{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T06:30:19Z","timestamp":1770359419999,"version":"3.49.0"},"reference-count":105,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T00:00:00Z","timestamp":1678924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Academy of Finland","award":["307331 UPFORMET"],"award-info":[{"award-number":["307331 UPFORMET"]}]},{"name":"Academy of Finland","award":["351311 GHGSUPER"],"award-info":[{"award-number":["351311 GHGSUPER"]}]},{"name":"Academy of Finland","award":["281255 ICOS Finland"],"award-info":[{"award-number":["281255 ICOS Finland"]}]},{"name":"Academy of Finland","award":["345531 FIRI 2022-2025"],"award-info":[{"award-number":["345531 FIRI 2022-2025"]}]},{"name":"Academy of Finland","award":["331829 CitySpot"],"award-info":[{"award-number":["331829 CitySpot"]}]},{"name":"Academy of Finland","award":["4000125046\/18\/I-NB ESA-MethEO"],"award-info":[{"award-number":["4000125046\/18\/I-NB ESA-MethEO"]}]},{"name":"Academy of Finland","award":["4000126450\/19\/I-NB GHG-CCI+"],"award-info":[{"award-number":["4000126450\/19\/I-NB GHG-CCI+"]}]},{"name":"Academy of Finland","award":["776810 EU-H2020 VERIFY"],"award-info":[{"award-number":["776810 EU-H2020 VERIFY"]}]},{"name":"Academy of Finland","award":["958927 EU-H2020 CoCO2"],"award-info":[{"award-number":["958927 EU-H2020 CoCO2"]}]},{"name":"European Space Agency","award":["307331 UPFORMET"],"award-info":[{"award-number":["307331 UPFORMET"]}]},{"name":"European Space Agency","award":["351311 GHGSUPER"],"award-info":[{"award-number":["351311 GHGSUPER"]}]},{"name":"European Space Agency","award":["281255 ICOS Finland"],"award-info":[{"award-number":["281255 ICOS Finland"]}]},{"name":"European Space Agency","award":["345531 FIRI 2022-2025"],"award-info":[{"award-number":["345531 FIRI 2022-2025"]}]},{"name":"European Space Agency","award":["331829 CitySpot"],"award-info":[{"award-number":["331829 CitySpot"]}]},{"name":"European Space Agency","award":["4000125046\/18\/I-NB ESA-MethEO"],"award-info":[{"award-number":["4000125046\/18\/I-NB ESA-MethEO"]}]},{"name":"European Space Agency","award":["4000126450\/19\/I-NB GHG-CCI+"],"award-info":[{"award-number":["4000126450\/19\/I-NB GHG-CCI+"]}]},{"name":"European Space Agency","award":["776810 EU-H2020 VERIFY"],"award-info":[{"award-number":["776810 EU-H2020 VERIFY"]}]},{"name":"European Space Agency","award":["958927 EU-H2020 CoCO2"],"award-info":[{"award-number":["958927 EU-H2020 CoCO2"]}]},{"name":"European Union","award":["307331 UPFORMET"],"award-info":[{"award-number":["307331 UPFORMET"]}]},{"name":"European Union","award":["351311 GHGSUPER"],"award-info":[{"award-number":["351311 GHGSUPER"]}]},{"name":"European Union","award":["281255 ICOS Finland"],"award-info":[{"award-number":["281255 ICOS Finland"]}]},{"name":"European Union","award":["345531 FIRI 2022-2025"],"award-info":[{"award-number":["345531 FIRI 2022-2025"]}]},{"name":"European Union","award":["331829 CitySpot"],"award-info":[{"award-number":["331829 CitySpot"]}]},{"name":"European Union","award":["4000125046\/18\/I-NB ESA-MethEO"],"award-info":[{"award-number":["4000125046\/18\/I-NB ESA-MethEO"]}]},{"name":"European Union","award":["4000126450\/19\/I-NB GHG-CCI+"],"award-info":[{"award-number":["4000126450\/19\/I-NB GHG-CCI+"]}]},{"name":"European Union","award":["776810 EU-H2020 VERIFY"],"award-info":[{"award-number":["776810 EU-H2020 VERIFY"]}]},{"name":"European Union","award":["958927 EU-H2020 CoCO2"],"award-info":[{"award-number":["958927 EU-H2020 CoCO2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recent advances in satellite observations of methane provide increased opportunities for inverse modeling. However, challenges exist in the satellite observation optimization and retrievals for high latitudes. In this study, we examine possibilities and challenges in the use of the total column averaged dry-air mole fractions of methane (XCH4) data over land from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite in the estimation of CH4 fluxes using the CarbonTracker Europe-CH4 (CTE-CH4) atmospheric inverse model. We carry out simulations assimilating two retrieval products: Netherlands Institute for Space Research\u2019s (SRON) operational and University of Bremen\u2019s Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). For comparison, we also carry out a simulation assimilating the ground-based surface data. Our results show smaller regional emissions in the TROPOMI inversions compared to the prior and surface inversion, although they are roughly within the range of the previous studies. The wetland emissions in summer and anthropogenic emissions in spring are lesser. The inversion results based on the two satellite datasets show many similarities in terms of spatial distribution and time series but also clear differences, especially in Canada, where CH4 emission maximum is later, when the SRON\u2019s operational data are assimilated. The TROPOMI