Articles | Volume 26, issue 5
https://doi.org/10.5194/acp-26-3489-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-3489-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Measurement report: Observational analysis of mode-dependent fog droplet size distribution evolution and improved parameterization using segmented gamma and lognormal fitting
Jingwen Zhang
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China
CMAWMC-NUIST Innovation Research Institute of Weather Modification, Nanjing University of Information Science and Technology, Nanjing, 210044, China
China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing, 210044, China
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China
CMAWMC-NUIST Innovation Research Institute of Weather Modification, Nanjing University of Information Science and Technology, Nanjing, 210044, China
China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Zhenya An
College of Meteorology and Oceanology, National University of Defense Technology, Changsha, 410000, China
Jingjing Lv
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China
CMAWMC-NUIST Innovation Research Institute of Weather Modification, Nanjing University of Information Science and Technology, Nanjing, 210044, China
China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Dan Xu
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China
CMAWMC-NUIST Innovation Research Institute of Weather Modification, Nanjing University of Information Science and Technology, Nanjing, 210044, China
China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Related authors
No articles found.
Zhuoxuan Shen, Xiaoli Liu, Yan Li, Jingyuan Xiong, Kerui Min, Hengjia Cai, and Nan Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-3034, https://doi.org/10.5194/egusphere-2025-3034, 2025
Preprint archived
Short summary
Short summary
This research combines aircraft observations and numerical simulations, particularly using the McSnow model, to track ice particle growth and evolution in stratocumulus clouds. Our findings show that the maximum rime density in convective regions reaches 0.5 to 0.55 g cm-3, leading to a thicker melting layer (400 to 500 m thicker than in stratiform regions). The study also identifies key relationships between spectral parameters, offering insights into cloud microphysical parameterization.
Yi Li, Xiaoli Liu, and Hengjia Cai
Atmos. Chem. Phys., 24, 13525–13540, https://doi.org/10.5194/acp-24-13525-2024, https://doi.org/10.5194/acp-24-13525-2024, 2024
Short summary
Short summary
The influence of different aerosol modes on cloud processes remains controversial. We modified the aerosol spectra and concentrations to simulate a warm stratiform cloud process in Jiangxi, China, using the WRF-SBM scheme. Research shows that different aerosol spectra have diverse effects on cloud droplet spectra, cloud development, and the correlation between dispersion (ε) and cloud physics quantities. Compared to cloud droplet concentration, ε is more sensitive to the volume radius.
Yi Li and Xiaoli Liu
EGUsphere, https://doi.org/10.5194/egusphere-2024-1592, https://doi.org/10.5194/egusphere-2024-1592, 2024
Preprint withdrawn
Short summary
Short summary
We studied how coarse-mode aerosols affect macro and micro characteristics in Jiangxi, China using a numerical model. Results indicate that higher coarse-mode aerosol concentrations (Ncm) accelerate cloud development. Yet, the response of cloud microphysics to Ncm changes is not linear, depending on the combined effects of updraft strength and cloud droplet activation. This research improves our understanding of coarse-mode aerosols' impact on cloud formation and weather.
Zhenya An and Xiaoli Liu
EGUsphere, https://doi.org/10.5194/egusphere-2023-2516, https://doi.org/10.5194/egusphere-2023-2516, 2023
Preprint archived
Short summary
Short summary
The study of warm fog helps to provide some theoretical support for cloud microphysics observational studies and scientific understanding. In this study, the microphysical characteristics of fog were explored using data sampled by the instrument FM-120 and WPS-1000XP, and it was found that the relationship between the microphysical quantities of fog is strongly influenced by the collision process, and the size of the fog droplets in turn affects the collision process between the droplets.
