process Ozone Mapping and Profiler Suite (OMPS) limb observations for vertical ozone profiles. Developed as a complementary dataset for validating operational products, the algorithm is tailored to OMPS limb profiler's specific characteristics. The retrieval algorithm employs scattered solar radiance measurements from the OMPS/LP, focusing on the visible spectral range, normalizes this radiance to that at an upper tangent height, and retrieves ozone concentrations between 12–40 km. Additionally, it enables the identification of cloud-contaminated measurements at specific altitudes within the instrument field of view. A comprehensive error analysis reveals that prior uncertainty contributes ∼5 % error in the tropical lower stratosphere (based on a +5 % perturbation experiment), while a 30 % uncertainty in the aerosol extinction coefficient causes ∼ 5 % error at 15–25 km. Absorption cross-section uncertainties introduce localized biases of −3 % to −5 %, and random measurement noise exhibits strong altitude dependence, with values below 10 % in the mid-stratosphere and exceeding 20 % at high altitudes and in the tropical upper troposphere. OMPS data spanning the entire year of 2021 are processed, and the results are evaluated through comparisons with multiple independent datasets, including NASA official products, passive satellite observations, and in-situ measurements from balloon-borne ozonesondes. At 17–36 km, deviations from OMPS/LP v2.6 data are ≤5 %; at 18–35 km, consistency with Microwave Limb Sounder (MLS) v5.0 data ranges from 5 %–10 %; at 20–35 km, most deviations from OSIRIS v7.3 data are ≤5 % (except near 23 km). Comparisons with ozonesonde measurements reveal that differences in the 13–30 km range over northern mid-to-high latitudes are mostly <10 % (with 10 %–15 % differences at 22–25 km in polar regions). Over southern mid-latitudes, the consistency within the same altitude range is 2 %–10 %. Notably, deviations between the retrieved profiles and comparison products increase significantly in the tropics at low altitudes.
Understanding the vertical distribution of aerosol and clouds i.s critical for climate modeling, weather forecasting, and air quality monitoring. Lidar observations are central to profiling atmospheric composition, yet signal attenuation in optically thick layers limits the effective retrieval of some important properties above those layers. More complex measurement approaches, using a combination of Lidar and cloud radar systems, can be taken to support more inclusive and accurate inference. In this study, we develop a deep learning framework to address this trade-off and gap in the cost of data acquisition by enabling full-column aerosol and cloud classification using only standard lidar inputs, achieving particularly high skill for aerosol typing while demonstrating robust, physically consistent classification of ice-cloud fields even under conditions of strong lidar signal attenuation, with liquid-cloud uncertainties primarily arising from closely related microphysical classes. The approach is based on a U-Net architecture trained to predict combined aerosol and cloud types from vertical profiles of backscatter and depolarization. Classification targets integrate established aerosol typing from PollyXT with cloud and precipitation categorization from Cloudnet, facilitating a unified scheme. The model achieves high precision, recall, and F1-scores above 95 %. By evaluating numerous complex case studies, we establish the model's ability to exploit information embedded in the lidar signal below attenuating layers, including structural and contextual features, to infer atmospheric conditions at higher altitudes, offering a robust AI-based enhancement to lidar-based atmospheric profiling and target classification. The application of AI in this context closes the gap between the need for vertical cloud maps and the sparse availability of Cloudnet.
