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Russian Studies on Clouds and Precipitation in 2019–2022

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Abstract

Results of Russian studies on cloud physics, precipitation, and weather modification in 2019–2022 are presented based on a survey prepared for the Russian National Report on Meteorology and Atmospheric Sciences to the 28th General Assembly of the International Union of Geodesy and Geophysics. Results concerning general issues of the observation and modeling of clouds and precipitation, including convective clouds; issues of studying the microphysical and optical characteristics of clouds; and the weather modification are discussed.

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REFERENCES

  1. Abshaev, M.T., Zakinyan, R.G., Abshaev, A.M., et al., Influence of atmosphere near-surface layer properties on development of cloud convection, Atmosphere, 2019, vol. 10, p. 131. https://doi.org/10.3390/atmos10030131

    Article  ADS  Google Scholar 

  2. Abshaev, A.M., Adzhiev, A.Kh., Veremei, N.E., et al., Development of convective cloud electrification according to empirical and numerical models, Tr. VKA im. Mozhaiskogo, 2020a, no. S674, pp. 68–74.

  3. Abshaev, M.T., Abshaev, A.M., Mikhailovskii, Yu.P., et al., Features of the development of electrification and city formation processes in a supercell cloud using remote radio physical means, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2020b, no. 596, pp. 96–130.

  4. Abshaev, M.T., Abshaev, A.M., Zakinyan, R.G., et al., Investigating the feasibility of artificial convective cloud creation, Atmos. Res., 2020c, vol. 243, p. 104998. https://doi.org/10.1016/j.atmosres.2020.104998

    Article  Google Scholar 

  5. Abshaev, A.M., Abshaev, M.T., Sin’kevich, A.A., et al., Studying an effect of glaciogenic seeding on lightning activity of convective clouds, Russ. Meteorol. Hydrol., 2022a, vol. 47, no. 8, pp. 604–612.

    Article  Google Scholar 

  6. Abshaev, M.T., Abshaev, A.A., Sin’kevich, A.A., et al., Features of development of a supercell convective cloud at the stage of maximum lightning activity (August 19, 2015, the North Caucasus), Russ. Meteorol. Hydrol., 2022b, vol. 47, no. 4, pp. 315–325.

    Article  Google Scholar 

  7. Abshaev, M.T., Abshaev, A.M., Malkarova, A.M., and Tsikanov, Kh.A., Hail suppression to protect crops in the North Caucasus, Russ. Meteorol. Hydrol., 2022c, vol. 47, no. 7, pp. 487–498.

    Article  Google Scholar 

  8. Abshaev, M.T., Abshaev, A.M., and Aksenov, A.A., et al., CFD simulation of updrafts initiated by a vertically directed jet fed by the heat of water vapor condensation, Sci. Rep., 2022d, vol. 12, p. 9356. https://doi.org/10.1038/s41598-022-13185-2

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  9. Abshaev, M.T., Zakinyan, R.G., Abshaev, A.M., et al., Atmospheric conditions favorable for the creation of artificial clouds by a jet saturated with hygroscopic aerosol, Atmos. Res., 2022e, vol. 277, p. 106323. https://doi.org/10.1016/j.atmosres.2022.106323

    Article  Google Scholar 

  10. Adzhiev, A.Kh., Bekkiev, M.Yu., Kuliev D.D., et al., Hardware and software system for monitoring electrical and thunderstorm phenomena in the atmosphere, Radiotekh. Telekommun. Sist., 2019, no. 4, pp. 5–11.

  11. Adzhiev, A.Kh., Kupovykh, G.V., Gyatov, R.A., and Kerefova Z.M., Relationship between the number of thunderstorm days and the duration of thunderstorms according to visual and instrumental observations, Izv. Vyssh. Uchebn. Zaved., Sev.-Kavk. Reg., Estestv. Nauki, 2020, no. 3, pp. 30–36.

  12. Adzhiev, A.Kh., Dokukin, M.D., Kondrat’eva, N.V., and Kumukova, O.A., Active avalanche control: Results of research and operational activities, Russ. Meteorol. Hydrol., 2022, vol. 47, no. 8, pp. 576–581.

    Article  Google Scholar 

  13. Akvilonova, A.B., Egorov, D.P., Kutuza, B.G., and Smirnov, M.T., Studying characteristics of the cloudy atmosphere based on measuring its downwelling microwave radiation spectra in the 18.0–27.2 GHz water vapor resonant absorption band, Russ. Meteorol. Hydrol., 2022, vol. 47, no. 12, pp. 953–961.

    Article  Google Scholar 

  14. Alekhin, S.G., Semi-empirical method for short-term total cloud forecasting, Tr. VKA im. Mozhaiskogo, 2020, no. 672, pp. 148–157.

  15. Alekhin, S.G., Ivanov, R.D., and Shemelov, V.A., Method for constructing prognostic equations for determining the height of cloud base from semi-empirical dependencies,, Tr. VKA im. Mozhaiskogo, 2022, no. 684, pp. 62–68.

  16. Aleksandrova, M., Cloudiness over the oceans at subarctic latitudes as a visible part of atmospheric moisture transport, Russ. J. Earth Sci., 2021, vol. 21, no. 1, p. ES1004. https://doi.org/10.2205/2020ES000738

    Article  Google Scholar 

  17. Alekseeva, A.A., Features of the conditions for the occurrence of active convection with strong squalls, Gidrometeorol. Issled. Prognozy, 2019, no. 2, pp. 41–58.

  18. Alekseeva, A.A., Methods for estimating maximum convective speed in the diagnosis and forecast of dangerous convective weather phenomena, Gidrometeorol. Issled. Prognozy, 2020, no. 2, pp. 6–22.

  19. Alekseeva, A.A. and Losev, V.M., Forecast of dangerous convective weather phenomena in summer, Gidrometeorol. Issled. Prognozy, 2019, no. 4, pp. 127–143.

  20. Alekseeva, A.A. and Peskov, B.E., Physical and synoptic predictors that control the formation of heavy rainfall, Gidrometeorol. Issled. Prognozy, 2021, no. 3, pp. 24–43.

  21. Alekseeva, A.A., Bukharov, V.M., Vasil’ev, E.V., and Losev, V.M., Diagnostics of squalls in snow charges based on data from DMRL-C Doppler meteorological radars, Gidrometeorol. Issled. Prognozy, 2020, no. 3, pp. 6–18.

  22. Alekseeva, A.A., Bukharov, V.M., and Losev, V.M., Diagnosis of severe squalls on the basis of DMRL-C data and numerical modeling results, Gidrometeorol. Issled. Prognozy, 2021, no. 3, pp. 6–23.

  23. Alekseeva, A.A., Bukharov, V.M., and Losev, V.M., The convective storm in the Moscow region on June 28, 2021, Gidrometeorol. Issled. Prognozy, 2022, no. 1, pp. 22–42.

