Data Assimilation of AOD and Estimation of Surface Particulate Matters over the Arctic
Source abstract
Abstract
In this study, more accurate information on the levels of aerosol optical depth (AOD) was calculated from the assimilation of the modeled AOD based on the optimal interpolation method. Additionally, more realistic levels of surface particulate matters over the Arctic were estimated using the assimilated AOD based on the linear relationship between the particulate matters and AODs. In comparison to the MODIS observation, the assimilated AOD was much improved compared with the modeled AOD (e.g., increase in correlation coefficients from −0.15–0.26 to 0.17–0.76 over the Arctic). The newly inferred monthly averages of PM10 and PM2.5 for April–September 2008 were 2.18–3.70 μg m−3 and 0.85–1.68 μg m−3 over the Arctic, respectively. These corresponded to an increase of 140–180%, compared with the modeled PMs. In comparison to in-situ observation, the inferred PMs showed better performances than those from the simulations, particularly at Hyytiala station. Therefore, combining the model simulation and data assimilation provided more accurate concentrations of AOD, PM10, and PM2.5 than those only calculated from the model simulations.
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Research context
OpenAlex topics: Atmospheric chemistry and aerosols · Atmospheric aerosols and clouds · Atmospheric Ozone and Climate
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea
Center for Earth Environmental Modeling Studies (CEMOS), Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea
Department of Health Management, Kyung-in Women’s University, Incheon 21041, Korea
Korea Institute of Atmospheric Prediction System, Seoul 07071, Korea
Department of Safety and Environment Research, The Seoul Institute, Seoul 06756, Korea
Institute for Atmospheric and Earth System Research/ Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
Department of Magnetism, Institute of Geophysics, Polish Academy of Sciences, Księcia Janusza St. 64, 01-452 Warsaw, Poland
Department of Physics of The Atmosphere, Institute of Geophysics Polish Academy of Sciences, Księcia Janusza St. 64, 01-452 Warsaw, Poland
Korea Polar Research Institute (KOPRI), Incheon 21990, Korea
Funding
National Research Foundation of Korea
References (96)
Show source-provided references
1. Arctic haze: Perturbation of the polar radiation budget10.1111/j.1749-6632.1980.tb17145.x ↗
2. Evidence for a central Eurasian source area of Arctic haze in Alaska10.1038/299815a0 ↗
3. Arctic air pollution: An overview of current knowledge10.1016/0004-6981(86)90180-0 ↗
4. The Arctic haze phenomenon10.1175/1520-0477(1995)076<2403:TAHP>2.0.CO;2 ↗
5. Arctic haze: Current trends and knowledge gaps10.1111/j.1600-0889.2006.00236.x ↗
6. Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, A.B., Tignor, M., and Miller, H.L. (2007). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press.
7. Local and remote contributions to Arctic warming10.1029/2007GL030221 ↗
8. Pollution and the planetary albedo10.1016/0004-6981(74)90004-3 ↗
9. Aerosols, cloud microphysics and fractional cloudiness10.1126/science.245.4923.1227 ↗
10. Effect of precipitation on the albedo susceptibility of clouds in the marine boundary layer10.1038/372250a0 ↗
11. Soot climate forcing via snow and ice albedos10.1073/pnas.2237157100 ↗
12. (2020, November 23). International Arctic Systems for Observing the Atmosphere, Available online: https://psl.noaa.gov/iasoa/.
