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A Review of Data Assimilation on Aerosol Optical, Radiative, and Climatic Effects Study

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Abstract

Aerosol plays an important role in many significant environmental and climate-related issues. Accurate simulation and prediction are critical to understanding aerosol behaviors, aerosol processes, and aerosol effects. However, large uncertainties in the simplified parameterizations of aerosol processes, the optical calculations, and radiation calculations hinder the accurate simulations of aerosol distributions and their influences. The observation limitations in spatio-temporal scales also make it difficult to reproduce the three-dimensional aerosol features. The aerosol data assimilation method can combine both the modeling information with multi-platform aerosol observations to improve the accurate predictions and behaviors of aerosols, hence optimizing the evaluations of aerosol radiative and climatic effects. In this paper, we briefly introduce the developments of the principal methods for aerosol data assimilation and their implementations in investigating the aerosol optical properties, radiative effects, and climatic effects in China. The challenges and development trends in improvements of aerosol data assimilation are also highlighted.

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References

  • Adhikary B, Kulkarni S, Dallura A, Tang Y, Chai T, Leung LR, Qian Y, Chung CE, Ramanathan V, Carmichael GR (2008) A regional scale chemical transport modeling of asian aerosols with data assimilation of AOD observations using optimal interpolation technique. Atmos Environ 42(37):8600–8615

    Google Scholar 

  • Barnes WL, Pagano TS, Salomonson VV (1998) Prelaunch characteristics of the moderate resolution imaging spectroradiometer (MODIS) on EOS-AM1. IEEE Trans Geosci Remote Sens 36(4):1088–1100

    Google Scholar 

  • Benedetti A, Morcrette J-J, Boucher O, Dethof A, Engelen RJ, Fisher M, Flentje H et al (2009) Aerosol analysis and forecast in the european centre for medium-range weather forecasts integrated forecast system: 2. Data assimilation. J Geophys Res. https://doi.org/10.1029/2008JD011115

    Article  Google Scholar 

  • Bessho K, Date K, Hayashi M, Ikeda A, Imai T, Inoue H, Kumagai Y et al (2016) An introduction to Himawari-8/9— Japan’s new-generation geostationary meteorological satellites. J Meteorol S Japan. Ser II 94(2):151–183

    Google Scholar 

  • Buchard V, Randles CA, da Silva AM, Darmenov A, Colarco PR, Govindaraju R, Ferrare R et al (2017) The MERRA-2 aerosol reanalysis, 1980 onward. Part II: Evaluation and case studies. J Climate 30(17):6851–6872

    Google Scholar 

  • Cao JJ, Cui L (2021) Current status, characteristics and causes of particulate air pollution in the fenwei plain, china: a review. J Geophys Resh: Atmos. https://doi.org/10.1029/2020JD034472

    Article  Google Scholar 

  • Cao J-J, Shen Z-X, Chow JC, Watson JG, Lee S-C, Tie X-X, Ho K-F, Wang G-H, Han Y-M (2012) Winter and summer PM 2.5 chemical compositions in fourteen Chinese Cities. J Air Waste Manag Assoc 62(10):1214–1226

    Google Scholar 

  • Chen D, Liu Z, Schwartz CS, Lin H-C, Cetola JD, Gu Y, Xue L (2014) The impact of aerosol optical depth assimilation on aerosol forecasts and radiative effects during a wild fire event over the United States. Geosci Model Dev 7(6):2709–2715

    Google Scholar 

  • Chen D, Liu Z, Davis C, Yu Gu (2017) Dust radiative effects on atmospheric thermodynamics and tropical cyclogenesis over the atlantic ocean using WRF-chem coupled with an AOD data assimilation system. Atmos Chem Phys 17(12):7917–7939

    Google Scholar 

  • Cheng Y, Dai T, Goto D, Schutgens NAJ, Shi G, Nakajima T (2019b) Investigating the assimilation of CALIPSO global aerosol vertical observations using a four-dimensional ensemble kalman filter. Atmos Chem Phys 19(21):13445–13467

