{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T16:03:27Z","timestamp":1772208207909,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,16]],"date-time":"2023-12-16T00:00:00Z","timestamp":1702684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Russian Science Foundation","award":["23-77-30008"],"award-info":[{"award-number":["23-77-30008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The increase of the CO2 content in the atmosphere caused by anthropogenic emissions from the territories of large cities (~70%) is the critical factor in determining the accuracy of emission estimations. Advanced experiment-based methods of anthropogenic CO2 emission estimation are based on the solution of an inverse problem, using accurate measurements of CO2 content and numerical models of atmospheric transport and chemistry. The accuracy of such models decreases the errors of the emission estimations. The aim of the current study is to adapt numerical weather prediction and atmospheric chemistry model WRF-Chem and validate its capability to simulate atmospheric CO2 for the territories of the two large coastal cities of the Gulf of Finland\u2014St. Petersburg (Russia) and Helsinki (Finland). The research has demonstrated that the WRF-Chem model is able to simulate annual variation, as well as the mean seasonal and diurnal variations of the near-surface CO2 mixing ratio, in Helsinki, at a high spatial resolution (2 km). Correlation between the modelled and measured CO2 mixing ratio is relatively high, at ~0.73, with a mean difference and its standard deviation of 0.15 \u00b1 0.04 and 1.7%, respectively. The differences between the WRF-Chem data and the measurements might be caused by errors in the modelling of atmospheric transport and in a priori CO2 emissions and biogenic fluxes. The WRF-Chem model simulates well the column-averaged CO2 mixing ratio (XCO2) in St. Petersburg (January 2019\u2013March 2020), with a correlation of ~0.95 relative to ground-based spectroscopic measurements by the IR\u2013Fourier spectrometer Bruker EM27\/SUN. The error of the XCO2 modelling constitutes ~0.3%, and most likely is related to inaccuracies in chemical boundary conditions and a priori anthropogenic CO2 emissions. The XCO2 time series in St. Petersburg by the WRF-Chem model fits well with global CAMS reanalysis and CarbonTracker-modelled data (the differences are less than ~1%). However, due to much higher spatial resolution (2 vs. over 100 km), the WRF-Chem data are in the best agreement with the ground-based remote measurements of XCO2. According to the study, the modelling errors of XCO2 in St. Petersburg during the whole simulated period are sufficiently minimal to fit the requirement of \u201cError \u2264 0.2%\u201d in 60% of cases. This requirement should be satisfied to evaluate properly the anthropogenic CO2 emissions of St. Petersburg on a city-scale.<\/jats:p>","DOI":"10.3390\/rs15245757","type":"journal-article","created":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T10:04:47Z","timestamp":1702893887000},"page":"5757","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Complex Validation of Weather Research and Forecasting\u2014Chemistry Modelling of Atmospheric CO2 in the Coastal Cities of the Gulf of Finland"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3947-3801","authenticated-orcid":false,"given":"Georgii","family":"Nerobelov","sequence":"first","affiliation":[{"name":"Faculty of Physics, Saint Petersburg University, St. Petersburg 199034, Russia"},{"name":"Meteoforecast Department, Russian State Hydrometeorological University, St. Petersburg 195196, Russia"},{"name":"SRC RAS\u2014Scientific Research Centre for Ecological Safety of the Russian Academy of Sciences, St. Petersburg 197110, Russia"}]},{"given":"Yuri","family":"Timofeyev","sequence":"additional","affiliation":[{"name":"Faculty of Physics, Saint Petersburg University, St. Petersburg 199034, Russia"}]},{"given":"Stefani","family":"Foka","sequence":"additional","affiliation":[{"name":"Faculty of Physics, Saint Petersburg University, St. Petersburg 199034, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8291-6738","authenticated-orcid":false,"given":"Sergei","family":"Smyshlyaev","sequence":"additional","affiliation":[{"name":"Meteoforecast Department, Russian State Hydrometeorological University, St. Petersburg 195196, Russia"}]},{"given":"Anatoliy","family":"Poberovskiy","sequence":"additional","affiliation":[{"name":"Faculty of Physics, Saint Petersburg University, St. Petersburg 199034, Russia"}]},{"given":"Margarita","family":"Sedeeva","sequence":"additional","affiliation":[{"name":"Meteoforecast Department, Russian State Hydrometeorological University, St. Petersburg 195196, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,16]]},"reference":[{"key":"ref_1","unstructured":"(2021, February 11). Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., P\u00e9an, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I.; et al., Eds.; In Press. Available online: https:\/\/www.ipcc.ch\/report\/sixth-assessment-report-working-group-i\/."},{"key":"ref_2","unstructured":"(2021, February 13). Methods for Remote Determination of CO2 Emissions. The MITRE Corporation JASON Program Office 7515 Colshire Drive McLean, Virginia 22102, 13 January 2011. Available online: https:\/\/irp.fas.org\/agency\/dod\/jason\/emissions.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1007\/s11027-019-09877-2","article-title":"Errors and uncertainties in a gridded carbon dioxide emissions inventory","volume":"24","author":"Oda","year":"2019","journal-title":"Mitig. Adapt. Strat. Glob. Chang."},{"key":"ref_4","first-page":"9591","article-title":"Tracking city CO2 emissions from space using a high-resolution inverse modelling approach: A case study for Berlin, Germany","volume":"16","author":"Pillai","year":"2016","journal-title":"Atmos. Meas. Tech."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.5194\/amt-14-1047-2021","article-title":"Emission Monitoring Mobile Experiment (EMME): An overview and first results of the St. Petersburg megacity campaign 2019","volume":"14","author":"Makarova","year":"2021","journal-title":"Atmos. Meas. Tech."},{"key":"ref_6","first-page":"10939","article-title":"The CO2 integral emission by the megacity of St Petersburg as quantified from ground-based FTIR measurements combined with dispersion modelling","volume":"21","author":"Ionov","year":"2021","journal-title":"Atmos. Meas. Tech."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Enting, I.G. (2002). Inverse Problems in Atmospheric Constituent Transport, Cambridge University Press.","DOI":"10.1017\/CBO9780511535741"},{"key":"ref_8","first-page":"6029","article-title":"Inverse modeling of CO2 sources and sinks using satellite observations of CO2 from TES and surface flask measurements","volume":"11","author":"Nassar","year":"2011","journal-title":"Atmos. Meas. Tech."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"10045","DOI":"10.1002\/2017GL074702","article-title":"Quantifying CO2 Emissions From Individual Power Plants From Space","volume":"44","author":"Nassar","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"085001","DOI":"10.1088\/1748-9326\/ab25ae","article-title":"Estimating power plant CO2 emission using OCO-2 XCO2 and high resolution WRF-Chem simulations","volume":"14","author":"Zheng","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"095010","DOI":"10.1088\/1748-9326\/ab9cfe","article-title":"Anthropogenic CO2 emissions assessment of Nile Delta using XCO2 and SIF data from OCO-2 satellite","volume":"15","author":"Shekhar","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1134\/S1028334X20090184","article-title":"Estimates of CO2 Anthropogenic Emission from the Megacity St. Petersburg","volume":"494","author":"Timofeyev","year":"2020","journal-title":"Dokl. Earth Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1134\/S0001433822030100","article-title":"Experimental Estimates of Integral Anthropogenic CO2 Emissions in the City of St. Petersburg","volume":"58","author":"Timofeyev","year":"2022","journal-title":"Izv. Atmos. Ocean. Phys."},{"key":"ref_14","first-page":"9981","article-title":"The importance of transport model uncertainties for the estimation of CO2 sources and sinks using satellite measurements","volume":"10","author":"Houweling","year":"2010","journal-title":"Atmos. Meas. Tech."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6699","DOI":"10.5194\/bg-10-6699-2013","article-title":"Global atmospheric carbon budget: Results from an ensemble of atmospheric CO2 inversions","volume":"10","author":"Peylin","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_16","unstructured":"(2021, March 10). Federal State Statistics Service, Available online: https:\/\/rosstat.gov.ru\/."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6957","DOI":"10.1016\/j.atmosenv.2005.04.027","article-title":"Fully coupled \u2018online\u2019 chemistry in the WRF model","volume":"39","author":"Grell","year":"2005","journal-title":"Atmos. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7763","DOI":"10.5194\/acp-22-7763-2022","article-title":"Analysis of CO2, CH4, and CO surface and column concentrations observed at R\u00e9union Island by assessing WRF-Chem simulations","volume":"22","author":"Callewaert","year":"2022","journal-title":"Atmos. Chem. Phys."