inversions show higher CH4 emissions from oil and gas production and coal mining from Russia and Kazakhstan. The location of hotspots in the TROPOMI inversions did not change compared to the prior, but all inversions indicated spatially more homogeneous high wetland emissions in northern Fennoscandia. In addition, we find that the regional monthly wetland emissions in the TROPOMI inversions do not correlate with the anthropogenic emissions as strongly as those in the surface inversion. The uncertainty estimates in the TROPOMI inversions are more homogeneous in space, and the regional uncertainties are comparable to the surface inversion. This indicates the potential of the TROPOMI data to better separately estimate wetland and anthropogenic emissions, as well as constrain spatial distributions. This study emphasizes the importance of quantifying and taking into account the model and retrieval uncertainties in regional levels in order to improve and derive more robust emission estimates.<\/jats:p>","DOI":"10.3390\/rs15061620","type":"journal-article","created":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T02:29:59Z","timestamp":1679020199000},"page":"1620","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["CH4 Fluxes Derived from Assimilation of TROPOMI XCH4 in CarbonTracker Europe-CH4: Evaluation of Seasonality and Spatial Distribution in the Northern High Latitudes"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9197-3005","authenticated-orcid":false,"given":"Aki","family":"Tsuruta","sequence":"first","affiliation":[{"name":"Climate Research, Finnish Meteorological Institute, P.O. Box 503, FI-00101 Helsinki, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8150-8775","authenticated-orcid":false,"given":"Ella","family":"Kivim\u00e4ki","sequence":"additional","affiliation":[{"name":"Earth Observation Research, Finnish Meteorological Institute, P.O. Box 503, FI-00101 Helsinki, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9202-906X","authenticated-orcid":false,"given":"Hannakaisa","family":"Lindqvist","sequence":"additional","affiliation":[{"name":"Earth Observation Research, Finnish Meteorological Institute, P.O. Box 503, FI-00101 Helsinki, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6570-1615","authenticated-orcid":false,"given":"Tomi","family":"Karppinen","sequence":"additional","affiliation":[{"name":"Earth Observation Research, Finnish Meteorological Institute, P.O. Box 503, FI-00101 Helsinki, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1501-2958","authenticated-orcid":false,"given":"Leif","family":"Backman","sequence":"additional","affiliation":[{"name":"Climate Research, Finnish Meteorological Institute, P.O. Box 503, FI-00101 Helsinki, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5281-8985","authenticated-orcid":false,"given":"Janne","family":"Hakkarainen","sequence":"additional","affiliation":[{"name":"Earth Observation Research, Finnish Meteorological Institute, P.O. Box 503, FI-00101 Helsinki, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1725-8246","authenticated-orcid":false,"given":"Oliver","family":"Schneising","sequence":"additional","affiliation":[{"name":"Institute of Environmental Physics (IUP), University of Bremen, 28359 Bremen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7616-1837","authenticated-orcid":false,"given":"Michael","family":"Buchwitz","sequence":"additional","affiliation":[{"name":"Institute of Environmental Physics (IUP), University of Bremen, 28359 Bremen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6327-6950","authenticated-orcid":false,"given":"Xin","family":"Lan","sequence":"additional","affiliation":[{"name":"Global Monitoring Laboratory, National Oceanic and Atmospheric Administration, 325 Broadway, Boulder, CO 80305-0450, USA"},{"name":"Cooperative Institute for Research in Environmental Sciences, University of Colorado, 216 UCB, Boulder, CO 80309, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8828-2759","authenticated-orcid":false,"given":"Rigel","family":"Kivi","sequence":"additional","affiliation":[{"name":"Earth Observation Research, Finnish Meteorological Institute, P.O. Box 503, FI-00101 Helsinki, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1573-6673","authenticated-orcid":false,"given":"Huilin","family":"Chen","sequence":"additional","affiliation":[{"name":"Centre for Isotope Research (CIO), Energy and Sustainability Research Institute Groningen (ESRIG), University of Groningen, 9711 Groningen, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5077-9524","authenticated-orcid":false,"given":"Matthias","family":"Buschmann","sequence":"additional","affiliation":[{"name":"Institute of Environmental Physics (IUP), University of Bremen, 28359 Bremen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5784-2127","authenticated-orcid":false,"given":"Benedikt","family":"Herkommer","sequence":"additional","affiliation":[{"name":"Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe Institute of Technology (KIT), 76344 Eggenstein-Leopoldshafen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3324-885X","authenticated-orcid":false,"given":"Justus","family":"Notholt","sequence":"additional","affiliation":[{"name":"Institute of Environmental Physics (IUP), University of Bremen, 28359 Bremen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5383-8462","authenticated-orcid":false,"given":"Coleen","family":"Roehl","sequence":"additional","affiliation":[{"name":"California Institute of Technology, Pasadena, CA 91125, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6405-8074","authenticated-orcid":false,"given":"Yao","family":"T\u00e9","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Etudes du Rayonnement et de la Mati\u00e8re en Astrophysique et Atmosph\u00e8res (LERMA-IPSL), Sorbonne Universit\u00e9, CNRS, Observatoire de Paris, PSL Universit\u00e9, 75005 Paris, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4924-0377","authenticated-orcid":false,"given":"Debra","family":"Wunch","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Toronto, Toronto, ON M5S 1A7, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3095-0069","authenticated-orcid":false,"given":"Johanna","family":"Tamminen","sequence":"additional","affiliation":[{"name":"Earth Observation Research, Finnish Meteorological Institute, P.O. 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Scientists raise alarm over \u2018dangerously fast\u2019 growth in atmospheric methane. Nature.","DOI":"10.1038\/d41586-022-00312-2"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"20200440","DOI":"10.1098\/rsta.2020.0440","article-title":"What do we know about the global methane budget? Results from four decades of atmospheric CH4 observations and the way forward","volume":"379","author":"Lan","year":"2021","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_5","unstructured":"P\u00f6rtner, H.O., Roberts, D., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegr\u00eda, A., Nicolai, M., and Okem, A. (2019). IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, Cambridge University Press."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s43247-022-00498-3","article-title":"The Arctic has warmed nearly four times faster than the globe since 1979","volume":"3","author":"Rantanen","year":"2022","journal-title":"Commun. Earth Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"115009","DOI":"10.1088\/1748-9326\/aa8c85","article-title":"Warmer spring conditions increase annual methane emissions from a boreal peat landscape with sporadic permafrost","volume":"12","author":"Helbig","year":"2017","journal-title":"Environ. Res. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.gloplacha.2006.03.012","article-title":"The effect of climate change on carbon in Canadian peatlands","volume":"53","author":"Tarnocai","year":"2006","journal-title":"Glob. Planet. Chang."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9647","DOI":"10.1073\/pnas.1618765114","article-title":"Emerging role of wetland methane emissions in driving 21st century climate change","volume":"114","author":"Zhang","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1038\/nature14338","article-title":"Climate change and the permafrost carbon feedback","volume":"520","author":"Schuur","year":"2015","journal-title":"Nature"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1038\/s43017-021-00230-3","article-title":"Permafrost carbon emissions in a changing Arctic","volume":"3","author":"Miner","year":"2022","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ahmed, M., Shuai, C., and Ahmed, M. (2022). Analysis of energy consumption and greenhouse gas emissions trend in China, India, the USA, and Russia. Int. J. Environ. Sci. Technol.","DOI":"10.1007\/s13762-022-04159-y"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.5194\/essd-12-1561-2020","article-title":"The Global Methane Budget 2000\u20132017","volume":"12","author":"Saunois","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"071002","DOI":"10.1088\/1748-9326\/ab9ed2","article-title":"Increasing anthropogenic methane emissions arise equally from agricultural and fossil fuel sources","volume":"15","author":"Jackson","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1111\/gcb.15901","article-title":"Regional trends and drivers of the global methane budget","volume":"28","author":"Stavert","year":"2022","journal-title":"Glob. Chang. Biol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"18101","DOI":"10.5194\/acp-21-18101-2021","article-title":"Estimating 2010\u20132015 anthropogenic and natural methane emissions in Canada using ECCC surface and GOSAT satellite observations","volume":"21","author":"Baray","year":"2021","journal-title":"Atmos. Chem. Phys."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, F., Maksyutov, S., Tsuruta, A., Janardanan, R., Ito, A., Sasakawa, M., Machida, T., Morino, I., Yoshida, Y., and Kaiser, J.W. (2019). Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories. Remote Sens., 11.","DOI":"10.3390\/rs11212489"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"14899","DOI":"10.1021\/acs.est.0c04117","article-title":"Eight-Year Estimates of Methane Emissions from Oil and Gas Operations in Western Canada Are Nearly Twice Those Reported in Inventories","volume":"54","author":"Chan","year":"2020","journal-title":"Environ. Sci. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.atmosenv.2017.02.036","article-title":"A high-resolution (0.1\u00b0 \u00d7 0.1\u00b0) inventory of methane emissions from Canadian and Mexican oil and gas systems","volume":"158","author":"Sheng","year":"2017","journal-title":"Atmos. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3321","DOI":"10.5194\/bg-12-3321-2015","article-title":"WETCHIMP-WSL: Intercomparison of wetland methane emissions models over West Siberia","volume":"12","author":"Bohn","year":"2015","journal-title":"Biogeosciences"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.5194\/gmd-10-2141-2017","article-title":"A global wetland methane emissions and uncertainty dataset for atmospheric chemical transport models (WetCHARTs version 1.0)","volume":"10","author":"Bloom","year":"2017","journal-title":"Geosci. Model Dev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3553","DOI":"10.5194\/acp-17-3553-2017","article-title":"Methane fluxes in the high northern latitudes for 2005\u20132013 estimated using a Bayesian atmospheric inversion","volume":"17","author":"Thompson","year":"2017","journal-title":"Atmos. Chem. Phys."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1565030","DOI":"10.1080\/16000889.2018.1565030","article-title":"Methane budget estimates in Finland from the CarbonTracker Europe-CH4 data assimilation system","volume":"71","author":"Tsuruta","year":"2019","journal-title":"Tellus B Chem. Phys. Meteorol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3643","DOI":"10.5194\/acp-21-3643-2021","article-title":"Attribution of the accelerating increase in atmospheric methane during 2010\u20132018 by inverse analysis of GOSAT observations","volume":"21","author":"Zhang","year":"2021","journal-title":"Atmos. Chem. Phys."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"11461","DOI":"10.3168\/jds.2017-13881","article-title":"Short-term methane emissions from 2 dairy farms in California estimated by different measurement techniques and US Environmental Protection Agency inventory methodology: A case study","volume":"101","author":"Arndt","year":"2018","journal-title":"J. Dairy Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.wasman.2020.12.026","article-title":"Methane emissions from the storage of liquid dairy manure: Influences of season, temperature and storage duration","volume":"121","author":"Ammon","year":"2021","journal-title":"Waste Manag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5393","DOI":"10.5194\/bg-12-5393-2015","article-title":"Natural and anthropogenic methane fluxes in Eurasia: A mesoscale quantification by generalized atmospheric inversion","volume":"12","author":"Berchet","year":"2015","journal-title":"Biogeosciences"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"959","DOI":"10.5194\/essd-11-959-2019","article-title":"EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970\u20132012","volume":"11","author":"Crippa","year":"2019","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1038\/s41597-020-0462-2","article-title":"High resolution temporal profiles in the Emissions Database for Global Atmospheric Research","volume":"7","author":"Crippa","year":"2020","journal-title":"Sci. Data"},{"key":"ref_30","unstructured":"(2022, August 05). Canadian Environmental Sustainability Indicators: Greenhouse Gas Concentrations. Available online: www.canada.ca\/en\/environment-climate-change\/services\/environmental-indicators\/greenhouse-gasconcentrations.html."},{"key":"ref_31","unstructured":"(2022, August 29). ICOS-EU Atmosphere Stations. Available online: https:\/\/www.icos-cp.eu\/observations\/atmosphere\/stations."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1111\/j.1600-0889.2010.00494.x","article-title":"Continuous measurements of methane from a tower network over Siberia","volume":"62","author":"Sasakawa","year":"2010","journal-title":"Tellus B"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.5194\/amt-3-1113-2010","article-title":"Continuous low-maintenance CO2\/CH4\/H2O measurements at the Zotino Tall Tower Observatory (ZOTTO) in Central Siberia","volume":"3","author":"Winderlich","year":"2010","journal-title":"Atmos. Meas. Tech."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"255","DOI":"10.5194\/bg-16-255-2019","article-title":"Interpreting eddy covariance data from heterogeneous Siberian tundra: Land-cover-specific methane fluxes and spatial representativeness","volume":"16","author":"Tuovinen","year":"2019","journal-title":"Biogeosciences"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"eaaz5120","DOI":"10.1126\/sciadv.aaz5120","article-title":"Quantifying methane emissions from the largest oil-producing basin in the United States from space","volume":"6","author":"Zhang","year":"2020","journal-title":"Sci. Adv."