Articles | Volume 13, issue 12
https://doi.org/10.5194/gmd-13-6285-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-6285-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
10 Dec 2020
Development and technical paper |
| 10 Dec 2020
| 10 Dec 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
Yanfei Liang
Institute of Meteorology and Oceanography, National University of
Defense Technology, Nanjing, China
PLA Unit 32145, People's Liberation Army, Xinxiang, China
Zengliang Zang
CORRESPONDING AUTHOR
Institute of Meteorology and Oceanography, National University of
Defense Technology, Nanjing, China
Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and
Fine Mechanics, Chinese Academy of Sciences, Hefei, China
Peng Yan
Meteorological Observation Center, Chinese Meteorological
Administration, Beijing, China
Yiwen Hu
School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing,
China
Yan Zhou
PLA Unit 78127, People's Liberation Army, Beijing, China
Wei You
CORRESPONDING AUTHOR
Institute of Meteorology and Oceanography, National University of
Defense Technology, Nanjing, China
Related authors
Yiwen Hu, Zengliang Zang, Xiaoyan Ma, Yi Li, Yanfei Liang, Wei You, Xiaobin Pan, and Zhijin Li
Atmos. Chem. Phys., 22, 13183–13200, https://doi.org/10.5194/acp-22-13183-2022, https://doi.org/10.5194/acp-22-13183-2022, 2022
Short summary
Short summary
This study developed a four-dimensional variational assimilation (4DVAR) system based on WRF–Chem to optimise SO2 emissions. The 4DVAR system was applied to obtain the SO2 emissions during the early period of the COVID-19 pandemic over China. The results showed that the 4DVAR system effectively optimised emissions to describe the actual changes in SO2 emissions related to the COVID lockdown, and it can thus be used to improve the accuracy of forecasts.
Daichun Wang, Wei You, Zengliang Zang, Xiaobin Pan, Yiwen Hu, and Yanfei Liang
Geosci. Model Dev., 15, 1821–1840, https://doi.org/10.5194/gmd-15-1821-2022, https://doi.org/10.5194/gmd-15-1821-2022, 2022
Short summary
Short summary
This paper presents a 3D variational data assimilation system for aerosol optical properties, including aerosol optical thickness (AOT) retrievals and lidar-based aerosol profiles, which was developed for a size-resolved sectional model in WRF-Chem. To directly assimilate aerosol optical properties, an observation operator based on the Mie scattering theory was designed. The results show that Himawari-8 AOT assimilation can significantly improve model aerosol analyses and forecasts.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
Short summary
Short summary
Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Lang Liu, Xin Long, Yi Li, Zengliang Zang, Fengwen Wang, Yan Han, Zhier Bao, Yang Chen, Tian Feng, and Jinxin Yang
Atmos. Chem. Phys., 25, 1569–1585, https://doi.org/10.5194/acp-25-1569-2025, https://doi.org/10.5194/acp-25-1569-2025, 2025
Short summary
Short summary
This study uses WRF-Chem to assess how meteorological conditions and emission reductions affected fine particulate matter (PM2.5) in the North China Plain (NCP). It highlights regional disparities: in the northern NCP, adverse weather negated emission reduction effects. In contrast, the southern NCP featured a PM2.5 decrease due to favorable weather and emission reductions. The research highlighted the interaction between emissions, meteorology, and PM2.5.
Yiwen Hu, Zengliang Zang, Xiaoyan Ma, Yi Li, Yanfei Liang, Wei You, Xiaobin Pan, and Zhijin Li
Atmos. Chem. Phys., 22, 13183–13200, https://doi.org/10.5194/acp-22-13183-2022, https://doi.org/10.5194/acp-22-13183-2022, 2022
Short summary
Short summary
This study developed a four-dimensional variational assimilation (4DVAR) system based on WRF–Chem to optimise SO2 emissions. The 4DVAR system was applied to obtain the SO2 emissions during the early period of the COVID-19 pandemic over China. The results showed that the 4DVAR system effectively optimised emissions to describe the actual changes in SO2 emissions related to the COVID lockdown, and it can thus be used to improve the accuracy of forecasts.
