Investigating Temporal-Spatial Distribution and the Possibility of Wind Gust Prediction in Iran

Document Type : Research Article


1 PhD Candidate, Department of Earth Sciences, Islamic Azad University, Science and Research Branch, Tehran, Iran

2 Associate Professor, Department of Earth Sciences, Islamic Azad University, Science and Research Branch, Tehran, Iran

3 Associate Professor, Department of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran

4 Associate Professor, Atmospheric Science and Meteorological Research Center (ASMERC), Tehran,


Today we are witnessing a multitude of destructive natural meteorological and hydrological phenomena that cause more financial and environmental losses to human life. One of the atmospheric phenomena that can have a direct impact on flight safety, transportation, structures, energy and many other aspects of human life is wind gust. The aim of this study was to investigate the temporal and spatial distribution of wind gust in Iran over a period of 15 years and to evaluate an experimental method called WPD to predict this phenomenon using the output of the WRF model. For this purpose, the data recorded in 32 synoptic stations between 2004 and 2018 were studied. The results showed that the number of wind gusts occurred in the southeast and northwest Iran was much higher than other regions, while the frequency of convective wind gusts has been higher in the western half of Iran. In general, the frequency of wind gust had an increasing trend during the studied period and reached its maximum in 2018. Moreover, most convective wind gust reports have been related to spring. The highest number of wind gust reports with 67% belonged to the first half of the year. However, only 13% of the reports belonged to the autumn. Most of the wind gusts were reported between 12:00 and 18:00 local standard time (0800 to 1400 UTC). Among several wind gust forecasting methods, the relationship used in the WRF post processing system (WPD) was selected and its performance in Iran was evaluated. The results of the method on 885 non-convective wind gust indicated the optimal performance of the method for forecasting wind gust in Iran (RMSE=3.23, MAE=2.83, MSE=13.4 and R=0.71).

Graphical Abstract

Investigating Temporal-Spatial Distribution and the Possibility of Wind Gust Prediction in Iran


حسین­زاده، سیدرضا. (1376). بادهای 120روزه سیستان، فصلنامه تحقیقات جغرافیایی، شماره 46، 102-127.
حمیدیان­پور، محسن؛ مفیدی، عباس؛ سلیقه، محمد. (1395). تحلیل ماهیت و ساختار باد سیستان، مجله ژئوفیزیک ایران، 10، (2)، 83-109.
علیجانی، بهلول. (1373). آب و هوای ایران، انتشارات دانشگاه پیام نور، 236 ص.
محمدی، محمد حسام؛ مشکوتی، امیرحسین؛ قادر، سرمد؛ آزادی، مجید. (1399). بررسی آماری جست‌بادهای همرفتی و غیرهمرفتی در محدوده ایران، مجموعه مقالات نوزدهمین کنفرانس ژئوفیزیک ایران، آبان 1399، 91-88.
محمدی، محمد حسام؛ مشکوتی، امیرحسین؛ قادر، سرمد؛ آزادی، مجید. (1399). پیش­بینی تندی جست­باد در ایران با استفاده از مدل WRF، مجموعه مقالات دوازدهمین کنفرانس ملی فرماندهی و کنترل ایران، آذر 1399.
مفیدی، عباس؛ حمیدیان­پور، محسن؛ سلیقه، محمد؛ علیجانی، بهلول. (1392). تعیین زمان آغاز، خاتمه و طول مدت وزش باد سیستان با بهره­گیری از روش­های تخمین نقطه تغییر، نشریه جغرافیا و مخاطرات محیطی، شماره 8، صص. 112-87.
Brasseur, O. 2001. Development and Application of a Physical Approach to Estimating Wind Gusts, Monthly Weather Review, 129 (1): 5-25.
Burton, T., D. Sharpe, N. Jenkins, E. Bossanyi, 2011. Wind Energy Handbook, John Wiley & Sons, Chichester, UK, 742 PP, edition 2.
Choi, E. C. C., Hidayat, F. A., 2002. Gust factors for thunderstorm and non-thunderstorm winds. Journal of Wind Engineering and Industrial Aerodynamics, 90 (12): 1683–1696.
Cook, N. J., Harris, R. I., and Whiting, R., 2003. Extreme wind speeds in mixed climates revisited. Journal of Wind Engineering and Industrial Aerodynamics, 91 (3): 403– 422.
De Meutter, P., Gerard, L., Smet, G., Hamid, K., Hamdi, R., Degrauwe, D., and Termonia, P., 2015. Predicting small-scale, short-lived downbursts: case study with the NWP limited-area ALARO model for the Pukkelpop thunderstorm. Mon. Weather Rev., 143 (3): 742–756.
Ferreira, V., Nascimento, E., 2016. Convectively-Induced Severe Wind Gusts in Southern Brazil, Surface Observations. Atmospheric Environment, and Association with Distinct Convective Modes: 28th Conference on Severe Local Storms, At Portland/OR, USA.
Ghavidel, Y., Baghbanan, P., Farajzadeh, M., 2017. The spatial analysis of thunderstorm hazard in Iran. Arabian Journal of Geosciences. 10 (5): 1-13. http://
Jolliffe, I. T., and D. B. Stephenson, 2003. Forecast Verifcation: A Practitioner’s Guide in Atmospheric Science. John Wiley and Sons, 240pp.
Kolendowicz, L., Taszarek, M., Czernecki, Bartosz., 2016. Convective and non-convective wind gusts in Poland, 2001-2015. Meteorology Hydrology and Water Management. 4 (2): 15-21.
Kurbatova, M., Konstantin, R., Gubenko, I. and Kurbatov, G., 2018. Comparison of seven wind gust parameterizations over the European part of Russia. Advances in Science and Research, 15: 251-255.
Mohr, S., Kunz, M., Richter, A., and Ruck, B., 2017. Statistical characteristics of convective wind gusts in Germany. Natural Hazards and Earth System Sciences, 17 (6): 957-969.
NCO, 1997. Subroutine calgust, Available from the National Weather Service, NCO Production, source code:
RUC20, 2007. diagnostic output fields for the Rapid Refresh and HRRR, Available from the National Oceanic and Atmospheric Administration website:
Sheridan, P., 2011. Review of Techniques and Research for Gust Forecasting and Parameterisation, Forecasting Research Technical Report 570, Met Office, Exeter.
Skamarock W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X., Wang, W., Powers, J. G., 2008. A Description of the Advanced Research WRF Version 3, NCAR THECNICAL NOTE.
Stucki, P., Dierer, S., Welker, C., Navarro, J. J. G., Raible, C. C., Martius, O. and Brönnimann, S., 2016. Evaluation of downscaled wind speeds and parameterised gusts for recent and historical windstorms in Switzerland. Tellus A. Dynamic Meteorology and Oceanography, 68 (1).