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پیشبینی همادی آذرخش در دامنههای جنوبی رشتهکوه البرز با استفاده از مدل WRF | ||
| فیزیک زمین و فضا | ||
| مقاله 12، دوره 52، شماره 1، خرداد 1405، صفحه 195-210 اصل مقاله (1.42 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jesphys.2026.408935.1007747 | ||
| نویسندگان | ||
| مجتبی جلالی کوتنائی؛ فرحناز تقوی* ؛ علیرضا محب الحجه؛ مریم قرایلو؛ سرمد قادر | ||
| گروه فیزیک فضا، مؤسسه ژئوفیزیک، دانشگاه تهران، تهران، ایران. | ||
| چکیده | ||
| آذرخش پدیده تخلیه آنی الکتریکی در فواصل طولانی در توفانهای تندری است که منجر به خسارات بسیاری در سطح جهان میشود. این مطالعه اختصاص به کاربست شاخص پتانسیل رخداد آذرخش (LPI) برای پیشبینی همادی آذرخش و اصلاح آن با روشهای یادگیری ماشین از جمله ماشین بردار پشتیبان (SVM) و جنگل تصادفی (RF) بر روی دامنه جنوبی رشتهکوه البرز با استفاده از مدل منطقهای WRF دارد. در اجرای مدل از دوازده طرحواره پارامترسازی همراه با دادههای اولیه GFS، GEFS و ERA5 با تفکیک 25/0 درجه در سه حوزه تودرتو با تفکیکهای 9، 3 و 1 کیلومتر و برای درستیسنجی نتایج از دادههای شبکههای زمینی (Earth Networks) استفاده شده است. برای ارتباط آماری میان LPI و تعداد درخش، بیشترین مقدار ضریب تعیّن R2 با مقدار41/0 همراه با ریشه میانگینمربعات خطای بهنجارشده (NRMSE) برابر 77/0 مربوط به داده ورودی GEFS با مجموعه پارامترسازیهای خُردفیزیک گودارد، همرفت کین-فریچ، دودهیه برای تابش طول موج کوتاه، RRTM برای تابش طول موج بلند، میسو برای لایه مرزی و لایه سطحیNoah میباشد. پس از هماد سازی وزندار و اصلاح توسط یادگیری ماشین با روش ماشین بردار پشتیبان (SVM)، مقدار R2 به ترتیب به 44/0 و 59/0 افزایش و مقدار NRMSE بهترتیب به 75/0 و 65/0 کاهش مییابد. با افزایش وزن شاخص انرژی پتانسیل دسترسپذیر همرفتی (CAPE) در روش SVM و برازش مربعی، R2 به 63/0 افزایش و NRMSE به 62/0 کاهش مییابد. در مجموع، نتایج بیانگر ارتباط آماری مناسب LPI با مشاهدات آذرخش و بهبود پیشبینی آذرخش با همادسازی وزندار و اصلاح از طریق یادگیری ماشین است. | ||
| کلیدواژهها | ||
| آذرخش؛ پیشبینی همادی؛ مدل WRF؛ شاخص LPI | ||
| عنوان مقاله [English] | ||
| Ensemble lightning forecasting in the southern foothills of the Alborz mountain range using WRF model | ||
| نویسندگان [English] | ||
| Mojtaba Jalali Koutanaei؛ Farahnaz Taghavi؛ Ali Reza Mohebalhojeh؛ Maryam Gharaylou؛ Sarmad Ghader | ||
| Department of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran. | ||
| چکیده [English] | ||
| Lightning is a phenomenon of instantaneous electrical discharge over long distances associated with thunderstorms which causes significant human and financial losses worldwide. In this study, the Lightning Potential Index (LPI), which measures the ability to charge within a cloud, was used to predict lightning occurrences on the southern slopes of the Alborz Mountains. This was carried out using a regional WRF model at three nested domains with resolutions of 9, 3, and 1 km. Twelve physics parameterization schemes were utilized in the model with initial and boundary conditions taken from Global Forecast System (GFS), 21 members of Global Ensemble Forecast System (GEFS), and the ECAMWF ERA5 data at a resolution of 0.25 degrees. Additionally, lightning occurrence prediction was enhanced using machine learning methods, including support vector machines (SVM) and random forests (RF). The Earth Networks data was used for the real lightning data. The highest value of the squared correlation R2, or coefficient of determination, was 0.41 with a NRMSE (normalized root mean square error: RMSE divided by the standard deviation) of 0.77 for the GEFS input data set using the Goddard microphysics parameterization, Kain–Fritsch (KF) convection, Dudiha for shortwave radiation, RRTM for longwave radiation, MYJ for boundary layer, and Noah LSM surface layer. The lowest value of R2 was 0.06 with the NRMSE of 0.97 for the GFS input data set and the Morrison–Morr microphysics parameterization, Grell–Devenyi ensemble