  24. Alekseeva, A.V., Davydov, V.E., Zinkina, M.D., et al., A laboratory experiment to study ion wind effects on the warm fog in an enclosed volume, Russ. Meteorol. Hydrol., 2022, vol. 47, no. 8, pp. 637–640.

    Article  Google Scholar 

  25. Aleshina, M.A. and Semenov, V.A., Changes in precipitation characteristics on the territory of Russia in the XX–XXI centuries according to ensemble of CMIP6 models, Fundam. Prikl. Klimatol., 2022, vol. 8, no. 4, pp. 424–440.

    Google Scholar 

  26. Aleshina, M.A., Semenov, V.A., and Chernokul’skii, A.V., The role of global and regional factors in changes in the extreme summer precipitation on the Black Sea coast of the Caucasus according to the results of experiments with a climate model, Fundam. Prikl. Klimatol., 2019, vol. 3, pp. 59–75.

    Google Scholar 

  27. Aleshina, M.A., Semenov, V.A., and Chernokulsky, A.V., A link between surface air temperature and extreme precipitation over Russia from station and reanalysis data, Environ. Res. Lett., 2021, vol. 16, p. 105004. https://doi.org/10.1088/1748-9326/ac1cba

    Article  ADS  Google Scholar 

  28. Alita, S.L. and Appaeva, Zh.Yu., Spatial evolution of the region of hail formation in single-cell hail clouds, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2021, no. 601, pp. 116–124.

  29. Alita, S.L. and Borisova, N.A., Development of a concept for the location of mobile points of influence on hail processes, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2020, no. 599, pp. 151–161.

  30. Andreev, A.I. and Shamilova, Yu.A., Cloud detection from the Himawari-8 satellite data using a convolutional neural network, Izv., Atmos. Ocean. Phys., 2021, vol. 57, no. 9, pp. 1162–1170.

    Article  Google Scholar 

  31. Andreev, A.I., Shamilova, Yu.A., and Kholodov, E.I., Using convolutional neural networks for cloud detection from Meteor-M No. 2 MSU-MR data, Russ. Meteorol. Hydrol., 2019, vol. 44, no. 7, pp. 459–466.

    Article  Google Scholar 

  32. Andreev, A.I., Pererva, N.I., and Kuchma, M.O., Development of precipitation nowcasting method using geostationary satellite data, Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2020, vol. 17, no. 6, pp. 18–22.

    Article  Google Scholar 

  33. Andreev, Yu.V., Vasil’eva, M.A., Ivanov, V.N., et al., Results of experimental studies on the dispersal of warm fogs using gauze electrostatic precipitators, Russ. Meteorol. Hydrol., 2021, vol. 46, no. 11, pp. 716–721.

    Article  Google Scholar 

  34. Aniskina, O.G., Stognieva, V.V., and Tolstobrova, N.B., Forecasting thunderstorms using mesoscale hydrodynamic models, Tr. VKA im. Mozhaiskogo, 2022, no. S685, pp. 6–10.

  35. Antokhina, O.Yu., Atmospheric precipitation within the Selenga River basin and large-scale atmospheric circulation over Eurasia in July, Geogr. Nat. Resour., 2019, vol. 40, no. 4, pp. 373–383.

    Article  Google Scholar 

  36. Appaeva, Zh.Yu., Results of statistical studies of the main characteristics of thunderstorm–hail clouds according to radar observations, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2020, no. 598, pp. 188–196.

  37. Arshinov, M.Yu., Belan, B.D., Davydov, D.K., et al., Automated precipitation collector, Meteorol. Gidrol., 2019, no. 7, pp. 118–123.

  38. Artyushina, A.V., Zhuravleva, T.B., and Nasrtdinov, I.M., Influence of 3D cloud effects on the intensity of solar radiation in the Earth limb sensing scheme: Results of numerical experiments, Tr. VKA im. Mozhaiskogo, 2020, no. S674, pp. 87–91.

  39. Arzhanova, N.M. and Korshunova, N.N., Assessment of long-term changes in the characteristics of ice-frost deposits in Russia, Tr. Vseross. Nauchno-Issled. Inst. Gidrometeorol. Inf., 2021, no. 188, pp. 18–29.

  40. Ashabokov, B.A., Khibiev, A.Kh., and Shkhanukov-Lafishev, M.Kh., Total approximation method for an equation describing droplet breakup and freezing in convective clouds, Comput. Math. Math. Phys., 2020, vol. 60, no. 9, pp. 1518–1527.

    Article  MathSciNet  Google Scholar 

  41. Ashabokov, B.A., Fedchenko, L.M., Shapovalov, V.A., et al., Numerical modeling of the influence of the atmospheric wind field structure on the macro- and microstructure characteristics of convective clouds, Izv., Atmos. Ocean. Phys., 2022, vol. 58, no. 6, pp. 569–577.

    Article  Google Scholar 

  42. Asmus, V.V., Ioffe, G.M., Kramareva, L.S., et al., Satellite monitoring of natural hazards on the territory of Russia, Russ. Meteorol. Hydrol., 2019, vol. 44, no. 11, pp. 719–728.

    Article  Google Scholar 

  43. Astafurov, V.G. and Skorokhodov, A.V., Using the results of cloud classification based on satellite data for solving climatological and meteorological problems, Russ. Meteorol. Hydrol., 2021, vol. 46, no. 12, pp. 839–848.

    Article  Google Scholar 

  44. Astafurov, V.G., Skorokhodov, A.V., and Kur’yanovich, K.V., Summer statistical models of cloud parameters over Western Siberia according to MODIS data, Russ. Meteorol. Hydrol., 2021, vol. 46, no. 11, pp. 735–746.

    Article  Google Scholar 

  45. Barekova, M.V., Inyukhin, V.S., Kalov, Kh.M., et al., Radar studies of an intense hail process developing over the central part of the North Caucasus on June 6, 2012, Dokl. Adygskoi (Cherkessk.) Mezhdunar. Akad. Nauk, 2019, vol. 19, no. 2, pp. 64–78.

    Google Scholar 

  46. Basharin, D. and Stankūnavičius, G., European precipitation response to Indian Ocean dipole events, Atmos. Res., 2022, vol. 273, p. 106142. https://doi.org/10.1016/j.atmosres.2022.106142

    Article  Google Scholar 

  47. Bazzaev, T.V., Vladimirov, S.A., Kochetov, N.M., et al., Method for enlarging particles of hygroscopic reagents generated by pyrotechnic products for dispersing warm fogs, in Vserossiiskaya konferentsiya po fizike oblakov i aktivnym vozdeistviyam na gidrometeorologicheskie protsessy, Sb. nauchn. tr. (Proceedings of All-Russian Conference on Cloud Physics and Active Impacts on Hydrometeorological Processes), Nalchik, 2021, pp. 302–307.

  48. Bekkiev, K.M., Shapovalov, V.A., Sherieva, M.A., and Lesev, V.N., Mathematical model of a convective cloud in hail suppression activities, Russ. Meteorol. Hydrol., 2022, vol. 47, no. 7, pp. 499–506.