Naifu Shao, Chunsong Lu, Xingcan Jia, Yuan Wang, Yubin Li, Yan Yin, Bin Zhu, Tianliang Zhao, Duanyang Liu, Shengjie Niu, Shuxian Fan, Shuqi Yan, and Jingjing Lv
Atmos. Chem. Phys., 23, 9873–9890, https://doi.org/10.5194/acp-23-9873-2023, https://doi.org/10.5194/acp-23-9873-2023, 2023
Short summary
Short summary
Fog is an important meteorological phenomenon that affects visibility. Aerosols and the planetary boundary layer (PBL) play critical roles in the fog life cycle. In this study, aerosol-induced changes in fog properties become more remarkable in the second fog (Fog2) than in the first fog (Fog1). The reason is that aerosol–cloud interaction (ACI) delays Fog1 dissipation, leading to the PBL meteorological conditions being more conducive to Fog2 formation and to stronger ACI in Fog2.
Zefeng Zhang, Hengnan Guo, Hanqing Kang, Jing Wang, Junlin An, Xingna Yu, Jingjing Lv, and Bin Zhu
Atmos. Meas. Tech., 15, 7259–7264, https://doi.org/10.5194/amt-15-7259-2022, https://doi.org/10.5194/amt-15-7259-2022, 2022
Short summary
Short summary
In this study, we first analyze the relationship between the visibility, the extinction coefficient, and atmospheric compositions. Then we propose to use the harmonic average of visibility data as the average visibility, which can better reflect changes in atmospheric extinction coefficients and aerosol concentrations. It is recommended to use the harmonic average visibility in the studies of climate change, atmospheric radiation, air pollution, environmental health, etc.
Cited articles
Akaike, H.: A new look at the statistical model identification, IEEE Trans. Automat. Contr., 19, 716–723, https://doi.org/10.1109/TAC.1974.1100705, 1974.
Boudala, F. S., Wu, D., Isaac, G. A., and Gultepe, I.: Seasonal and Microphysical Characteristics of Fog at a Northern Airport in Alberta, Canada, Remote Sens., 14, 4865, https://doi.org/10.3390/rs14194865, 2022.
Boutle, I., Price, J., Kudzotsa, I., Kokkola, H., and Romakkaniemi, S.: Aerosol–fog interaction and the transition to well-mixed radiation fog, Atmos. Chem. Phys., 18, 7827–7840, https://doi.org/10.5194/acp-18-7827-2018, 2018.
Boutle, I., Angevine, W., Bao, J.-W., Bergot, T., Bhattacharya, R., Bott, A., Ducongé, L., Forbes, R., Goecke, T., Grell, E., Hill, A., Igel, A. L., Kudzotsa, I., Lac, C., Maronga, B., Romakkaniemi, S., Schmidli, J., Schwenkel, J., Steeneveld, G.-J., and Vié, B.: Demistify: a large-eddy simulation (LES) and single-column model (SCM) intercomparison of radiation fog, Atmos. Chem. Phys., 22, 319–333, https://doi.org/10.5194/acp-22-319-2022, 2022.
Brenguier, J.-L., Pawlowska, H., Schüller, L., Preusker, R., Fischer, J., and Fouquart, Y.: Radiative Properties of Boundary Layer Clouds: Droplet Effective Radius versus Number Concentration, J. Atmos. Sci., 57, 803–821, https://doi.org/10.1175/1520-0469(2000)057<0803:RPOBLC>2.0.CO;2, 2000.
Brown, R.: A numerical study of radiation fog with an explicit formulation of the microphysics, Q. J. Roy. Meteor. Soc., 106, 781–802, https://doi.org/10.1002/qj.49710645010, 1980.
Chakrabarti, A. and Ghosh, J. K.: AIC, BIC and Recent Advances in Model Selection, in: Philosophy of Statistics, vol. 7, edited by: Bandyopadhyay, P. S. and Forster, M. R., North-Holland, Amsterdam, 583–605, https://doi.org/10.1016/B978-0-444-51862-0.50018-6, 2011.
Chen, J., Liu, Y., Zhang, M., and Peng, Y.: New understanding and quantification of the regime dependence of aerosol-cloud interaction for studying aerosol indirect effects, Geophys. Res. Lett., 43, 1780–1787, https://doi.org/10.1002/2016GL067683, 2016.
