Articles | Volume 9, issue 2
https://doi.org/10.5194/gmd-9-749-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-749-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
19 Feb 2016
Model description paper |
| 19 Feb 2016
Adjoint of the global Eulerian–Lagrangian coupled atmospheric transport model (A-GELCA v1.0): development and validation
Dmitry A. Belikov
CORRESPONDING AUTHOR
National Institute for Environmental Studies, Tsukuba,
Japan
National Institute of Polar Research, Tokyo,
Japan
Tomsk State University, Tomsk, Russia
Shamil Maksyutov
National Institute for Environmental Studies, Tsukuba,
Japan
Alexey Yaremchuk
N. Andreev Acoustic Institute, Moscow,
Russia
Alexander Ganshin
Tomsk State University, Tomsk, Russia
Central Aerological Observatory, Dolgoprudny,
Russia
Thomas Kaminski
The Inversion Lab, Hamburg, Germany
previously at: FastOpt GmbH, Hamburg, Germany
Simon Blessing
FastOpt GmbH, Hamburg, Germany
Motoki Sasakawa
National Institute for Environmental Studies, Tsukuba,
Japan
Angel J. Gomez-Pelaez
Izaña Atmospheric Research Center (IARC),
Meteorological State Agency of Spain (AEMET), Izaña, 38311,
Spain
Alexander Starchenko
Tomsk State University, Tomsk, Russia
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Cited
15 citations as recorded by crossref.
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- Indian Land Carbon Sink Estimated from Surface and GOSAT Observations L. Nayagam et al. 10.3390/rs17030450
- 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. 10.1016/j.jandt.2025.03.004
- The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies A. Berchet et al. 10.5194/gmd-14-5331-2021
- Atmospheric observations suggest methane emissions in north-eastern China growing with natural gas use F. Wang et al. 10.1038/s41598-022-19462-4
- Sources of uncertainty in atmospheric dispersion modeling in support of Comprehensive Nuclear–Test–Ban Treaty monitoring and verification system S. Mekhaimr & M. Abdel Wahab 10.1016/j.apr.2019.03.008
- Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories F. Wang et al. 10.3390/rs11212489
- Interannual variability on methane emissions in monsoon Asia derived from GOSAT and surface observations F. Wang et al. 10.1088/1748-9326/abd352
- Variational regional inverse modeling of reactive species emissions with PYVAR-CHIMERE-v2019 A. Fortems-Cheiney et al. 10.5194/gmd-14-2939-2021
- Attribution of PM2.5 exposure in Beijing–Tianjin–Hebei region to emissions: implication to control strategies X. Li et al. 10.1016/j.scib.2017.06.005
- Country-Scale Analysis of Methane Emissions with a High-Resolution Inverse Model Using GOSAT and Surface Observations R. Janardanan et al. 10.3390/rs12030375
- Technical note: A high-resolution inverse modelling technique for estimating surface CO2 fluxes based on the NIES-TM–FLEXPART coupled transport model and its adjoint S. Maksyutov et al. 10.5194/acp-21-1245-2021
- Country-level methane emissions and their sectoral trends during 2009–2020 estimated by high-resolution inversion of GOSAT and surface observations R. Janardanan et al. 10.1088/1748-9326/ad2436
- Study of the footprints of short-term variation in XCO2 observed by TCCON sites using NIES and FLEXPART atmospheric transport models D. Belikov et al. 10.5194/acp-17-143-2017
- Algorithmic differentiation for cloud schemes (IFS Cy43r3) using CoDiPack (v1.8.1) M. Baumgartner et al. 10.5194/gmd-12-5197-2019
15 citations as recorded by crossref.
- Plant responses to volcanically elevated CO2 in two Costa Rican forests R. Bogue et al. 10.5194/bg-16-1343-2019
- Indian Land Carbon Sink Estimated from Surface and GOSAT Observations L. Nayagam et al. 10.3390/rs17030450
- 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. 10.1016/j.jandt.2025.03.004
- The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies A. Berchet et al. 10.5194/gmd-14-5331-2021
- Atmospheric observations suggest methane emissions in north-eastern China growing with natural gas use F. Wang et al. 10.1038/s41598-022-19462-4
- Sources of uncertainty in atmospheric dispersion modeling in support of Comprehensive Nuclear–Test–Ban Treaty monitoring and verification system S. Mekhaimr & M. Abdel Wahab 10.1016/j.apr.2019.03.008
- Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories F. Wang et al. 10.3390/rs11212489
- Interannual variability on methane emissions in monsoon Asia derived from GOSAT and surface observations F. Wang et al. 10.1088/1748-9326/abd352
- Variational regional inverse modeling of reactive species emissions with PYVAR-CHIMERE-v2019 A. Fortems-Cheiney et al. 10.5194/gmd-14-2939-2021
- Attribution of PM2.5 exposure in Beijing–Tianjin–Hebei region to emissions: implication to control strategies X. Li et al. 10.1016/j.scib.2017.06.005
- Country-Scale Analysis of Methane Emissions with a High-Resolution Inverse Model Using GOSAT and Surface Observations R. Janardanan et al. 10.3390/rs12030375
- Technical note: A high-resolution inverse modelling technique for estimating surface CO2 fluxes based on the NIES-TM–FLEXPART coupled transport model and its adjoint S. Maksyutov et al. 10.5194/acp-21-1245-2021
- Country-level methane emissions and their sectoral trends during 2009–2020 estimated by high-resolution inversion of GOSAT and surface observations R. Janardanan et al. 10.1088/1748-9326/ad2436
- Study of the footprints of short-term variation in XCO2 observed by TCCON sites using NIES and FLEXPART atmospheric transport models D. Belikov et al. 10.5194/acp-17-143-2017
- Algorithmic differentiation for cloud schemes (IFS Cy43r3) using CoDiPack (v1.8.1) M. Baumgartner et al. 10.5194/gmd-12-5197-2019
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