    Article  Google Scholar 

  49. Beryulev, G.P. and Danelyan, B.G., Precipitation enhancement: The results of studies and operational activities, Russ. Meteorol. Hydrol., 2021, vol. 46, no. 9, pp. 579–587.

    Article  Google Scholar 

  50. Bezrukova, N.A. and Chernokulsky, A.V., Russian studies on clouds and precipitation in 2011–2014, Izv., Atmos. Ocean. Phys., 2016, vol. 52, no. 5, pp. 512–523.

    Article  Google Scholar 

  51. Bezrukova, N.A. and Chernokulsky, A.V., Russian studies on clouds and precipitation in 2015–2018, Izv., Atmos. Ocean. Phys., 2020, vol. 56, no. 4, pp. 344–363.

    Article  Google Scholar 

  52. Bezrukova, N.A. and Chernokulsky, A.V., Clouds and precipitation, in Russian National Report: Meteorology and Atmospheric Sciences: 2019–2022, Mokhov, I.I. and Krivolutsky, A.A., Eds., Moscow: MAKS Press, 2023, pp. 86–150. https://doi.org/10.29003/m3460.978-5-317-07017-5.

  53. Biryukov, E.Yu. and Kostsov, V.S., The use of linear regression relations derived from model and experimental data for retrieval of the water content of clouds from ground-based microwave measurements, Atmos. Oceanic Opt., 2019, vol. 32, no. 5, pp. 569–577.

    Article  Google Scholar 

  54. Biryukov, E.Yu. and Kostsov, V.S., Application of the regression algorithm to the problem of studying horizontal inhomogeneity of the cloud liquid water path by ground-based microwave measurements in the angular scanning mode, Atmos. Oceanic Opt., 2020, vol. 33, no. 8, pp. 602–609.

    Article  ADS  Google Scholar 

  55. Bloshchinskiy, V.D., Kuchma, M.O., Andreev, A.I., and Sorokin, A.A., Snow and cloud detection using a convolutional neural network and low-resolution data from the Electro-l No. 2 satellite, J. Appl. Remote Sens., 2020, vol. 14, no. 3, p. 034506. https://doi.org/10.1117/1.JRS.14.034506

    Article  ADS  Google Scholar 

  56. Bobrova, D.A. and Kazakova, E.N., History of research into avalanche processes on Sakhalin Island, Meteorol. Gidrol., 2022, no. 8, pp. 112–119.

  57. Bolgov, Yu.V., Mathematical modeling of snow avalanche dynamics using cellular automata, Russ. Meteorol. Hydrol., 2022, vol. 47, no. 8, pp. 590–595.

    Article  Google Scholar 

  58. Bolgov, M.V., Trubetskova, M.D., and Kharlamov, M.A., Estimation of statistical characteristics of rainfall in the Moscow region, Russ. Meteorol. Hydrol., 2020, vol. 45, no. 7, pp. 508–514.

    Article  Google Scholar 

  59. Brusova, N.E., Kuznetsov, I.N., and Nakhaev, M.I., Features of the precipitation regime in the Moscow region in 2008–2017, Gidrometeorol. Issled. Prognozy, 2019, no. 1, pp. 127–142.

  60. Bukharov, M.V. and Bukharov, V.M., Analysis of a rapidly growing mesoscale deep convection system using satellite diagnostic maps, Gidrometeorol. Issled. Prognozy, 2020, no. 2, pp. 23–38.

  61. Busygin, V.P., Krasnokutskaya, L.D., and Kuz’mina, I.Yu., Transfer of lightning optical radiation into space through the cloud layer, Izv., Atmos. Ocean. Phys., 2019, vol. 55, no. 5, pp. 453–461.

    Article  Google Scholar 

  62. Bychkov, A.A., Petrunin, A.M., Chastukhin, A.V., et al., Prospects of using ground-based generators in cloud seeding, Russ. Meteorol. Hydrol., 2022, vol. 47, no. 7, pp. 535–541.

    Article  Google Scholar 

  63. Bykov, V.Yu., Il’in, G.N., Karavaev, D.M., Shchukin, and G.G., Microwave radiometric measurements of vapor and liquid moisture content in the troposphere, Tr. VKA im. Mozhaiskogo, 2020, no. S674, pp. 128–132.

  64. Chechko, V.A. and Topchaya, V.Yu., Distribution and composition of aerosol particles in rainfall over the coast of the Kaliningrad oblast, Russ. Meteorol. Hydrol., 2019, vol. 43, no. 5, pp. 337–337.

    Google Scholar 

  65. Chernokulsky, A. and Esau, I., Cloud cover and cloud types in the Eurasian Arctic in 1936–2012, Int. J. Climatol., 2019, vol. 39, no. 15, pp. 5771–5790. https://doi.org/10.1002/joc.6187

    Article  Google Scholar 

  66. Chernokulsky, A.V., Kozlov, F.A., Zolina, O.G., et al., Observed changes in convective and stratiform precipitation in Northern Eurasia over the last five decades, Environ. Res. Lett., 2019, vol. 4, no. 4, p. 045001. https://doi.org/10.1088/1748-9326/aafb82

    Article  ADS  CAS  Google Scholar 

  67. Chernokulsky, A., Shikhov, A., Bykov, A., and Azhigov, I., Satellite-based study and numerical forecasting of two tornado outbreaks in the Ural region in June 2017, Atmosphere, 2020a, vol. 11, p. 1146. https://doi.org/10.3390/atmos11111146

    Article  ADS  Google Scholar 

  68. Chernokulsky, A., Kurgansky, M., Mokhov, I., et al., Tornadoes in northern Eurasia: From the middle age to the information era, Mon. Weather Rev., 2020b, vol. 148, no. 8, pp. 3081–3110. https://doi.org/10.1175/MWR-D-19-0251.1

    Article  ADS  Google Scholar 

  69. Chernokulsky, A.V., Kurgansky, M.V., Mokhov, I.I., et al., Tornadoes in the Russian regions, Russ. Meteorol. Hydrol., 2021, vol. 46, no. 1, pp. 69–82.

    Article  Google Scholar 

  70. Chernokulsky, A.V., Eliseev, A.V., Kozlov, F.A., et al., Atmospheric severe convective events in Russia: Changes observed from different data, Russ. Meteorol. Hydrol., 2022a, vol. 47, no. 5, pp. 343–354.

    Article  Google Scholar 

  71. Chernokulsky, A.V., Shikhov, A.N., Azhigov, I.O., et al., Squalls and tornadoes over the European territory of Russia on May 15, 2021: Diagnosis and modeling, Russ. Meteorol. Hydrol., 2022b, vol. 47, no. 11, pp. 867–881.