    Google Scholar 

  • Cheng X, Liu Y, Xiangde Xu, You W, Zang Z, Gao L, Chen Y, Debin Su, Yan P (2019a) Lidar data assimilation method based on CRTM and WRF-chem models and its application in PM2.5 forecasts in Beijing. Sci Total Environ 682:541–552

    Google Scholar 

  • Cheng Y, Dai T, Goto D, Murakami H, Yoshida M, Shi G, Nakajima T (2021) Enhanced simulation of an Asian dust storm by assimilating GCOM-C observations. Remote Sensing 13(15):3020. https://doi.org/10.3390/rs13153020

    Article  Google Scholar 

  • Choi Y, Chen S-H, Huang C-C, Earl K, Chen C-Y, Schwartz CS, Matsui T (2020) Evaluating the impact of assimilating aerosol optical depth observations on dust forecasts over north africa and the east atlantic using different data assimilation methods. J Adv Model Earth Syst. https://doi.org/10.1029/2019MS001890

    Article  Google Scholar 

  • Christian K, Wang J, Ge C, Peterson D, Hyer E, Yorks J, McGill M (2019) Radiative forcing and stratospheric warming of pyrocumulonimbus smoke aerosols: first modeling results with multisensor (EPIC, CALIPSO, and CATS) views from space. Geophys Res Lett 46(16):10061–10071

    Google Scholar 

  • Chung CE, Ramanathan V, Carmichael G, Kulkarni S, Tang Y, Adhikary B, Leung LR, Qian Y (2010) Anthropogenic aerosol radiative forcing in Asia derived from regional models with atmospheric and aerosol data assimilation. Atmos Chem Phys 10(13):6007–6024

    Google Scholar 

  • Collins WD, Rasch PJ, Eaton BE, Khattatov BV, Lamarque J-F, Zender CS (2001) Simulating aerosols using a chemical transport model with assimilation of satellite aerosol retrievals: methodology for INDOEX. J Geophys Res: Atmos 106(D7):7313–7336

    Google Scholar 

  • Dai T, Schutgens NAJ, Goto D, Shi G, Nakajima T (2014) Improvement of aerosol optical properties modeling over eastern Asia with MODIS AOD assimilation in a global non-hydrostatic icosahedral aerosol transport model. Environ Pollut 195:319–329

    Google Scholar 

  • Dai T, Cheng Y, Suzuki K, Goto D, Kikuchi M, Schutgens NAJ, Yoshida M et al (2019) Hourly aerosol assimilation of Himawari-8 AOT using the four-dimensional local ensemble transform kalman filter. J Adv Model Earth Syst. https://doi.org/10.1029/2018MS001475

    Article  Google Scholar 

  • Descombes G, Auligné T, Vandenberghe F, Barker DM, Barré J (2015) Generalized background error covariance matrix model (GEN_BE v2.0). Geosci Model Dev 8(3):669–696

    Google Scholar 

  • Di Tomaso E, Schutgens NAJ, Jorba O, García-Pando CP (2017) Assimilation of MODIS dark target and deep blue observations in the dust aerosol component of NMMB-MONARCH version 1.0. Geosci Model Dev 10(3):1107–1129

    Google Scholar 

  • Diner DJ, Abdou WA, Bruegge CJ, Conel JE, Crean KA, Gaitley BJ, Helmlinger MC et al (2001) MISR aerosol optical depth retrievals over Southern Africa during the SAFARI-2000 dry season campaign. Geophys Res Lett 28(16):3127–3130

    Google Scholar 

  • El Amraoui L, Sic B, Piacentini A, Marécal V, Frebourg N, Attié J-L (2020) Aerosol data assimilation in the MOCAGE chemical transport model during the TRAQA/ChArMEx campaign: lidar observations. Atmos Meas Tech 13(9):4645–4667

    Google Scholar 

  • Evensen G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using monte carlo methods to forecast error statistics. J Geophys Res 99(C5):10143. https://doi.org/10.1029/94JC00572

    Article  Google Scholar 

  • Flemming J, Benedetti A, Inness A, Engelen RJ, Jones L, Huijnen V, Remy S et al (2017) The CAMS interim reanalysis of carbon monoxide, ozone and aerosol for 2003–2015. Atmos Chem Phys 17(3):1945–1983

