105 citations as recorded by crossref.

1. A comparative study of anthropogenic CH4 emissions over China based on the ensembles of bottom-up inventories X. Lin et al. 10.5194/essd-13-1073-2021
2. Accelerating methane growth rate from 2010 to 2017: leading contributions from the tropics and East Asia Y. Yin et al. 10.5194/acp-21-12631-2021
3. Assimilating a blended dataset of satellite-based estimations and in situ observations to improve WRF-Chem PM2.5 prediction X. Ma et al. 10.1016/j.atmosenv.2023.120284
4. Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015 J. Maasakkers et al. 10.5194/acp-19-7859-2019
5. U.S. CH4 emissions from oil and gas production: Have recent large increases been detected? L. Bruhwiler et al. 10.1002/2016JD026157
6. Satellite-derived methane hotspot emission estimates using a fast data-driven method M. Buchwitz et al. 10.5194/acp-17-5751-2017
7. The update of the line positions and intensities in the line list of carbon dioxide for the HITRAN2020 spectroscopic database E. Karlovets et al. 10.1016/j.jqsrt.2021.107896
8. East Asian methane emissions inferred from high-resolution inversions of GOSAT and TROPOMI observations: a comparative and evaluative analysis R. Liang et al. 10.5194/acp-23-8039-2023
9. Intercomparison of Remote Sensing Retrievals: An Examination of Prior-Induced Biases in Averaging Kernel Corrections H. Nguyen & J. Hobbs 10.3390/rs12193239
10. Spatial and temporal distribution of carbon dioxide gas using GOSAT data over IRAN S. Falahatkar et al. 10.1007/s10661-017-6285-8
11. The Global Methane Budget 2000–2017 M. Saunois et al. 10.5194/essd-12-1561-2020
12. Error Budget of the MEthane Remote LIdar missioN and Its Impact on the Uncertainties of the Global Methane Budget P. Bousquet et al. 10.1029/2018JD028907
13. Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data A. Turner et al. 10.5194/acp-15-7049-2015
14. Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling S. Henne et al. 10.5194/acp-16-3683-2016
15. Emissions of methane in Europe inferred by total column measurements D. Wunch et al. 10.5194/acp-19-3963-2019
16. Diagnostic methods for atmospheric inversions of long-lived greenhouse gases A. Michalak et al. 10.5194/acp-17-7405-2017
17. Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts C. Ma et al. 10.1007/s00376-017-7179-y
18. Application of a Common Methodology to Select in Situ CO2 Observations Representative of the Atmospheric Background to an Italian Collaborative Network P. Trisolino et al. 10.3390/atmos12020246
19. Inverse modelling of European CH4 emissions during 2006–2012 using different inverse models and reassessed atmospheric observations P. Bergamaschi et al. 10.5194/acp-18-901-2018
20. Methodology for selecting near-surface CH4, CO, and CO2 observations reflecting atmospheric background conditions at the WMO/GAW station in Lamezia Terme, Italy L. Malacaria et al. 10.1016/j.apr.2025.102515
21. Global distribution of methane emissions: a comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments Z. Qu et al. 10.5194/acp-21-14159-2021
22. Interpreting contemporary trends in atmospheric methane A. Turner et al. 10.1073/pnas.1814297116
23. Monitoring Methane Concentrations with High Spatial Resolution over China by Using Random Forest Model Z. Jin et al. 10.3390/rs16142525
24. Methane emissions from China: a high-resolution inversion of TROPOMI satellite observations Z. Chen et al. 10.5194/acp-22-10809-2022
25. Large XCH4 anomaly in summer 2013 over northeast Asia observed by GOSAT M. Ishizawa et al. 10.5194/acp-16-9149-2016
26. Improved Gaussian regression model for retrieving ground methane levels by considering vertical profile features H. He et al. 10.3389/feart.2024.1352498
27. The global methane budget 2000–2012 M. Saunois et al. 10.5194/essd-8-697-2016
28. Consistent regional fluxes of CH4 and CO2 inferred from GOSAT proxy XCH4 : XCO2 retrievals, 2010–2014 L. Feng et al. 10.5194/acp-17-4781-2017