    Article  Google Scholar 

  72. Chernokulsky, A., Shikhov, A., Bykov, A., et al., Diagnosis and modelling of two destructive derecho events in European Russia in the summer of 2010, Atmos. Res., 2022c, vol. 267, p. 105928. https://doi.org/10.1016/j.atmosres.2021.105928

    Article  Google Scholar 

  73. Chernous, P.A., Experience of artificial avalanche release: Problems and prospects, Russ. Meteorol. Hydrol., 2022, vol. 47, no. 8, pp. 582–589.

    Article  Google Scholar 

  74. Chernykh, I.V. and Aldukhov, O.A., Annual variation of parameters of continuous cloud layers over the Arctic against the background of their global estimates from long-term radiosonde data, Tr. Vseross. Nauchno-Issled. Inst. Gidrometeorol. Inf., 2019, no. 185, pp. 115–135

  75. Chernykh, I.V. and Aldukhov, O.A., Long-term estimates of the number of cloud layers from radiosonde data for 1964–2017 in different latitudinal zones, Russ. Meteorol. Hydrol., 2020a, vol. 45, no. 4, pp. 227–238.

    Article  Google Scholar 

  76. Chernykh, I.V. and Aldukhov, O.A., Description of the database “Urgent data on the boundaries of cloud layers reconstructed from radiosonde observations of temperature and humidity at 58 stations in the Russian Federation and neighboring regions”, Tr. Vseross. Nauchno-Issled. Inst. Gidrometeorol. Inf., 2020b, no. 186, pp. 21–34.

  77. Danelyan, B.G., Bankova, N.Yu., Khizhnyak, A.N., and Lomakin, I.V., Analysis of the frequency of days with fogs at major airports in southern Russia for planning and deployment of works on active impacts (fog dispersal and precipitation regulation), in Vserossiiskaya konferentsiya po fizike oblakov i aktivnym vozdeistviyam na gidrometeorologicheskie protsessy, Sb. nauchn. tr. (Proceedings of All-Russian Conference on Cloud Physics and Active Impacts on Hydrometeorological Processes), Nalchik, 2021a, pp. 298–302.

  78. Danelyan, B.G., Kirin, D.V., Kolokutin, G.E., and Sprygin, A.A., Resource cloudiness for active impacts in the main agricultural production regions of the European part of Russia, in Vserossiiskaya konferentsiya po fizike oblakov i aktivnym vozdeistviyam na gidrometeorologicheskie protsessy, Sb. nauchn. tr. (Proceedings of All-Russian Conference on Cloud Physics and Active Impacts on Hydrometeorological Processes), Nalchik, 2021b, pp. 281–286.

  79. Danilova, I.V. and Onuchin, A.A., The estimation of solid precipitation distribution in the taiga zone of the Yenisei River basin using satellite data, Russ. Meteorol. Hydrol., 2019, vol. 44, no. 1, pp. 71–77.

    Article  Google Scholar 

  80. Dement’eva, T.V. and Korshunova, N.N., Empirical and statistical analysis of total and low-level cloudiness on the territory of Russia, Tr. Vseross. Nauchno-Issled. Inst. Gidrometeorol. Inf., 2020, no. 187, pp. 197–204.

  81. Dement’eva, S.O., Il’in, N.V., Shatalina, M.V., and Mareev, E.A., Forecast of convective events and its verification against atmospheric electricity observations, Izv., Atmos. Ocean. Phys., 2020, vol. 56, no. 2, pp. 123–129.

    Article  Google Scholar 

  82. Denisenkov, D.A., Zhukov, V.Yu., and Shchukin, G.G., Wind shear recognition from weather radar data, Russ. Meteorol. Hydrol., 2019, vol. 44, no. 11, pp. 782–789.

    Article  Google Scholar 

  83. Doronin, A.P., Kozlova, N.A., Petrochenko, V.M., Assessment of the suitability of supercooled clouds for scattering over the central region of European Russia for solving applied problems, Tr. VKA im. Mozhaiskogo, 2019, no. 671, pp. 163–171.

  84. Doronin, A.P., Petrochenko, V.M., Goncharov, I.V., et al., Assessment of the suitability for dispersal of billow and stratus clouds in the northwestern region of European Russia for hydrometeorological support, Navig. Gidrogr., 2020, no. 59, pp. 70–80.

  85. Dovgalyuk, Yu.A., Veremei, N.E., Toropova, M.L., et al., Numerical modeling of the influence of electrical processes on hazardous weather phenomena associated with convective clouds, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2019, no. 595, pp. 63–82.

  86. Dovgalyuk, Yu.A., Veremei, N.E., Sinkevich, A.A., et al., Numerical simulation of evolution and electric structure of the cumulonimbus cloud in northwestern Russia, Russ. Meteorol. Hydrol., 2020a, vol. 45, no. 4, pp. 239–244.

    Article  Google Scholar 

  87. Dovgalyuk, Yu.A., Veremei, N.E., and Sinkevich, A.A., Investigation of electrification mechanisms and relationship between the electrical discharge frequency and radar characteristics of the thunderstorm in China, Russ. Meteorol. Hydrol., 2020b, vol. 45, no. 10, pp. 712–719.

    Article  Google Scholar 

  88. Dovgalyuk, Yu.A., Veremei, N.E., Sinkevich, A.A., et al., Effects of high aerosol air pollution on the evolution of convective clouds during a thunderstorm in China according to three-dimensional numerical simulations, Russ. Meteorol. Hydrol., 2022, vol. 47, no. 3, pp. 197–206.

    Article  Google Scholar 

  89. Drofa, A.S., On the efficiency of the impact of ice-forming reagents on convective clouds, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2020, no. 597, pp. 34–50.

  90. Drofa, A.S., Kozlov, S.V., and Sprygin, A.A., Forecast of resource convective clouds for weather modification, Russ. Meteorol. Hydrol., 2022, vol. 47, no. 7, pp. 516–522.

    Article  Google Scholar 

  91. Ehrlich, A., Wendisch, M., Lüpkes, C., et al., A comprehensive in situ and remote sensing data set from the Arctic CLoud Observations using airborne measurements during polar Day (ACLOUD) campaign, Earth Syst. Sci. Data, 2019, vol. 11, pp. 1853–1881. https://doi.org/10.5194/essd-11-1853-2019

    Article  ADS  Google Scholar 

  92. Ekaikin, A.A., Teben’kova, N.A., Lipenkov, V.Ya., et al., Underestimation of snow accumulation rate in Central Antarctica (Vostok station) derived from stake measurements, Russ. Meteorol. Hydrol., 2020, vol. 45, no. 2, pp. 132–140.

    Article  Google Scholar 

  93. Eliseev, A.V., Ploskov, A.N., Chernokulsky, A.V., and Mokhov, I.I., A correlation between lightning flash frequencies and the statistical characteristics of convective activity in the atmosphere, Dokl. Earth Sci., 2019, vol. 485, no. 1, pp. 273–278.