Daichun Wang, Wei You, Zengliang Zang, Xiaobin Pan, Yiwen Hu, and Yanfei Liang
Geosci. Model Dev., 15, 1821–1840, https://doi.org/10.5194/gmd-15-1821-2022, https://doi.org/10.5194/gmd-15-1821-2022, 2022
Short summary
Short summary
This paper presents a 3D variational data assimilation system for aerosol optical properties, including aerosol optical thickness (AOT) retrievals and lidar-based aerosol profiles, which was developed for a size-resolved sectional model in WRF-Chem. To directly assimilate aerosol optical properties, an observation operator based on the Mie scattering theory was designed. The results show that Himawari-8 AOT assimilation can significantly improve model aerosol analyses and forecasts.
Xiangde Xu, Wenyue Cai, Tianliang Zhao, Xinfa Qiu, Wenhui Zhu, Chan Sun, Peng Yan, Chunzhu Wang, and Fei Ge
Atmos. Chem. Phys., 21, 14131–14139, https://doi.org/10.5194/acp-21-14131-2021, https://doi.org/10.5194/acp-21-14131-2021, 2021
Short summary
Short summary
We found that the structure of atmospheric thermodynamics in the troposphere can be regarded as a strong forewarning signal for variations of surface PM2.5 concentration in heavy air pollution.
Xinghong Cheng, Zilong Hao, Zengliang Zang, Zhiquan Liu, Xiangde Xu, Shuisheng Wang, Yuelin Liu, Yiwen Hu, and Xiaodan Ma
Atmos. Chem. Phys., 21, 13747–13761, https://doi.org/10.5194/acp-21-13747-2021, https://doi.org/10.5194/acp-21-13747-2021, 2021
Short summary
Short summary
We develop a new inversion method of emission sources based on sensitivity analysis and the three-dimension variational technique. The novel explicit observation operator matrix between emission sources and the receptor’s concentrations is established. Then this method is applied to a typical heavy haze episode in North China, and spatiotemporal variations of SO2, NO2, and O3 concentrations simulated using a posterior emission sources are compared with results using an a priori inventory.
Rongmin Ren, Zhanqing Li, Peng Yan, Yuying Wang, Hao Wu, Maureen Cribb, Wei Wang, Xiao'ai Jin, Yanan Li, and Dongmei Zhang
Atmos. Chem. Phys., 21, 9977–9994, https://doi.org/10.5194/acp-21-9977-2021, https://doi.org/10.5194/acp-21-9977-2021, 2021
Short summary
Short summary
We analyzed the effect of the proportion of components making up the chemical composition of aerosols on f(RH) in southern Beijing in 2019. Nitrate played a more significant role in affecting f(RH) than sulfate. The ratio of the sulfate mass fraction to the nitrate mass fraction (mostly higher than ~ 4) was a sign of the deliquescence of aerosol. A piecewise parameterized scheme was proposed, which could better describe deliquescence and reduce uncertainties in simulating aerosol hygroscopicity.
Yuying Wang, Zhanqing Li, Qiuyan Wang, Xiaoai Jin, Peng Yan, Maureen Cribb, Yanan Li, Cheng Yuan, Hao Wu, Tong Wu, Rongmin Ren, and Zhaoxin Cai
Atmos. Chem. Phys., 21, 915–926, https://doi.org/10.5194/acp-21-915-2021, https://doi.org/10.5194/acp-21-915-2021, 2021
Short summary
Short summary
The unexpected increase in surface ozone concentration was found along with the reduced anthropogenic emissions during the 2019 Chinese Spring Festival in Beijing. The enhanced atmospheric oxidation capacity could promote the formation of secondary aerosols, especially sulfate, which offset the decrease in PM2.5 mass concentration. This phenomenon was likely to exist throughout the entire Beijing–Tianjin–Hebei (BTH) region to be a contributing factor to the haze during the COVID-19 lockdown.
Cited articles
Bannister, R. N.: A review of operational methods of variational and
ensemble-variational data assimilation, Q. J. Roy. Meteor. Soc., 143,
607–633, https://doi.org/10.1002/qj.2982, 2017.
Baraskar, A., Bhushan, M., Venkataraman, C., and Cherian, R.: An offline
constrained data assimilation technique for aerosols: Improving GCM
simulations over South Asia using observations from two satellite sensors,
Atmos. Environ., 132, 36–48, https://doi.org/10.1016/j.atmosenv.2016.02.026, 2016.
Benedetti, A., Morcrette, J. J., Boucher, Dethof, O., Engelen, R. J.,
Fisher, M., Flentje, H., Huneeus, N., Jones, L., Kaiser, J. W., Kinne,S.,
Mangold, A., Razinger, M., Simmons, A. J., and Suttie, M.: Aerosol analysis and
forecast in the European Centre for Medium-Range Weather Forecasts
Integrated Forecast System: 2. Data assimilation, J. Geophys. Res., 114,
D13205, https://doi.org/10.1029/2008JD011115, 2009.
