Skip to main content
Log in

WRF Model Prediction of a Dense Fog Event Occurred During the Winter Fog Experiment (WIFEX)

  • Published:
Pure and Applied Geophysics Aims and scope Submit manuscript

Abstract

In this study, the sensitivity of the Weather Research and Forecasting (WRF) model to simulate the life cycle of a dense fog event that occurred on 23–24 January 2016 is evaluated using different model configurations. For the first time, intensive observational periods (IOPs) were made during the unique winter fog experiment (WIFEX) that took place over Delhi, India, where air quality is serious during the winter months. The multiple sensitivity experiments to evaluate the WRF model performance included parameters such as initial model and boundary conditions, vertical resolution in the lower boundary layer (BL), and the planetary BL (PBL) physical parameterizations. In addition, the model sensitivity was tested using various configurations that included domain size and grid resolution. Results showed that simulations with a high number of vertical levels within the lower PBL height (i.e., 10 levels below 300 m) simulated the accurate timing of fog formation, development, and dissipation. On the other hand, simulations with less vertical levels in the PBL captured only the mature physical characteristics of the fog cycle. A comparison of six local PBL schemes showed little variation in the onset of fog life cycle in comparison to observations of visibility. However, comparisons of observations with thermodynamical values such as 2-m temperature and longwave radiation showed poor relationships. Overall, quasi-normal scale elimination (QNSE) and MYNN 2.5 PBL schemes simulated the complete fog life cycle correctly with high liquid water content (LWC; 0.5/0.35 g m−3), while other schemes only responded during the mature phase.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Aditi, S., George, J. P., Gupta, M. D., Rajagopal, E. N., & Basu, S. (2015). Verification of visibility forecasts from NWP model with satellite and surface observations. Mausam, 66(3), 603–616.

    Google Scholar 

  • Badarinath, K. V. S., Shailesh, K. K., Anu Rani, S., & Roy, P. S. (2009). Fog over Indo-Gangetic plains: A study using multisatellite data and ground observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2(3), 185–195.

    Article  Google Scholar 

  • Balzarini, A., Angelini, F., Ferrero, L., Moscatelli, M., Perrone, M. G., Pirovano, G., et al. (2014). Sensitivity analysis of PBL schemes by comparing WRF model and experimental data. Geoscientific Model Development: Discussion, 7, 6133–6171.

    Article  Google Scholar 

  • Benjamin, S. G., Dévényi, D., Weygandt, S. S., Brundage, K. J., Brown, J. M., Grell, G. A., et al. (2004). An hourly assimilation–forecast cycle: The RUC. Monthly Weather Review, 132, 495–518.

    Article  Google Scholar 

  • Bhowmik, S. K. R., Sud, A. M., & Singh, C. (2004). Forecasting fog over Delhi—an objective method. Mausam, 55(2), 313–322.

    Google Scholar 

  • Bosveld, F., Baas, P., Steeneveld, G. J., Holtslag, A., Angevine, W., Bazile, E., et al. (2014). The third GABLS intercomparison case for evaluation studies of boundary-layer models: part B: results and process understanding. Boundary-Layer Meteorology, 152, 157–187.

    Article  Google Scholar 

  • Bougeault, P., & Lacarrere, P. (1989). Parameterization of orographyinduced turbulence in a mesobeta-scale model. Monthly Weather Reviews, 117, 1872–1890. https://doi.org/10.1175/1520-0493(1989)1171872:POOITI.2.0.CO;2.

    Article  Google Scholar 

  • Bretherton, C. S., & Park, S. (2009). A new moist turbulence parameterization in the Community Atmosphere Model. Journal of Climate, 22, 3422–3448. https://doi.org/10.1175/2008JCLI2556.1.

    Article  Google Scholar 

  • Carvalho, D., Rocha, A., Gomez-Gesteira, M., & Santos, C. (2012). A sensitivity study of the WRF model in wind simulation for an area of high wind energy. Environmental Modelling and Software, 33, 23–34. https://doi.org/10.1016/j.envsoft.2012.01.019.

    Article  Google Scholar 

  • Chandra, et al. (2018). Odd-even traffic rule implementation during winter 2016 in Delhi did not reduce traffic emissions of VOCs, carbon dioxide, methane, and carbon monoxide. Current Science, 114(1318), 6.