    Article  ADS  CAS  Google Scholar 

  94. Essery, R., Kim, H., Wang, L., et al., Snow cover duration trends observed at sites and predicted by multiple models, Cryosphere, 2020, vol. 14, pp. 4687–4698. https://doi.org/10.5194/tc-14-4687-2020

    Article  ADS  Google Scholar 

  95. Evstigneev, V.P., Naumova, V.A., Voronin, D.Y., et al., Severe precipitation phenomena in Crimea in relation to atmospheric circulation, Atmosphere, 2022, vol. 13, p. 1712. https://doi.org/10.3390/atmos13101712

    Article  ADS  Google Scholar 

  96. Filei, A.A., Retrieval of the cloud optical depth and particle effective radii from MSU-MR daytime measurements, Opt. Atmos. Okeana, 2019a, vol. 32, no. 8, pp. 650–656.

    Google Scholar 

  97. Filei, A.A., Determination of the cloud phase state using MSU-MR radiometer data onboard Meteor-M no. 2 spacecraft, Opt. Atmos. Okeana, 2019b, vol. 32, no. 5, pp. 376–380.

    Google Scholar 

  98. Filei, A.A., Retrieval of the cloud top height from using Meteor-M No. 2-2 MSU-MR measurements, Opt. Atmos. Okeana, 2020, vol. 33, no. 12, pp. 918–925.

    Google Scholar 

  99. Flossmann, A.I., Manton, M., Abshaev, A., et al., Review of advances in precipitation enhancement research, Bull. Am. Meteorol. Soc., 2019, vol. 100, no. 8, pp. 1465–1480. https://doi.org/10.1175/BAMS-D-18-0160.1

    Article  ADS  Google Scholar 

  100. Gabyshev, D.N., Fedorets, A.A., Aktaev, N.E., et al., Acceleration of the condensational growth of water droplets in an external electric field, J. Aerosol Sci., 2019, vol. 135, pp. 103–112.

    Article  ADS  CAS  Google Scholar 

  101. Gabyshev, D.N., Szakáll, M., Shcherbakov, D.V., et al., Oscillatory signatures in the raindrop motion relative to the air medium with terminal velocity, Atmosphere, 2022, pp. 13, 1137. https://doi.org/10.3390/atmos13071137

  102. Galileiskii, V.P., Grishin, A.I., Elizarov, A.I., et al., Experimental study of the reflection of light radiation from crystalline particles in the lower troposphere, Atmos. Oceanic Opt., 2023, vol. 36, no. 1, pp. 41–46.

    Article  ADS  Google Scholar 

  103. Galin, V.Ya. and Dymnikov, P.V., Dynamic–stochastic parametrization of cloudiness in the general circulation model of the atmosphere, Izv., Atmos. Ocean. Phys., 2019, vol. 55, no. 5, pp. 381–385.

    Article  Google Scholar 

  104. Gavrikov, A.V., Zolina, O.G., Razorenova, O.A., et al., Extreme precipitation in June 2021 over the Black Sea in the context of long-term climate change, Oceanology (Engl. Transl.), 2022, vol. 62, no. 3, pp. 303–309.

  105. Golitsyn, G.S., Chkhetiani, O.G., and Vazaeva, N.V., Clouds and turbulence theory: Peculiar self-similarity, 4/3 fractal exponent and invariants, Izv., Atmos. Ocean. Phys., 2022, vol. 58, pp. 645–648.

    Article  Google Scholar 

  106. Gotyur, I.A., Meshkov, A.N., Rud’, M.Yu., and Yaremenko, I.A., A method for searching for foci of cumulonimbus clouds using data from hydrometeorological spacecraft with artificial neural network technologies, Tr. VKA im. Mozhaiskogo, 2020, no. S674, pp. 146–151.

  107. Grigor’ev, V.Yu., Frolova, N.L., Kireeva, M.B., and Stepanenko, V.M., Spatial and temporal variability of ERA5 precipitation accuracy over Russia, Izv. Ross. Akad. Nauk, Ser. Geogr., 2022, vol. 86, no. 3, pp. 435–446.

    Google Scholar 

  108. Gubenko, I.M. and Rubinshtein, K.G., Analysis of comprehensive forecast of lightning activity, Atmos. Oceanic Opt., 2020, vol. 33, no. 12, pp. 949–957.

    Google Scholar 

  109. Hosseini-Moghari, S.-M., Sun, S., Tang, Q., and Groisman, P.Ya., Scaling of precipitation extremes with temperature in China’s mainland: Evaluation of satellite precipitation data, J. Hydrol., 2022, vol. 606, p. 127391. https://doi.org/10.1016/j.jhydrol.2021.127391

    Article  Google Scholar 

  110. Ianchenko, N.I., Features of the fluoride behavior in the snow cover under the action of technological and weather conditions, Pure Appl. Chem., 2022, vol. 94, no. 9, pp. 1071–1077. https://doi.org/10.1515/pac-2021-0901

    Article  CAS  Google Scholar 

  111. Ianchenko, N.I., Talovskaya, A.V., and Zanin, A.A., Comparative assessment of fluorine, sodium, and lithium distributions in snow cover in Siberia, Pure Appl. Chem., 2022, vol. 94, no. 3, pp. 261–267. https://doi.org/10.1515/pac-2021-0319

    Article  CAS  Google Scholar 

  112. Ignatov, R.Yu., Rubinshtein, K.G., and Yusupov, Yu.I., Numerical experiments on forecasting glaze phenomena, Atmos. Oceanic Opt., 2020, vol. 33, no. 9, pp. 682–689.

    Article  ADS  Google Scholar 

  113. Ignatov, R.Yu., Rubinshtein, K.G., and Yusupov, Yu.I., Forecasting the maximum thickness of ice accretions, Atmos. Oceanic Opt., 2022, vol. 35, no. 5, pp. 541–549.

    Article  ADS  Google Scholar 

  114. Ilin, N.V. and Kuterin, F.A., Accuracy of thunderstorm detection based on DMRL-C weather radar data, Russ. Meteorol. Hydrol., 2020, vol. 45, no. 9, pp. 669–675.

    Article  Google Scholar 

  115. IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Masson-Delmotte, V., Zhai, P., Pirani, A., et al., Eds., Cambridge: Cambridge Univ. Press, 2021. https://doi.org/10.1017/9781009157896

  116. Ivanova, A.R., International practices of thunderstorm nowcasting, Russ. Meteorol. Hydrol., 2019, vol. 44, no. 11, pp. 756–763.

    Article  Google Scholar 

  117. Ivanova, A.R., Icing effects on air transport operation: State-of-the-art and prediction problems, Russ. Meteorol. Hydrol., 2021, vol. 46, no. 7, pp. 461–473.

    Article  Google Scholar 

  118. Ivanova, A.R. and Denisenko, I.A., On the possibility of nowcasting thunderstorms at Moscow airfields using radar and lightning direction data, Gidrometeorol. Issled. Prognozy, 2020, no. 1, pp. 142–161.

