103 citations as recorded by crossref.

1. A comparative study of anthropogenic CH<sub>4</sub> 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 CH<sub>4</sub> emissions during 2006–2012 using different inverse models and reassessed atmospheric observations P. Bergamaschi et al. 10.5194/acp-18-901-2018
20. 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
21. Interpreting contemporary trends in atmospheric methane A. Turner et al. 10.1073/pnas.1814297116
22. Monitoring Methane Concentrations with High Spatial Resolution over China by Using Random Forest Model Z. Jin et al. 10.3390/rs16142525
23. Methane emissions from China: a high-resolution inversion of TROPOMI satellite observations Z. Chen et al. 10.5194/acp-22-10809-2022
24. Large XCH<sub>4</sub> anomaly in summer 2013 over northeast Asia observed by GOSAT M. Ishizawa et al. 10.5194/acp-16-9149-2016
25. Improved Gaussian regression model for retrieving ground methane levels by considering vertical profile features H. He et al. 10.3389/feart.2024.1352498
26. The global methane budget 2000–2012 M. Saunois et al. 10.5194/essd-8-697-2016
27. Consistent regional fluxes of CH<sub>4</sub> and CO<sub>2</sub> inferred from GOSAT proxy XCH<sub>4</sub> : XCO<sub>2</sub> retrievals, 2010–2014 L. Feng et al. 10.5194/acp-17-4781-2017
28. Satellite observations of atmospheric methane and their value for quantifying methane emissions D. Jacob et al. 10.5194/acp-16-14371-2016
29. 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
30. Long-Term Trends and Spatiotemporal Variations in Atmospheric XCH4 over China Utilizing Satellite Observations J. Xu et al. 10.3390/atmos13040525
31. A decade of GOSAT Proxy satellite CH<sub>4</sub> observations R. Parker et al. 10.5194/essd-12-3383-2020
32. 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
33. Inverse modeling of GOSAT-retrieved ratios of total column CH<sub>4</sub> and CO<sub>2</sub> for 2009 and 2010 S. Pandey et al. 10.5194/acp-16-5043-2016
34. Detectability of Arctic methane sources at six sites performing continuous atmospheric measurements T. Thonat et al. 10.5194/acp-17-8371-2017
35. 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
36. A Bayesian framework for deriving sector-based methane emissions from top-down fluxes D. Cusworth et al. 10.1038/s43247-021-00312-6
37. Emulation of greenhouse‐gas sensitivities using variational autoencoders L. Cartwright et al. 10.1002/env.2754
38. Determination of Methane sources globally by SCIAMACHY J. PARK & S. PARK 10.4287/jsprs.55.104
39. CH4 concentrations over the Amazon from GOSAT consistent with in situ vertical profile data A. Webb et al. 10.1002/2016JD025263
40. 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
41. 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
42. Assessing 5 years of GOSAT Proxy XCH<sub>4</sub> data and associated uncertainties R. Parker et al. 10.5194/amt-8-4785-2015
43. 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
44. 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
45. Recent methane surges reveal heightened emissions from tropical inundated areas X. Lin et al. 10.1038/s41467-024-55266-y
46. 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
47. On the use of satellite-derived CH<sub>4</sub> : CO<sub>2</sub> columns in a joint inversion of CH<sub>4</sub> and CO<sub>2</sub> fluxes S. Pandey et al. 10.5194/acp-15-8615-2015
48. MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane G. Ehret et al. 10.3390/rs9101052
49. 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
50. Methane production and estimation from livestock husbandry: A mechanistic understanding and emerging mitigation options S. Kumari et al. 10.1016/j.scitotenv.2019.136135
51. 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
52. Assessment of seasonal variations of carbon dioxide concentration in Iran using GOSAT data S. Mousavi et al. 10.1111/1477-8947.12121
53. Quantification of CH<sub>4</sub> coal mining emissions in Upper Silesia by passive airborne remote sensing observations with the Methane Airborne MAPper (MAMAP) instrument during the CO<sub>2</sub> and Methane (CoMet) campaign S. Krautwurst et al. 10.5194/acp-21-17345-2021
54. 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
55. 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
56. 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
57. 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
58. On the Causes and Consequences of Recent Trends in Atmospheric Methane H. Schaefer 10.1007/s40641-019-00140-z
59. 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
60. Spatial and Temporal Variations of Atmospheric CO2 Concentration in China and Its Influencing Factors Z. Lv et al. 10.3390/atmos11030231