},{"key":"ref_36","first-page":"100114","article-title":"Satellite-based estimates of nitrogen oxide and methane emissions from gas flaring and oil production activities in Sakha Republic, Russia","volume":"11","author":"Ialongo","year":"2021","journal-title":"Atmos. Environ. X"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"9169","DOI":"10.5194\/acp-20-9169-2020","article-title":"Remote sensing of methane leakage from natural gas and petroleum systems revisited","volume":"20","author":"Schneising","year":"2020","journal-title":"Atmos. Chem. Phys."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"113","DOI":"10.5194\/acp-15-113-2015","article-title":"Inverse modelling of CH4 emissions for 2010\u20132011 using different satellite retrieval products from GOSAT and SCIAMACHY","volume":"15","author":"Alexe","year":"2015","journal-title":"Atmos. Chem. Phys."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"D02304","DOI":"10.1029\/2006JD007268","article-title":"Satellite chartography of atmospheric methane from SCIAMACHY on board ENVISAT: 2. Evaluation based on inverse model simulations","volume":"112","author":"Bergamaschi","year":"2007","journal-title":"J. Geophys. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5043","DOI":"10.5194\/acp-16-5043-2016","article-title":"Inverse modeling of GOSAT-retrieved ratios of total column CH4 and CO2 for 2009 and 2010","volume":"16","author":"Pandey","year":"2016","journal-title":"Atmos. Chem. Phys."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3991","DOI":"10.5194\/acp-14-3991-2014","article-title":"A multi-year methane inversion using SCIAMACHY, accounting for systematic errors using TCCON measurements","volume":"14","author":"Houweling","year":"2014","journal-title":"Atmos. Chem. Phys."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"7859","DOI":"10.5194\/acp-19-7859-2019","article-title":"Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010\u20132015","volume":"19","author":"Maasakkers","year":"2019","journal-title":"Atmos. Chem. Phys."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"395","DOI":"10.5194\/acp-22-395-2022","article-title":"Methane emissions in the United States, Canada, and Mexico: Evaluation of national methane emission inventories and 2010\u20132017 sectoral trends by inverse analysis of in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) atmospheric observations","volume":"22","author":"Lu","year":"2022","journal-title":"Atmos. Chem. Phys."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"7741","DOI":"10.1002\/2014JD021551","article-title":"Mapping of North American methane emissions with high spatial resolution by inversion of SCIAMACHY satellite data","volume":"119","author":"Wecht","year":"2014","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"7049","DOI":"10.5194\/acp-15-7049-2015","article-title":"Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data","volume":"15","author":"Turner","year":"2015","journal-title":"Atmos. Chem. Phys."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"D22301","DOI":"10.1029\/2009JD012287","article-title":"Inverse modeling of global and regional CH4 emissions using SCIAMACHY satellite retrievals","volume":"114","author":"Bergamaschi","year":"2009","journal-title":"J. Geophys. Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"235","DOI":"10.5194\/acp-17-235-2017","article-title":"Global inverse modeling of CH4 sources and sinks: An overview of methods","volume":"17","author":"Houweling","year":"2017","journal-title":"Atmos. Chem. Phys."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4339","DOI":"10.5194\/acp-21-4339-2021","article-title":"2010\u20132015 North American methane emissions, sectoral contributions, and trends: A high-resolution inversion of GOSAT observations of atmospheric methane","volume":"21","author":"Maasakkers","year":"2021","journal-title":"Atmos. Chem. Phys."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"14159","DOI":"10.5194\/acp-21-14159-2021","article-title":"Global distribution of methane emissions: A comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments","volume":"21","author":"Qu","year":"2021","journal-title":"Atmos. Chem. Phys."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5423","DOI":"10.5194\/amt-9-5423-2016","article-title":"The operational methane retrieval algorithm for TROPOMI","volume":"9","author":"Hu","year":"2016","journal-title":"Atmos. Meas. Tech."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.5194\/gmd-10-1261-2017","article-title":"Global methane emission estimates for 2000\u20132012 from CarbonTracker Europe-CH4 v1.0","volume":"10","author":"Tsuruta","year":"2017","journal-title":"Geosci. Model Dev."