convection, RRTM for radiation, MYNN boundary layer and NOAH LSM surface layer. In addition to LPI, other quantities related to static instability were also examined for their statistical relation with the number of lightning flashes. The quantities examined included Convective Avaliable Potential Energy (CAPE), Cloud Physics Thunder Parameter (CPTP), K index (KI), Convection Inhibition (CIN) and equivalent reflectivity factor (DBZ). The R2 values for the linear regression between the number of flashes and CAPE, CPTP, KI, CIN and DBZ were 0.14, 0.07, 0.02, 0.03 and 0.07, respectively. Therefore, only CAPE exhibited modest statistical relation with the lightning flashes. After weighted matching and machine learning correction with the support vector machine (SVM) method, the R2 value increased to 0.44 and 0.59, respectively, while the corresponging NRMSE values were 0.75 and 0.65. Given the significant impact of CAPE on the formation of convective clouds, its weight was doubled in applying the SVM. Consequently, R2 for the quadratic regression between the LPI and the actual lightning data was increased to 0.63, while NRMSE was decreased slightly to 0.62. Overall, the results suggest that the LPI index is a suitable indicator for predicting lightning occurrence on the southern slopes of the Alborz Mountains, as there is a sufficiently strong statistical relation between the actual lightning data and the LPI index. Moreover, following weighted matching and correction by machine learning, the accuracy of lightning prediction is significantly improved. | ||
| کلیدواژهها [English] | ||
| Lightning, Ensemble Prediction, WRF Model, LPI Index | ||
| مراجع | ||
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قرایلو، م.؛ ثابت قدم، س. و قادر، س. (1395). امکانسنجی پیشبینی رخداد آذرخش با استفاده از مدل میان مقیاس WRF در منطقه ایران، مجله فیزیک زمین و فضا،42)1)، 213-220.
Babuňková Uhlířová, I., Popová, J., & Sokol, Z. (2022). Lightning Potential Index and its spatial and temporal characteristics in COSMO NWP model. Atmospheric Research, 268, 106025. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. Bright, D. R., M. S. Wandishin, R. E. Jewell, & Weiss, S. J. (2005). A physically based parameter for lightning prediction and its calibration in ensemble forecasts, paper presented at Conference on Meteorological Applications of Lightning Data, Am. Meteorol. Soc., San Diego, Calif. Cortes, C., & Vapnik, V. (1995). Support-vectot networks. Machine Learning, 20(3), 273–297. Eugene W. McCaul Jr., Georgios Priftis, Jonathan L. Case, Themis Chronis, Patrick N. Gatlin, Steven J. Goodman, & Fanyou K., (2020). Sensitivities of the WRF Lightning Forecasting Algorithm to Parameterized Microphysics and Boundary Layer Schemes. AMS Publications, 1545–1560 Curran, E. B., Holle, R., & Lopez ,E. (1999). Lightning casualties and damages in the United States from 1959 to 1994. Journal o E. B f Climate, 13, 3448-3464. Frisbie, P.R., Colton, J.D., Pringle, J.R., Daniels, J.A., Ramey Jr, J.D., & Meyers, M.P., (2009), Lightning Prediction by WFO Grand Junction using Model Data and Graphical Forecast Editor Smart Tools, viewed 2 May (2013), https://ams.confex.com/ams/pdfpapers/149101.pdf. Gatlin, P. N., & Goodman, S. J. (2010). A total lightning trending algorithm to identify severe thunderstorms. J. Atmos. Oceanic Technol., 27, 3–22. Gibergans-Báguena, J., & Liast,M. C. (2007). Improvement of the analog forecasting method by using local thermodynamic data. Application to autumn precipitation in Catalonia, Atmos. Res., 86, 173–193. Gofa, F., Boucouvala, D., Samos, L., & Louka, p. (2023). Lightning Potential Forecast Evaluation and Its Correlation with Thermodynamic Indices. Environmental Sciences Proceedings (MDPI). Holle, R., (2008). annual rates of lightning fatalities by