The molecules NO2, O3, OClO and BrO play a major role in the photochemistry of stratospheric ozone, notably in the formation of the springtime Antarctic ozone hole. For this reason, these species have been monitored by Differential Optical Absorption Spectroscopy (DOAS) instrumentation for many decades. To transform DOAS Slant Column Densities (SCDs) into Vertical Column Densities (VCDs), independent of the viewing geometry, the Air Mass Factors (AMFs) relating these quantities are needed. Ground-based stratospheric trace gas measurements are performed in zenith-viewing geometry at twilight, around and beyond 90° solar zenith angle (SZA). At those solar angles, the Earth's sphericity and the rapid changes in photochemical parameters (e.g., photolysis rate coefficients) affect the calculation of the AMFs, particularly for photochemically active species such as NO2, OClO and BrO. This study presents a methodology to infer AMFs that account for sphericity and photochemical effects. We estimate stratospheric AMFs of NO2, O3, OClO and BrO for Belgrano and Marambio Antarctic stations using the MYSTIC (Mayer, 2009; Emde et al., 2010) Radiative Transfer Model (RTM). The photochemical changes taking place during twilight are considered using a photochemical box-model based on the SLIMCAT chemistry transport model (Chipperfield, 1999, 2006). Vertical profile concentrations obtained by this model are “averaged” over the optical paths. That is, for each SZA observed at the station, a vertical concentration equivalent to all the concentrations encountered by the solar beams in different parts of the atmosphere is calculated, considering the different “local” SZAs and the partial optical paths in each layer. These concentration profiles, representative of a complete two-dimensional atmosphere, are then used as input for the one-dimensional fully-spherical version of MYSTIC RTM. The robustness of the proposed methodology is tested against measurements of NO2, O3, OClO and BrO SCDs obtained at Marambio Antarctic station. A good agreement is observed between modelled and measured values of NO2, O332
This paper presents a multi-instrument synergistic technique to retrieve atmospheric dust aerosol columnar effective imaginary refractive index (k), single scattering albedo (SSA), and absorption aerosol optical depth (AAOD). The technique combines: (a) aerosol information derived from the narrow field-of-view measurements by filter sun-moon-sky radiometer within the Aerosol Robotic Network (AERONET): spectral aerosol optical depth (AOD) and inversion properties; (b) the total, direct, and diffuse sky irradiance measurements from UV- and Vis-Multifilter Rotating Shadowband Radiometer (MFRSR); (c) trace gas columns from satellite measurements (OMI and OMPS). The approach is demonstrated on the data collected at the Izaña Atmospheric Observatory (IZO), located at an altitude of 2.4 km on Tenerife Island, a unique site for transported Saharan dust column optical properties retrievals due to very clean background condition for calibrating the instrument. This multi-instrument synergy enables consistent column absorption retrievals from ultraviolet (UV) to visible (VIS) wavelengths, while effectively accounting separately for aerosol and gaseous (Ozone – O3, and Nitrogen Dioxide – NO2) absorption. The MFRSR on-site calibration procedure relies on observations acquired on cleaner days (AOD < 0.1 at 440 nm) to eliminate the observed dependency of the calibration constant on increasing dust aerosol loading due to an inefficient correction for the forward scattering (aureole effect). The retrieval algorithm (1) integrates the temporally collocated AERONET-retrieved particle size distribution and the real part of the refractive index into the radiative transfer simulations, while accounting for the pre-defined spheroidal shape distribution of the dust aerosols, and (2) fits the measured ratio of diffuse to direct-normal irradiance for discrete wavelengths (325 to 440 nm) to the pre-calculated, on-the-fly look-up table to retrieve column effective spectral imaginary part of the refractive index. The sensitivity analysis reveals that the uncertainties in the AERONET spectral AOD (±0.01 at 440 nm ±0.02 at shorter UV wavelengths) and assumed particle shape distribution constitute the dominant source of error, followed by estimated 1 % error in the MFRSR-measured diffuse-to-direct irradiance ratios, in the MFRSR-retrieved k±0.03 for AOD larger than 0.4. The derived SSA at 440 nm shows good agreement with AERONET inversions, mostly within ±0.03 for AOD > 0.2, and ±0.02 at higher AOD (> 0.4). This close correspondence confirms the consistency between the two fundamentally distinct inversion techniques and enhances confidence in the concurrent MFRSR UV wavelength inversions. We present a multi-year (2019–2023) MFRSR Saharan aerosol absorption record revealing enhanced dust absorption at UV wavelengths with noticeable intraseasonal and interannual variabilities, which are indicative of a varying composition of minerals (iron oxides) in the dust. The spectral aerosol absorption effects reduce the amount of surface-reaching UV radiation and slow down tropospheric photochemistry, which can have implications for air quality, human health, and ecosystem dynamics. The ongoing AERONET and MFRSR measurements currently made at the Santa Cruz ground-level site on Tenerife Island will continue to produce a unique, long-term ground-based UV spectral Saharan dust absorption dataset, providing a valuable reference for evaluating space-based UV aerosol absorption retrievals from instruments such as DSCOVR-EPIC, S5P-TROPOMI, and the most recently launched PACE-OCI. In addition to deriving spectral absorption properties, the enhanced sensitivity of UV measurements to the dust spectral absorption, demonstrated with the MFRSR inversion in this work, can be exploited for inferring the mineralogical composition of the dust aerosols, which is critical to improving the dust representation in Earth System Models.