29. Satellite observations of atmospheric methane and their value for quantifying methane emissions D. Jacob et al. 10.5194/acp-16-14371-2016
30. Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations Y. Zhang et al. 10.5194/acp-21-3643-2021
31. Long-Term Trends and Spatiotemporal Variations in Atmospheric XCH4 over China Utilizing Satellite Observations J. Xu et al. 10.3390/atmos13040525
32. A decade of GOSAT Proxy satellite CH4 observations R. Parker et al. 10.5194/essd-12-3383-2020
33. Using an Inverse Model to Reconcile Differences in Simulated and Observed Global Ethane Concentrations and Trends Between 2008 and 2014 S. Monks et al. 10.1029/2017JD028112
34. Inverse modeling of GOSAT-retrieved ratios of total column CH4 and CO2 for 2009 and 2010 S. Pandey et al. 10.5194/acp-16-5043-2016
35. Detectability of Arctic methane sources at six sites performing continuous atmospheric measurements T. Thonat et al. 10.5194/acp-17-8371-2017
36. CH4 Fluxes Derived from Assimilation of TROPOMI XCH4 in CarbonTracker Europe-CH4: Evaluation of Seasonality and Spatial Distribution in the Northern High Latitudes A. Tsuruta et al. 10.3390/rs15061620
37. A Bayesian framework for deriving sector-based methane emissions from top-down fluxes D. Cusworth et al. 10.1038/s43247-021-00312-6
38. Emulation of greenhouse‐gas sensitivities using variational autoencoders L. Cartwright et al. 10.1002/env.2754
39. Determination of Methane sources globally by SCIAMACHY J. PARK & S. PARK 10.4287/jsprs.55.104
40. CH4 concentrations over the Amazon from GOSAT consistent with in situ vertical profile data A. Webb et al. 10.1002/2016JD025263
41. Inverse modeling of pan-Arctic methane emissions at high spatial resolution: what can we learn from assimilating satellite retrievals and using different process-based wetland and lake biogeochemical models? Z. Tan et al. 10.5194/acp-16-12649-2016
42. A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application S. Feng et al. 10.5194/gmd-16-5949-2023
43. Assessing 5 years of GOSAT Proxy XCH4 data and associated uncertainties R. Parker et al. 10.5194/amt-8-4785-2015
44. Use of Assimilation Analysis in 4D-Var Source Inversion: Observing System Simulation Experiments (OSSEs) with GOSAT Methane and Hemispheric CMAQ S. Voshtani et al. 10.3390/atmos14040758
45. Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003–2018) for carbon and climate applications M. Reuter et al. 10.5194/amt-13-789-2020
46. Recent methane surges reveal heightened emissions from tropical inundated areas X. Lin et al. 10.1038/s41467-024-55266-y
47. Evaluating year-to-year anomalies in tropical wetland methane emissions using satellite CH4 observations R. Parker et al. 10.1016/j.rse.2018.02.011
48. On the use of satellite-derived CH4 : CO2 columns in a joint inversion of CH4 and CO2 fluxes S. Pandey et al. 10.5194/acp-15-8615-2015
49. MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane G. Ehret et al. 10.3390/rs9101052
50. A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China G. Jia et al. 10.1016/j.jes.2021.08.048
51. Spatiotemporal variations of atmospheric XCH4 in China based on multiple spatially continuous satellite-derived products Y. Liu et al. 10.1016/j.jenvman.2025.126309
52. Methane production and estimation from livestock husbandry: A mechanistic understanding and emerging mitigation options S. Kumari et al. 10.1016/j.scitotenv.2019.136135
53. Historical trend of China's CH4 concentrations and emissions during 2003–2020 based on satellite observations, and their implications D. Chen et al. 10.1016/j.apr.2022.101615
54. Assessment of seasonal variations of carbon dioxide concentration in Iran using GOSAT data S. Mousavi et al. 10.1111/1477-8947.12121
55. Quantification of CH4 coal mining emissions in Upper Silesia by passive airborne remote sensing observations with the Methane Airborne MAPper (MAMAP) instrument during the CO2 and Methane (CoMet) campaign S. Krautwurst et al. 10.5194/acp-21-17345-2021
