Vol. 38, issue 10, article # 10

Abstract:

The work is devoted to the study of the effect of aerosols on various processes in the atmosphere, in particular, the formation of convective clouds and their electrification. The special cases of thunderstorms in the Krasnoyarsk Territory during wildfires are considered. This region is characterized by the highest risk of increased wildfires due to the climate change among Russian regions. The thunderstorm events were selected based on the analysis of lightning activity in the selected area from 2015 to 2022 in comparison with wildfire data. A series of numerical experiments on simulating thunderstorms in the WRF model with different aerosol concentrations in the atmosphere have been performed. Based on the simulation results, electrical parameters of clouds have been calculated. To find correlations between the density of lightning discharges and the parameters of convective systems we suggest methods for estimating lightning activity based on the volume of thunderstorm cells with a certain radar reflectivity and the area of high electrical potential. We have revealed that an increase in the aerosol load in the atmosphere increases the time of convection development, as well as a significant effect of aerosol concentration on electric potential maximum at near constant radar reflectivity maximum. The results can be used to develop fundamental ideas about the relationship between lightning activity and wildfires and to improve methods for predicting lightning activity.

Keywords:

aerosol load, smoke aerosol, wildfires, convective cloud, thunderstorm, cloud electrification, weather forecast, numerical simulation

Figures:

References:

1. Rosenfeld D., Lohmann U., Raga G., O'Dowd C., Kulmala M., Sandro F., Reissell A., Andreae M. Flood or drought: How do aerosols affect precipitation? // Science. 2008. V. 321. P. 1309–1313. DOI: 10.1126/science.1160606.
2. Thompson G., Eidhammer T.A. Study of aerosol impacts on clouds and precipitation development in a large winter cyclone // J. Atmos. Sci. 2014. V. 71, N 10. P. 3636–3658. DOI: 10.1175/JAS-D-13-0305.1.
3. Poliukhov A.A., Chubarova N.E., Volodin E.M. The effects of aerosol-cloud interaction and its influence on radiation in the INMCM5 climate model // Proc. SPIE. 2019. V. 11208. P. 1120810. DOI: 10.1117/12.2540377.
4. Da Silva N., Mailler S., Drobinski P. Aerosol indirect effects on the temperature-precipitation scaling // Atmos. Chem. Phys. 2019. V. 20, N 10. P. 6207–6223. DOI: 10.5194/acp-2018-1334.
5. Takahashi T. Riming electrification as a charge generation mechanism in thunderstorms // J. Atmos. Sci. 1978. V. 35. P. 1536–1548. DOI: 10.1175/1520-0469(1978)035<1536:REAACG>2.0.CO;2.
6. Saunders C.P.R., Peck S.L. Laboratory studies of the influence of the rime accretion rate on charge transfer during crystal/graupel collisions // J. Geophys. Res.: Atmos. 1998. V. 103. P. 13949–13956. DOI: 10.1029/97JD02644.
7. Mansell E.R., MacGorman D.R., Ziegler C.L., Straka J.M. Charge structure and lightning sensitivity in a simulated multicell thunderstorm // J. Geophys. Res.: Atmos. 2005. V. 110, D12101. DOI: 10.1029/2004JD005287.
8. Zhao P., Yin Y., Xiao H. The effects of aerosol on development of thunderstorm electrification: A numerical study // Atmos. Res. 2015. V. 153. P. 376–391. DOI: 10.1016/j.atmosres.2014.09.011.
9. Konovalov I.B., Golovushkin N.A., Beekmann M., Andreae M.O. Insights into the aging of biomass burning aerosol from satellite observations and 3D atmospheric modeling: Evolution of the aerosol optical properties in Siberian wildfire plumes // Atmos. Chem. Phys. 2021. V. 21. P. 357–392. DOI: 10.5194/acp-21-357-2021.
10. Konovalov I.B., Golovushkin N.A., Beekmann M., Siour G., Zhuravleva T.B., Nasrtdinov I.M., Kuznetsova I.N. On the importance of the model representation of organic aerosol in simulations of the direct radiative effect of Siberian biomass burning aerosol in the eastern Arctic // Atmos. Environ. 2023. V. 309. P. 119910. DOI: 10.1016/j.atmosenv.2023.119910.
11. Vasil'ev R.V., Tashchilin M.A., Tatarnikov A.V. Sopostavlenie dinamiki termal'nykh tochek i zaregistrirovannykh lesnykh pozharov s dinamikoj molnievykh razryadov na Bajkal'skoj prirodnoj territorii // Vychislitel'nye tekhnologii. 2023. V. 28, N 6. P. 37–45. DOI: 10.25743/ICT.2023.28.6.004.
12. Makarov I.A., Chernokul'skij A.V. Vliyanie izmeneniya klimata na ekonomiku Rossii: rejting regionov po neobkhodimosti adaptatsii // Zhurnal Novoj ekonomicheskoj assotsiatsii. 2023. V. 61, N 4. P. 145–202. DOI: 10.31737/22212264_2023_4_145-202.
13. Zhang Y., Fan J., Shrivastava M., Homeyer C.R., Wang Y., Seinfeld J.H. Notable impact of wildfires in the western United States on weather hazards in the central United States // Proc. Nat. Acad. Sci. USA. 2022. V. 119, N 44. P. e2207329119. DOI: 10.1073/pnas.2207329119.
14. Huang X., Ding K., Liu J., Wang Z., Tang R., Xue L., Wang H., Zhang Q., Tan Z.-M., Fu C., Davis S.J., Andreae M.O., Ding A. Smoke-weather interaction affects extreme wildfires in diverse coastal regions // Science. 2023. V. 379. P. 457–461. DOI: 10.1126/science. add9843.
15. Gidromettsentr Rossii. М., 2024. URL: https:// meteoinfo.ru/glossary/5036-2012-04-26-12-51-35 (data obrashcheniya: 16.10.2024).
16. Holzworth R.H., Brundell J.B., McCarthy M.P., Jacobson A.R., Rodger C.J., Anderson T.S. Lightning in the Arctic // Geophys. Res. Lett. 2021. V. 48. P. e2020GL091366. DOI: 10.1029/2020GL091366.
17. Giglio L., Schroeder W., Justice C.O. The Collection 6 MODIS active fire detection algorithm and fire products // Remote Sens. Environ. 2016. V. 178. P. 31–41. DOI: 10.1016/j.rse.2016.02.054.
18. Lesopozharnyj tsentr Krasnoyarskogo kraya. Krasnoyarsk, 2024. URL: https://lpcentr.ru/ (data obrashcheniya: 15.04.2025).
19. Thompson G., Eidhammer T. A study of aerosol impacts on clouds and precipitation development in a large winter cyclone // J. Atmos. Sci. 2014. V. 71, N 10. P. 3636–3658. DOI: 10.1175/JAS-D-13-0305.1.
20. Ginoux P., Chin M., Tegen I., Prospero J., Holben B., Dubovik O., Lin S.-J. Sources and distributions of dust aerosols simulated with the GOCART model // J. Geophys. Res. 2001. V. 106. P. 20255–20274. DOI: 10.1029/2000JD000053.
21. Dement'eva S.O., Il'in N.V., Mareev E.A. Raschet elektricheskogo polya i indeksa molnievoj aktivnosti v modelyakh prognoza pogody // Izv. RAN. Fiz. atmosf. i okeana. 2015. V. 51, N 2. P. 210–217.
22. Fan J., Rosenfeld D., Zhang Y., Giangrande S.E., Li Z., Machado L.A.T., Martin S.T., Yang Y., Wang J., Artaxo P., Barbosa H.M.J., Braga R.C., Comstock J.M., Feng Z., Gao W., Gomes H.B., Mei F., Pöhlker C., Pöhlker M.L., Pöschl U., de Souza R.A.F. Substantial convection and precipitation enhancements by ultrafine aerosol particles // Science. 2018. V. 359, N 6374. P. 411–418. DOI: 10.1126/science.aan8461.
23. Liu D., Yu H., Sun C. Estimation of lightning activity of squall lines by different lightning parameterization schemes in the Weather Research and Forecasting Model // Remote Sens. 2023. V. 15. P. 5070. DOI: 10.3390/rs15205070.
24. Sin'kevich A.A., Mikhajlovskij Yu.P., Matrosov S.Yu., Popov V.B., Snegurov V.S., Snegurov A.V., Dovgalyuk Yu.A., Veremej N.E. Svyaz' struktury konvektivnykh oblakov s chastotoj molnij po rezul'tatam radiofizicheskikh izmerenij // Meteorol. i gidrol. 2019. N 6. P. 37–51. DOI: 10.3103/S1068373919060049.
25. Dement'eva S.O., Il'in N.V., Shatalina M.V., Mareev E.A. Prognoz konvektivnykh yavlenij i ego verifikatsiya po dannym nablyudenij atmosfernogo elektrichestva // Izv. RAN. Fiz. atmosf. i okeana. 2020. V. 56, N 2. P. 150–157.
26. Fehr T., Dotzek N., Holler H. Comparison of lightning activity and radar-retrieved microphysical properties in EULINOX storms // Atmos. Res. 2005. V. 76, N 2. P. 167–189. DOI: 10.1016/j.atmosres.2004.11.027.
27. Liu C., Cecil D.J., Zipser E.J., Kronfeld K., Robertson R. Relationships between lightning flash rates and radar reflectivity vertical structures in thunderstorms over the tropics and subtropics // J. Geophys. Res. 2012. V. 117, N D06. DOI: 10.1029/2011JD017123.
28. Kitagawa Y.K., Nascimento E.G., Souza N.B., Zucatelli P., Kumar P., de Almeida Albuquerque T.T., de Moraes M., Moreira D. Evaluation of the WRF-ARW model during an extreme rainfall event: Subtropical storm Guará // Atmósfera. 2022. V. 35, N 4. P. 651–672. DOI: 10.20937/atm.52977.