The Characterising CiRrus and icE cloud acrosS the specTrum-Microwave (CCREST-M) aircraft campaign (February–March 2024) was based around the Chilbolton Observatory, UK, using the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 aircraft together with ground-based multi-frequency radars to provide a testbed for ice-cloud scattering and radiative transfer models across the microwave and sub-millimetre spectrum. Ice particle size distributions (PSDs) are retrieved from the ground-based zenith-pointing radars at the time of the radiometric overpasses, and the aircraft in-situ PSDs are used as an independent validation dataset.We present a novel hybrid retrieval framework for mid-latitude ice PSD parameters (slope λ, intercept No, and shape μGHzGHz-retrieved ice water content (IWC), and the mean mass-weighted dimension. The ML predictions are converted into first guess gamma-PSD parameters at each height. A subsequent deterministic optimisation then adjusts Noλ, using a randomly oriented rosette-aggregate scattering model, to enforce simultaneous agreement with the observed 35 and 94 GHzApplication of the above method to three CCREST-M cases show that the ML ensemble reproduces PSD moments well for two cases but fails when extrapolating beyond its trained temperature range in the third case. Retrieved IWCs from the 3 GHzμ=0) and gamma PSD assumptions show comparable performance overall. Retrieved mean PSDs show generally good agreement with in-situ PSDs as a function of temperature for two of the cases, with IWCs within about 50 % of the in-situ measured IWCs over much of the −50−10 °CGHzkm. Below 4.5 km
Observations of neutral density in the mesosphere and lower thermosphere (MLT) region of the terrestrial atmosphere are important for understanding lower atmospheric, geomagnetic, and anthropogenic forcing. This study introduces a statistical method for measuring neutral density variations using an extensive dataset of meteor head echoes that were observed using the MAARSY high-power large-aperture (HPLA) mesosphere–stratosphere–troposphere (MST) radar. The method relies on observing the mean geocentric velocity of meteor head echoes as a function of initial detection altitude and day-of-year. The meteor head echo catalog used contains 1.4 million meteor head echoes between 2016–2023. Neutral density variations are observed with a 6 d time and 2 km altitude resolution between 85–115 km. The measurements show variations in neutral density potentially due to geomagnetic and atmospheric events. Variations of up to 20 % are common in the dataset, and agree with the magnitude of atmospheric neutral density fluctuations from an Upper-Atmosphere ICOsahedral Non-hydrostatic (UA-ICON) atmosphere model run.
The Fraunhofer absorption feature at 430 nmNO2) from measurements by satellite-based instruments such as the Tropospheric Monitoring Instrument (TROPOMI) and Ozone Monitoring Instrument (OMI). The width and depth of the feature in the measured spectrum are affected by rotational Raman scattering (RRS) throughout the atmosphere and by vibrational Raman scattering (VRS) in open water bodies. RRS, or the Ring-effect, is accounted for in the Differential Optical Absorption Spectroscopy (DOAS) retrieval of the NO2430 nm430 nmNO2In this paper we investigate the benefits of removing the wavelength range 428–433 nm2-gap approach” reduces the SCD error and the RMS error of the fit over water bodies by 10 %–20 % and the fit residual for the remaining parts of the window improves. Over some land scenes, where the residual outside the 428–433 nm2-gap approach. Over ocean waters the SCD is seen to decrease by a few percent, which leads to a decrease of the stratospheric NO2−2 µmol m−2NO2±2 µmol m−2. These changes are too small to alter the general conclusions of the routine validation of TROPOMI data. Because of the improvement of the SCD error and systematic improvements over open water it has been decided to implement the NO2-gap approach in the new processor versions of TROPOMI (since 22 November 2025) and OMI (since April 2026, with full mission reprocessing).