    Google Scholar 

  • Chen F (2007). The Noah Land Surface Model in WRF: A short tutorial. NCAR, LSM group meeting, 30 pp. http://www.atmos.illinois.edu/~snesbitt/ATMS597R/notes/noahLSM-tutorial.pdf. Accessed 19 Mar 2012

  • Chou, S.-H. (2011). An example of vertical resolution impact on the WRF-Var analysis. Electronic Journal of Operational Meteorology, 12, 1–20.

    Google Scholar 

  • Cohen, A. E., Cavallo, S. M., Coniglio, M. C., & Brooks, H. E. (2015). A review of planetary boundary layer parameterization schemes and their sensitivity in simulating a southeast U.S. cold season severe weather environment. Weather Forecasting, 5, 5. https://doi.org/10.1175/waf-d-14-00105.1(150224120634008).

    Google Scholar 

  • Collins, W. D., et al. (2004). Description of the NCAR Community Atmosphere Model (CAM3), Tech. Note NCAR-TN-464+STR, Natl. Cent. for Atmos. Res., Boulder, Colo: National Center For Atmospheric Research

    Google Scholar 

  • Dimitrova, R., et al. (2016). Assessment of planetary boundary-layer schemes in the weather research and forecasting mesoscale model using MATERHORN field data. Boundary-Layer Meteorology, 176–177, 185–201.

    Google Scholar 

  • Dimri, A. P., Niyogi, D., Barros, A. P., Ridley, J., Mohanty, U. C., Yasunari, T., et al. (2015). Western disturbances: a review. Reviews of Geophysics, 53, 225–246. https://doi.org/10.1002/2014rg000460.

    Article  Google Scholar 

  • Dudhia, J. (1989). Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. Journal of Atmospheric Science, 46, 3077–3107.

    Article  Google Scholar 

  • Garratt, J. R. (1994). The atmospheric boundary layer. Cambridge University Press, 316 pp (Cambridge Atmospheric and Space Science Series). Europe. Quarterly Journal of the Royal Meteorological Society, 139(671), 501–514.

    Google Scholar 

  • George, J. P., Indira Rani, S., Jayakumar, A., Mohandas, S., Mallick, S., Lodh, A., et al. (2016). NCUM data assimilation system. Monsoon, Report, NMRF/TR/01/2016.

    Google Scholar 

  • Ghude, S. D., Bhat, G. S., Prabhakaran, T., Jenamani, R. K., Chate, D. M., Safai, P. D., et al. (2017). Winter fog experiment over the Indo-Gangetic plains of India. Current Science, 112(04), 767–784.

    Article  Google Scholar 

  • Gilliam, R. C., & Pleim, J. E. (2010). Performance assessment of new land surface and planetary boundary layer physics in the WRF-ARW. Journal of Applied Meteorology and Climatology, 49(4), 760–774.

    Article  Google Scholar 

  • Goswami, P., & Sarkar, S. (2017). An analogue dynamical model for forecasting fog-induced visibility: validation over Delhi. Meteorological Applications, 24, 360–375.

    Article  Google Scholar 

  • Goswami, P., & Tyagi, A. (2007). “Advance forecasting of onset, duration and hourly fog intensity over Delhi”, Research Report RR CM 0714. Bangalore, India: Centre for Mathematical Modelling and Computer Simulation.

    Google Scholar 

  • Grenier, H., & Bretherton, C. S. (2001). A moist PBL parameterization for large-scale models and its application to subtropical cloud-topped marine boundary layers. Monthly Weather Reviews, 129, 357–377. https://doi.org/10.1175/1520-0493(2001)129,0357:AMPPFL.2.0.CO;2.

    Article  Google Scholar 

  • Gultepe, I., Agelin-Chaab, M. J., Komar, G., Elfstrom, F., & Boudala, B. Zhou. (2018). A meteorological supersite for aviation and cold weather applications. Pure and Applied Geophysics. https://doi.org/10.1007/s00024-018-1880-3. (in press).

    Google Scholar 

  • Gultepe, I., Pearson, G. J. A., Milbrandt, B., Hansen, S., Platnick, P., Taylor, M., et al. (2009). The fog remote sensing and modeling (FRAM) field project. Bulletin of the American Meteorological Society, 90, 341–359.

