69 citations as recorded by crossref.

1. The offline Lagrangian particle model FLEXPART–NorESM/CAM (v1): model description and comparisons with the online NorESM transport scheme and with the reference FLEXPART model M. Cassiani et al. https://doi.org/10.5194/gmd-9-4029-2016
2. Quantifying European SF6 emissions from 2005 to 2021 using a large inversion ensemble M. Vojta et al. https://doi.org/10.5194/acp-25-15197-2025
3. Global methane emission estimates for 2000–2012 from CarbonTracker Europe-CH4 v1.0 A. Tsuruta et al. https://doi.org/10.5194/gmd-10-1261-2017
4. Characterization of German SF6 Emissions K. Meixner et al. https://doi.org/10.1021/acsestair.5c00234
5. Surface networks in the Arctic may miss a future methane bomb S. Wittig et al. https://doi.org/10.5194/acp-24-6359-2024
6. Comparison of four inverse modelling systems applied to the estimation of HFC-125, HFC-134a, and SF6 emissions over Europe D. Brunner et al. https://doi.org/10.5194/acp-17-10651-2017
7. The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2019 A. Petrescu et al. https://doi.org/10.5194/essd-15-1197-2023
8. Do State‐Of‐The‐Art Atmospheric CO2 Inverse Models Capture Drought Impacts on the European Land Carbon Uptake? W. He et al. https://doi.org/10.1029/2022MS003150
9. Quantifying the UK's carbon dioxide flux: an atmospheric inverse modelling approach using a regional measurement network E. White et al. https://doi.org/10.5194/acp-19-4345-2019
10. The carbon sink in China as seen from GOSAT with a regional inversion system based on the Community Multi-scale Air Quality (CMAQ) and ensemble Kalman smoother (EnKS) X. Kou et al. https://doi.org/10.5194/acp-23-6719-2023
11. Greenhouse gas observation network design for Africa A. Nickless et al. https://doi.org/10.1080/16000889.2020.1824486
12. Strong monsoon influence on South Asian methane emissions in 2020 revealed by a Bayesian inversion constrained by satellite observations R. Subramanian et al. https://doi.org/10.5194/acp-26-9757-2026
13. A methane monitoring station siting method based on WRF-STILT and genetic algorithm L. Fan et al. https://doi.org/10.3389/fenvs.2024.1394281
14. Unidentified Halon-2402 emissions in East Asia are driving the global trend H. Choi et al. https://doi.org/10.1038/s41612-026-01399-4
15. Methane emissions in East Asia for 2000–2011 estimated using an atmospheric Bayesian inversion R. Thompson et al. https://doi.org/10.1002/2014JD022394
16. Extensive release of methane from Arctic seabed west of Svalbard during summer 2014 does not influence the atmosphere C. Myhre et al. https://doi.org/10.1002/2016GL068999
17. A comprehensive evaluation of the use of Lagrangian particle dispersion models for inverse modeling of greenhouse gas emissions M. Vojta et al. https://doi.org/10.5194/gmd-15-8295-2022
18. Effects of extreme meteorological conditions in 2018 on European methane emissions estimated using atmospheric inversions R. Thompson et al. https://doi.org/10.1098/rsta.2020.0443
19. Source–receptor matrix calculation for deposited mass with the Lagrangian particle dispersion model FLEXPART v10.2 in backward mode S. Eckhardt et al. https://doi.org/10.5194/gmd-10-4605-2017
20. Regional CO2 inversions with LUMIA, the Lund University Modular Inversion Algorithm, v1.0 G. Monteil & M. Scholze https://doi.org/10.5194/gmd-14-3383-2021
21. GONGGA-Lagrange: A novel atmospheric carbon inversion system integrating NLS-4DVar assimilation method and the Lagrangian particle dispersion model X. Yang et al. https://doi.org/10.1016/j.jclepro.2026.149198
22. Changes in black carbon emissions over Europe due to COVID-19 lockdowns N. Evangeliou et al. https://doi.org/10.5194/acp-21-2675-2021
23. Analysis of Regional Surface CO2 Fluxes Using the MEGA Satellite Data Assimilation System L. Hu et al. https://doi.org/10.3390/rs17223720
