A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China
Tài liệu tham khảo
Aleksankina, 2018, Global sensitivity and uncertainty analysis of an atmospheric chemistry transport model: the FRAME model (version 9.15.0) as a case study, Geosci. Model Dev., 11, 1653, 10.5194/gmd-11-1653-2018
Alexe, 2015, Inverse modelling of CH4 emissions for 2010–2011 using different satellite retrieval products from GOSAT and SCIAMACHY, Atmos. Chem. Phys., 15, 113, 10.5194/acp-15-113-2015
Bocquet, 2015, Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models, Atmos. Chem. Phys., 15, 5325, 10.5194/acp-15-5325-2015
Broquet, 2013, Regional inversion of CO2 ecosystem fluxes from atmospheric measurements: reliability of the uncertainty estimates, Atmos. Chem. Phys., 13, 9039, 10.5194/acp-13-9039-2013
Carrassi, 2018, Data assimilation in the geosciences: an overview of methods, issues, and perspectives, Wiley Interdiscip. Rev. Clim. Chang., 9, e535, 10.1002/wcc.535
Chen, 2019, The 2015 and 2016 wintertime air pollution in China: SO2 emission changes derived from a WRF-Chem/EnKF coupled data assimilation system, Atmos. Chem. Phys., 19, 8619, 10.5194/acp-19-8619-2019
Cohen, 2014, Estimating global black carbon emissions using a top-down Kalman filter approach, J. Geophys. Res., 119, 307, 10.1002/2013JD019912
Crippa, 2018, Gridded emissions of air pollutants for the period 1970–2012 within EDGAR v4.3.2, Earth Syst. Sci. Data, 10, 1987, 10.5194/essd-10-1987-2018
Crippa, 2020, High resolution temporal profiles in the emissions database for global atmospheric research, Sci. Data, 7, 1, 10.1038/s41597-020-0462-2
Dai, 2019, Inverting the east Asian dust emission fluxes using the ensemble Kalman smoother and Himawari-8 AODs: a case study with WRF-Chem v3.5.1, Atmosphere (Basel), 10, 543, 10.3390/atmos10090543
Deng, 2020, A big data approach to improving the vehicle emission inventory in China, Nat. Commun., 11, 1, 10.1038/s41467-020-16579-w
Elguindi, 2020, Intercomparison of magnitudes and trends in anthropogenic surface emissions from bottom-up inventories, top-down estimates, and emission scenarios, Earth’s Future, 8, 1, 10.1029/2020EF001520
Elissavet Koukouli, 2018, Updated SO2 emission estimates over China using OMI/Aura observations, Atmos. Meas. Tech., 11, 1817, 10.5194/amt-11-1817-2018
Evensen, 2004, Sampling strategies and square root analysis schemes for the EnKF, Ocean Dyn., 54, 539, 10.1007/s10236-004-0099-2
Fang, 2016, Top-down estimates of benzene and toluene emissions in the Pearl River Delta and Hong Kong, China, Atmos. Chem. Phys., 16, 3369, 10.5194/acp-16-3369-2016
Faragher, 2012, Understanding the basis of the Kalman filter via a simple and intuitive derivation, IEEE Signal Process. Mag., 29, 128, 10.1109/MSP.2012.2203621
Feng, 2020, CO emissions inferred from surface CO observations over China in December 2013 and 2017, J. Geophys. Res. Atmos., 125, 1, 10.1029/2019JD031808
Geng, 2017, Impact of spatial proxies on the representation of bottom-up emission inventories: a satellite-based analysis, Atmos. Chem. Phys., 17, 4131, 10.5194/acp-17-4131-2017
Gilliam, 2015, Impact of inherent meteorology uncertainty on air quality model predictions, J. Geophys. Res., 120, 12259, 10.1002/2015JD023674
Gilliland, 2006, Seasonal NH3 emissions for the continental united states: inverse model estimation and evaluation, Atmos. Environ., 40, 4986, 10.1016/j.atmosenv.2005.12.066