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"6771","DOI":"10.5194\/amt-12-6771-2019","article-title":"A scientific algorithm to simultaneously retrieve carbon monoxide and methane from TROPOMI onboard Sentinel-5 Precursor","volume":"12","author":"Schneising","year":"2019","journal-title":"Atmos. Meas. Tech."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2785","DOI":"10.5194\/gmd-10-2785-2017","article-title":"The CarbonTracker Data Assimilation Shell (CTDAS) v1.0: Implementation and global carbon balance 2001\u20132015","volume":"10","author":"Tsuruta","year":"2017","journal-title":"Geosci. Model Dev."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"D24304","DOI":"10.1029\/2005JD006157","article-title":"An ensemble data assimilation system to estimate CO2 surface fluxes from atmospheric trace gas observations","volume":"110","author":"Peters","year":"2005","journal-title":"J. Geophys. Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"417","DOI":"10.5194\/acp-5-417-2005","article-title":"The two-way nested global chemistry-transport zoom model TM5: Algorithm and applications","volume":"5","author":"Krol","year":"2005","journal-title":"Atmos. Chem. Phys."},{"key":"ref_56","first-page":"506","article-title":"Evaluating atmospheric methane inversion model results for Pallas, northern Finland","volume":"20","author":"Tsuruta","year":"2015","journal-title":"Boreal Environ. Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The ERA5 global reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"20200443","DOI":"10.1098\/rsta.2020.0443","article-title":"Effects of extreme meteorological conditions in 2018 on European methane emissions estimated using atmospheric inversions","volume":"380","author":"Thompson","year":"2022","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"5331","DOI":"10.5194\/gmd-14-5331-2021","article-title":"The Community Inversion Framework v1.0: A unified system for atmospheric inversion studies","volume":"14","author":"Berchet","year":"2021","journal-title":"Geosci. Model Dev."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2909","DOI":"10.5194\/bg-15-2909-2018","article-title":"A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions","volume":"15","author":"Lienert","year":"2018","journal-title":"Biogeosciences"},{"key":"ref_61","unstructured":"Crippa, M., Guizzardi, D., Schaaf, E., Solazzo, E., Muntean, M., Monforti-Ferrario, F., Olivier, J., and Vignati, E. (2022). Technical Report, EDGAR\u2014Emissions Database for Global Atmospheric Research, United Nations Environment Programme. in prep."},{"key":"ref_62","unstructured":"Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Lo Vullo, E., Solazzo, E., Monforti-Ferrario, F., Olivier, J., and Vignati, E. (2021, July 01). EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC) [Dataset] PID. Available online: http:\/\/data.europa.eu\/89h\/97a67d67-c62e-4826-b873-9d972c4f670b."},{"key":"ref_63","unstructured":"(2022, September 24). EDGARv6.0. Available online: https:\/\/edgar.jrc.ec.europa.eu\/index.php\/dataset_ghg60."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1002\/jgrg.20042","article-title":"Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4)","volume":"118","author":"Giglio","year":"2013","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5194\/essd-11-1-2019","article-title":"Gridded maps of geological methane emissions and their isotopic signature","volume":"11","author":"Etiope","year":"2019","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"4584","DOI":"10.1038\/s41467-019-12541-7","article-title":"Global ocean methane emissions dominated by shallow coastal waters","volume":"10","author":"Weber","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_67","unstructured":"Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S., P\u00e9an, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., and Gomis, M. (2021). Climate Change 2021: The Physical Science Basis, Cambridge University Press."},{"key":"ref_68","unstructured":"Schuldt, K.N., Aalto, T., Andrews, A., Aoki, S., Arduini, J., Baier, B., Bergamaschi, P., Biermann, T., Biraud, S.C., and Boenisch, H. (2021). Multi-Laboratory Compilation of Atmospheric Methane Data for the Period 1983\u20132020; obspack_ch4_1_GLOBALVIEWplus_v3.0_2021-05-07."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1098\/rsta.2010.0240","article-title":"The Total Carbon Column Observing Network","volume":"369","author":"Wunch","year":"2011","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Laughner, J.L., Roche, S., Kiel, M., Toon, G.C., Wunch, D., Baier, B.C., Biraud, S., Chen, H., Kivi, R., and Laemmel, T. (Atmos. Meas. Tech. Discuss., 2022). A new algorithm to generate a priori trace gas profiles for the GGG2020 retrieval algorithm, Atmos. Meas. Tech. Discuss., in preprint.","DOI":"10.5194\/amt-2022-267"},{"key":"ref_71","unstructured":"Buschmann, M., Petri, C., Palm, M., Warneke, T., Notholt, J., and Engineers, A.S. (2022). TCCON Data from Ny-Alesund, Svalbard, Norway, Release GGG2020R0, CaltechDATA, California Institute of Technology. TCCON Data archive."