Copernicus Climate Change Service: ERA5 hourly data on single levels from 1940 to present, last access: 25 February 2026, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023a.
Copernicus Climate Change Service: ERA5 hourly data on pressure levels from 1940 to present, last access: 25 February 2026, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023b.
Cui, C., Bao, Y., Yuan, C., Li, Z., and Zong, C.: Comparison of the performances between the WRF and WRF-LES models in radiation fog – A case study, Atmos. Res., 226, 76–86, https://doi.org/10.1016/j.atmosres.2019.04.003, 2019.
Dorman, C. E., Hoch, S. W., Gultepe, I., Wang, Q., Yamaguchi, R. T., Fernando, H. J. S., and Krishnamurthy, R.: Large-Scale Synoptic Systems and Fog During the C-FOG Field Experiment, Bound.-Lay. Meteorol., 181, 171–202, https://doi.org/10.1007/s10546-021-00641-1, 2021.
Elias, T., Haeffelin, M., Drobinski, P., Gomes, L., Rangognio, J., Bergot, T., Chazette, P., Raut, J.-C., and Colomb, M.: Particulate contribution to extinction of visible radiation: Pollution, haze, and fog, Atmos. Res., 92, 443–454, https://doi.org/10.1016/j.atmosres.2009.01.006, 2009.
Elias, T., Dupont, J.-C., Hammer, E., Hoyle, C. R., Haeffelin, M., Burnet, F., and Jolivet, D.: Enhanced extinction of visible radiation due to hydrated aerosols in mist and fog, Atmos. Chem. Phys., 15, 6605–6623, https://doi.org/10.5194/acp-15-6605-2015, 2015.
Friedlein, M. T.: DENSE FOG CLIMATOLOGY, B. Am. Meteorol. Soc., 85, 515–517, 2004.
Ge, P., Zhang, Y., Fan, S., Wang, Y., Wu, H., Wang, X., and Zhang, S.: Observational study of microphysical and chemical characteristics of size-resolved fog in different regional backgrounds in China, Sci. Total Environ., 950, 175329, https://doi.org/10.1016/j.scitotenv.2024.175329, 2024.
Gultepe, I. and Milbrandt, J. A.: Microphysical Observations and Mesoscale Model Simulation of a Warm Fog Case during FRAM Project, in: Fog and Boundary Layer Clouds: Fog Visibility and Forecasting, Birkhäuser Basel, 1161–1178, https://doi.org/10.1007/978-3-7643-8419-7_4, 2007.
Gultepe, I. and Milbrandt, J. A.: Probabilistic Parameterizations of Visibility Using Observations of Rain Precipitation Rate, Relative Humidity, and Visibility, J. Appl. Meteorol. Clim., 49, 36–46, https://doi.org/10.1175/2009JAMC1927.1, 2010.
Gultepe, I., Müller, M. D., and Boybeyi, Z.: A New Visibility Parameterization for Warm-Fog Applications in Numerical Weather Prediction Models, J. Appl. Meteorol. Clim., 45, 1469–1480, https://doi.org/10.1175/JAM2423.1, 2006.
Gultepe, I., Tardif, R., Michaelides, S. C., Cermak, J., Bott, A., Bendix, J., Müller, M. D., Pagowski, M., Hansen, B., Ellrod, G., Jacobs, W., Toth, G., and Cober, S. G.: Fog Research: A Review of Past Achievements and Future Perspectives, Pure Appl. Geophys., 164, 1121–1159, https://doi.org/10.1007/s00024-007-0211-x, 2007.
Gultepe, I., Pearson, G., Milbrandt, J. A., Hansen, B., Platnick, S., Taylor, P., Gordon, M., Oakley, J. P., and Cober, S. G.: The Fog Remote Sensing and Modeling Field Project, B. Am. Meteorol. Soc., 90, 341–360, https://doi.org/10.1175/2008BAMS2354.1, 2009.