Burton, S. P., Ferrare, R. A., Hostetler, C. A., Hair, J. W., Kittaka, C.,
Vaughan, M. A., Obland, M. D., Rogers, R. R., Cook, A. L., Harper, D. B.,
and Remer, L. A.: Using airborne high spectral resolution lidar data to evaluate
combined active plus passive retrievals of aerosol extinction profiles, J.
Geophys. Res.-Atmos., 115, D00H15, https://doi.org/10.1029/2009JD012130, 2010.
Cao, J. J., Wang, Q. Y., Chow, J. C., Watson, J. G., Tie, X. X., Shen, Z.
X., Wang, P., and An, Z. S.: Impacts of aerosol compositions on visibility
impairment in Xi'an, China, Atmos. Environ., 59, 559–566,
https://doi.org/10.1016/j.atmosenv.2012.05.036, 2012a.
Cao, J. J., Shen, Z. X., Chow, J. C., Watson, J. G., Lee, S. C., Tie, X. X.,
Ho, K. F., Wang, G. H., and Han, Y. M.: Winter and Summer PM2.5 Chemical
Compositions in Fourteen Chinese Cities, J. Air. Waste. Manage., 62,
1214–1226, https://doi.org/10.1080/10962247.2012.701193, 2012b.
Carmichael, G. R., Sandu, A., Chai, T., Daescu, D. N., Constantinescu, E.
M., and Tang, Y.: Predicting air quality: Improvements through advanced methods
to integrate models and measurements, J. Comput. Phys., 227, 3540–3571,
https://doi.org/10.1016/j.jcp.2007.02.024, 2008.
Cheng, X. H., Liu, Y. L., Xu, X. D., You, W., Zang, Z. Z., Gao, L. N., Chen,
Y. B., Su, D. B., and Yan, P.: 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,
https://doi.org/10.1016/j.scitotenv.2019.05.186, 2019.
Chen, Y., Li, F. F., Shao, N., Wang, X. P., Wang, Y. M., Hu, X. Y., and Wang,
X.: Aerosol Lidar Intercomparison in the Framework of the MEMO Project. 1.
Lidar Self Calibration and 1st Comparison Observation Calibration Based on
Statistical Analysis Method, in: 2019 International Conference on
Meteorology Observations (ICMO), Chengdu, China, 28–31 December 2019, 1–5,
https://doi.org/10.1109/ICMO49322.2019.9026086, 2019.
Chen, Z. J., Zhang, J. S., Zhang, T. S., Liu, W. Q., and Liu, J. G.: Haze
observations by simultaneous lidar and WPS in Beijing before and during
APEC, 2014, Sci. China. Chem., 58, 1385–1392,
https://doi.org/10.1007/s11426-015-5467-x, 2015.
Ganguly, D., Ginoux, P., Ramaswamy, V., Dubovik, O., Welton, J., Reid, E.
A., and Holben, B. N.: Inferring the composition and concentration of aerosols
by combining AERONET and MPLNET data: Comparison with other measurements and
utilization to evaluate GCM output, J. Geophys. Res.-Atmos. 114, D16203,
https://doi.org/10.1029/2009JD011895, 2009.
Fernald, F. G.: Analysis of atmospheric lidar observations: some comments,
Appl. Optics., 23, 652–653, https://doi.org/10.1364/AO.23.000652, 1984.
Gordon T. D., Prenni A. J., Renfro J. R., McClure, E., Hicks, B., Onasch, T.
B., Freedman, A., McMeeking, G. R., and Chen, P.: Open-path, closed-path and
reconstructed aerosol extinction at a rural site, J. Air. Waste. Manage.,
68, 824–835, https://doi.org/10.1080/10962247.2018.1452801, 2018.
Haywood, J. and Boucher, O.: Estimates of the direct and indirect radiative
forcing due to tropospheric aerosols: A review, Rev. Geophys., 38, 513–543,
https://doi.org/10.1029/1999RG00:0078, 2000.
Jiang, Z. Q., Liu, Z. Q., Wang, T. J., Schwartz, C. S., Lin, H. C., and Jiang,
F.: Probing into the impact of 3-DVAR assimilation of surface PM10
observations over China using process analysis, J. Geophys. Res.-Atmos.,
118, 6738–6749, https://doi.org/10.1002/jgrd.50495, 2013.