24. Spatio-temporal bivariate statistical models for atmospheric trace-gas inversion A. Zammit-Mangion et al. https://doi.org/10.1016/j.chemolab.2015.09.006
25. Comparison of observation- and inventory-based methane emissions for eight large global emitters A. Petrescu et al. https://doi.org/10.5194/essd-16-4325-2024
26. Monitoring the impact of EU F-gas regulation on HFC-134a emissions through a comparison of top-down and bottom-up estimates S. Annadate et al. https://doi.org/10.1186/s12302-025-01081-1
27. Investigation of Volcanic Emissions in the Mediterranean: “The Etna–Antikythera Connection” A. Kampouri et al. https://doi.org/10.3390/atmos12010040
28. Regional estimation of methane emissions over the peninsular India using atmospheric inverse modelling A. Raju et al. https://doi.org/10.1007/s10661-022-10323-1
29. The Lagrangian particle dispersion model FLEXPART version 10.4 I. Pisso et al. https://doi.org/10.5194/gmd-12-4955-2019
30. Estimating methane emissions in the Arctic nations using surface observations from 2008 to 2019 S. Wittig et al. https://doi.org/10.5194/acp-23-6457-2023
31. Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling S. Henne et al. https://doi.org/10.5194/acp-16-3683-2016
32. The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2017 A. Petrescu et al. https://doi.org/10.5194/essd-13-2307-2021
33. Three-dimensional methane distribution simulated with FLEXPART 8-CTM-1.1 constrained with observation data C. Groot Zwaaftink et al. https://doi.org/10.5194/gmd-11-4469-2018
34. Adjoint of the global Eulerian–Lagrangian coupled atmospheric transport model (A-GELCA v1.0): development and validation D. Belikov et al. https://doi.org/10.5194/gmd-9-749-2016
35. Top-down estimates of black carbon emissions at high latitudes using an atmospheric transport model and a Bayesian inversion framework N. Evangeliou et al. https://doi.org/10.5194/acp-18-15307-2018
36. A Sensitivity Study of a Bayesian Inversion Model Used to Estimate Emissions of Synthetic Greenhouse Gases at the European Scale S. Annadate et al. https://doi.org/10.3390/atmos15010051
37. A global re-analysis of regionally resolved emissions and atmospheric mole fractions of SF6 for the period 2005–2021 M. Vojta et al. https://doi.org/10.5194/acp-24-12465-2024
38. Improved understanding of anthropogenic and biogenic carbonyl sulfide (COS) fluxes in western Europe from long-term continuous mixing ratio measurements A. Berchet et al. https://doi.org/10.5194/acp-25-7499-2025
39. Emissions of HFC-23 do not reflect commitments made under the Kigali Amendment B. Adam et al. https://doi.org/10.1038/s43247-024-01946-y
40. Towards improving top–down national CO2 estimation in Europe: potential from expanding the ICOS atmospheric network in Italy Y. Villalobos et al. https://doi.org/10.1088/1748-9326/adc41e
41. Long-term analysis of atmospheric propane over Southern Europe based on observations conducted at the WMO-GAW station of Monte Cimone E. Mancinelli et al. https://doi.org/10.5194/acp-26-4105-2026
42. The consolidated European synthesis of CO2 emissions and removals for the European Union and United Kingdom: 1990–2020 M. McGrath et al. https://doi.org/10.5194/essd-15-4295-2023
43. A Review of City-Scale Methane Flux Inversion Based on Top-Down Methods X. Li et al. https://doi.org/10.3390/rs17183152
44. Assessing Lagrangian inverse modelling of urban anthropogenic CO2 fluxes using in situ aircraft and ground-based measurements in the Tokyo area I. Pisso et al. https://doi.org/10.1186/s13021-019-0118-8
45. Objectified quantification of uncertainties in Bayesian atmospheric inversions A. Berchet et al. https://doi.org/10.5194/gmd-8-1525-2015
46. The regional European atmospheric transport inversion comparison, EUROCOM: first results on European-wide terrestrial carbon fluxes for the period 2006–2015 G. Monteil et al. https://doi.org/10.5194/acp-20-12063-2020
