Abstract
The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), a type of online coupled chemistry-meteorology model (CCMM), considers the interaction between air quality and meteorology to improve air quality forecasting. Meteorological data assimilation (DA) can be used to reduce uncertainty in meteorological field, which is one factor causing prediction uncertainty in the CCMM. In this study, WRF-Chem and three-dimensional variational DA were used to examine the impact of meteorological DA on air quality and meteorological forecasts over the Korean Peninsula. The nesting model domains were configured over East Asia (outer domain) and the Korean Peninsula (inner domain). Three experiments were conducted by using different DA domains to determine the optimal model domain for the meteorological DA. When the meteorological DA was performed in the outer domain or both the outer and inner domains, the root-mean-square error (RMSE), bias of the predicted particulate matter (PM) concentrations, and the RMSE of predicted meteorological variables against the observations were smaller than those in the experiment where the meteorological DA was performed only in the inner domain. This indicates that the improvement of the synoptic meteorological fields by DA in the outer domain enhanced the meteorological initial and boundary conditions for the inner domain, subsequently improving air quality and meteorological predictions. Compared to the experiment without meteorological DA, the RMSE and bias of the meteorological and PM variables were smaller in the experiments with DA. The effect of meteorological DA on the improvement of PM predictions lasted for approximately 58–66 h, depending on the case. Therefore, the uncertainty reduction in the meteorological initial condition by the meteorological DA contributed to a reduction of the forecast errors of both meteorology and air quality.
This is a preview of subscription content, log in via an institution to check access.
References
Ackermann, I. J., H. Hass, M. Memmesheimer, et al., 1998: Modal aerosol dynamics model for Europe: Development and first applications. Atmos. Environ., 32, 2981–2999, doi: https://doi.org/10.1016/S1352-2310(98)00006-5.
Ahmadov, R., S. A. McKeen, A. L. Robinson, et al., 2012: A volatility basis set model for summertime secondary organic aerosols over the eastern United States in 2006. J. Geophys. Res. Atmos., 117, D06301, doi: https://doi.org/10.1029/2011JD016831.
Baklanov, A., D. Brunner, G. Carmichael, et al., 2017: Key issues for seamless integrated chemistry–meteorology modeling. Bull. Amer. Meteor. Soc., 98, 2285–2292, doi: https://doi.org/10.1175/BAMS-D-15-00166.1.
Barker, D. M., W. Huang, Y.-R. Guo, et al., 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897–914, doi:https://doi.org/10.1175/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2.
Bei, N., B. de Foy, W. Lei, et al., 2008: Using 3DVAR data assimilation system to improve ozone simulations in the Mexico City basin. Atmos. Chem. Phys., 8, 7353–7366, doi: https://doi.org/10.5194/acp-8-7353-2008.
Bei, N., W. Lei, M. Zavala, et al., 2010: Ozone predictabilities due to meteorological uncertainties in the Mexico City basin using ensemble forecasts. Atmos. Chem. Phys., 10, 6295–6309, doi: https://doi.org/10.5194/acp-10-6295-2010.
Bocquet, M., H. Elbern, H. Eskes, et al., 2015: Data assimilation in atmospheric chemistry models: Current status and future prospects for coupled chemistry meteorology models. Atmos. Chem. Phys., 15, 5325–5358, doi: https://doi.org/10.5194/acp-15-5325-2015.
Chen, S.-H., and W.-Y. Sun, 2002: A one-dimensional time dependent cloud model. J. Meteor. Soc. Japan Ser. II, 80, 99–118, doi: https://doi.org/10.2151/jmsj.80.99.
Choi, J., R. J. Park, H.-M. Lee, et al., 2019: Impacts of local vs. trans-boundary emissions from different sectors on PM2.5 exposure in South Korea during the KORUS-AQ campaign. Atmos. Environ., 203, 196–205, doi: https://doi.org/10.1016/j.atmosenv.2019.02.008.
Eltahan, M., and S. Alahmadi, 2019: Numerical dust storm simulation using modified geographical domain and data assimilation: 3DVAR and 4DVAR (WRF-Chem/WRFDA). IEEE Access, 7, 128,980–128,989, doi: https://doi.org/10.1109/access.2019.2930812.
Forkel, R., J. Werhahn, A. B. Hansen, et al., 2012: Effect of aerosol–radiation feedback on regional air quality—a case study with WRF/Chem. Atmos. Environ., 53, 202–211, doi: https://doi.org/10.1016/j.atmosenv.2011.10.009.
Grell, G. A., and D. Dévényi, 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29, 1693, doi: https://doi.org/10.1029/2002GL015311.