Gultepe, I., Kuhn, T., Pavolonis, M., Calvert, C., Gurka, J., Heymsfield, A. J., Liu, P. S. K., Zhou, B., Ware, R., Ferrier, B., Milbrandt, J., and Bernstein, B.: Ice Fog in Arctic During FRAM–Ice Fog Project: Aviation and Nowcasting Applications, B. Am. Meteorol. Soc., 95, 211–226, https://doi.org/10.1175/BAMS-D-11-00071.1, 2014.
Gultepe, I., Heymsfield, A. J., Fernando, H. J. S., Pardyjak, E., Dorman, C. E., Wang, Q., Creegan, E., Hoch, S. W., Flagg, D. D., Yamaguchi, R., Krishnamurthy, R., Gaberšek, S., Perrie, W., Perelet, A., Singh, D. K., Chang, R., Nagare, B., Wagh, S., and Wang, S.: A Review of Coastal Fog Microphysics During C-FOG, Bound.-Lay. Meteorol., 181, 227–265, https://doi.org/10.1007/s10546-021-00659-5, 2021.
Guo, L., Guo, X., Fang, C., and Zhu, S.: Observation analysis on characteristics of formation, evolution and transition of a long-lasting severe fog and haze episode in North China, Sci. China Earth Sci., 58, 329–344, https://doi.org/10.1007/s11430-014-4924-2, 2015.
Haeffelin, M., Bergot, T., Elias, T., Tardif, R., Carrer, D., Chazette, P., Colomb, M., Drobinski, P., Dupont, E., Dupont, J.-C., Gomes, L., Musson-Genon, L., Pietras, C., Plana-Fattori, A., Protat, A., Rangognio, J., Raut, J.-C., Rémy, S., Richard, D., Sciare, J., and Zhang, X.: Parisfog: Shedding new Light on Fog Physical Processes, B. Am. Meteorol. Soc., 91, 767–783, https://doi.org/10.1175/2009BAMS2671.1, 2010.
Hammer, E., Gysel, M., Roberts, G. C., Elias, T., Hofer, J., Hoyle, C. R., Bukowiecki, N., Dupont, J.-C., Burnet, F., Baltensperger, U., and Weingartner, E.: Size-dependent particle activation properties in fog during the ParisFog 2012/13 field campaign, Atmos. Chem. Phys., 14, 10517–10533, https://doi.org/10.5194/acp-14-10517-2014, 2014.
Jia, X., Quan, J., Zheng, Z., Liu, X., Liu, Q., He, H., and Liu, Y.: Impacts of Anthropogenic Aerosols on Fog in North China Plain, J. Geophys. Res.-Atmos., 124, 252–265, https://doi.org/10.1029/2018JD029437, 2019.
Kessler, E.: On the Distribution and Continuity of Water Substance in Atmospheric Circulations, in: On the Distribution and Continuity of Water Substance in Atmospheric Circulations, edited by: Kessler, E., American Meteorological Society, Boston, MA, 1–84, https://doi.org/10.1007/978-1-935704-36-2_1, 1969.
Koenig, L. R.: Numerical Experiments Pertaining to Warm-Fog Clearing, Mon. Weather Rev., 99, 227–241, https://doi.org/10.1175/1520-0493(1971)099<0227:NEPTWC>2.3.CO;2, 1971.
Koračin, D., Dorman, C. E., Lewis, J. M., Hudson, J. G., Wilcox, E. M., and Torregrosa, A.: Marine fog: A review, Atmos. Res., 143, 142–175, https://doi.org/10.1016/j.atmosres.2013.12.012, 2014.
Khain, A. P., Beheng, K. D., Heymsfield, A., Korolev, A., Krichak, S. O., Levin, Z., Pinsky, M., Phillips, V., Prabhakaran, T., Teller, A., van den Heever, S. C., and Yano, J.-I.: Representation of microphysical processes in cloud-resolving models: Spectral (bin) microphysics versus bulk parameterization, Rev. Geophys., 53, 247–322, https://doi.org/10.1002/2014RG000468, 2015.