47. Emissions of Tetrafluoromethane (CF4) and Hexafluoroethane (C2F6) From East Asia: 2008 to 2019 J. Kim et al. https://doi.org/10.1029/2021JD034888
48. FLEXPART version 11: improved accuracy, efficiency, and flexibility L. Bakels et al. https://doi.org/10.5194/gmd-17-7595-2024
49. 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. https://doi.org/10.3390/rs15061620
50. High-Resolution Bayesian Inversion of Carbon Dioxide Flux Over Peninsular India S. Sijikumar et al. https://doi.org/10.1016/j.atmosenv.2023.119868
51. A rise in HFC-23 emissions from eastern Asia since 2015 H. Park et al. https://doi.org/10.5194/acp-23-9401-2023
52. Efficient use of a Lagrangian particle dispersion model for atmospheric inversions using satellite observations of column mixing ratios R. Thompson et al. https://doi.org/10.5194/acp-25-12737-2025
53. Non-Gaussian bivariate modelling with application to atmospheric trace-gas inversion A. Zammit-Mangion et al. https://doi.org/10.1016/j.spasta.2016.06.005
54. Estimation of trace gas fluxes with objectively determined basis functions using reversible-jump Markov chain Monte Carlo M. Lunt et al. https://doi.org/10.5194/gmd-9-3213-2016
55. Review of spatial scale dispersion models (ATDMs) to simulate environmental dispersion and deposition of radionuclides and the overview of GIS coupling with dispersion models H. Muhammad et al. https://doi.org/10.1016/j.jandt.2025.03.004
56. Methane fluxes in the high northern latitudes for 2005–2013 estimated using a Bayesian atmospheric inversion R. Thompson et al. https://doi.org/10.5194/acp-17-3553-2017
57. Black Carbon Emission Reduction Due to COVID‐19 Lockdown in China M. Jia et al. https://doi.org/10.1029/2021GL093243
58. Observations of 1,2-dichloroethane from the AGAGE and NOAA networks and derived global and regional emissions J. Pitt et al. https://doi.org/10.5194/acp-26-10167-2026
59. Revealing the significant acceleration of hydrofluorocarbon (HFC) emissions in eastern Asia through long-term atmospheric observations H. Choi et al. https://doi.org/10.5194/acp-24-7309-2024
60. Atmospheric observations show accurate reporting and little growth in India’s methane emissions A. Ganesan et al. https://doi.org/10.1038/s41467-017-00994-7
61. The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies A. Berchet et al. https://doi.org/10.5194/gmd-14-5331-2021
62. Modeling water column gas transformation, migration and atmospheric flux from seafloor seepage K. Dølven et al. https://doi.org/10.5194/os-21-3031-2025
63. Inversion of CO emissions in Greater Bay Area over southern China using a WRF-STILT-Bayesian framework X. Lu et al. https://doi.org/10.1016/j.cacint.2026.100308
64. Comparative Review of Global Methane Budget Estimation: Top-Down, Bottom-Up, and Integrated Approaches B. Alem et al. https://doi.org/10.3390/rs18091336
65. Accounting for model error in air quality forecasts: an application of 4DEnVar to the assimilation of atmospheric composition using QG-Chem 1.0 E. Emili et al. https://doi.org/10.5194/gmd-9-3933-2016
66. Changes in net ecosystem exchange over Europe during the 2018 drought based on atmospheric observations R. Thompson et al. https://doi.org/10.1098/rstb.2019.0512
67. Reduced-cost construction of Jacobian matrices for high-resolution inversions of satellite observations of atmospheric composition H. Nesser et al. https://doi.org/10.5194/amt-14-5521-2021
68. Utilizing Earth Observations of Soil Freeze/Thaw Data and Atmospheric Concentrations to Estimate Cold Season Methane Emissions in the Northern High Latitudes M. Tenkanen et al. https://doi.org/10.3390/rs13245059
69. Rapid decline of carbon monoxide emissions in the Fenwei Plain in China during the three-year Action Plan on defending the blue sky M. Jia et al. https://doi.org/10.1016/j.jenvman.2023.117735