Grell, G. A., S. E. Peckham, R. Schmitz, et al., 2005: Fully coupled “online” chemistry within the WRF model. Atmos. Environ., 39, 6957–6975, doi: https://doi.org/10.1016/j.atmosenv.2005.04.027.
Guenther, A., T. Karl, P. Harley, et al., 2006: Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature). Atmos. Chem. Phys., 6, 3181–3210, doi: https://doi.org/10.5194/acp-6-3181-2006.
Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318–2341, doi: https://doi.org/10.1175/MWR3199.1.
Iacono, M. J., J. S. Delamere, E. J. Mlawer, et al., 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res. Atmos., 113, D13103, doi: https://doi.org/10.1029/2008JD009944.
Jeon, W., Y. Choi, H. W. Lee, et al., 2015: A quantitative analysis of grid nudging effect on each process of PM2.5 production in the Korean Peninsula. Atmos. Environ., 122, 763–774, doi: https://doi.org/10.1016/j.atmosenv.2015.10.050.
Jiménez, P. A., J. Dudhia, J. F. González-Rouco, et al., 2012: A revised scheme for the WRF surface layer formulation. Mon. Wea. Rev., 140, 898–918, doi: https://doi.org/10.1175/MWR-D-11-00056.1.
Jung, B.-J., H. M. Kim, F. Q. Zhang, et al., 2012: Effect of targeted dropsonde observations and best track data on the track forecasts of Typhoon Sinlaku (2008) using an ensemble Kalman filter. Tellus A, 64, 14984, doi: https://doi.org/10.3402/tellusa.v64i0.14984.
Kim, D.-H., and H. M. Kim, 2018: Effect of assimilating Himawari-8 atmospheric motion vectors on forecast errors over East Asia. J. Atmos. Oceanic Technol., 35, 1737–1752, doi: https://doi.org/10.1175/JTECH-D-17-0093.1.
Kim, D.-H., and H. M. Kim, 2022: Effect of data assimilation in the polar WRF with 3DVAR on the prediction of radiation, heat flux, cloud, and near surface atmospheric variables over Svalbard. Atmos. Res., 272, 106155, doi: https://doi.org/10.1016/j.atmosres.2022.106155.
Kim, H. M., M. C. Morgan, and R. E. Morss, 2004: Evolution of analysis error and adjoint-based sensitivities: Implications for adaptive observations. J. Atmos. Sei., 61, 795–812, doi: https://doi.org/10.1175/1520-0469(2004)061<0795:EOAEAA>2.0.CO;2.
Kim, H. M., J. K. Kay, and B.-J. Jung, 2008: Application of adjoint-based forecast sensitivities to Asian dust transport events in Korea. Water Air Soil Pollut., 195, 335–343, doi: https://doi.org/10.1007/s11270-008-9750-8.
Kim, S.-M., and H. M. Kim, 2018: Effect of observation error variance adjustment on numerical weather prediction using forecast sensitivity to error covariance parameters. Tellus A, 70, 1492839, doi: https://doi.org/10.1080/16000870.2018.1492839.
Kim, S.-M., and H. M. Kim, 2019: Forecast sensitivity observation impact in the 4DVAR and hybrid-4DVAR data assimilation systems. J. Atmos. Oceanic Technol., 36, 1563–1575, doi: https://doi.org/10.1175/JTECH-D-18-0240.1.
Lee, H.-J., H.-Y. Jo, S.-W. Kim, et al., 2019: Impacts of atmospheric vertical structures on transboundary aerosol transport from China to South Korea. Sci. Rep., 9, 13040, doi: https://doi.org/10.1038/s41598-019-49691-z.
Lelieveld, J., A. Pozzer, U. Pöschl, et al., 2020: Loss of life expectancy from air pollution compared to other risk factors: A worldwide perspective. Cardiovasc. Res., 116, 1910–1917, doi: https://doi.org/10.1093/cvr/cvaa025.
Liu, J., J. Hong, F. Y. Mao, et al., 2020: Impact of assimilating multi-source observations on meteorological and PM2.5 forecast over Central China. Atmos. Res., 241, 104945, doi: https://doi.org/10.1016/j.atmosres.2020.104945.
Martínez-Sabari, E. E., and J. A. García-Reynoso, 2021: Meteorological data assimilation for air quality modeling with WRF-Chem: Central Mexico case study. Atmósfera, 34, 311–336, doi: https://doi.org/10.20937/atm.52804.
Nam, K.-M., and H. Yoon, 2019: Air pollution in East Asia and its regional and socioeconomic impacts: An introduction. Ann. Reg. Sci., 63, 249–254, doi: https://doi.org/10.1007/s00168-019-00935-w.