Kunkel, B. A.: Microphysical properties of fog at Otis AFB, Air Force Geophysics Laboratories, Air Force Systems Command, United States, Report no. AFGL-TR-82-0026, 1982.
Lakra, K. and Avishek, K.: A review on factors influencing fog formation, classification, forecasting, detection and impacts, Rend. Lincei-Sci. Fis., 33, 319–353, https://doi.org/10.1007/s12210-022-01060-1, 2022.
Li, J., Wang, X., Chen, J., Zhu, C., Li, W., Li, C., Liu, L., Xu, C., Wen, L., Xue, L., Wang, W., Ding, A., and Herrmann, H.: Chemical composition and droplet size distribution of cloud at the summit of Mount Tai, China, Atmos. Chem. Phys., 17, 9885–9896, https://doi.org/10.5194/acp-17-9885-2017, 2017.
Liu, Y. and Daum, P. H.: Indirect warming effect from dispersion forcing, Nature, 419, 580–581, https://doi.org/10.1038/419580a, 2002.
Lu, C., Liu, Y., Niu, S., Zhao, L., Yu, H., and Cheng, M.: Examination of microphysical relationships and corresponding microphysical processes in warm fogs, Acta Meteorol. Sin., 27, 832–848, https://doi.org/10.1007/s13351-013-0610-0, 2013.
Lu, C., Liu, Y., Yum, S. S., Chen, J., Zhu, L., Gao, S., Yin, Y., Jia, X., and Wang, Y.: Reconciling Contrasting Relationships Between Relative Dispersion and Volume-Mean Radius of Cloud Droplet Size Distributions, J. Geophys. Res.-Atmos., 125, e2019JD031868, https://doi.org/10.1029/2019JD031868, 2020.
Martinet, P., Cimini, D., Burnet, F., Ménétrier, B., Michel, Y., and Unger, V.: Improvement of numerical weather prediction model analysis during fog conditions through the assimilation of ground-based microwave radiometer observations: a 1D-Var study, Atmos. Meas. Tech., 13, 6593–6611, https://doi.org/10.5194/amt-13-6593-2020, 2020.
Mazoyer, M., Lac, C., Thouron, O., Bergot, T., Masson, V., and Musson-Genon, L.: Large eddy simulation of radiation fog: impact of dynamics on the fog life cycle, Atmos. Chem. Phys., 17, 13017–13035, https://doi.org/10.5194/acp-17-13017-2017, 2017.
Mazoyer, M., Burnet, F., and Denjean, C.: Experimental study on the evolution of droplet size distribution during the fog life cycle, Atmos. Chem. Phys., 22, 11305–11321, https://doi.org/10.5194/acp-22-11305-2022, 2022.
Meyer, M. B., Jiusto, J. E., and Lala, G. G.: Measurements of visual range and radiation-fog (haze) microphysics, J. Atmos. Sci., 37, 622–629, 1980.
Nelli, N., Francis, D., Abida, R., Fonseca, R., Masson, O., and Bosc, E.: In-situ measurements of fog microphysics: Visibility parameterization and estimation of fog droplet sedimentation velocity, Atmos. Res., 309, 107570, https://doi.org/10.1016/j.atmosres.2024.107570, 2024.
Niu, S., Lu, C., Liu, Y., Zhao, L., Lü, J., and Yang, J.: Analysis of the microphysical structure of heavy fog using a droplet spectrometer: A case study, Adv. Atmos. Sci., 27, 1259–1275, https://doi.org/10.1007/s00376-010-8192-6, 2010.
Niu, S. J., Liu, D. Y., Zhao, L. J., Lu, C. S., Lü, J. J., and Yang, J.: Summary of a 4-Year Fog Field Study in Northern Nanjing, Part 2: Fog Microphysics, Pure Appl. Geophys., 169, 1137–1155, https://doi.org/10.1007/s00024-011-0344-9, 2012.