    Google Scholar 

  • Gandin LS (1963) Objective analysis of meteorological fields. Leningr Gidrometeoizdat 287:242–257

    Google Scholar 

  • Generoso S, Bréon F-M, Chevallier F, Balkanski Y, Schulz M, Bey I (2007) Assimilation of POLDER aerosol optical thickness into the LMDz-INCA Model: implications for the arctic aerosol burden. J Geophys Res. https://doi.org/10.1029/2005JD006954

    Article  Google Scholar 

  • Giles DM, Sinyuk A, Sorokin MG, Schafer JS, Smirnov A, Slutsker I, Eck TF et al (2019) 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) measurements. Atmos Meas Tech 12(1):169–209

    Google Scholar 

  • Holben BN, Eck TF, Slutsker I, Tanré D, Buis JP, Setzer A, Vermote E et al (1998) AERONET—a federated instrument network and data archive for aerosol characterization. Remote Sens Environ 66(1):1–16

    Google Scholar 

  • Huang J, Lin B, Minnis P, Wang T, Wang X, Yongxiang Hu, Yi Y, Kirk Ayers J (2006) Satellite-based assessment of possible dust aerosols semi-direct effect on cloud water path over East Asia. Geophys Res Lett 33(19):L19802. https://doi.org/10.1029/2006GL026561

    Article  Google Scholar 

  • Huang J, Li Y, Fu C, Chen F, Fu Q, Dai A, Shinoda M et al (2017) Dryland climate change: recent progress and challenges: dryland climate change. Rev Geophys 55(3):719–778

    Google Scholar 

  • Huneeus N, Schulz M, Balkanski Y, Griesfeller J, Prospero J, Kinne S, Bauer S, Boucher O, Chin M, Dentener F, Diehl T, Easter R, Fillmore D, Ghan S, Ginoux P, Grini A, Horowitz L, Koch D, Krol M C, Landing W, Liu X, Mahowald N, Miller R, Morcrette J-J, Myhre G, Penner J, Perlwitz J, Stier P, Takemura T, Zender CS (2011) Global dust model intercomparison in AeroCom phase I. Atmos Chem Phys 11(15):7781–7816

    Google Scholar 

  • Hunt BR, Kostelich EJ, Szunyogh I (2007) Efficient data assimilation for spatio-temporal chaos: a local ensemble transform Kalman filter. Physica D 230:112–126

    Google Scholar 

  • Imaoka K, Kachi M, Fujii H, Murakami H, Hori M, Ono A, Igarashi T et al (2010) Global change observation mission (GCOM) for monitoring carbon, water cycles, and climate change. Proc IEEE 98(5):717–734

    Google Scholar 

  • Ji Z, Kang S, Cong Z, Zhang Q, Yao T (2015) Simulation of carbonaceous aerosols over the third pole and adjacent regions: distribution, transportation, deposition, and climatic effects. Clim Dyn 45(9–10):2831–2846

    Google Scholar 

  • Kalnay E (2003) Atmospheric modeling, data assimilation and predictability. Cambridge university press, Cambridge. https://doi.org/10.1017/CBO9780511802270

    Book  Google Scholar 

  • Kidwell KB (1998) NOAA polar orbiter data user guide. NOAA. NESDIS. NCDC, Suitland, Md. (Available as http://www2.ncdc.noaa.gov/docs/podug)

  • Liang Y, Zang Z, Liu D, Yan P, Hu Y, Zhou Y, You W (2020) Development of a three-dimensional variational assimilation system for lidar profile data based on a size-resolved aerosol model in WRF–Chem model v3.9.1 and its application in PM2.5 forecasts across China. Geosci Model Dev 13(12):6285–6301

  • Lier Ph, Bach M (2008) PARASOL a microsatellite in the a—train for earth atmospheric observations. Acta Astronaut 62(2–3):257–263

    Google Scholar 

  • Liu Z, Liu Q, Lin H-C, Schwartz CS, Lee Y-H, Wang T (2011) Three-dimensional variational assimilation of MODIS aerosol optical depth: implementation and application to a dust storm over East Asia: AOD DATA ASSIMILATION. J Geophys Res: Atmos. https://doi.org/10.1029/2011JD016159