Griscom, 2017, Natural climate solutions, Proc. Natl. Acad. Sci. USA, 114, 11645, 10.1073/pnas.1710465114
Grudzien, 2018, Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error, Nonlinear Process. Geophys., 25, 633, 10.5194/npg-25-633-2018
Hu, 2017, Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China, Atmos. Chem. Phys., 17, 13103, 10.5194/acp-17-13103-2017
Hu, 2015, Long-term particulate matter modeling for health effect studies in California-Part 1: model performance on temporal and spatial variations, Atmos. Chem. Phys., 15, 3445, 10.5194/acp-15-3445-2015
Hu, 2021, Spatial-temporal heterogeneity of air pollution and its relationship with meteorological factors in the Pearl River Delta, China, Atmos. Environ., 254, 10.1016/j.atmosenv.2021.118415
Huang, 2019, A feasible methodological framework for uncertainty analysis and diagnosis of atmospheric chemical transport models, Environ. Sci. Technol., 53, 3110, 10.1021/acs.est.8b06326
Huang, 2021, An updated model-ready emission inventory for Guangdong province by incorporating big data and mapping onto multiple chemical mechanisms, Sci. Total Environ., 769, 10.1016/j.scitotenv.2020.144535
2015
Jacob, 2016, Satellite observations of atmospheric methane and their value for quantifying methane emissions, Atmos. Chem. Phys., 16, 14371, 10.5194/acp-16-14371-2016
Jia, 2019, A dynamic dust emission allocation method and holiday profiles applied to emission processing for improving air quality model performance, Aerosol Air Qual. Res., 19, 2531, 10.4209/aaqr.2019.01.0021
Kaiser, 2018, High-resolution inversion of OMI formaldehyde columns to quantify isoprene emission on ecosystem-relevant scales: application to the southeast US, Atmos. Chem. Phys., 18, 5483, 10.5194/acp-18-5483-2018
Kepert, 2009, Covariance localization and balance in an ensemble Kalman filter, Q. J. R. Meteorol. Soc., 135, 1157, 10.1002/qj.443
Kong, 2019, Improved inversion of monthly ammonia emissions in China based on the Chinese ammonia monitoring network and ensemble Kalman filter, Environ. Sci. Technol., 53, 12529, 10.1021/acs.est.9b02701
Kurokawa, 2013, Emissions of air pollutants and greenhouse gases over Asian regions during 2000-2008: regional emission inventory in Asia (REAS) version 2, Atmos. Chem. Phys., 13, 11019, 10.5194/acp-13-11019-2013
Ledoux, 2005, An efficient natural neighbour interpolation algorithm for geoscientific modelling, 97
Lee, 2015, Meteorological controls on the diurnal variability of carbon monoxide mixing ratio at a mountaintop monitoring site in the Appalachian Mountains, Tellus Ser. B Chem. Phys. Meteorol., 67, 25659, 10.3402/tellusb.v67.25659
Li, 2019, Estimation of representative errors of surface observations of air pollutant concentrations based on high-density observation network over Beijing-Tianjin-Hebei region (Chinese), J. Atmos. Sci., 43, 277
Li, 2019, Persistent growth of anthropogenic non-methane volatile organic compound (NMVOC) emissions in China during 1990-2017: drivers, speciation and ozone formation potential, Atmos. Chem. Phys., 19, 8897, 10.5194/acp-19-8897-2019
Liao, 2020, High gaseous nitrous acid (HONO) emissions from light-duty diesel vehicles, Environ. Sci. Technol., 55, 200, 10.1021/acs.est.0c05599
Liu, 2018, Time series forecasting of air quality based on regional numerical modeling in Hong Kong, J. Geophys. Res. Atmos., 123, 4175, 10.1002/2017JD028052