56. Investigation of monthly and seasonal changes of methane gas with respect to climate change using satellite data S. Javadinejad et al. 10.1007/s13201-019-1067-9
57. Inverse modeling of 2010–2022 satellite observations shows that inundation of the wet tropics drove the 2020–2022 methane surge Z. Qu et al. 10.1073/pnas.2402730121
58. National quantifications of methane emissions from fuel exploitation using high resolution inversions of satellite observations L. Shen et al. 10.1038/s41467-023-40671-6
59. Assessing the capability of different satellite observing configurations to resolve the distribution of methane emissions at kilometer scales A. Turner et al. 10.5194/acp-18-8265-2018
60. On the Causes and Consequences of Recent Trends in Atmospheric Methane H. Schaefer 10.1007/s40641-019-00140-z
61. Characterizing model errors in chemical transport modeling of methane: impact of model resolution in versions v9-02 of GEOS-Chem and v35j of its adjoint model I. Stanevich et al. 10.5194/gmd-13-3839-2020
62. Spatial and Temporal Variations of Atmospheric CO2 Concentration in China and Its Influencing Factors Z. Lv et al. 10.3390/atmos11030231
63. Global methane budget and trend, 2010–2017: complementarity of inverse analyses using in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) observations X. Lu et al. 10.5194/acp-21-4637-2021
64. An integrated analysis of contemporary methane emissions and concentration trends over China using in situ and satellite observations and model simulations H. Tan et al. 10.5194/acp-22-1229-2022
65. Global inverse modeling of CH4 sources and sinks: an overview of methods S. Houweling et al. 10.5194/acp-17-235-2017
66. Influences of Uncertainties in the STT Flux on Modeled Tropospheric Methane Z. Wang 10.1029/2023JD039107
67. Gridded National Inventory of U.S. Methane Emissions J. Maasakkers et al. 10.1021/acs.est.6b02878
68. Atmospheric CH4 and CO2 enhancements and biomass burning emission ratios derived from satellite observations of the 2015 Indonesian fire plumes R. Parker et al. 10.5194/acp-16-10111-2016
69. Detecting high-emitting methane sources in oil/gas fields using satellite observations D. Cusworth et al. 10.5194/acp-18-16885-2018
70. Exploiting the Matched Filter to Improve the Detection of Methane Plumes with Sentinel-2 Data H. Wang et al. 10.3390/rs16061023
71. Constraints and biases in a tropospheric two-box model of OH S. Naus et al. 10.5194/acp-19-407-2019
72. Interannual variability on methane emissions in monsoon Asia derived from GOSAT and surface observations F. Wang et al. 10.1088/1748-9326/abd352
73. Region-dependent seasonal pattern of methane over Indian region as observed by SCIAMACHY M. Kavitha & P. Nair 10.1016/j.atmosenv.2016.02.008
74. Contributions of the troposphere and stratosphere to CH4 model biases Z. Wang et al. 10.5194/acp-17-13283-2017
75. Revising the line-shape parameters for air- and self-broadened CO2 lines toward a sub-percent accuracy level R. Hashemi et al. 10.1016/j.jqsrt.2020.107283
76. Comparative analysis of low-Earth orbit (TROPOMI) and geostationary (GeoCARB, GEO-CAPE) satellite instruments for constraining methane emissions on fine regional scales: application to the Southeast US J. Sheng et al. 10.5194/amt-11-6379-2018
77. Comparison of Atmospheric Carbon Dioxide Concentration Trend and Accuracy from GOSAT and AIRS data over the Korean Peninsula S. Lee et al. 10.7780/kjrs.2015.31.6.5
78. A high-resolution satellite-based map of global methane emissions reveals missing wetland, fossil fuel, and monsoon sources X. Yu et al. 10.5194/acp-23-3325-2023
79. Constraining sector-specific CO2 and CH4 emissions in the US S. Miller & A. Michalak 10.5194/acp-17-3963-2017