Payra, S. and Mohan, M.: Multirule Based Diagnostic Approach for the Fog Predictions Using WRF Modelling Tool, Adv. Meteorol., 2014, 456065, https://doi.org/10.1155/2014/456065, 2014.
Peterka, A., Thompson, G., and Geresdi, I.: Numerical prediction of fog: A novel parameterization for droplet formation, Q. J. Roy. Meteor. Soc., 150, 2203–2222, https://doi.org/10.1002/qj.4704, 2024.
Pinnick, R. G., Hoihjelle, D. L., Fernandez, G., Stenmark, E. B., Lindberg, J. D., Hoidale, G. B., and Jennings, S. G.: Vertical Structure in Atmospheric Fog and Haze and Its Effects on Visible and Infrared Extinction, Journal of Atmospheric Sciences, 35, 2020–2032, https://doi.org/10.1175/1520-0469(1978)035<2020:VSIAFA>2.0.CO;2, 1978.
Price, J. D.: On the Formation and Development of Radiation Fog: An Observational Study, Bound.-Lay. Meteorol., 172, 167–197, https://doi.org/10.1007/s10546-019-00444-5, 2019.
Roach, W. T.: On the effect of radiative exchange on the growth by condensation of a cloud or fog droplet, Q. J. Roy. Meteor. Soc., 102, 361–372, https://doi.org/10.1002/qj.49710243207, 1976.
Roach, W. T., Brown, R., Caughey, S. J., Garland, J. A., and Readings, C. J.: The physics of radiation fog: I – a field study, Q. J. Roy. Meteor. Soc., 102, 313–333, https://doi.org/10.1002/qj.49710243204, 1976.
Román-Cascón, C., Steeneveld, G. J., Yagüe, C., Sastre, M., Arrillaga, J. A., and Maqueda, G.: Forecasting radiation fog at climatologically contrasting sites: evaluation of statistical methods and WRF, Q. J. Roy. Meteor. Soc., 142, 1048–1063, https://doi.org/10.1002/qj.2708, 2016.
Ryznar, E.: Advection-radiation fog near lake Michigan, Atmos. Environ., 11, 427–430, https://doi.org/10.1016/0004-6981(77)90004-X, 1977.
Sampurno Bruijnzeel, L., Eugster, W., and Burkard, R.: Fog as a Hydrologic Input, in: Encyclopedia of Hydrological Sciences, John Wiley & Sons, https://doi.org/10.1002/0470848944.hsa041, 2006.
Schwarz, G.: Estimating the Dimension of a Model, Ann. Stat., 6, 461–464, 1978.
Shao, N., Lu, C., Jia, X., Wang, Y., Li, Y., Yin, Y., Zhu, B., Zhao, T., Liu, D., Niu, S., Fan, S., Yan, S., and Lv, J.: Radiation fog properties in two consecutive events under polluted and clean conditions in the Yangtze River Delta, China: a simulation study, Atmos. Chem. Phys., 23, 9873–9890, https://doi.org/10.5194/acp-23-9873-2023, 2023.
Shen, C., Zhao, C., Ma, N., Tao, J., Zhao, G., Yu, Y., and Kuang, Y.: Method to Estimate Water Vapor Supersaturation in the Ambient Activation Process Using Aerosol and Droplet Measurement Data, J. Geophys. Res.-Atmos., 123, 10606–10619, https://doi.org/10.1029/2018JD028315, 2018.
Stephens, G. L.: Radiation Profiles in Extended Water Clouds. I: Theory, J. Atmos. Sci., 35, 2111–2122, https://doi.org/10.1175/1520-0469(1978)035<2111:RPIEWC>2.0.CO;2, 1978.
Stephens, G. L.: The Parameterization of Radiation for Numerical Weather Prediction and Climate Models, Mon. Weather Rev., 112, 826–867, https://doi.org/10.1175/1520-0493(1984)112<0826:TPORFN>2.0.CO;2, 1984.
Stewart, D. A. and Essenwanger, O. M.: A survey of fog and related optical propagation characteristics, Rev. Geophys., 20, 481–495, https://doi.org/10.1029/RG020i003p00481, 1982.