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"11279","DOI":"10.5194\/acp-19-11279-2019","article-title":"Analysis of total column CO2 and CH4 measurements in Berlin with WRF-GHG","volume":"19","author":"Zhao","year":"2019","journal-title":"Atmos. Chem. Phys."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nerobelov, G., Timofeyev, Y., Smyshlyaev, S., Foka, S., Mammarella, I., and Virolainen, Y. (2021). Validation of WRF-Chem Model and CAMS Performance in Estimating Near-Surface Atmospheric CO2 Mixing Ratio in the Area of Saint Petersburg (Russia). Atmosphere, 12.","DOI":"10.5194\/egusphere-egu21-1497"},{"key":"ref_21","first-page":"860","article-title":"Temporal variations in CO2, CH4 and CO concentrations in Saint-Petersburg suburb (Peterhof)","volume":"32","author":"Foka","year":"2012","journal-title":"Opt. Atmos. I Okeana"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1134\/S0001433823020056","article-title":"Comparison of CO2 Content in the Atmosphere of St. Petersburg According to Numerical Modeling and Observations","volume":"59","author":"Nerobelov","year":"2023","journal-title":"Izv. Atmos. Ocean. Phys."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4977","DOI":"10.1109\/TGRS.2011.2158548","article-title":"Data Fusion of Different Spatial Resolution Remote Sensing Images Applied to Forest-Type Mapping","volume":"49","author":"Kempeneers","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","unstructured":"(2021, April 11). Center for Earth Observation and Modeling (CEOM). Available online: https:\/\/www.ceom.ou.edu\/."},{"key":"ref_25","first-page":"227","article-title":"Atmospheric CO2 observations at Finnish urban and rural sites","volume":"20","author":"Kilkki","year":"2015","journal-title":"Boreal Env. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1111\/j.1600-0889.2007.00306.x","article-title":"Determining the contribution of vertical advection to the net ecosystem exchange at Hyyti\u00e4l\u00e4 forest, Finland","volume":"59","author":"Mammarella","year":"2007","journal-title":"Tellus B Chem. Phys. Meteorol."},{"key":"ref_27","unstructured":"(2021, April 11). Available online: https:\/\/www.campbellsci.com.au\/wxt536."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hari, P., Nikinmaa, E., Pohja, T., Siivola, E., B\u00e4ck, J., Vesala, T., and Kulmala, M. (2013). Physical and Physiological Forest Ecology, Springer.","DOI":"10.1007\/978-94-007-5603-8"},{"key":"ref_29","unstructured":"(2021, April 11). List of SMEAR III Measurements. Available online: https:\/\/www.atm.helsinki.fi\/smear\/index.php\/smear-iii\/measurements."},{"key":"ref_30","unstructured":"University of Wyoming, College of Engineering (2021, March 10). Sounding Data. Available online: http:\/\/weather.uwyo.edu\/upperair\/sounding.html."},{"key":"ref_31","unstructured":"Rella, C. (2021, July 21). Accurate Greenhouse Gas Measurements in Humid Gas Streams Using the Picarro G1301 Carbon Dioxide\/Methane\/Water Vapor Gas Analyzer. 2010 PICARRO, INC. Available online: http:\/\/www.cen-sun.com\/ueditor\/php\/upload\/file\/20190806\/1565079076987536.pdf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.5194\/amt-12-1513-2019","article-title":"Building the COllaborative Carbon Column Observing Network (COCCON): Long-term stability and ensemble performance of the EM27\/SUN Fourier transform spectrometer","volume":"12","author":"Frey","year":"2019","journal-title":"Atmos. Meas. Tech."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2969","DOI":"10.5194\/amt-5-2969-2012","article-title":"XCO2-measurements with a tabletop FTS using solar absorption spectroscopy","volume":"5","author":"Gisi","year":"2012","journal-title":"Atmos. Meas. Tech."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3047","DOI":"10.5194\/amt-8-3047-2015","article-title":"Calibration and instrumental line shape characterization of a set of portable FTIR spectrometers for detecting greenhouse gas emissions","volume":"8","author":"Frey","year":"2015","journal-title":"Atmos. Meas. Tech."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.5194\/amt-15-2199-2022","article-title":"Investigation of spaceborne trace gas products over St Petersburg and Yekaterinburg, Russia, by using Collaborative Column Carbon Observing Network (COCCON) observations","volume":"15","author":"Alberti","year":"2022","journal-title":"Atmos. Meas. Tech."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e2020JD034362","DOI":"10.1029\/2020JD034362","article-title":"Implementation of improved parameterization of terrestrial flux in WRF-VPRM improves the simulation of nighttime CO2 peaks and a daytime CO2 band ahead of a cold front","volume":"126","author":"Hu","year":"2021","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"D21307","DOI":"10.1029\/2010JD013887","article-title":"CO2 surface fluxes at grid point scale estimated from a global 21 year reanalysis of atmospheric measurements","volume":"115","author":"Chevallier","year":"2010","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_38","unstructured":"Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Zhiquan, L., Berner, J., Wang, W., Powers, J.G., Duda, M.G., and Barker, D.M. (2021, August 15). A Description of the Advanced Research WRF Model Version 4.3 (No. NCAR\/TN-556+STR). Available online: https:\/\/opensky.ucar.edu\/islandora\/object\/opensky:2898."