    Article  Google Scholar 

  • Lorenc AC (1986) Analysis methods for numerical weather prediction. Q J R Meteorol Soc 112(474):1177–1194

    Google Scholar 

  • Lynch P, Reid JS, Westphal DL, Zhang J, Hogan TF, Hyer EJ, Curtis CA, Hegg DA, Shi Y, Campbell JR, Rubin JI, Sessions WR, Turk FJ, Walker AL (2016) An 11-year global gridded aerosol optical thickness reanalysis (v1.0) for atmospheric and climate sciences. Geosci Model Dev 9(4):1489–1522

    Google Scholar 

  • Myhre G, Samset BH, Schulz M, Balkanski Y, Bauer S, Berntsen TK, Bian H, Bellouin N, Chin M, Diehl T, Easter RC, Feichter J, Ghan SJ, Hauglustaine D, Iversen T, Kinne S, Kirkevåg A, Lamarque J-F, Lin G, Liu X, Lund MT, Luo G, Ma X, van Noije T, Penner JE, Rasch PJ, Ruiz A, Seland Ø, Skeie RB, Stier P, Takemura T, Tsigaridis K, Wang P, Wang Z, Xu L, Yu H, Yu F, Yoon J-H, Zhang K, Zhang H, Zhou C (2013) Radiative forcing of the direct aerosol effect from AeroCom Phase II simulations. Atmos Chem Phys 13(4):1853–187

    Google Scholar 

  • Nakajima T, Yoon S-C, Ramanathan V, Shi G-Y, Takemura T, Higurashi A, Takamura T et al (2007) Overview of the atmospheric brown cloud East Asian regional experiment 2005 and a study of the aerosol direct radiative forcing in East Asia. J Geophys Res 112(D24):D24S91. https://doi.org/10.1029/2007JD009009

    Article  Google Scholar 

  • Oikawa E, Nakajima T, Winker D (2018) An evaluation of the shortwave direct aerosol radiative forcing using CALIOP and MODIS observations. J Geophys Res: Atmos 123(2):1211–1233

    Google Scholar 

  • Park RS, Song CH, Han KM, Park ME, Lee S-S, Kim S-B, Shimizu A (2011) 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 technique. Atmos Chem Phys 11(23):12275–12296

    Google Scholar 

  • Reale O, Lau KM, da Silva A, Matsui T (2014) Impact of assimilated and interactive aerosol on tropical cyclogenesis. Geophys Res Lett 41:3282–3288

    Google Scholar 

  • Remer LA, Kaufman YJ, Tanré D, Mattoo S, Chu DA, Martins JV, Li R-R et al (2005) The MODIS aerosol algorithm, products, and validation. J Atmos Sci 62(4):947–973

    Google Scholar 

  • Rubin JI, Collins WD (2014) Global simulations of aerosol amount and size using MODIS observations assimilated with an ensemble kalman filter. J Geophy Res: Atmos 119(22):12780–12806

    Google Scholar 

  • Rubin JI, Reid JS, Hansen JA, Anderson JL, Collins N, Hoar TJ, Hogan T et al (2016) Development of the ensemble navy aerosol analysis prediction system (ENAAPS) and Its application of the data assimilation research testbed (DART) in support of aerosol forecasting. Atmos Chem Phys 16(6):3927–3951

    Google Scholar 

  • Rubin JI, Reid JS, Hansen JA, Anderson JL, Holben BN, Xian P, Westphal DL, Zhang J (2017) Assimilation of AERONET and MODIS AOT observations using variational and ensemble data assimilation methods and its impact on aerosol forecasting skill: modis, aeronet da for aerosol forecasting. J Geophys Res: Atmos 122(9):4967–4992

    Google Scholar 

  • Schutgens NAJ, Miyoshi T, Takemura T, Nakajima T (2010a) Applying an ensemble kalman filter to the assimilation of AERONET observations in a global aerosol transport model. Atmos Chem Phys 16. https://doi.org/10.5194/acp-10-2561-2010

