Feasibility study of lightning event prediction using WRF mesoscale model in Iran



Lightning is a characteristic of severe weather and often associated with hail and heavy rainfall. It is a natural hazard with potential threat to human life and considerable damages to aviation structures. Therefore, lightning prediction is critical and the real-time lightning detection systems are able to determine the location of cloud-to-ground (CG) lightning strikes accurately.
Generally many indices are used to predict the thunderstorms such as K-Index (KI), Convective Available Potential Energy (CAPE) and Cloud Physics Thunder Parameter (CPTP) that are based on thermodynamic instability parameters. Lightning Potential Index (LPI) is an advanced index for evaluating the potential for lightning activity introduced by Yair et al. (2010) based on the dynamics and microphysics of clouds. According to Yair et al. (2010), LPI is estimated within the charge separation zone of clouds, between 0oC and 20oC, where the non-inductive mechanism involving collisions of ice and graupel particles in the presence of super-cooled water is dominant (Saunders et al., 1991).
In the current study, the meso-scale Weather Research and Forecasting (WRF) model has been used to predict LPI over the northern part of Iran for two case studies of thundercloud event on 9 December 2013 and 25 May 2014. The WRF model is a fully compressible, nonhydrostatic atmospheric model, which uses a terrain-following hydrostatic vertical pressure coordinates (Skamarock et al., 2008). In the present research, WRF version 3.6.1 is used to simulate historical thundercloud event in Iran region.
The model was run at 36 km, 12 km, 4 km and 1.333 km grid spacing. The inner domain is containing Tehran urban area. The Rapid radiative transfer model (Mlawer et al., 1997) with the Dudhia scheme (Dudhia, 1989) was used to simulate the long- and short-wave radiation, respectively. The Monin-Obukhov scheme was used to simulate surface layer fluxes (Janjic, 1996) and the Mellor-Yamada-Janjic turbulent kinetic energy (TKE) scheme was used to simulate boundary layer fluxes (Mellor and Yamada, 1982; Janjic, 1990, 1994). The land surface fluxes were obtained from NOAA model (Chen and Dudhia, 2001, modified by Liu et al., 2006). The Kain-Fritsch scheme was used on the 36 and 12 km grids to parameterize moist convection (Kain and Fritsch, 1993) and the Thompson microphysical scheme was used on the 4 and 1.333 km grids to parameterize microphysical processes. The simulated values of mixing ratios of hydrometers and vertical velocity have been used to calculate the LPI.
Results were evaluated using Cloud-to-ground (CG) lightning flash data from NASA Lightning Imaging Sensor (LIS) and one of the common indices used for forecasting thunderstorms which rely on stability and thermodynamical indices such as K index. Results show that there is a good consistency of both the location of lightning occurrence between the model outputs and LIS data for both understudied cases. Besides, the LPI gives more localized estimation of the location of lightning occurrence compared to the KI. Since K index is not derived from the microphysical fields, it seems to be much less useful for accurate prediction of lightning. Thus LPI provided important information to predict the potential for lightning.


Main Subjects

قرایلو، م.، پگاه فر، ن. و بیدختی، ع. ع، 1393، مدل‌سازی انتقال بار الکتریکی درون ابر (صاعقه) و پیاده‌سازی آن در یک مدل پیش‌یابی یک‌بعدی ابر قائم، م. فیزیک زمین و فضا، 40(1)، 137-148.
Ackerman, A. S. and Knox, A. J., 2003, Meteorology understanding the atmosphere, Toronto, Ontorio, Canada.
Bright, D. R., Wandishin, M. S., Jewell, R. E. and Weiss, S. J., 2005, A physically based parameter for lightning prediction and its calibration in ensemble forecasts. Preprints, Conf. on Meteor, Applications of Lightning Data, San Diego, CA, Amer. Meteor. Soc., 4.3.
Chen, F. and Dudhia, J., 2001, Coupling an advanced land-surface/hydrology model with the Penn State/NCAR MM5 modeling system. Part I: model implementation and sensitivity, Mon. Weather Rev., 129, 569-585.
Christian, H. J., Blakeslee, R. J. and Goodman, S. J., 1992, Lightning imaging sensor (LIS) for the Earth Observing System, NASA TM-4350, 44 pp.
Dudhia, J., 1989, Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model, J. Atmos. Sci., 46, 3077-3107.
George, J. J., 1960, Weather forecasting for aeronautics, Academic Press, 673 pp.
Janjic, Z., 1990, The step-mountain coordinate: Physical package, Mon. Weather Rev., 118, 1429-1443.
Janjic, Z., 1994, The step-mountain eta coordinate model: Further development of the convection, viscous sublayer, and turbulent closure schemes, Mon. Weather Rev., 122, 927-945.
Janjic, Z., 1996, The Mellor-Yamada level 2.5 scheme in the NCEP eta model, paper presented at 11th Conference on Numerical Weather Prediction, Am. Meteorol. Soc., Norfolk, Va., 19-23 Aug.
Kain, J. S. and Fritsch, J. M., 1993, Convective parameterization for mesoscale models: The Kain-Fritsch scheme, in The Representation of Cumulus in Numerical Models, Meteorol. Monogr, 46, 165-177.
Liu, Y., F. Chen, Warner, T. and Basara, J., 2006, Verification of a mesoscale data-assimilation and forecasting system for the Oklahoma City area during the joint urban 2003 project, J. Appl. Meteorol. Climatol, 45(7), 912-929.
Lynn, B. H. and Yair, Y., 2008, Lightning potential index: a new tool for predicting the lightning density and the potential for extreme rainfall, Geophysical Research Abstracts, EGU General Assembly, Vienna.
Lynn, B. H. and Yair, Y., 2010, Prediction of lightning flash density with the WRF model, Adv. Geosci., 23, 11-16.
Mellor, G. L. and Yamada, T., 1982, Development of a turbulence closure model for geophysical fluid problems, Rev. Geophys. Space Phys., 20, 851-875.
Miller, K., Gadian, A., Saunders, C., Latham, J. and Christian, H., 2001, Modeling and observations of thundercloud electrification and lightning, Atmos. Res., 58, 89-115.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J. and S. A., 1997, Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res., 102(D14), 16,663-16,682.
Saunders, C. P. R., Keith, W. D. and Mitzeva, R.

P., 1991, The effect of liquid water on thunderstorm charging, J. Geophys. Res., 96, 11007-11017.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X.-Y., Wang, W., and Powers, J. G., 2008, A description of the advanced research WRF version 3. National Center for Atmospheric Research Boulder Co Mesoscale and Microscale Meteorology Div.
Thompson, G., Rasmussen, R. M. and Manning, K., 2004, Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis, Mon. Weather Rev., 132, 519-542.
Yair, Y., Lynn, B., Price, C., Kotroni, V., Lagouvardos, K., Morin, E., Mugnai, A. and Llasat, M. C., 2010, Predicting lightning density in Mediterranean storms based on the WRF model dynamic and microphysical fields, J. Geophys. Res., Atmospheres (1984-2012), 115(D4).