  119. Ivanova, A.R. and Skriptunova, E.N., Dynamics of episodes of low clouds and limited visibility at airfields of the Russian Federation in 2001–2020, Gidrometeorol. Issled. Prognozy, 2022, no. 2, pp. 53–68.

  120. Kagermazov, A.Kh. and Sozaeva, L.T., Estimation of the contribution of various hydrometeors to the total radar reflectivity in hail clouds, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2019, no. 594, pp. 107–119.

  121. Kagermazov, A.Kh. and Sozaeva, L.T., Hail forecast and estimate of its size using a global mathematical model of the atmosphere, Tr. VKA im. Mozhaiskogo, 2022, no. S685, pp. 133–140.

  122. Kagermazov, A.Kh. and Sozaeva, L.T., Hail forecast of up to three days according to global atmospheric model output data, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2020, no. 598, pp. 204–214.

  123. Kagermazov, A.Kh., Sozaeva, L.T., and Zharashuev, M.V., Forecast of flood-forming precipitation in the North Caucasus using the global atmosphere model, Meteorol. Gidrol., 2019, no. 6, pp. 80–86.

  124. Kalchikhin, V.V. and Kobzev, A.A., Estimation of parameters of dangerous weather phenomena associated with atmospheric precipitates using the optical precipitation gage, Atmos. Oceanic Opt., 2020, vol. 33, no. 2, pp. 216–218.

    Article  Google Scholar 

  125. Kalchikhin, V., Kobzev, A., Nagorskiy, P., et al., Connected variations of meteorological and electrical quantities of surface atmosphere under the influence of heavy rain, Atmosphere, 2020, vol. 11, p. 1195. https://doi.org/10.3390/atmos11111195

    Article  ADS  Google Scholar 

  126. Kalchikhin, V.V., Kobzev, A.A., Tikhomirov, A.A., and Filatov, D.E., Element-by-element calibration of an optoelectronic precipitation gage, Atmos. Oceanic Opt., 2022, vol. 35, no. 1, pp. 77–80.

    Article  ADS  Google Scholar 

  127. Kalinin, N.A. and Sivkov, B.A., Numerical forecast of summer precipitation of different intensities using the WRF model and atmospheric instability indices, Geogr. Vestn., 2022, no. 3, pp. 92–108.

  128. Kalinin, N.A., Shikhov, A.N., Bykov, A.V., and Azhigov, I.O., Conditions for the appearance and short-time prediction of strong squalls and tornadoes in the European Part of Russia, Atmos. Oceanic Opt., 2019a, vol. 32, no. 3, pp. 334–344.

    Article  Google Scholar 

  129. Kalinin, N.A., Shikhov, A.N., Bykov, A.V., and Tarasov, A.V., Analysis of the results of numerical forecast of rainfall using the WRF model with various convection parameterizations (the test case of the Perm Territory), Gidrometeorol. Issled. Prognozy, 2019b, no. 3, pp. 43–59.

  130. Kalinin, N.A., Shikhov, A.N., Chernokulsky, A.V., et al., Environments of formation of severe squalls and tornadoes causing large-scale windthrows in the forest zone of European Russia and the Ural, Russ. Meteorol. Hydrol., 2021, vol. 46, no. 2, pp. 83–93.

    Article  Google Scholar 

  131. Kalinin, N.A., Bykov, A.V., and Shikhov, A.N., Object-oriented estimation of the short-term forecast of convective hazardous weather events in Perm Krai by the WRF model, Atmos. Oceanic Opt., 2022, vol. 35, no. 3, pp. 423–433.

    Article  ADS  Google Scholar 

  132. Kalmykova, O.V., Fedorova, V.V., and Fadeev, R.O., Analysis of the conditions for the outbreak of tornadoes over the Black Sea on July 16, 2019, and assessment of the forecast performance, Gidrometeorol. Issled. Prognozy, 2021, no. 1, pp. 112-129.

  133. Kalmykova, O.V., Shershakov, V.M., Novitskii, M.A., and Shmerlin, B.Ya., Automated forecasting of waterspouts off the Black Sea coast of Russia and its performance assessment, Russ. Meteorol. Hydrol., 2019, vol. 44, no. 11, pp. 764–771.

    Article  Google Scholar 

  134. Kalnajs, L.E., Davis, S.M., Goetz, J.D., et al., A reel-down instrument system for profile measurements of water vapor, temperature, clouds, and aerosol beneath constant-altitude scientific balloons, Atmos. Meas. Tech., 2021, vol. 14, pp. 2635–2648. https://doi.org/10.5194/amt-14-2635-2021

    Article  CAS  Google Scholar 

  135. Karagodin, A., Rozanov, E., and Mironova, I., On the possibility of modeling the IMF by-weather coupling through GEC-related effects on cloud droplet coalescence rate, Atmosphere, 2022, vol. 13, p. 881. https://doi.org/10.3390/atmos13060881

    Article  ADS  Google Scholar 

  136. Karashtin, A.N., Shlyugaev, Yu.V., and Karashtina, O.S., Cloud-to-ground lightning discharge indicator in the radio frequency emission of thunderclouds as observed in the Upper Volga region of Russia, Atmos. Res., 2021, vol. 256, p. 105559. https://doi.org/10.1016/j.atmosres.2021.105559

    Article  Google Scholar 

  137. Karavaev, D.M. and Shchukin, G.G., Study of variations in atmospheric moisture and cloud water reserves using microwave radiometry, Atmos. Oceanic Opt., 2019, vol. 32, no. 11, pp. 930–935.

    Google Scholar 

  138. Karavaev, D.M., Lebedev, A.B., Shchukin, G.G., and Il’in, G.N., Prospects for application of ground-based microwave radiometry for analysis of atmospheric fronts and early prediction of severe weather events, Russ. Meteorol. Hydrol., 2022, vol. 47, no. 12, pp. 946–952.

    Article  Google Scholar 

  139. Kazantseva, A.S., Kadebskaya, O.I., Dublyanskii, Yu.V., and Kataev, V.N., The results of precipitation isotope composition monitoring in the Northern and Middle Urals, Russ. Meteorol. Hydrol., 2020, no. vol. 45, 3, pp. 201–206.

  140. Kharyutkina, E., Pustovalov, K., Moraru, E., and Nechepurenko, O., Analysis of spatiotemporal variability of lightning activity and wildfires in Western Siberia during 2016–2021, Atmosphere, 2022, vol. 13, p. 669. https://doi.org/10.3390/atmos13050669

    Article  ADS  Google Scholar 

  141. Khaustov, A. and Redina, M., Polycyclic aromatic hydrocarbons in the snow cover of Moscow: (Case study of the RUDN University campus), Polycyclic Aromat. Compd., 2021, vol. 45, no. 5, pp. 1030–1041. https://doi.org/10.1080/10406638.2019.1645707

    Article  CAS  Google Scholar 

  142. Khaykin, S.M., Moyer, E., Kramer, M., et al., Persistence of moist plumes from overshooting convection in the Asian monsoon anticyclone, Atmos. Chem. Phys., 2022, vol. 22, pp. 3169–3189. https://doi.org/10.5194/acp-22-3169-2022

    Article  ADS  CAS  Google Scholar 

  143. Khlebnikova, E.I., Rudakov, Yu.L., and Shkol’nik, I.M., Changes in precipitation regime over the territory of Russia: Data of regional climate modeling and observations, Russ. Meteorol. Hydrol., 2019, vol. 44, no. 7, pp. 431–439.