  • Schutgens NAJ, Miyoshi T, Takemura T, Nakajima T (2010b) Sensitivity tests for an ensemble kalman filter for aerosol assimilation. Atmos Chem Phys 18. https://doi.org/10.5194/acp-10-6583-2010

  • Sekiyama TT, Tanaka TY, Shimizu TY, Miyoshi T (2010) Data assimilation of CALIPSO aerosol observations. Atmos Chem Phys 11. https://doi.org/10.5194/acp-10-39-2010

  • Sekiyama TT, Yumimoto K, Tanaka TY, Nagao T, Kikuchi M, Murakami H (2016) Data assimilation of Himawari-8 aerosol observations: asian dust forecast in June 2015. SOLA 12:86–90

    Google Scholar 

  • Shao Y, Wyrwoll K-H, Chappell A, Huang J, Lin Z, McTainsh GH, Mikami M, Tanaka TY, Wang X, Yoon S (2011) Dust cycle: an emerging core theme in earth system science. Aeol Res 2(4):181–204

    Google Scholar 

  • Shimizu A (2004) Continuous observations of asian dust and other aerosols by polarization lidars in China and Japan during ACE-Asia. J Geophys Res 109(D19):D19S17. https://doi.org/10.1029/2002JD003253

    Article  Google Scholar 

  • Sugimoto N, Uno I, Nishikawa M, Shimizu A, Matsui I, Dong X, Chen Y, Quan H (2003) Record heavy asian dust in Beijing in 2002: observations and model analysis of recent events: record heavy dust in Beijing in 2002. Geophy Res Lett. https://doi.org/10.1029/2002GL016349

    Article  Google Scholar 

  • Tang Y, Pagowski M, Chai T, Pan L, Lee P, Baker B, Kumar R, Monache LD, Tong D, Kim H-C (2017) A case study of aerosol data assimilation with the community multi-scale air quality model over the contiguous united states using 3D-Var and optimal interpolation methods. Geosci Model Dev 16. https://doi.org/10.5194/gmd-10-4743-2017

  • Textor C, Schulz M, Guibert S, Kinne S, Balkanski Y, Bauer S, Berntsen T, Berglen T, Boucher O, Chin M, Dentener F, Diehl T, Easter R, Feichter H, Fillmore D, Ghan S, Ginoux P, Gong S, Grini A, Hendricks J, Horowitz L, Huang P, Isaksen I, Iversen T, Kloster S, Koch D, Kirkeva A, Kristjansson JE, Krol M, Lauer A, Lamarque JF, Liu X, Montanaro V, Myhre G, Penner J, Pitari G, Reddy S, Seland Ø, Stier P, Takemura T, Tie X (2006) Analysis and quantification of the diversities of aerosol life cycles within AeroCom. Atmos Chem Phys 6:1777–1813

  • Tsikerdekis A, Schutgens NAJ, Hasekamp OP (2020) Assimilating aerosol optical properties related to size and absorption from POLDER/PARASOL with an ensemble dataassimilation system. Aerosols/atmos Model/tropos/phys (physl Prop Process)

  • Twomey SP (1974) Pollution and the planetary albedo. Atmos Environ 8:1251–1256

    Google Scholar 

  • Winker DM, Hunt WH, McGill MJ (2007) Initial performance assessment of CALIOP. Geophys Res Lett. https://doi.org/10.1029/2007GL030135

    Article  Google Scholar 

  • Xia X, Min J, Shen F, Wang Y, Zhang P (2020) Aerosol data assimilation using data from fengyun-4a, a next-generation geostationary meteorological satellite. Atmos Environ 237:117695

    Google Scholar 

  • Xin J, Wang Y, Li Z, Wang P, Hao WM, Nordgren BL, Wang S et al (2007) Aerosol optical depth (AOD) and Ångström exponent of aerosols observed by the Chinese sun hazemeter network from August 2004 to September 2005. J Geophys Res 112(D5):D05203. https://doi.org/10.1029/2006JD007075