},{"key":"ref_72","unstructured":"Kivi, R., Heikkinen, P., and Kyro, E. (2017). TCCON Data from Sodankyla, Finland, Release GGG2020R0, CaltechDATA, California Institute of Technology. TCCON data archive."},{"key":"ref_73","unstructured":"Wunch, D., Mendonca, J., Colebatch, O., Allen, N., Blavier, J.F.L., Kunz, K., Roche, S., Hedelius, J., Neufeld, G., and Springett, S. (2020). TCCON Data from East Trout Lake, Canada, Release GGG2020R0, CaltechDATA, California Institute of Technology. TCCON data archive."},{"key":"ref_74","unstructured":"Hase, F., Blumenstock, T., Dohe, S., Gro\u00df, J., and Kiel, M. (2017). TCCON data from Karlsruhe, Germany, Release GGG2020R0, CaltechDATA, California Institute of Technology. TCCON data archive."},{"key":"ref_75","unstructured":"Te, Y., Jeseck, P., and Janssen, C. (2017). TCCON Data from Paris, France, Release GGG2020R0, CaltechDATA, California Institute of Technology. TCCON data archive."},{"key":"ref_76","unstructured":"Warneke, T., Messerschmidt, J., Notholt, J., Weinzierl, C., Deutscher, N., Petri, C., Grupe, P., Vuillemin, C., Truong, F., and Schmidt, M. (2017). TCCON Data from Orleans, France, Release GGG2020R0, CaltechDATA, California Institute of Technology. TCCON data archive."},{"key":"ref_77","unstructured":"Wennberg, P.O., Roehl, C., Wunch, D., Toon, G.C., Blavier, J.F., Washenfelder, R., Keppel-Aleks, G., Allen, N., and Ayers, J. (2017). TCCON Data from Park Falls, Wisconsin, USA, CaltechDATA, California Institute of Technology. TCCON data archive."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Rodgers, C.D., and Connor, B.J. (2003). Intercomparison of remote sounding instruments. J. Geophys. Res., 108.","DOI":"10.1029\/2002JD002299"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1839","DOI":"10.1175\/2010JTECHA1448.1","article-title":"AirCore: An Innovative Atmospheric Sampling System","volume":"27","author":"Karion","year":"2010","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"4997","DOI":"10.5194\/amt-9-4997-2016","article-title":"Radiocarbon analysis of stratospheric CO2 retrieved from AirCore sampling","volume":"9","author":"Paul","year":"2016","journal-title":"Atmos. Meas. Tech."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"4791","DOI":"10.5194\/amt-13-4791-2020","article-title":"Intercomparison of low- and high-resolution infrared spectrometers for ground-based solar remote sensing measurements of total column concentrations of CO2, CH4, and CO","volume":"13","author":"Sha","year":"2020","journal-title":"Atmos. Meas. Tech."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"4751","DOI":"10.5194\/amt-13-4751-2020","article-title":"Intercomparison of atmospheric CO2 and CH4 abundances on regional scales in boreal areas using Copernicus Atmosphere Monitoring Service (CAMS) analysis, COllaborative Carbon Column Observing Network (COCCON) spectrometers, and Sentinel-5 Precursor satellite observations","volume":"13","author":"Tu","year":"2020","journal-title":"Atmos. Meas. Tech."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1080\/02723646.1981.10642213","article-title":"On the Validation of Models","volume":"2","author":"Willmott","year":"1981","journal-title":"Phys. Geogr."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Lindqvist, H., Kivim\u00e4ki, E., Tsuruta, A., Karppinen, T., Backman, L., Schneising, O., Buchwitz, M., Lorente Delgado, A., Kivi, R., and Chen, H. (Remote Sensing, 2023). Evaluation of Sentinel 5P TROPOMI methane observations at high latitudes, Remote Sensing, in preperation.","DOI":"10.3390\/rs16162979"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"4465","DOI":"10.5194\/bg-10-4465-2013","article-title":"Seasonal dynamics of methane emissions from a subarctic fen in the Hudson Bay Lowlands","volume":"10","author":"Hanis","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"2420","DOI":"10.1111\/j.1365-2486.2009.02083.x","article-title":"Diurnal and seasonal variation in methane emissions in a northern Canadian peatland measured by eddy covariance","volume":"16","author":"Long","year":"2010","journal-title":"Glob. Chang. Biol."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1029\/2017GB005747","article-title":"Temporal Variation of Ecosystem Scale Methane Emission From a Boreal Fen in Relation to Temperature, Water Table Position, and Carbon Dioxide Fluxes","volume":"32","author":"Rinne","year":"2018","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"20190517","DOI":"10.1098\/rstb.2019.0517","article-title":"Effect of the 2018 European drought on methane and carbon dioxide exchange of northern mire ecosystems","volume":"375","author":"Rinne","year":"2020","journal-title":"Philos. Trans. R. Soc. B Biol. Sci."