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"16663","DOI":"10.1029\/97JD00237","article-title":"Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave","volume":"102","author":"Mlawer","year":"1997","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3077","DOI":"10.1175\/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2","article-title":"Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model","volume":"46","author":"Dudhia","year":"1989","journal-title":"J. Atmos. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1175\/1520-0493(1994)122<0927:TSMECM>2.0.CO;2","article-title":"The Step\u2013Mountain Eta Coordinate Model: Further developments of the convection, viscous sublayer, and turbulence closure schemes","volume":"122","year":"1994","journal-title":"Mon. Wea. Rev."},{"key":"ref_42","first-page":"163","article-title":"Basic laws of turbulent mixing in the surface layer of the atmosphere","volume":"151","author":"Monin","year":"1954","journal-title":"Contrib. Geophys. Inst. Acad. Sci. USSR"},{"key":"ref_43","unstructured":"Janjic Zavisa, I. (1996, January 19\u201323). The surface layer in the NCEP Eta Model. Proceedings of the Eleventh Conference on Numerical Weather Prediction, Norfolk, VA, USA."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1175\/1520-0493(2001)129<0569:CAALSH>2.0.CO;2","article-title":"Coupling an Advanced Land Surface-Hydrology Model with the Penn State-NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity","volume":"129","author":"Chen","year":"2001","journal-title":"Mon. Wea. Rev."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1175\/1520-0493(1993)121<0764:PEOAUB>2.0.CO;2","article-title":"Prognostic Evaluation of Assumptions Used by Cumulus Parameterizations","volume":"121","year":"1993","journal-title":"Mon. Wea. Rev."},{"key":"ref_46","first-page":"129","article-title":"The WRF single\u2013moment 6\u2013class microphysics scheme (WSM6)","volume":"42","author":"Hong","year":"2006","journal-title":"J. Korean Meteor. Soc."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s00704-009-0143-8","article-title":"A new building energy model coupled with an urban canopy parameterization for urban climate simulations\u2013\u2013Part II. Validation with one dimension off\u2013line simulations","volume":"99","author":"Salamanca","year":"2010","journal-title":"Theor. Appl. Climatol."},{"key":"ref_48","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_49","unstructured":"Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Hor\u00e1nyi, A., Mu\u00f1oz Sabater, J., Nicolas, J., Peubey, C., Radu, R., and Rozum, I. (2021, May 05). ERA5 hourly data on single levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). Available online: https:\/\/cds.climate.copernicus.eu\/cdsapp#!\/dataset\/reanalysis-era5-single-levels?tab=overview."},{"key":"ref_50","unstructured":"Jacobson, A.R., Schuldt, K.N., Miller, J.B., Tans, P., Andrews, A., Mund, J., Aalto, T., Bakwin, P., Bergamaschi, P., and Biraud, S.C. (2021, May 05). CarbonTracker Near-Real Time, CT-NRT.v2020-1. NOAA Earth System Research Laboratory, Global Monitoring Division, Available online: https:\/\/gml.noaa.gov\/ccgg\/carbontracker\/CT-NRT.v2020-1\/."},{"key":"ref_51","unstructured":"Tomohiro, O., and Maksyutov, S. (2021, May 15). ODIAC Fossil Fuel CO2 Emissions Dataset (Version name: ODIAC2020b). Center for Global Environmental Research, National Institute for Environmental Studies. Available online: https:\/\/www.nies.go.jp\/doi\/10.17595\/20170411.001-e.html."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"B\u00f6ttcher, K., Markkanen, T., Thum, T., Aalto, T., Aurela, M., Reick, C.H., Kolari, P., Arslan, A.N., and Pulliainen, J. (2016). Evaluating Biosphere Model Estimates of the Start of the Vegetation Active Season in Boreal Forests by Satellite Observations. Remote Sens., 8.","DOI":"10.3390\/rs8070580"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Mahadevan, P., Wofsy, S.C., Matross, D.M., Xiao, X., Dunn, A.L., Lin, J.C., Gerbig, C., Munger, J.W., Chow, V.Y., and Gottlieb, E.W. (2008). A satellite-based biosphere parameterization for net ecosystem CO2 exchange: Vegetation Photosynthesis and Respiration Model (VPRM). Glob. Biogeochem. Cycles, 22.","DOI":"10.1029\/2006GB002735"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1134\/S1024856021050158","article-title":"Estimates of CO2 Emissions and Uptake by the Water Surface near St. Petersburg Megalopolis","volume":"34","author":"Nerobelov","year":"2021","journal-title":"Atmos. Ocean. Opt."