    Article  Google Scholar 

  • Yang J, Zhang Z, Wei C, Feng Lu, Guo Q (2017) Introducing the new generation of chinese geostationary weather satellites, Fengyun-4. Bull Am Meteor Soc 98(8):1637–1658

    Google Scholar 

  • Ye H, Pan X, You W, Zhu X, Zang Z, Wang D, Zhang X, Yiwen Hu, Jin S (2021) Impact of CALIPSO profile data assimilation on 3-D aerosol improvement in a size-resolved aerosol model. Atmos Res 264:105877. https://doi.org/10.1016/j.atmosres.2021.105877

    Article  Google Scholar 

  • Yu H, Dickinson RE, Chin M, Kaufman YJ, Holben BN, Geogdzhayev IV, Mishchenko MI (2003) Annual cycle of global distributions of aerosol optical depth from integration of MODIS retrievals and GOCART model simulations. J Geophys Res: Atmos 108(D3):4128

    Google Scholar 

  • Yu H, Kaufman YJ, Chin M, Feingold G, Remer LA, Anderson TL, Balkanski Y et al (2006) A review of measurement-based assessments of the aerosol direct radiative effect and forcing. Atmos Chem Phys 6(3):613–666

    Google Scholar 

  • Yumimoto K, Takemura T (2011) Direct radiative effect of aerosols estimated using ensemble-based data assimilation in a global aerosol climate model: assimilation with aerosol climate model. Geophys Res Lett. https://doi.org/10.1029/2011GL049258

    Article  Google Scholar 

  • Yumimoto K, Uno I, Sugimoto N, Shimizu A, Satake S (2007) Adjoint inverse modeling of dust emission and transport over East Asia: adjoint inverse modeling of dust. Geophys Res Lett. https://doi.org/10.1029/2006GL028551

    Article  Google Scholar 

  • Yumimoto K, Uno I, Sugimoto N, Shimizu A, Liu Z, Winker DM (2008) Adjoint inversion modeling of asian dust emission using lidar observations. Atmos Chem Phys 8(11):2869–2884. https://doi.org/10.5194/acp-8-2869-2008

    Article  Google Scholar 

  • Yumimoto K, Tanaka TY, Yoshida M, Kikuchi M, Nagao TM, Murakami H, Maki T (2018) Assimilation and forecasting experiment for heavy Siberian wildfire smoke in May 2016 with Himawari-8 aerosol optical thickness. J Meteorol Soc Japan. Ser II 96B:133–149

    Google Scholar 

  • Zhang J, Reid JS, Westphal DL, Baker NL, Hyer EJ (2008) A system for operational aerosol optical depth data assimilation over global oceans. J Geophys Res 113(D10):D10208. https://doi.org/10.1029/2007JD009065

    Article  Google Scholar 

  • Zhang J, Campbell JR, Hyer EJ, Reid JS, Westphal DL, Johnson RS (2014) Evaluating the impact of multisensor data assimilation on a global aerosol particle transport model: multi-sensor aerosol data assimilation. J Geophys Res: Atmos 119(8):4674–4689

    Google Scholar 

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Acknowledgements

The authors acknowledge the help given by colleagues and the constructive contribution of the anonymous reviewers.

Funding

The current research was funded by the International Partnership Program of Chinese Academy of Sciences (grant no. 134111KYSB20200006), the CAS “Light of West China” Program, the National Key Research and Development Program of China (grant nos. 2016YFC0202001 and 2017YFC0209803), the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA2006010302), the National Natural Science Funds of China (grant nos. 41875133, 41590875, 41605083, and 42030511), and the Youth Innovation Promotion Association CAS (grant no. 2020078).

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  1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China

    Yueming Cheng, Tie Dai, Junji Cao & Guangyu Shi

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  1. Yueming Cheng
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  2. Tie Dai
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TD and JC did conceptualize the idea for this research. YC wrote the manuscript, which was reviewed and revised by all of the contributors.

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Cheng, Y., Dai, T., Cao, J. et al. A Review of Data Assimilation on Aerosol Optical, Radiative, and Climatic Effects Study. Aerosol Sci Eng 6, 146–154 (2022). https://doi.org/10.1007/s41810-022-00142-9

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