    Article  Google Scholar 

  144. Khlebnikova, E.I., Shkol’nik, I.M., and Rudakova, Yu.L., Projected changes in rare precipitation extremes: Results of regional climate modeling for the territory of Russia, Russ. Meteorol. Hydrol., 2022, vol. 47, no. 5, pp. 355–362.

    Article  Google Scholar 

  145. Khuchunaev, B.M., Baisiev, Kh.M., Gekkieva, S.O., and Budaev, A.Kh., Experimental studies of the ice-forming efficiency of the pyrotechnic composition AD1 with zinc additives, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2020, no. 597, pp. 51–60.

  146. Khuchunaev, B.M., Baisiev, Kh.M., and Gekkieva, S.O., Laboratory studies of increased ice-forming efficiency of AD1-based pyrotechnic compositions, Nauka. Innovatsii. Tekhnol., 2021a, no. 2, pp. 125–140.

  147. Khuchunaev, B.M., Gekkieva, S.O., and Budaev, A.Kh., Methods for determining the ice-forming efficiency of anti-hail products in laboratory installations, Nauka. Innovatsii. Tekhnol., 2021b, no. 3, pp. 105–118.

  148. Khuchunaev, B.M., Gekkieva, S.O., and Budaev, A.Kh., Laboratory studies of the influence of electric field strength on the specific charge on reagent particles generated during the sublimation of pyrotechnic compositions, Nauka. Innovatsii. Tekhnol., 2021c, no. 4, pp. 209–226.

  149. Khutorova, O.G., Blizorukov, A.S., Dement’ev, V.V., and Khutorov, V.E., Sounding of the mesoscale structure of the troposphere during the passage of atmospheric fronts, Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2019, vol. 16, no. 6, pp. 254–262.

    Article  Google Scholar 

  150. Khutorova, O.G., Maslova, M.V., and Khutorov, V.E., On the monitoring of convective processes using satellite navigation system receivers, Opt. Atmos. Okeana, 2022, vol. 35, no. 6, pp. 505–509.

    Google Scholar 

  151. Kisel’nikova, V.Z., Object-oriented assessment of the quality of precipitation forecast for the warm period (May–September) of 2016–2020 according to the COSMO-Ru2 model, Gidrometeorol. Issled. Prognozy, 2021, no. 2, pp. 43–51.

  152. Kislov, A., Matveeva, T., and Antipina, U., Precipitation extremes and their synoptic models in the northwest European sector of the Arctic during the cold season, Atmosphere, 2022, pp. 13, 1116. https://doi.org/10.3390/atmos13071116

  153. Kleshcheva, T.I., Potalova, E.Yu., and Permyakov, M.S., Comparison of World Wide Lightning Location Network (WWLLN) data and standard observations at weather stations in the Southern Russian Far East, Russ. Meteorol. Hydrol., 2021, vol. 46, no. 6, pp. 403–409.

    Article  Google Scholar 

  154. Klimenko, D.E., Studying the areal rainfall reduction in the Urals based on radar data, Russ. Meteorol. Hydrol., 2019, vol. 44, no. 7, pp. 484–493.

    Article  Google Scholar 

  155. Klimenko, D.E., Estimating the probable maximum precipitation by physical methods using satellite and radiolocation observation data: Case study of the Middle Urals, Water Resour., 2020, vol. 47, no. 4, pp. 641–650.

    Article  CAS  Google Scholar 

  156. Klimenko, D.E., Cherepanova, E.S., and Kuz’minykh, A.Yu., Evaluating parameters of the distributions of extreme storms with several events per year taken into account, Water Resour., 2019a, vol. 46, no. 4, pp. 630–637.

    Article  CAS  Google Scholar 

  157. Klimenko, D.E., Cherepanova, E.S., and Kuznetsova, T.V., Assessment and mapping of parameters of flood-forming rainstorm in the Tobol River basin, Geogr. Prir. Resur., 2019b, no. 3, pp. 165–172.

  158. Klimenko, V.V., Lubyako, L.V., Mareev, E.A., and Shatalina, M.V., Ground-based measurements of microwave brightness temperature and electric field fluctuations for clouds with a different level of electrical activity, Atmos. Res., 2022, vol. 266, p. 105937. https://doi.org/10.1016/j.atmosres.2021.105937

    Article  Google Scholar 

  159. Korneev, V.P., Koloskov, B.P., Bychkov, A.A., et al., Cloud seeding for improving weather in megacities, Russ. Meteorol. Hydrol., 2022, vol. 47, no. 7, pp. 523–529.

    Article  Google Scholar 

  160. Korolev, V. and Gorshenin, A., Probability models and statistical tests for extreme precipitation based on generalized negative binomial distributions, Mathematics, 2020, vol. 8, p. 604. https://doi.org/10.3390/math8040604

    Article  Google Scholar 

  161. Korolev, V., Gorshenin, A., Belyaev, K., Statistical tests and extreme precipitation volumes, Mathematics, 2019, vol. 7, p. 648. https://doi.org/10.3390/math7070648

    Article  Google Scholar 

  162. Korshunov, V.A., Multiple scattering in cirrus clouds and taking it into account when interpreting lidar measurements in the stratosphere, Atmos. Oceanic Opt., 2021, vol. 35, no. 1, pp. 151–157.

    Article  ADS  Google Scholar 

  163. Korshunov, V.A. and Zubachev, D.S., Cirrus cloud parameters according to lidar measurements in Obninsk, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2021, no. 602, pp. 68–78.

  164. Kostarev, S.V., Vetrov, A.L., Sivkov, B.A., and Pomortseva, A.A., Radar characteristics of cloud systems during heavy rainfall, Geogr. Vestn., 2020, no. 3, pp. 113–124.

  165. Kostromitinov, A.V. and Yaremenko, I.A., Fog forecasting method using convolutional neural networks, Tr. VKA im. Mozhaiskogo, 2022, no. S685, pp. 186–193.