13. Aerosol chemical composition during tropospheric ozone depletion at Ny Ålesund/Svalbard10.3402/tellusb.v49i5.15987 ↗
14. Variations and sources of the equivalent black carbon in the high Arctic revealed by long-term observations at Alert and Barrow: 1989–2003
15. Aerosol black carbon in the European Arctic: Measurements at Zeppelin station, Ny-Ålesund, Svalbard from 1998–200710.1029/2008GL035741 ↗
16. Ozone dynamics and snow atmosphere exchanges during ozone depletion events at Barrow, Alaska
17. Recommendations for reporting “black carbon” measurements10.5194/acp-13-8365-2013 ↗
18. Becagli, S., Amore, A., Caiazzo, L., Iorio, T.D., Sarra, A., Lazzara, L., Marchese, C., Meloni, D., Mori, G., and Muscari, G. (2019). Biogenic Aerosol in the Arctic from Eight Years of MSA Data from Ny Ålesund (Svalbard Islands) and Thule (Greenland). Atmosphere, 10.10.3390/atmos10070349 ↗
19. Microphysical and optical properties of Arctic mixed-phase clouds. The 9 April 2007 case study10.5194/acp-9-6581-2009 ↗
20. Characteristics, sources, and transport of aerosols measured in spring 2008 during the aerosol, radiation, and cloud processes affecting Arctic Climate (ARCPAC) Project10.5194/acp-11-2423-2011 ↗
21. The Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) mission: Design, execution, and first results10.5194/acp-10-5191-2010 ↗
22. A meteorological overview of the ARCTAS 2008 mission10.5194/acp-10-817-2010 ↗
23. In-situ observation of Asian pollution transported into the Arctic lowermost stratosphere10.5194/acp-11-10975-2011 ↗
24. A multi-model assessment of pollution transport to the Arctic10.5194/acp-8-5353-2008 ↗
25. Monte Carlo estimates of uncertainties in predictions by a photochemical grid model (UAM-IV) due to uncertainties in input variables10.1016/S1352-2310(97)00419-6 ↗
26. Evaluating uncertainties in regional photochemical air quality modeling10.1146/annurev.energy.28.011503.163508 ↗
27. Adjoint inversion modeling of Asian dust emission using lidar observations10.5194/acp-8-2869-2008 ↗
28. A system for operational aerosol optical depth data assimilation over global oceans
29. Aerosol analysis and forecast in the European centre for medium-range weather forecasts integrated forecast system: 2. Data assimilation10.1029/2008JD011115 ↗
30. Three-dimensional variational assimilation of MODIS aerosol optical depth: Implementation and application to a dust storm over East Asia10.1029/2011JD016159 ↗
31. Probing into the impact of 3DVAR assimilation of surface PM10 observations over China using process analysis10.1002/jgrd.50495 ↗
32. A three-dimensional variational data assimilation system for multiple aerosol species with WRF/Chem and an application to PM2.5 prediction10.5194/acp-13-4265-2013 ↗
33. Aerosol optical depth assimilation for a size-resolved sectional model: Impacts of observationally constrained, multi-wavelength and fine mode retrievals on regional scale analyses and forecasts10.5194/acp-13-10425-2013 ↗
34. Aerosol data assimilation in the chemical transport model MOCAGE during the TRAQA/ChArMEx campaign: Aerosol optical depth10.5194/amt-9-5535-2016 ↗
35. Dust radiative effects on atmospheric thermodynamics and tropical cyclogenesis over the Atlantic Ocean using WRF-Chem coupled with an AOD data assimilation system10.5194/acp-17-7917-2017 ↗
36. Simulating aerosols using a chemical transport model with assimilation of satellite aerosol retrievals: Methodology for INDOEX10.1029/2000JD900507 ↗
37. Understanding the Indian Ocean Experiment (INDOEX) aerosol distributions with an aerosol assimilation10.1029/2000JD900508 ↗
38. A regional scale chemical transport modeling of Asian aerosols with data assimilation of AOD observations using optimal interpolation technique10.1016/j.atmosenv.2008.08.031 ↗
39. Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model10.5194/acp-10-2561-2010 ↗