Kahnert, M.: Variational data analysis of aerosol species in a regional CTM:
background error covariance constraint and aerosol optical observation
operators, Tellus B, 60, 753–770,
https://doi.org/10.1111/j.1600-0889.2008.00377.x, 2008.
Kim, Y. J., Kim, K. W., Kim, S. D., Lee, B. K., and Han, J. S.: Fine particulate
matter characteristics and its impact on visibility impairment at two urban
sites in Korea: Seoul and Incheon, Atmos. Environ., 40, 593–605,
https://doi.org/10.1016/j.atmosenv.2005.11.076, 2006.
Li, Z., Zang, Z., Li, Q. B., Chao, Y., Chen, D., Ye, Z., Liu, Y., and Liou, K. N.: A three-dimensional variational data assimilation system for multiple aerosol species with WRF/Chem and an application to PM2.5 prediction, Atmos. Chem. Phys., 13, 4265–4278, https://doi.org/10.5194/acp-13-4265-2013, 2013.
Liu, Q. H. and Weng, F. Z.: Advanced doubling-adding method for radiative
transfer in planetary atmosphere, J. Atmos. Sci., 63, 3459–3465,
https://doi.org/10.1175/JAS3808.1, 2006.
Liu, Z., Q. Liu, Q. H., Lin, H. C., Schwartz, C. S., Lee, Y. H., and Wang, T.
J.: Three-dimensional variational assimilationof MODIS aerosol optical
depth: Implementation and application to a dust storm over East Asia, J.
Geophys. Res.-Atmos., 116, D23206, https://doi.org/10.1029/2011JD016159,
2011.
Lowenthal, D. H. and Kumar, N.: PM2.5 Mass and Light Extinction Reconstruction
in IMPROVE, J. Air. Waste. Manage., 53, 1109–1120,
https://doi.org/10.1080/10473289.2003.10466264, 2003.
Matthias, V., Freudenthaler, V., Amodeo, A., Balin, I., Balis, D.,
Bösenberg, J., Chaikovsky, A., Chourdakis, G., Comeron, A., Delaval, A.,
Tomasi, F. D., Eixmann, R., Hågård, A., Komguem, L., Kreipl, S.,
Matthey, R., Rizi, V., Rodrigues, J. A., Wandinger, U., and Wang, X.:
Aerosol lidar intercomparison in the framework of the EARLINET project. 1.
Instruments, Appl. Optics., 43, 961–976,
https://doi.org/10.1364/AO.43.000961, 2004.
Milroy, C., Martucci, G., Lolli, S., Loaec, S., Sauvage, L., Xueref-Remy, I., Lavrič, J. V., Ciais, P., and O'Dowd, C. D.: On the ability of pseudo-operational ground-based light detection and ranging (LIDAR) sensors to determine boundary-layer structure: intercomparison and comparison with in-situ radiosounding, Atmos. Meas. Tech. Discuss., 4, 563–597, https://doi.org/10.5194/amtd-4-563-2011, 2011.
Niu, T., Gong, S. L., Zhu, G. F., Liu, H. L., Hu, X. Q., Zhou, C. H., and Wang, Y. Q.: Data assimilation of dust aerosol observations for the CUACE/dust forecasting system, Atmos. Chem. Phys., 8, 3473–3482, https://doi.org/10.5194/acp-8-3473-2008, 2008.
Parrish, D. F. and Derber, J. C.: The national meteorological center's
spectral statistical-interpolation analysis system, Mon. Wea. Rev., 120,
1747–1763, https://doi.org/10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.co;2, 1992.
Peng, Z., Liu, Z., Chen, D., and Ban, J.: Improving PM2.5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter, Atmos. Chem. Phys., 17, 4837–4855, https://doi.org/10.5194/acp-17-4837-2017, 2017.
Pitchford, M., Maim, W., Schichtel, B., Kumar, N., Lowenthal, D., and Hand, J.:
Revised algorithm for estimating light extinction from IMPROVE particle
speciation data, J. Air. Waste. Manage., 57, 1326–1336,
https://doi.org/10.3155/1047-3289.57.11.1326, 2007.
Raut, J.-C. and Chazette, P.: Assessment of vertically-resolved PM10 from mobile lidar observations, Atmos. Chem. Phys., 9, 8617–8638, https://doi.org/10.5194/acp-9-8617-2009, 2009.