Liu, 2019, Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1, Geosci. Model Dev., 12, 2899, 10.5194/gmd-12-2899-2019
Liu, 2020, Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic, Nat. Commun., 11, 1, 10.1038/s41467-020-20254-5
Lu, 2020, Development of a regional data assimilation system and its application in two distinct areas of china for estimating CO surface flux, Appl. Ecol. Environ. Res., 18, 5225, 10.15666/aeer/1804_52255246
Mao, 2014, Top-down estimates of biomass burning emissions of black carbon in the Western United States, Atmos. Chem. Phys., 14, 7195, 10.5194/acp-14-7195-2014
Miller, 2015, Biases in atmospheric CO2 estimates from correlated meteorology modeling errors, Atmos. Chem. Phys., 15, 2903, 10.5194/acp-15-2903-2015
Miyazaki, 2017, Decadal changes in global surface NOx emissions from multi-constituent satellite data assimilation, Atmos. Chem. Phys., 17, 807, 10.5194/acp-17-807-2017
Miyazaki, 2014, Global lightning NOx production estimated by an assimilation of multiple satellite data sets, Atmos. Chem. Phys., 14, 3277, 10.5194/acp-14-3277-2014
Mizzi, 2016, Assimilating compact phase space retrievals of atmospheric composition with WRF-Chem/DART: a regional chemical transport/ensemble Kalman filter data assimilation system, Geosci. Model Dev., 9, 965, 10.5194/gmd-9-965-2016
Mizzi, 2018, Assimilating compact phase space retrievals (CPSRs): comparison with independent observations (MOZAIC in situ and IASI retrievals) and extension to assimilation of truncated retrieval profiles, Geosci. Model Dev., 11, 3727, 10.5194/gmd-11-3727-2018
Müller, 2005, Inversion of CO and NOx emissions using the adjoint of the IMAGES model, Atmos. Chem. Phys., 5, 1157, 10.5194/acp-5-1157-2005
Otte, 2010, The meteorology-chemistry interface processor (MCIP) for the CMAQ modeling system: updates through MCIPv3.4.1, Geosci. Model Dev., 3, 243, 10.5194/gmd-3-243-2010
Ou, 2020, Role of export industries on ozone pollution and its precursors in China, Nat. Commun., 11, 1, 10.1038/s41467-020-19035-x
Pison, 2007, Inverse modeling of surface NOx anthropogenic emission fluxes in the Paris area during the air pollution over Paris region (ESQUIF) campaign, J. Geophys. Res. Atmos., 112, 1, 10.1029/2007JD008871
Resler, 2010, Inverse modeling of emissions and their time profiles, Atmos. Pollut. Res., 1, 288, 10.5094/APR.2010.036
Sakov, 2008, A deterministic formulation of the ensemble Kalman filter: an alternative to ensemble square root filters, Tellus Ser. A Dyn. Meteorol. Oceanogr., 60 A, 361, 10.1111/j.1600-0870.2007.00299.x
Stavrakou, 2013, Key chemical NOx sink uncertainties and how they influence top-down emissions of nitrogen oxides, Atmos. Chem. Phys., 13, 9057, 10.5194/acp-13-9057-2013
Streets, 2003, Biomass burning in Asia: annual and seasonal estimates and atmospheric emissions, Glob. Biogeochem. Cycles, 17, 1099, 10.1029/2003GB002040
Su, 2018, Relationships between the planetary boundary layer height and surface pollutants derived from lidar observations over China: regional pattern and influencing factors, Atmos. Chem. Phys., 18, 15921, 10.5194/acp-18-15921-2018
Su, 2019, Primary and secondary sources of ambient formaldehyde in the Yangtze River Delta based on Ozone Mapping and Profiler Suite (OMPS) observations, Atmos. Chem. Phys., 19, 6717, 10.5194/acp-19-6717-2019