Pandey, A., M. Brauer, M. L. Cropper, et al., 2021: Health and economic impact of air pollution in the states of India: The Global Burden of Disease Study 2019. Lancet Planet. Health, 5, e25–e38, doi: https://doi.org/10.1016/s2542-5196(20)30298-9.
Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center’s spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1747–1763, doi: https://doi.org/10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2.
Peterson, D. A., E. J. Hyer, S.-O. Han, et al., 2019: Meteorology influencing springtime air quality, pollution transport, and visibility in Korea. Elem. Sci. Anth., 7, 57, doi: https://doi.org/10.1525/elementa.395.
Seo, M.-G., and H. M. Kim, 2023: Effect of meteorological data assimilation using 3DVAR on high-resolution simulations of atmospheric CO2 concentrations in East Asia. Atmos. Pollut. Res., 14, 101759, doi: https://doi.org/10.1016/j.apr.2023.101759.
Son, S.-C., and S. Park, 2019: Mass size distributions of water-soluble aerosol particles during high fine particulate matter episode over Gwangju in November 2018. J. Korean Soc. Atmos. Environ., 35, 423–437, doi: https://doi.org/10.5572/KOSAE.2019.35.4.423.
Stockwell, W. R., F. Kirchner, M. Kuhn, et al., 1997: A new mechanism for regional atmospheric chemistry modeling. J. Geophys. Res. Atmos., 102, 25,847–25,879, doi: https://doi.org/10.1029/97JD00849.
Tewari, M., F. Chen, W. Wang, et al., 2004: Implementation and verification of the unified NOAH land-surface model in the WRF model. Proceedings of the 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction, Seattle, USA, 14 January 2004, Amer. Meteor. Soc., 14.2a.
Woo, J.-H., K.-C. Choi, H. K. Kim, et al., 2012: Development of an anthropogenic emissions processing system for Asia using SMOKE. Atmos. Environ., 58, 5–13, doi: https://doi.org/10.1016/j.atmosenv.2011.10.042.
Yang, E.-G., and H. M. Kim, 2017: Evaluation of a regional reanalysis and ERA-Interim over East Asia using in situ observations during 2013–14. J. Appl. Meteor. Climatol., 56, 2821–2844, doi: https://doi.org/10.1175/JAMC-D-16-0227.1.
Yang, E.-G., and H. M. Kim, 2019: Evaluation of short-range precipitation reforecasts from East Asia Regional Reanalysis. J. Hydrometeor., 20, 319–337, doi: https://doi.org/10.1175/JHM-D-18-0068.1.
Yang, E.-G., and H. M. Kim, 2021: A comparison of variational, ensemble-based, and hybrid data assimilation methods over East Asia for two one-month periods. Atmos. Res., 249, 105257, doi: https://doi.org/10.1016/j.atmosres.2020.105257.
Yang, E.-G., H. M. Kim, and D.-H. Kim, 2022: Development of East Asia Regional Reanalysis based on advanced hybrid gain data assimilation method and evaluation with E3DVAR, ERA-5, and ERA-Interim reanalysis. Earth Syst. Sci. Data, 14, 2109–2127, doi: https://doi.org/10.5194/essd-14-2109-2022.
Zhang, X., X. M. Ou, X. Yang, et al., 2017: Socioeconomic burden of air pollution in China: Province-level analysis based on energy economic model. Energy Econ., 68, 478–489, doi: https://doi.org/10.1016/j.eneco.2017.10.013.
Zhang, Y., M. Bocquet, V. Mallet, et al., 2012a: Real-time air quality forecasting, Part I: History, techniques, and current status. Atmos. Environ., 60, 632–655, doi: https://doi.org/10.1016/j.atmosenv.2012.06.031.
Zhang, Y., M. Bocquet, V. Mallet, et al., 2012b: Real-time air quality forecasting, Part II: State of the science, current research needs, and future prospects. Atmos. Environ., 60, 656–676, doi: https://doi.org/10.1016/j.atmosenv.2012.02.04.
Acknowledgments
The authors appreciate the reviewers’ valuable comments.
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by the National Research Foundation of Korea (2021R1A2C1012572) funded by the South Korean government (Ministry of Science and ICT), Yonsei Signature Research Cluster Program of 2023 (2023-22-0009), and National Institute of Environmental Research (NIER-2022-01-02-076) funded by the Ministry of Environment (MOE) of the Republic of Korea.
Rights and permissions
About this article
Cite this article
Cho, Y., Kim, H.M., Yang, EG. et al. Effect of Meteorological Data Assimilation on Regional Air Quality Forecasts over the Korean Peninsula. J Meteorol Res 38, 262–284 (2024). https://doi.org/10.1007/s13351-024-3152-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13351-024-3152-8