country. Presented at the 20th International Lightning Detection Conference, 21-23 April 2008, and the 2nd International Lightning Meterology Conference, 24-25 April 2008, Tuscon, Arizona. Khansalari, S., & Gharaylou, M. (2025). Lightning Prediction in the Tehran Region Using the WRF Model With Multiple Physical Parameterizations and an Ensemble Approach, Earth and Space Sience, 12(6), 1-25. Kumar, A., Das, S., & Panda, S. K. (2022). Numerical simulation of a widespread lightning event over north India using an ensemble of WRF modeling configurations. Journal of Atmospheric and Solar-Terrestrial Physics, 241, 105984. Laroche, P., Blanchet, P., Delannoy, A., & Issac, F., (2015). Experimental studies of lightning strikes to aircraft. AerospaceLab, Tech. Doc. hal-01184400f, 14 pp. McCaul, E. W., Priftis, G., Case, J. L., Chronis, T., Gatlin, P. N., Goodman, S. J., & Kong, F., (2020). Sensitivities of the WRF lightning forecasting algorithm to parameterized microphysics and boundary layer schemes. Weather Forecast, 35(4),1545–1560. Miller, K., Gadian, A., Saunders, C., Latham, J., & Christian, H., (2001). Modeling and observations of thundercloud electrification and lightning, Atmos. Res., 58, 89–115. Mondal, U., Panda, S. K., Banerjee, B. K., Kumar, A., & Sharma, D. (2023). Performance evaluation of lightning potential index and flash count using WRF microphysical parameters over Rajasthan and West Bengal, India. Dynamics of Atmospheres and Oceans, 104, 101404. Mondal, U., Panda, S. K., Terao, T., Kumar, M., & Sharma, D. (2024). Evaluating the performance and detection efficiency of Weather Research Forecasting model with lightning parameterization schemes for identifying lightning hotspots over Northeast region in India. Climate Dynamics, 62(11), 1–24. Moriasi, D.N., J. G. Arnold, M. W. Van Liew, R. L. Bingner, R. D. Harmel, & Veith T. L. (2007). Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Transactions of the ASABE, 50, 885–900. Morrison, H., Thompson G., & Tatarskii V. (2009). Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Mon. Wea. Rev., 137, 991–1007. Prasad, S. K., Saha, K., Shanker, G., Sarkar, A., George, J. P., & Prasad, V. S. (2024). Evaluating lightning forecasts of a convective scale ensemble prediction system over India. Theoretical and Applied Climatology, 155(6), 4407–4422. Sadeghi, B., Taghavi, F., & Shayegan, A., (2021). Effect of Earth’s Magnetic Field on Prerequisites for Lightning Initiation in Thunderstorm. Journal of Earth and Space Physics, 46(4), 173-188. Saha, K., Bisht, D. S., & Ashrit, R. (2025). Prediction of lightning events over Bangladesh: A machine learning perspective. Journal of Atmospheric and Solar-Terrestrial Physics, 268, 106448. Saunders, C. P. R., Keith, W. D. & 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., & Powers, J. G., (2008). A description of the Advanced Reasearch WRF Version 3. NCAR Technical Note, TN 475+STR, 113 pp. Sturtevant, J. S. (1995). The Severe Local Storm Forecasting Primer, 197 pp., Weather Scratch Meteorol. Serv., Florence, Ala. Williams, E. R. (1999). The behavior of total lightning activity in severe Florida thunderstorms. Atmospheric Research, 51, 245-265. Xiushu, Q., Qilin, Z., Tie, Y., &Tinglong, Z., (2016). Lightning Physics, Beijing Science Press. Yair, Y., Lynn, B., Price, C., Kotroni, V., Lagouvardos, K., Morin, E., Mugnai, A., & Del CarmenLlasat, M. (2010). Predicting the potential for lightning activity in Mediterranean storms based on the Weather Research and Forecasting (WRF) model dynamic and microphysical fields. Journal of Geophysical Research Atmospheres, 115(4), 1–13. Zhang, W., Meng, Q., Ma, M., & Zhang, Y., (2011). Lightning casualties and damages in China from 1997 to 2009. Nat. Hazards, 57, 465–476. | ||
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