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"265","DOI":"10.21046\/2070-7401-2021-18-6-265-272","article-title":"Analysis of ground-based spectroscopic measurements of CO2 in Peterhof","volume":"18","author":"Nikitenko","year":"2021","journal-title":"Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Iz Kosmosa"},{"key":"ref_56","unstructured":"(2021, June 10). Evaluation and Quality Control document for observation-based CO2 flux estimates for the period 1979\u20132021, v21r1 Version 2.0. Available online: https:\/\/atmosphere.copernicus.eu."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2067","DOI":"10.5194\/gmd-11-2067-2018","article-title":"WRF and WRF-Chem v3.5.1 simulations of meteorology and black carbon concentrations in the Kathmandu Valley","volume":"11","author":"Mues","year":"2018","journal-title":"Geosci. Model Dev."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"101229","DOI":"10.1016\/j.gsf.2021.101229","article-title":"A sensitivity study of the WRF model in offshore wind modeling over the Baltic Sea","volume":"12","author":"Li","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1029\/2003RG000124","article-title":"Sea breeze: Structure, forecasting, and impacts","volume":"41","author":"Miller","year":"2003","journal-title":"Rev. Geophys."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2654","DOI":"10.1175\/JAMC-D-13-038.1","article-title":"Urban Emissions of CO2 from Davos, Switzerland: The First Real-Time Monitoring System Using an Atmospheric Inversion Technique","volume":"52","author":"Lauvaux","year":"2013","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"e1918","DOI":"10.1002\/met.1918","article-title":"Seasonal and diurnal variation of marine wind characteristics based on lidar measurements","volume":"27","author":"Shu","year":"2020","journal-title":"Meteorol. Appl."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Dekking, F.M., Kraaikamp, C., Lopuha\u00e4, H.P., and Meester, L.E. (2005). A Modern Introduction to Probability and Statistics, Springer. Springer Texts in Statistics.","DOI":"10.1007\/1-84628-168-7"},{"key":"ref_63","unstructured":"(2022, June 12). CarbonTracker Documentation CT2022 Release, Available online: https:\/\/gml.noaa.gov\/ccgg\/carbontracker\/documentation.php#tth_sEc4.1."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1029\/2012JD018196","article-title":"Improving the temporal and spatial distribution of CO2 emissions from global fossil fuel emission data sets","volume":"118","author":"Nassar","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_65","unstructured":"(2022, July 10). Annual Report of Public Joint Stock Company of Generating Companies of the Wholesale Electricity Market for 2021. Available online: https:\/\/www.ogk2.ru\/upload\/iblock\/e9f\/2fl5rq2ylzvtevw1dh2tf89187c9s0jc\/2022_06_29_ogk_2_AR_RUS_spread_print.pdf."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1134\/S102485602106018X","article-title":"Comparison of CAMS Data on CO2 with Measurements in Peterhof","volume":"34","author":"Nerobelov","year":"2021","journal-title":"Atmos. Ocean. Opt."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agrformet.2019.02.029","article-title":"Inter-and intra-annual dynamics of photosynthesis differ between forest floor vegetation and tree canopy in a subarctic Scots pine stand","volume":"271","author":"Kulmala","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1175\/2009JTECHA1179.1","article-title":"Relative Humidity Effect on the High Frequency Attenuation of Water Vapor Flux Measured by a Closed-Path Eddy Covariance System","volume":"26","author":"Mammarella","year":"2009","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"4915","DOI":"10.5194\/amt-9-4915-2016","article-title":"Quantifying the uncertainty of eddy covariance fluxes due to the use of different software packages and combinations of processing steps in two contrasting ecosystems","volume":"9","author":"Mammarella","year":"2016","journal-title":"Atmos. Meas. Tech."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/24\/5757\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:40:11Z","timestamp":1760132411000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/24\/5757"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,16]]},"references-count":69,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["rs15245757"],"URL":"https:\/\/doi.org\/10.3390\/rs15245757","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,16]]}}}