    Article  Google Scholar 

  • Gultepe, I., Tardif, R., Michaelides, S. C., Cermak, J., Bott, A., Bendix, J., et al. (2007). Fog research: a review of past achievements and future perspectives. Pure and Applied Geophysics, 164, 1121–1159.

    Article  Google Scholar 

  • Holtslag, A. A. M., Svensson, G., Baas, P., Basu, S., Beare, B., Beljaars, A. C. M., et al. (2013). Stable atmospheric boundary layers and diurnal cycles challenges for weather and climate models. Bulletin of the American Meteorological Society, 94, 1691–1706.

    Article  Google Scholar 

  • Hong S-Y, Dudhia J, Chen S-H (2004). A Revised Approach to Ice Microphysical Processes for the Bulk Parameterization of Clouds and Precipitation. Monthly Weather Review, 132(1), 103–120.

    Article  Google Scholar 

  • Hong, S. Y., Noh, Y., & Dudhia, J. (2006). A new vertical diffusion package with an explicit treatment of entrainment processes. Monthly Weather Review, 134, 2318–2341.

    Article  Google Scholar 

  • Janjic, Z. I. (1990). The step-mountain coordinate: Physical package. Monthly Weather Review, 118, 1429–1443.

    Article  Google Scholar 

  • Jaswal, A. K., Kumar, N., Prasad, A. K., & Menas, K. (2013). Decline in horizontal surface visibility over India (1961–2008) and its association with meteorological variables. Nat: Hazards. https://doi.org/10.1007/s11069-013-0666-2.

    Book  Google Scholar 

  • Jayakumar, A., Rajagopal, E. N., Boutle, I. A., George, J. P., Mohandas, S., Webster, S., et al. (2018). An operational fog prediction system for Delhi using the 330 m unified model. Atmospheric Science Letters, 19, e796. https://doi.org/10.1002/asl.796.

    Article  Google Scholar 

  • Jenamani, R. K. (2007). Alarming rise in fog and pollution causing a fall in maximum temperature over Delhi. Current Science, 93, 314–322.

    Google Scholar 

  • Kleczek, M. A., Steeneveld, G. J., & Holtslag, A. A. M. (2014). Evaluation of the weather research and forecasting mesoscale model for GABLS3: Impact of boundarylayer schemes, boundary conditions and spin-up. Boundary-Layer Meteorology, 152, 213–243. https://doi.org/10.1007/s10546-014-9925-3.

    Article  Google Scholar 

  • Kulkarni, R. G. (2016). Wintertime fog in Delhi and its effect on aviation economy. Pune: M Sc Project Report Submitted to Savitribai Phule University.

    Google Scholar 

  • Kumar, P., Kishtawal, C. M., & Pal, P. K. (2015). Impact of ECMWF, NCEP, and NCMRWF global model analysis on the WRF model forecast over Indian region. Theoretical and Applied Climatology. https://doi.org/10.1007/s00704-015-1629-1.

    Google Scholar 

  • Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., & Collins, W. D. (2008). Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. Journal of Geophysical Research, 113, D13.

    Article  Google Scholar 

  • Leduc, M., & Laprise, R. (2009). Regional climate model sensitivity to domain size. Climate Dynamics, 32, 833–854.

    Article  Google Scholar 

  • Leduc, M., Laprise, R., Moretti-Poisson, M., & Morin, J. P. (2011). Sensitivity to domain size of mid-latitude summer simulations with a regional climate model. Climate Dynamics, 37, 343–356.

    Article  Google Scholar 

  • Lim K-S. S., & Hong S-Y (2010). Development of an Effective Double-Moment Cloud Microphysics Scheme with Prognostic Cloud Condensation Nuclei (CCN) for Weather and Climate Models. Monthly Weather Review, 138(5), 1587–1612.

    Article  Google Scholar 

  • Lin, C., Zhang, Z., Pu, Z., & Wang, Y. (2017). Numerical simulations of an advection fog event over Shanghai Pudong International Airport with the WRF model. Journal of Meteorological Research, 31, 874–889.

    Article  Google Scholar 

  • Milovac, J., Warrach-Sagi, K., Behrendt, A., Spath, F., Ingwersen, J., Wulfmeyer, V. (2016). Investigation of PBL schemes combining the WRF model simulations with scanning water vapor differential absorption laser measurements. Journal of Geophysical Research: Atmospheres.

  • Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., & Clough, S. A. (1997). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. Journal of Geophysical Research, 102, 16663–16682.

    Article  Google Scholar 

  • Mohan, M., & Bhati, S. (2011). Analysis of WRF model performance over subtropical region of Delhi, India. Advances in Meteorology, art. no. 621235.

  • Naira, Chaouch, Marouane, Temimi, Michael, Weston, & Hosni, Ghedira. (2017). Sensitivity of the meteorological model WRF-ARW to planetary boundary layer schemes during fog conditions in a coastal arid region. Atmosheric Research, 187(2017), 106–127.

    Google Scholar 

  • Nakanishi, M., & Niino, H. (2006). An improved Mellor-Yamada Level-3 model: its numerical stability and application to a regional prediction of advection fog. Boundary-Layer Meteorology, 119(2), 397–407.

    Article  Google Scholar 

  • Nieuwstadt, F. T. M. (1984). The turbulent structure of the stable, nocturnal boundary layer. Journal of Atmospheric Science, 41, 2202–2216.

    Article  Google Scholar 

  • Pasricha, P. K., Gera, B. S., Shastri, S., Maini, H. K., Ghosh, A. B., Tiwari, M. K., et al. (2003). Role of water vapour green house effect in the forecasting of fog occurrence. Boundary-Layer Meteorol., 107(2), 469–482.

    Article  Google Scholar 

  • Payra, S., & Mohan, M. (2014). Multirule based diagnostic approach for the fog predictions using WRF modelling tool. Advances in Meteorology. https://doi.org/10.1155/2014/456065.

    Google Scholar 

  • Philip, A., Bergot, T., Bouteloup, Y., & Bouyssel, F. (2016). The impact of vertical resolution on fog forecasting in the kilometric-scale model arome: a case study and statistics. Weather Forecast, 31, 1655–1671. https://doi.org/10.1175/WAF-D-16-0074.1.

    Article  Google Scholar 

  • Pithani, P., Ghude, S. D., Prabhakaran, T., et al. (2018). WRF model sensitivity to choice of PBL and microphysics parameterization for an advection fog event at Barkachha, rural site in the Indo-Gangetic basin. India: Theoretical and Applied Climatology. https://doi.org/10.1007/s00704-018-2530-5.

    Book  Google Scholar 

  • Powers, et al. (2017). The weather research and forecasting (WRF) model: overview, system efforts, and future directions. Bulletin of the American Meteorological Society. https://doi.org/10.1175/BAMS-D-15-00308.1.

    Google Scholar 

  • Pramod, D. Safai, et al. (2018). Two way relationship between aerosols and fog: A case study 2 at IGI Airport. New Delhi: Aerosol and Air Quality Research. https://doi.org/10.4209/aaqr.2017.11.0542.

    Google Scholar 

  • Prasad, V. S., Johny, C. J., & Sodhi, J. S. (2016). Impact of 3D Var GSI-ENKF hybrid data assimilation system. Journal of Earth System Science, 125(8), 1509–1521qj.1976.

    Article  Google Scholar 

  • Remy, S., & Bergot, T. (2009). Assesing the impact of observations on a local numerical fog prediction system. Quarterly Journal Royal Meteorological Society, 135, 1248–1265.

    Article  Google Scholar 

  • Roman-Cascon, C., Yague, C., Sastre, M., Maqueda, G., Salamanca, F., & Viana, S. (2012). Observations and WRF simulations of fog events at the Spanish Northern Plateau. Advances in Applied Science Research, 8(1), 11–18.

    Google Scholar 

  • Savijarvi, H. (2006). Radiative and turbulent heating rates in the clear-air boundary layer. Quarterly Journal Royal Meteorological Society, 132, 147–161.

    Article  Google Scholar 

  • Shin, H. H., & Hong, S. Y. (2011). Inter comparison of planetary boundary- layer parameterizations in WRF model for a single day from CASES-99. Boundary-Layer Meteorology, 139, 261–281.

    Article  Google Scholar 

  • Skamarock, W. C., Klemp, J. B., & Dudhia, J., et al. (2008). A description of the advanced research WRF version 3. NCAR Technical Note. NCAR/TN-475 + STR.