Volcanic clouds can influence the climate and pose a serious threat to air transportation. Detecting and distinguishing them from meteorological clouds is particularly challenging because they often are composed of water vapor and ice particles, along with ash and gases. This study presents a neural network (NN) model for the detection of volcanic clouds composed of ash, ice, and SO2, applied to data acquired by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) satellite instrument. A dataset of 1259 SEVIRI images related to Mount Etna volcano (Italy) eruptions spanning from 2020 to 2022, as well as 2024, was considered. The NN model, based on a multi-layer perceptron (MLP), was developed using 13 features, including thermal infrared channels and brightness temperature differences (BTDs). A post-processing step based on a plume-tracking algorithm and a Non-Local means filter was implemented to improve the performance of the NN model. The model was validated using three eruptive events that were not included in the training phase, achieving an overall balanced accuracy of up to 92.0 %. The validation results also showed that the model successfully detected 66.0 %, 48.5 %, and 84.1 % of the observed volcanic cloud (VC) pixels in the three analysed validation events, respectively. In addition, only 7.7 %, 4.0 %, and 21.9 % of the detected VC pixels corresponded to false alarms for the respective events. Thus, the model demonstrates the capability to detect volcanic clouds even under complex conditions of high meteorological cloud cover. The results are promising for the automatic detection of volcanic clouds, including those containing ice and SO2, as well as for improving volcanic cloud retrieval processes.
As part of the FENNEC programme, a field campaign was conducted on the Mediterranean coast of southern Spain, near Gibraltar, from June to August 2011. Using a straightforward ground-based N2–Raman lidar, several aerosol optical properties were retrieved at 355 nm, including the linear particle depolarisation ratio (PDR), the lidar ratio (LR), and the aerosol backscatter and extinction coefficients. From continuous sampling over 58 nights, several periods were identified in which aerosol events exhibited optical thicknesses greater than 0.5. The primary drivers of these events are the incursions of Saharan dust mixed with local polluted and marine air masses. Pairing PDR and LR has been shown to be effective in identifying three distinct bulk aerosol classes: dust, carbonaceous and soluble (predominantly marine) aerosols. After processing the night-time data to ensure sufficient lidar range, the study demonstrates the effectiveness of lidar profiles in evaluating the reliability of the Copernicus Atmosphere Monitoring Service (CAMS) reanalyses of atmospheric aerosols up to approximately 7 km above mean sea level (a.m.s.l.). The two datasets show excellent consistency in terms of the optical thickness and vertical profile of the aerosol extinction coefficient in the Saharan dust aerosol layers. CAMS reproduces the temporal evolution well, with a correlation coefficient (COR) greater than 0.8. However, this is less accurate for the layer below 2 km a.m.s.l. (COR = 0.55), where CAMS tends to underestimate compared to ground-based lidar.
Accurate global monitoring of atmospheric methane (CH4) is essential for tracking progress toward climate mitigation targets such as the Global Methane Pledge (GMP). Ground-based measurement networks are too sparse to provide sufficient spatial coverage, while satellite-derived retrievals are hindered by systematic biases and uncertainties, limiting their reliability for consistent global monitoring. We present the first global fusion of GOSAT, GOSAT-2, and TROPOMI to generate a globally consistent daily 0.1° land dataset for 2020–2023 for enhanced global column-averaged dry-air mole fraction of atmospheric methane (XCH4) mapping. The framework employs a three-step machine-learning (ML) approach: (1) sensor-specific bias correction using TCCON observations, (2) cross-sensor harmonization to GOSAT-2, the sensor with the strongest post-correction TCCON agreement, and (3) priority-based fusion. Tree-based ensemble regressors were trained with satellite retrieval parameters to reduce systematic biases and inter-sensor discrepancies. Independent validation at three withheld TCCON stations demonstrates robust generalization of the Fused product (R2 = 0.81, RMSE = 10.78 ppb), outperforming standard and operational bias-corrected satellite products and previously reported ML-based approaches. Regional assessments show that fusion substantially improves data availability and reduces systematic errors, delivering up to 9.5 % relative coverage gains compared to TROPOMI operational products in challenging regions (South Asia, Amazon Basin, Eastern Siberia). The Fused dataset reveals intensifying positive XCH4+60 ppb) over South Asia, East Asia, and Central Africa during 2020–2023, linked to MODIS-derived agricultural and urban land classes as well as known oil and gas fields. The dataset provides a scalable resource for regional CH4