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.
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.
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.
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.
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.
Bhowmik, S. K. R., Sud, A. M., & Singh, C. (2004). Forecasting fog over Delhi—an objective method. Mausam, 55(2), 313–322.
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.
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.
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.
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.
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.
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.
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).
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
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.
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.
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.
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.
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.
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.
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.
Goswami, P., & Sarkar, S. (2017). An analogue dynamical model for forecasting fog-induced visibility: validation over Delhi. Meteorological Applications, 24, 360–375.
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.
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.
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).
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.
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.
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.
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.
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.
Janjic, Z. I. (1990). The step-mountain coordinate: Physical package. Monthly Weather Review, 118, 1429–1443.
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.
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.
Jenamani, R. K. (2007). Alarming rise in fog and pollution causing a fall in maximum temperature over Delhi. Current Science, 93, 314–322.
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.
Kulkarni, R. G. (2016). Wintertime fog in Delhi and its effect on aviation economy. Pune: M Sc Project Report Submitted to Savitribai Phule University.
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.
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.
Leduc, M., & Laprise, R. (2009). Regional climate model sensitivity to domain size. Climate Dynamics, 32, 833–854.
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.
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.
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.
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.
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.
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.
Nieuwstadt, F. T. M. (1984). The turbulent structure of the stable, nocturnal boundary layer. Journal of Atmospheric Science, 41, 2202–2216.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Savijarvi, H. (2006). Radiative and turbulent heating rates in the clear-air boundary layer. Quarterly Journal Royal Meteorological Society, 132, 147–161.
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.
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.
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.
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.
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.
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.
Syed, F. S., Kornich, H., & Tjernstrom, M. (2012). On the fog variability over South Asia. Climate Dynamics, 39, 2993–3005.
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.
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.
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.
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.
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.
Zhou, B., & Du, J. (2010). Fog prediction from a multimodel mesoscale ensemble prediction system. Weather and Forecasting, 25, 303–322.
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
Corresponding author
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.
Rights and permissions
About this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00024-018-2053-0