  • Steeneveld, G. J., & Bode, M. (2018). Unravelling the relative roles of physical processes in modelling the life cycle of a warm radiation fog. Quarterly Journal of the Royal Meteorological Society. https://doi.org/10.1002/qj.3300.

    Google Scholar 

  • Steeneveld, G. J., Holtslag, A. A. M., Nappo, C. J., Van de Wiel, B. J. H., & Mahrt, L. (2008). Exploring the possible role of small-scale terrain drag on stable boundary layers over land. Journal of Applied Meteorology and Climatology, 47, 2518–2530.

    Article  Google Scholar 

  • Steeneveld, G. J., Ronda, R. J., & Holtslag, A. A. M. (2015). The challenge of forecasting the onset and development of radiation fog using mesoscale atmospheric models. Boundary-Layer Meteorology, 154, 265–289. https://doi.org/10.1007/s10546-014-9973-8.

    Article  Google Scholar 

  • Sterk, H. A. M., Steeneveld, G. J., & Holtslag, A. A. M. (2013). The role of snow-surface coupling, radiation, and turbulent mixing in modeling a stable boundary layer over Arctic sea ice. Journal of Geophysical Research: Atmospheres, 118, 1199–1217. https://doi.org/10.1002/jgrd.50158.

    Google Scholar 

  • Sukorianski, S., Galperin, B., & Perov, V. (2005). Application of a new spectral theory of stable stratified turbulence to the atmospheric boundary layer over sea ice. Boundary-Layer Meteorology, 117, 231–257.

    Article  Google Scholar 

  • Syed, F. S., Kornich, H., & Tjernstrom, M. (2012). On the fog variability over South Asia. Climate Dynamics, 39, 2993–3005.

    Article  Google Scholar 

  • Tardif, R., & Rasmussen, R. M. (2007). Event-based climatology and typology of fog in the New York City region. Journal of Applied Meteorology and Climatology, 46, 1141–1168.

    Article  Google Scholar 

  • Van der Velde, I. R., Steeneveld, G. J., & Holtslag, A. A. M. (2010). Modeling and forecasting the onset and duration of severe radiation fog under frost conditions. Monthly Weather Reviews, 38(11), 4237–4253.

    Article  Google Scholar 

  • Warner, T. T., Peterson, R. A., & Treadon, R. E. (1997). A tutorial on lateral boundary conditions as a basic and potentially serious limitation to regional numerical weather prediction. Bulletin of the American Meteorological Society, 78, 2599–2617.

    Article  Google Scholar 

  • Wild, M., Ohmura, A., Gilgen, H., Morcrette, J. J., & Slingo, A. (2001). Evaluation of downward longwave radiation in general circulation models. Journal of Climate, 14, 3227–3239.

    Article  Google Scholar 

  • Zhong, S., In, H., & Clements, C. (2007). Impact of turbulence, land surface, and radiation parameterizations on simulated boundary layer properties in a coastal environment. Journal of Geophysical Research: Atmospheres, 112(D13), D13110.

    Article  Google Scholar 

  • Zhou, B., & Du, J. (2010). Fog prediction from a multimodel mesoscale ensemble prediction system. Weather and Forecasting, 25, 303–322.

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank the Director, IITM, for his encouragement during the study. Observational data used in this study were gathered as part of the MoES-IITM-IMD collaboration which jointly conducted the winter fog experiment (WIFEX) campaign funded by MoES. The authors also acknowledge ECMWF ERA-Interim data used in this study. We thank Sunitha Devi, India Meteorological Department (IMD), and NASA for providing the satellite images and synoptic charts. The authors appreciate Dr. Anupam Hazra for multiple useful discussions that helped prepare the manuscript. All simulations and data processing were carried out on an Aditya high-performance computing system at the Indian Institute of Tropical Meteorology (IITM), Pune, India.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sachin D. Ghude.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 2055 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pithani, P., Ghude, S.D., Chennu, V.N. et al. WRF Model Prediction of a Dense Fog Event Occurred During the Winter Fog Experiment (WIFEX). Pure Appl. Geophys. 176, 1827–1846 (2019). https://doi.org/10.1007/s00024-018-2053-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00024-018-2053-0

Keywords

Navigation