40. Sensitivity tests for an ensemble Kalman filter for aerosol assimilation10.5194/acp-10-6583-2010 ↗
41. Data assimilation of CALIPSO aerosol observations10.5194/acp-10-39-2010 ↗
42. A study on the aerosol optical properties over East Asia using a combination of CMAQ-simulated aerosol optical properties and remote-sensing data via a data assimilation technique10.5194/acp-11-12275-2011 ↗
43. Experiments with the assimilation of fine aerosols using an ensemble Kalman filter
44. Improvement of aerosol optical properties modeling over Eastern Asia with MODIS AOD assimilation in a global non-hydrostatic icosahedral aerosol transport model10.1016/j.envpol.2014.06.021 ↗
45. Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS) and its application of the Data Assimilation Research Testbed (DART) in support of aerosol forecasting10.5194/acp-16-3927-2016 ↗
46. Assimilation of MODIS dark target and deep blue observations in the dust aerosol component of NMMB-MONARCH Version 1.010.5194/gmd-10-1107-2017 ↗
47. Impact of Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) and AirNow PM2:5 assimilation on Community Multi-scale Air Quality (CMAQ) aerosol predictions over the contiguous United States10.1002/2016JD026295 ↗
48. Improving PM2.5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter10.5194/acp-17-4837-2017 ↗
49. Assimilation of AERONET and MODIS AOT observations using variational and ensemble data assimilation methods and its impact on aerosol forecasting skill10.1002/2016JD026067 ↗
50. Assimilating aerosol observations with a “hybrid” variational-ensemble data assimilation system10.1002/2013JD020937 ↗
51. Evaluating the impact of assimilating aerosol optical depth observations on dust forecasts over North Africa and East Atlantic using different data assimilation methods10.1029/2019MS001890 ↗
52. Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D., Duda, M.G., Huang, X.Y., Wang, W., and Powers, J.G. (2008). A Description of the Advanced Research WRF Version 3 (No. NCAR/TN-475+STR), University Corporation for Atmospheric Research.
53. Use of four-dimensional data assimilation in a limited-areas mesoscale model. Part I: Experiments with synoptic-scale data10.1175/1520-0493(1990)118<1250:UOFDDA>2.0.CO;2 ↗
54. Mmulti-scale four-dimensional data assimilation10.1175/1520-0450(1994)033<0416:MFDDA>2.0.CO;2 ↗
55. A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2 ↗
56. Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models
57. The Kain-Fritsch convective parameterization: An update10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2 ↗
58. A new vertical diffusion package with an explicit treatment of entrainment processes10.1175/MWR3199.1 ↗
59. Development of a condensed SAPRC-07 chemical mechanism10.1016/j.atmosenv.2010.01.024 ↗
60. Models-3 Community Multiscale Air Quality (CMAQ) model aerosol component 1. Model description
61. Review of the governing equations, computational algorithms, and other components of the models-3 community multiscale air quality (CMAQ) modeling system10.1115/1.2128636 ↗
62. Arctic air pollution New insights from POLARCAT-IPY10.1175/BAMS-D-13-00017.1 ↗
63. Atmosphere [in “State of the Climate in 2008]
64. Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4)10.5194/gmd-3-43-2010 ↗
65. Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: Methodology and application10.5194/acp-10-7017-2010 ↗
66. Global anthropogenic emissions of particulate matter including black carbon10.5194/acp-17-8681-2017 ↗
67. Global data set of biogenic VOC emissions calculated by the MEGAN model over the last 30 years10.5194/acp-14-9317-2014 ↗
68. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009)10.5194/acp-10-11707-2010 ↗
69. Assimilation of POLDER aerosol optical thickiness into the LMDz–INCA model: Implications for the Arctic aerosol burden