61. Global methane budget and trend, 2010–2017: complementarity of inverse analyses using in situ (GLOBALVIEWplus CH<sub>4</sub> ObsPack) and satellite (GOSAT) observations X. Lu et al. 10.5194/acp-21-4637-2021
62. 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
63. Global inverse modeling of CH<sub>4</sub> sources and sinks: an overview of methods S. Houweling et al. 10.5194/acp-17-235-2017
64. Influences of Uncertainties in the STT Flux on Modeled Tropospheric Methane Z. Wang 10.1029/2023JD039107
65. Gridded National Inventory of U.S. Methane Emissions J. Maasakkers et al. 10.1021/acs.est.6b02878
66. Atmospheric CH<sub>4</sub> and CO<sub>2</sub> 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
67. Detecting high-emitting methane sources in oil/gas fields using satellite observations D. Cusworth et al. 10.5194/acp-18-16885-2018
68. Exploiting the Matched Filter to Improve the Detection of Methane Plumes with Sentinel-2 Data H. Wang et al. 10.3390/rs16061023
69. Constraints and biases in a tropospheric two-box model of OH S. Naus et al. 10.5194/acp-19-407-2019
70. Interannual variability on methane emissions in monsoon Asia derived from GOSAT and surface observations F. Wang et al. 10.1088/1748-9326/abd352
71. Region-dependent seasonal pattern of methane over Indian region as observed by SCIAMACHY M. Kavitha & P. Nair 10.1016/j.atmosenv.2016.02.008
72. Contributions of the troposphere and stratosphere to CH<sub>4</sub> model biases Z. Wang et al. 10.5194/acp-17-13283-2017
73. 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
74. 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
75. 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
76. 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
77. Constraining sector-specific CO<sub>2</sub> and CH<sub>4</sub> emissions in the US S. Miller & A. Michalak 10.5194/acp-17-3963-2017
78. 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
79. Attribution of the 2020 surge in atmospheric methane by inverse analysis of GOSAT observations Z. Qu et al. 10.1088/1748-9326/ac8754
80. Monitoring global tropospheric OH concentrations using satellite observations of atmospheric methane Y. Zhang et al. 10.5194/acp-18-15959-2018
81. Variability and quasi-decadal changes in the methane budget over the period 2000–2012 M. Saunois et al. 10.5194/acp-17-11135-2017
82. Exploring constraints on a wetland methane emission ensemble (WetCHARTs) using GOSAT observations R. Parker et al. 10.5194/bg-17-5669-2020
83. Space-based Earth observation in support of the UNFCCC Paris Agreement M. Hegglin et al. 10.3389/fenvs.2022.941490
84. High-resolution inversion of methane emissions in the Southeast US using SEAC<sup>4</sup>RS aircraft observations of atmospheric methane: anthropogenic and wetland sources J. Sheng et al. 10.5194/acp-18-6483-2018
85. 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
86. 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
87. 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
88. 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
89. Observation and integrated Earth-system science: A roadmap for 2016–2025 A. Simmons et al. 10.1016/j.asr.2016.03.008
90. 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
91. 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
92. Bias Correction of the Ratio of Total Column CH4 to CO2 Retrieved from GOSAT Spectra H. Oshio et al. 10.3390/rs12193155
93. 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
94. 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
95. Overtone spectroscopy of molecular complexes containing small polyatomic molecules M. Herman et al. 10.1080/0144235X.2016.1171039
96. 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
97. 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
98. Development of Algorithms for Atmospheric Methane Distribution Retrieval from METOP/IASI Spectra M. Khamatnurova et al. 10.1134/S1024856018010074
99. Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part I: Design of the Assimilation System S. Voshtani et al. 10.3390/rs14020371
100. Validation of TANSO-FTS/GOSAT XCO<sub>2</sub> and XCH<sub>4</sub> glint mode retrievals using TCCON data from near-ocean sites M. Zhou et al. 10.5194/amt-9-1415-2016
101. Social cost of methane: Method and estimates for Indian livestock S. Kumari et al. 10.1016/j.envdev.2019.100462
102. Characterizing model errors in chemical transport modeling of methane: using GOSAT XCH<sub>4</sub> data with weak-constraint four-dimensional variational data assimilation I. Stanevich et al. 10.5194/acp-21-9545-2021
103. Spatiotemporal distribution patterns of atmospheric methane using GOSAT data in Iran S. Mousavi & S. Falahatkar 10.1007/s10668-019-00378-5