Sun, 2017, The impact of meteorological persistence on the distribution and extremes of ozone, Geophys. Res. Lett., 44, 1545, 10.1002/2016GL071731
Tandeo, 2020, A review of innovation-based methods to jointly estimate model and observation error covariance matrices in ensemble data assimilation, Mon. Weather Rev., 148, 3973, 10.1175/MWR-D-19-0240.1
Tang, 2011, Improvement of ozone forecast over Beijing based on ensemble Kalman filter with simultaneous adjustment of initial conditions and emissions, Atmos. Chem. Phys., 11, 12901, 10.5194/acp-11-12901-2011
Tang, 2016, Limitations of ozone data assimilation with adjustment of NOx emissions: mixed effects on NO2 forecasts over Beijing and surrounding areas, Atmos. Chem. Phys., 16, 6395, 10.5194/acp-16-6395-2016
Vrac, 2015, Multivariate-intervariable, spatial, and temporal-bias correction, J. Clim., 28, 218, 10.1175/JCLI-D-14-00059.1
Wang, 2017, Differentiating local and regional sources of Chinese urban air pollution based on effect of spring festival, Atmos. Chem. Phys., 17, 9103, 10.5194/acp-17-9103-2017
Wang, 2019, Sensitivities of the NCEP global forecast system, Mon. Weather Rev., 147, 1237, 10.1175/MWR-D-18-0239.1
Worden, 2019, New constraints on biogenic emissions using satellite-based estimates of carbon monoxide fluxes, Atmos. Chem. Phys., 19, 13569, 10.5194/acp-19-13569-2019
Wu, 2020, Development of the real-time on-road emission (ROEv1.0) model for street-scale air quality modeling based on dynamic traffic big data, Geosci. Model Dev., 13, 23, 10.5194/gmd-13-23-2020
Wu, 2017, On-road vehicle emissions and their control in China: a review and outlook, Sci. Total Environ., 574, 332, 10.1016/j.scitotenv.2016.09.040
Xu, 2019, Regional discrepancies in spatiotemporal variations and driving forces of open crop residue burning emissions in China, Sci. Total Environ., 671, 536, 10.1016/j.scitotenv.2019.03.199
Yang, 2017, Monitoring carbon dioxide from space: retrieval algorithm and flux inversion based on GOSAT data and using CarbonTracker-China, Adv. Atmos. Sci., 34, 965, 10.1007/s00376-017-6221-4
Yang, 2019, High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets, Atmos. Chem. Phys., 19, 8831, 10.5194/acp-19-8831-2019
Yoshida, 2018, Correlation-cutoff method for covariance localization in strongly coupled data assimilation, Mon. Weather Rev., 146, 2881, 10.1175/MWR-D-17-0365.1
Zadra, 2018, Systematic errors in weather and climate models: nature, origins, and ways forward, Bull. Am. Meteorol. Soc., ES67, 10.1175/BAMS-D-17-0287.1
Zhang, 2015, A global carbon assimilation system using a modified ensemble Kalman filter, Geosci. Model Dev., 8, 805, 10.5194/gmd-8-805-2015
Zhao, 2020, Quantification and evaluation of atmospheric ammonia emissions with different methods: a case study for the Yangtze River Delta region, China, Atmos. Chem. Phys., 20, 4275, 10.5194/acp-20-4275-2020
Zheng, 2018, Rapid decline in carbon monoxide emissions and export from East Asia between years 2005 and 2016, Environ. Res. Lett., 13, 10.1088/1748-9326/aab2b3
Zheng, 2020, Changes in China's anthropogenic emissions during the COVID-19 pandemic in 2020, Earth Syst. Sci. Data, 13, 2895, 10.5194/essd-13-2895-2021
Zheng, 2009, A highly resolved temporal and spatial air pollutant emission inventory for the Pearl River Delta region, China and its uncertainty assessment, Atmos. Environ., 43, 5112, 10.1016/j.atmosenv.2009.04.060