70. An investigation into seasonal and regional aerosol characteristics in East Asia using model-predicted and remotely-sensed aerosol properties10.5194/acp-8-6627-2008 ↗
71. Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing10.1080/10962247.2015.1096862 ↗
72. Wei, J., Peng, Y., Guo, J., and Sun, L. (2019). Performance of MODIS Collection 6.1 Level 3 aerosol products in spatial temporal variations over land. Atmos. Environ., 30–44.10.1016/j.atmosenv.2019.03.001 ↗
73. The MODIS 2.1 channel–Correlation with visible reflectance for use in remote sensing of aerosol10.1109/36.628795 ↗
74. The Collection 6 MODIS aerosol products over land and ocean10.5194/amt-6-2989-2013 ↗
75. Enhanced Deep Blue aerosol retrieval algorithm: The second generation10.1002/jgrd.50712 ↗
76. Remote sensing of aerosol properties over oceans using the MODIS/EOS spectral radiances10.1029/96JD03437 ↗
77. MODIS Collection 6 aerosol products: Comparison between Aqua’s e-Deep Blue, Dark Target, and “merged” data sets, and usage recommendations10.1002/2014JD022453 ↗
78. AERONET: A federated instrument network and data archive for aerosol characterization10.1016/S0034-4257(98)00031-5 ↗
79. Advancements in the Aerosol Robotic Network (AERONET) Version 3 database–automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements10.5194/amt-12-169-2019 ↗
80. Assessment of the Level-3 MODIS daily aerosol optical depth in the context of surface solar radiation and numerical weather modeling10.5194/acp-13-675-2013 ↗
81. The extinction coefficient of multicomponent aerosols10.1016/0004-6981(82)90131-7 ↗
82. Revised algorithm for estimating light extinction from IMPROVE particle speciation data10.3155/1047-3289.57.11.1326 ↗
83. Light scattering from sea-salt aerosols at interagency monitoring of protected visual environments (IMPROVE) sites10.1080/10473289.2006.10464478 ↗
84. GIST-PM-Asia v1: Development of a numerical system to improve particulate matter forecasts in South Korea using geostationary satellite-retrieved aerosol optical data over Northeast Asia10.5194/gmd-9-17-2016 ↗
85. A European aerosol phenomenology-2: Chemical characteristics of particulate matter at kerbside, urban, rural and background sites in Europe10.1016/j.atmosenv.2004.01.041 ↗
86. A parameterization of sea-slat aerosol source function for sub- and super-micron particles10.1029/2003GB002079 ↗
87. A comparison of sea salt emission parameterizations in northwestern Europe using a chemistry transport model setup10.5194/acp-16-9905-2016 ↗
88. Data assimilation of ground-level ozone in Europe with a Kalman filter and chemistry transport model
89. New approach to monitor transboundary particulate pollution over Northeast Asia10.5194/acp-14-659-2014 ↗
90. Li, L. (2020). Optimal Inversion of Conversion Parameters from Satellite AOD to Ground Aerosol Extinction Coefficient Using Automatic Differentiation. Remote Sens., 12.10.3390/rs12030492 ↗
91. Impact of a nitrogen emission control area (NECA) on the future air quality and nitrogen deposition to seawater in the Baltic Sea region10.5194/acp-19-1721-2019 ↗
92. NOAA’s HYSPLIT atmospheric transport and dispersion modeling system10.1175/BAMS-D-14-00110.1 ↗
93. Aerosol optical depth retrieval in the Arctic region using MODIS data over snow10.1016/j.rse.2012.10.009 ↗
94. Shi, Z., Xing, T., Guang, J., Xue, Y., and Che, Y. (2019). Aerosol Optical Depth over the Arctic Snow-Covered Regions Derived from Dual-Viewing Satellite Observations. Remote Sens., 11.10.3390/rs11080891 ↗
95. A nonparametric approach to filling gaps in satellite-retrieved aerosol optical depth for estimating ambient PM2.5 levels10.1016/j.envpol.2018.09.052 ↗
96. Kianian, B., Liu, Y., and Chang, H.H. (2021). Imputing Satellite-Derived Aerosol Optical Depth Using a Multi-Resolution Spatial Model and Random Forest for PM2.5 Prediction. Remote Sens., 13.10.3390/rs13010126 ↗