Symonds, M. R. E. and Moussalli, A.: A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike's information criterion, Behav. Ecol. Sociobiol., 65, 13–21, https://doi.org/10.1007/s00265-010-1037-6, 2011.
Tampieri, F. and Tomasi, C.: Size distribution models of fog and cloud droplets in terms of the modified gamma function, Tellus, 28, 333–347, https://doi.org/10.3402/tellusa.v28i4.10300, 1976.
Tudor, M.: Impact of horizontal diffusion, radiation and cloudiness parameterization schemes on fog forecasting in valleys, Meteorol. Atmos. Phys., 108, 57–70, https://doi.org/10.1007/s00703-010-0084-x, 2010.
Turton, J. D. and Brown, R.: A comparison of a numerical model of radiation fog with detailed observations, Q. J. Roy. Meteor. Soc., 113, 37–54, https://doi.org/10.1002/qj.49711347504, 1987.
Twomey, S. and Bohren, C. F.: Simple Approximations for Calculations of Absorption in Clouds, J. Atmos. Sci., 37, 2086–2095, https://doi.org/10.1175/1520-0469(1980)037<2086:SAFCOA>2.0.CO;2, 1980.
Wang, H., Zhang, Z., Liu, D., Zhu, Y., Zhang, X., and Yuan, C.: Study on a Large-Scale Persistent Strong Dense Fog Event in Central and Eastern China, Adv. Meteorol., 2020, 8872334, https://doi.org/10.1155/2020/8872334, 2020.
Wang, Y., Niu, S., Lv, J., Lu, C., Xu, X., Wang, Y., Ding, J., Zhang, H., Wang, T., and Kang, B.: A New Method for Distinguishing Unactivated Particles in Cloud Condensation Nuclei Measurements: Implications for Aerosol Indirect Effect Evaluation, Geophys. Res. Lett., 46, 14185–14194, https://doi.org/10.1029/2019GL085379, 2019.
Wang, Y., Niu, S., Lu, C., Lv, J., Zhang, J., Zhang, H., Zhang, S., Shao, N., Sun, W., Jin, Y., and Song, Q.: Observational study of the physical and chemical characteristics of the winter radiation fog in the tropical rainforest in Xishuangbanna, China, Sci. China Earth Sci., 64, 1982–1995, https://doi.org/10.1007/s11430-020-9766-4, 2021.
Wendisch, M., Mertes, S., Heintzenberg, J., Wiedensohler, A., Schell, D., Wobrock, W., Frank, G., Martinsson, B. G., Fuzzi, S., and Orsi, G.: Drop size distribution and LWC in Po Valley fog, Contributions to Atmospheric Physics, 71, 87–100, 1998.
Willett, H. C.: Fog and Haze, their Causes, Distribution, and Forecasting, Mon. Weather Rev., 56, 435–468, https://doi.org/10.1175/1520-0493(1928)56<435:FAHTCD>2.0.CO;2, 1928.
Zhang, J.: Fog droplet size distribution for Fog Case 1-1, Fog Case 4, Fog Case 10, Fog Case 18, and Fog Case 20, Zenodo [data set], https://doi.org/10.5281/zenodo.18552099, 2026.
Zhang, J., Yang, Y., and Ding, J.: Information criteria for model selection, WIREs Comput. Stat., 15, e1607, https://doi.org/10.1002/wics.1607, 2023.
Short summary
Fog affects human activities and regional climate, yet our understanding of it remains limited. Based on 27 fog events in Nanjing, this study shows that droplet size distributions evolve among unimodal, bimodal, and trimodal during the fog lifecycle. A new segmented fitting significantly improves the estimation of key microphysical and radiative parameters. These results improve our understanding of fog microphysics and droplet size distribution parameterizations in weather and climate models.
Fog affects human activities and regional climate, yet our understanding of it remains limited....
Altmetrics
Final-revised paper
Preprint