Roy, B., Mathur, R., Gilliland, A. B., and Howard, S. C.: A comparison of
CMAQ-based aerosol properties with IMPROVE, MODIS, and AERONET data, J.
Geophys. Res., 112, D14301, https://doi.org/10.1029/2006JD008085, 2007.
Ryan, P. A., Lowenthal, D., and Kumar, N.: Improved Light Extinction
Reconstruction in Interagency Monitoring of Protected Visual Environments,
J. Air. Waste. Manage., 55, 1751–1759, https://doi.org/10.1080/10473289.2005.10464768, 2005.
Saide, P. E., Carmichael, G. R., Liu, Z., Schwartz, C. S., Lin, H. C., da Silva, A. M., and Hyer, E.: 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 forecasts, Atmos. Chem. Phys., 13, 10425–10444, https://doi.org/10.5194/acp-13-10425-2013, 2013.
Sandu, A. and Chai, T.: Chemical Data Assimilation – An Overview,
Atmos., 2, 426–463, https://doi.org/10.3390/atmos2030426, 2011.
Sekiyama, T. T., Tanaka, T. Y., Shimizu, A., and Miyoshi, T.: Data assimilation of CALIPSO aerosol observations, Atmos. Chem. Phys., 10, 39–49, https://doi.org/10.5194/acp-10-39-2010, 2010.
Schwartz, C. S., Liu, Z. Q., Lin, H. C., and McKeen, S. A.: Simultaneous
three-dimensional variational assimilation of surface fine particulate
matter and MODIS aerosol optical depth, J. Geophys. Res.-Atmos., 117, D13202, https://doi.org/10.1029/2011JD017383, 2012.
Sugimoto, N., Matsui, I., Shimizu, A., Nishizawa, T., and Yoon, S. C.: Lidar
network observations of tropospheric aerosols, Proc. SPIE, 7153,
https://doi.org/10.1117/12.806540, 2008.
Sugimoto, N. and Huang Z. W.: Lidar methods for observing mineral dust, J.
Meteor. Res., 28, 173–184, https://doi.org/10.1007/s13351-014-3068-9, 2014.
Tang, Y., Pagowski, M., Chai, T., Pan, L., Lee, P., Baker, B., Kumar, R., Delle Monache, L., Tong, D., and Kim, H.-C.: 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., 10, 4743–4758, https://doi.org/10.5194/gmd-10-4743-2017, 2017.
Tao, J., Ho, K. F., Chen, L., Zhu, L., Han, J., and Xu, Z.: Effect of chemical
composition of PM2.5 on visibility in Guangzhou, China, 2007 spring,
Particuology, 7, 68–75, https://doi.org/10.1016/j.partic.2008.11.002,
2009.
Tao, J., Cao J. J., Zhang, R. J., Zhu, L. H., Zang, T., Shi, S., Chan, C.
Y.: Reconstructed light extinction coefficients using chemical compositions
of PM2.5 in winter in urban Guangzhou, China, Adv. Atmos. Sci., 29, 359–368,
https://doi.org/10.1007/s00376-011-1045-0, 2012.
Tao, J., Zhang, L. M., Ho, K. F., Zhang, R. J., Lin, Z. J., Zhang, Z. S.,
Lin, M., Cao, J. J., Liu, S. X., and Wang, G. H.: Impact of PM2.5 chemcial
compositions on aerosol light scattering in Guangzhou-the largest megacity
in South China, Atmos. Res., 135–136, 48–58,
https://doi.org/10.1016/j.atmosres.2013.08.015, 2014.
Tesche, M., Ansmann, A., Müller, D., Althausen, D., and Engelman, R.:
Particle backscatter, extinction, and lidar ratio profiling with Raman lidar
in south and north China, Appl. Optics., 46, 6302–6308,
https://doi.org/10.1364/AO.46.006302, 2007.
Tombette, M., Mallet, V., and Sportisse, B.: PM10 data assimilation over Europe with the optimal interpolation method, Atmos. Chem. Phys., 9, 57–70, https://doi.org/10.5194/acp-9-57-2009, 2009.
Wang, D., You, W., Zang, Z., Pan, X., He, H., and Liang, Y.: A three-dimensional
variational data assimilation system for a size-resolved aerosol model:
Implementation and application for particulate matter and gaseous pollutant
forecasts across China, Sci. China. Earth. Sci., 63, 1366–1380,
https://doi.org/10.1007/s11430-019-9601-4, 2020.