80. Satellite quantification of oil and natural gas methane emissions in the US and Canada including contributions from individual basins L. Shen et al. 10.5194/acp-22-11203-2022
81. Attribution of the 2020 surge in atmospheric methane by inverse analysis of GOSAT observations Z. Qu et al. 10.1088/1748-9326/ac8754
82. Monitoring global tropospheric OH concentrations using satellite observations of atmospheric methane Y. Zhang et al. 10.5194/acp-18-15959-2018
83. Variability and quasi-decadal changes in the methane budget over the period 2000–2012 M. Saunois et al. 10.5194/acp-17-11135-2017
84. Exploring constraints on a wetland methane emission ensemble (WetCHARTs) using GOSAT observations R. Parker et al. 10.5194/bg-17-5669-2020
85. Space-based Earth observation in support of the UNFCCC Paris Agreement M. Hegglin et al. 10.3389/fenvs.2022.941490
86. High-resolution inversion of methane emissions in the Southeast US using SEAC4RS aircraft observations of atmospheric methane: anthropogenic and wetland sources J. Sheng et al. 10.5194/acp-18-6483-2018
87. Spatio-Temporal Consistency Evaluation of XCO2 Retrievals from GOSAT and OCO-2 Based on TCCON and Model Data for Joint Utilization in Carbon Cycle Research Y. Kong et al. 10.3390/atmos10070354
88. Evaluation of column-averaged methane in models and TCCON with a focus on the stratosphere A. Ostler et al. 10.5194/amt-9-4843-2016
89. Spatial-temporal variation in XCH4 during 2009–2021 and its driving factors across the land of the Northern Hemisphere X. Cao et al. 10.1016/j.atmosres.2023.106811
90. Spatiotemporal analysis of atmospheric methane concentrations and key influencing factors using machine learning in the Middle East (2010–2021) S. Mousavi 10.1016/j.rsase.2024.101406
91. Observation and integrated Earth-system science: A roadmap for 2016–2025 A. Simmons et al. 10.1016/j.asr.2016.03.008
92. A high-resolution (0.1° × 0.1°) inventory of methane emissions from Canadian and Mexican oil and gas systems J. Sheng et al. 10.1016/j.atmosenv.2017.02.036
93. Reduced-cost construction of Jacobian matrices for high-resolution inversions of satellite observations of atmospheric composition H. Nesser et al. 10.5194/amt-14-5521-2021
94. Bias Correction of the Ratio of Total Column CH4 to CO2 Retrieved from GOSAT Spectra H. Oshio et al. 10.3390/rs12193155
95. Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane D. Jacob et al. 10.5194/acp-22-9617-2022
96. Methane fluxes in the high northern latitudes for 2005–2013 estimated using a Bayesian atmospheric inversion R. Thompson et al. 10.5194/acp-17-3553-2017
97. Overtone spectroscopy of molecular complexes containing small polyatomic molecules M. Herman et al. 10.1080/0144235X.2016.1171039
98. Global satellite observations of column-averaged carbon dioxide and methane: The GHG-CCI XCO2 and XCH4 CRDP3 data set M. Buchwitz et al. 10.1016/j.rse.2016.12.027
99. Bottom-Up Estimates of Coal Mine Methane Emissions in China: A Gridded Inventory, Emission Factors, and Trends J. Sheng et al. 10.1021/acs.estlett.9b00294
100. Development of Algorithms for Atmospheric Methane Distribution Retrieval from METOP/IASI Spectra M. Khamatnurova et al. 10.1134/S1024856018010074
101. Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part I: Design of the Assimilation System S. Voshtani et al. 10.3390/rs14020371
102. Validation of TANSO-FTS/GOSAT XCO2 and XCH4 glint mode retrievals using TCCON data from near-ocean sites M. Zhou et al. 10.5194/amt-9-1415-2016
103. Social cost of methane: Method and estimates for Indian livestock S. Kumari et al. 10.1016/j.envdev.2019.100462
104. Characterizing model errors in chemical transport modeling of methane: using GOSAT XCH4 data with weak-constraint four-dimensional variational data assimilation I. Stanevich et al. 10.5194/acp-21-9545-2021
105. Spatiotemporal distribution patterns of atmospheric methane using GOSAT data in Iran S. Mousavi & S. Falahatkar 10.1007/s10668-019-00378-5