84 citations as recorded by crossref.

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47. A case study on the application of SCIAMACHY satellite methane measurements for regional studies: the Greater Area of the Eastern Mediterranean A. Georgoulias et al. https://doi.org/10.1080/01431161.2010.517791
48. First ground-based FTIR observations of methane in the inner tropics over several years A. Petersen et al. https://doi.org/10.5194/acp-10-7231-2010
49. Estimating UK methane and nitrous oxide emissions from 1990 to 2007 using an inversion modeling approach A. Manning et al. https://doi.org/10.1029/2010JD014763
50. Estimating methane emissions in California's urban and rural regions using multitower observations S. Jeong et al. https://doi.org/10.1002/2016JD025404
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52. Atmospheric inverse estimates of methane emissions from Central California C. Zhao et al. https://doi.org/10.1029/2008JD011671
53. CarbonTracker-CH4: an assimilation system for estimating emissions of atmospheric methane L. Bruhwiler et al. https://doi.org/10.5194/acp-14-8269-2014
54. Global atmospheric methane: budget, changes and dangers E. Dlugokencky et al. https://doi.org/10.1098/rsta.2010.0341
55. A three-dimensional-model inversion of methyl chloroform to constrain the atmospheric oxidative capacity S. Naus et al. https://doi.org/10.5194/acp-21-4809-2021
56. Four‐dimensional variational data assimilation for inverse modeling of atmospheric methane emissions: Analysis of SCIAMACHY observations J. Meirink et al. https://doi.org/10.1029/2007JD009740
57. Dynamic biomass burning emission factors and their impact on atmospheric CO mixing ratios T. van Leeuwen et al. https://doi.org/10.1002/jgrd.50478
58. Source attribution of the changes in atmospheric methane for 2006–2008 P. Bousquet et al. https://doi.org/10.5194/acp-11-3689-2011
59. Four-dimensional variational data assimilation for inverse modelling of atmospheric methane emissions: method and comparison with synthesis inversion J. Meirink et al. https://doi.org/10.5194/acp-8-6341-2008
60. Top-Down Versus Bottom-Up E. Nisbet & R. Weiss https://doi.org/10.1126/science.1189936
61. Inverse modeling of global and regional CH4 emissions using SCIAMACHY satellite retrievals P. Bergamaschi et al. https://doi.org/10.1029/2009JD012287
62. The challenge of estimating regional trace gas emissions from atmospheric observations A. Manning https://doi.org/10.1098/rsta.2010.0321
63. Satellite Measurement of GHG Emissions: Prospects for Enhancing Transparency and Answerability under International Law T. Aganaba-Jeanty & A. Huggins https://doi.org/10.1017/S2047102519000104
64. Application of Gauss's theorem to quantify localized surface emissions from airborne measurements of wind and trace gases S. Conley et al. https://doi.org/10.5194/amt-10-3345-2017
65. On variational data assimilation for estimating the model initial conditions and emission fluxes for short-term forecasting of SOx concentrations J. Vira & M. Sofiev https://doi.org/10.1016/j.atmosenv.2011.09.066
66. A pragmatic protocol for characterising errors in atmospheric inversions of methane emissions over Europe B. Szénási et al. https://doi.org/10.1080/16000889.2021.1914989
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68. Evaluating Weather and Chemical Transport Models at High Latitudes using MAGIC2021 Airborne Measurements F. Langot et al. https://doi.org/10.5194/amt-18-5955-2025
69. Top-down CO emission estimates using TROPOMI CO data in the TM5-4DVAR (r1258) inverse modeling suit J. Nüß et al. https://doi.org/10.5194/gmd-18-2861-2025
70. Urban carbon accounting: An overview L. Yin et al. https://doi.org/10.1016/j.uclim.2022.101195
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72. 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. https://doi.org/10.5194/acp-16-12649-2016
73. Application of stable isotope analysis for improved understanding of the methane budget: comparison of TROICA measurements with TM3 model simulations O. Tarasova et al. https://doi.org/10.1007/s10874-010-9157-y
74. A three-dimensional synthesis inversion of the molecular hydrogen cycle: Sources and sinks budget and implications for the soil uptake P. Bousquet et al. https://doi.org/10.1029/2010JD014599
75. Evaluation of methane emissions from West Siberian wetlands based on inverse modeling H. Kim et al. https://doi.org/10.1088/1748-9326/6/3/035201
76. A multitower measurement network estimate of California's methane emissions S. Jeong et al. https://doi.org/10.1002/jgrd.50854
77. Satellite chartography of atmospheric methane from SCIAMACHY on board ENVISAT: 2. Evaluation based on inverse model simulations P. Bergamaschi et al. https://doi.org/10.1029/2006JD007268
78. Inverse modelling of New Zealand's carbon dioxide balance estimates a larger than expected carbon sink B. Bukosa et al. https://doi.org/10.5194/acp-25-6445-2025
79. Satellite observations of atmospheric methane and their value for quantifying methane emissions D. Jacob et al. https://doi.org/10.5194/acp-16-14371-2016
80. Validation of TES methane with HIPPO aircraft observations: implications for inverse modeling of methane sources K. Wecht et al. https://doi.org/10.5194/acp-12-1823-2012
81. Atmospheric carbon gases retrieved from SCIAMACHY by WFM-DOAS: version 0.5 CO and CH4and impact of calibration improvements on CO2retrieval M. Buchwitz et al. https://doi.org/10.5194/acp-6-2727-2006
82. Technical note: The CAMS greenhouse gas reanalysis from 2003 to 2020 A. Agustí-Panareda et al. https://doi.org/10.5194/acp-23-3829-2023
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84. Impact of transport model errors on the global and regional methane emissions estimated by inverse modelling R. Locatelli et al. https://doi.org/10.5194/acp-13-9917-2013