Wang, Y., Sartelet, K. N., Bocquet, M., and Chazette, P.: Assimilation of ground versus lidar observations for PM10 forecasting, Atmos. Chem. Phys., 13, 269–283, https://doi.org/10.5194/acp-13-269-2013, 2013.
Wang, Y., Sartelet, K. N., Bocquet, M., and Chazette, P.: Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign, Atmos. Chem. Phys., 14, 3511–3532, https://doi.org/10.5194/acp-14-3511-2014, 2014a.
Wang, Y., Sartelet, K. N., Bocquet, M., Chazette, P., Sicard, M., D'Amico, G., Léon, J. F., Alados-Arboledas, L., Amodeo, A., Augustin, P., Bach, J., Belegante, L., Binietoglou, I., Bush, X., Comerón, A., Delbarre, H., García-Vízcaino, D., Guerrero-Rascado, J. L., Hervo, M., Iarlori, M., Kokkalis, P., Lange, D., Molero, F., Montoux, N., Muñoz, A., Muñoz, C., Nicolae, D., Papayannis, A., Pappalardo, G., Preissler, J., Rizi, V., Rocadenbosch, F., Sellegri, K., Wagner, F., and Dulac, F.: Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin, Atmos. Chem. Phys., 14, 12031–12053, https://doi.org/10.5194/acp-14-12031-2014, 2014b.
Wiscombe, W. J.: Improved MIE scattering algorithms, Appl. Opt.,
19, 1505–1509, https://doi.org/10.1364/AO.19.001505, 1980.
Wu, J. B., Xu, J. M., Pagowski, M., Geng, F. H., Gu, S. Q., Zhou, G. Q.,
Xie, Y., and Yu, Z. Q.: Modeling study of a severe aerosol pollution event in
December 2013 over Shanghai China:An application of chemical data
assimilation, Particuology, 20, 41–51, https://doi.org/10.1016/j.partic.2014.10.008, 2015.
Xia, X. L., Min, J. Z., Shen, F. F., Wang, Y. B., and Yang, C.: Aerosol data
assimilation using data from Fengyun-3A and MODIS: application to a dust
storm over East Asia in 2011, Adv. Atmos. Sci., 36, 1–14,
https://doi.org/10.1007/s00376-018-8075-9, 2019.
Young, S. A. and Vaughan, M. A.: The retrieval of profiles of particulate
extinction from Cloud-Aerosol Lidar Infrared Pathfinder Satellite
Observations (CALIPSO) data: Algorithm description, J. Atmos. Ocean. Tech.,
26, 1105–1119, https://doi.org/10.1175/2008JTECHA1221.1, 2009.
Yumimoto, K., Murakami, H., Tanaka, T. Y., Sekiyama, T. T., Ogi, A., and Maki,
T.: Forecasting of Asian dust storm that occurred on May 10–13, 2011, using
an ensemble-based data assimilation system, Particuology, 28, 121–130,
https://doi.org/10.1016/j.partic.2015.09.001, 2015.
Yumimoto, K., Nagao, T. M., Kikuchi, M., Sekiyama, T. T., Murakami, H.,
Tanaka, T. Y., Ogi, A., Irie, H., Khatri, P., Okumura, H., Arai, K., Morino,
I., Uchino, O., and Maki, T.: Aerosol data assimilation using data from
Himawari-8, a next-generation geostationary meteorological satellite,
Geophys. Res. Lett., 43, 5886–5894, https://doi.org/10.1002/2016GL069298,
2016.
Zhang, S., Zhou, Z. M., Ye, C. L., Shi, J. B., Wang, P., and Liu, D.: Analysis
of a Pollution Transmission Process in Hefei City Based on Mobile Lidar, in:
EPJ Web Conf., 237, 02006, https://doi.org/10.1051/epjconf/202023702006,
2020.
Zang, Z. Z., Li, Z. J., Pan, X. B., Hao, Z. L., and You, W.: Aerosol data
assimilation and forecasting experiments using aircraft and surface
observations during CalNex, Tellus B, 68, 1–14,
https://doi.org/10.3402/tellusb.v68.29812, 2016.
Zou, X., Vandenberghe, F., Pondeca, M., and Kuo, Y. H.: Introduction to Adjoint
Techniques and the MM5 Adjoint Modeling System, NCAR Technical Note
NCAR/TN-435-STR 122, https://doi.org/10.5065/D6F18WNM, 1997.