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Kivim\u00e4ki, E., Lindqvist, H., Hakkarainen, J., Laine, M., Sussmann, R., Tsuruta, A., Detmers, R., Deutscher, N.M., Dlugokencky, E.J., and Hase, F. (2019). Evaluation and Analysis of the Seasonal Cycle and Variability of the Trend from GOSAT Methane Retrievals. Remote Sens., 11.","DOI":"10.3390\/rs11070882"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"665","DOI":"10.5194\/amt-14-665-2021","article-title":"Methane retrieved from TROPOMI: Improvement of the data product and validation of the first 2 years of measurements","volume":"14","author":"Lorente","year":"2021","journal-title":"Atmos. Meas. Tech."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"3839","DOI":"10.5194\/gmd-13-3839-2020","article-title":"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","volume":"13","author":"Stanevich","year":"2020","journal-title":"Geosci. Model Dev."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"4843","DOI":"10.5194\/amt-9-4843-2016","article-title":"Evaluation of column-averaged methane in models and TCCON with a focus on the stratosphere","volume":"9","author":"Ostler","year":"2016","journal-title":"Atmos. Meas. Tech."},{"key":"ref_93","unstructured":"Zhumabayev, D., Bakdolotov, A., De Miglio, R., Litvak, V., Baibakisheva, A., Sarbassov, Y., and Baigarin, K. (2022). Kazakhstan\u2019s Road to Net Zero GHG Emissions, NUR."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1913","DOI":"10.1175\/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2","article-title":"Ensemble Data Assimilation without Perturbed Observations","volume":"130","author":"Whitaker","year":"2002","journal-title":"Mon. Wea. Rev."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"2223","DOI":"10.5194\/gmd-7-2223-2014","article-title":"FLEXINVERT: An atmospheric Bayesian inversion framework for determining surface fluxes of trace species using an optimized grid","volume":"7","author":"Thompson","year":"2014","journal-title":"Geosci. Model Dev."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.5194\/acp-6-1275-2006","article-title":"Sensitivity analysis of methane emissions derived from SCIAMACHY observations through inverse modelling","volume":"6","author":"Meirink","year":"2006","journal-title":"Atmos. Chem. Phys."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1002\/2013JD019760","article-title":"Comparison of CH4 inversions based on 15 months of GOSAT and SCIAMACHY observations","volume":"118","author":"Monteil","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.rse.2013.04.024","article-title":"The Greenhouse Gas Climate Change Initiative (GHG-CCI): Comparison and quality assessment of near-surface-sensitive satellite-derived CO2 and CH4 global data sets","volume":"162","author":"Buchwitz","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1723","DOI":"10.5194\/amt-7-1723-2014","article-title":"The Greenhouse Gas Climate Change Initiative (GHG-CCI): Comparative validation of GHG-CCI SCIAMACHY\/ENVISAT and TANSO-FTS\/GOSAT CO2 and CH4 retrieval algorithm products with measurements from the TCCON","volume":"7","author":"Dils","year":"2014","journal-title":"Atmos. Meas. Tech."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"2907","DOI":"10.5194\/amt-7-2907-2014","article-title":"Derivation of tropospheric methane from TCCON CH4 and HF total column observations","volume":"7","author":"Saad","year":"2014","journal-title":"Atmos. Meas. Tech."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"3295","DOI":"10.5194\/amt-7-3295-2014","article-title":"Retrieval of tropospheric column-averaged CH4 mole fraction by solar absorption FTIR-spectrometry using N2O as a proxy","volume":"7","author":"Wang","year":"2014","journal-title":"Atmos. Meas. Tech."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.5194\/amt-9-1961-2016","article-title":"Methane cross-validation between three Fourier transform spectrometers: SCISAT ACE-FTS, GOSAT TANSO-FTS, and ground-based FTS measurements in the Canadian high Arctic","volume":"9","author":"Holl","year":"2016","journal-title":"Atmos. Meas. Tech."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Kuze, A., Kikuchi, N., Kataoka, F., Suto, H., Shiomi, K., and Kondo, Y. (2020). Detection of Methane Emission from a Local Source Using GOSAT Target Observations. Remote Sens., 12.","DOI":"10.3390\/rs12020267"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"4063","DOI":"10.5194\/amt-15-4063-2022","article-title":"On the influence of underlying elevation data on Sentinel-5 Precursor TROPOMI satellite methane retrievals over Greenland","volume":"15","author":"Hachmeister","year":"2022","journal-title":"Atmos. Meas. Tech."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"669","DOI":"10.5194\/amt-16-669-2023","article-title":"Advances in retrieving methane and carbon monoxide from TROPOMI onboard Sentinel-5 Precursor","volume":"16","author":"Schneising","year":"2022","journal-title":"Atmos. Meas. Tech. 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