  166. Kostsov, V.S., Kniffka, A., Stengel, M., and Ionov, D.V., Cross-comparison of cloud liquid water path derived from observations by two space-borne and one ground-based instrument in Northern Europe, Atmos. Meas. Tech., 2019, vol. 12, pp. 5927–5946. https://doi.org/10.5194/amt-12-5927-2019

    Article  Google Scholar 

  167. Kostsov, V.S., Ionov, D.V., and Kniffka, A., Detection of the cloud liquid water path horizontal inhomogeneity in a coastline area by means of ground-based microwave observations: feasibility study, Atmos. Meas. Tech., 2020, vol. 13, pp. 4565–4587. https://doi.org/10.5194/amt-13-4565-2020

    Article  Google Scholar 

  168. Kotova, E.I. and Topchaya, V.Yu., Chemical and algological composition of the snow cover at the mouth of the Onega River (White Sea basin), Pure Appl. Chem., 2022, vol. 94, no. 3, pp. 291–295. https://doi.org/10.1515/pac-2021-0309

    Article  CAS  Google Scholar 

  169. Kovalev, N.A., Netyagin, O.V., and Sazhin, I.V., Experience of cloud seeding to extinguish wildfires in Siberia and the Far East in 2017–2021: Preliminary results and performance assessment issues, Russ. Meteorol. Hydrol., 2022, vol. 47, no. 7, pp. 71–77.

    Article  Google Scholar 

  170. Kozhevnikov, V.N., Clouds as manifestation of wave disturbance above mountain ridges, Izv., Atmos. Ocean. Phys., 2022, vol. 58, no. 2, pp. 121–130.

    Article  Google Scholar 

  171. Kozhevnikov, A.Y., Falev, D.I., Sypalov, S.A., et al., Polycyclic aromatic hydrocarbons in the snow cover of the northern city agglomeration, Sci. Rep., 2021, vol. 11, p. 19074. https://doi.org/10.1038/s41598-021-98386-x

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  172. Krinitskiy, M., Aleksandrova, M., Verezemskaya, P., et al., On the generalization ability of data-driven models in the problem of total cloud cover retrieval, Remote Sens., 2021, vol. 13, p. 326. https://doi.org/10.3390/rs13020326

    Article  ADS  Google Scholar 

  173. Krupnova, T.G., Rakova, O.V., Struchkova, G.P., et al., Insights into particle-bound metal(loid)s in winter snow cover: Geochemical monitoring of the Korkinsky coal mine area, South Ural Region, Russia, Sustainability, 2021, vol. 13, p. 4596. https://doi.org/10.3390/su13094596

    Article  Google Scholar 

  174. Kulichkov, S.N., Chunchuzov, I.P., Popov, O.E., et al., Internal gravity and infrasound waves during the hurricane of May 29, 2017, in Moscow, Izv., Atmos. Ocean. Phys., 2019, vol. 55, no. 2, pp. 166–177.

    Article  Google Scholar 

  175. Kulikov, M.Y., Belikovich, M.V., Skalyga, N.K., et al., Skills of thunderstorm prediction by convective indices over a metropolitan area: Comparison of microwave and radiosonde data, Remote Sens., 2020, vol. 12, p. 604. https://doi.org/10.3390/rs12040604

    Article  ADS  Google Scholar 

  176. Kurov A.B., Gekkieva Zh.M., Sin’kevich A.A., et al., Study of operational features of the Blitzortung lightning direction system, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2022, no. 606, pp. 50–62.

  177. Kuryatnikova, N.A., Malygina, N.S., and Mitrofanova, E.Yu., Atmospheric input and diversity of bioaerosols in winter precipitation in the south of Western Siberia, Atmos. Oceanic Opt., 2022, vol. 35, no. 1, pp. 146–150.

    Article  ADS  CAS  Google Scholar 

  178. Kustova, N., Konoshonkin, A., Shishko, V., et al., Coherent backscattering by large ice crystals of irregular shapes in cirrus clouds, Atmosphere, 2022a, vol. 13, p. 1279. https://doi.org/10.3390/atmos13081279

    Article  ADS  Google Scholar 

  179. Kustova, N., Konoshonkin, A., Shishko, V., et al., Depolarization ratio for randomly oriented ice crystals of cirrus clouds, Atmosphere, 2022b, vol. 13, p. 1551. https://doi.org/10.3390/atmos13101551

    Article  ADS  Google Scholar 

  180. Kuz’min, V.A., Study of the electric field strength of the atmosphere during thunderstorms, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2021, no. 601, pp. 104–115.

  181. Kuzhevskaya, I.V., Zhukova, V.A., Koshikova, T.S., et al., The spatio-temporal distribution of mesoscale convective complexes over the southeastern Western Siberia, Geosphere Res., 2021, no. 3. P. 115–124.

  182. Kuznetsov, A.D., Kryukova, S.V., and Simakina, T.E., Modeling the size of hailstones under active influences on clouds, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2019, no. 595, pp. 132–144.

  183. Kuznetsov, A.D., Lyalyushkin, A.S., and Mikhailushkin, S.Yu., On aircraft motion impact on the development of cumulonimbus clouds, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2020, no. 599, pp. 162–175.

  184. Ladokhina, E.M. and Rubinshtein, K.G., Analysis of the effect of the St. Petersburg megalopolis on precipitation and wind for validation of numerical weather forecasts, Atmos. Oceanic Opt., 2021, vol. 34, no. 3, pp. 239–249.

    Article  ADS  Google Scholar 

  185. Leonov, I.I. and Sokolikhina, N.N., Ice storm formation conditions in Vladivostok in November 2020, Gidrometeorol. Issled. Prognozy, 2021, no. 4, pp. 69–83.

  186. Lesev, V.N., Shapovalov, V.A., Ashabokov, B.A., et al., 3D model of a convective cloud: The interaction of microphysical and electrical processes, J. Heat Mass Transfer, 2021, vol. 23, no. 1, pp. 1–18. https://doi.org/10.17654/HM023010001

    Article  Google Scholar 

  187. Liang, H., Abshaev, M., Abshaev, A., et al., Water vapor harvesting nanostructures through bioinspired gradient-driven mechanism, Chem. Phys. Lett., 2019, vol. 728, pp. 167–173. https://doi.org/10.1016/j.cplett.2019.05.008

    Article  ADS  CAS  Google Scholar 

  188. Live, K.B. and Kushchev, S.A., Analysis of the economic efficiency of anti-hail activities in the Russian Federation, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2021, no. 602, pp. 124–133.

  189. Liu, Y., Zhu, Y., Wang, H., et al., Role of autumn Arctic sea ice in the subsequent summer precipitation variability over East Asia, Int. J. Climatol., 2020, vol. 40, pp. 706–722. https://doi.org/10.1002/joc.6232

    Article  Google Scholar 

  190. Lockhoff, M., Zolina, O., Simmer, C., and Schulz, J., Representation of precipitation characteristics and extremes in regional reanalyses and satellite- and gauge-based estimates over Western and Central Europe, J. Hydrometeorol., 2019, vol. 20, pp. 1123–1145. https://doi.org/10.1175/JHM-D-18-0200.1

    Article  ADS  Google Scholar 

  191. Makhotina, I.A., Chechin, D.G., and Makshtas, A.P., Cloud radiative forcing over sea ice in the Arctic during the polar night according to North Pole-37, -39, and ‑40 drifting stations, Izv., Atmos. Ocean. Phys., 2021, vol. 57, no. 5, pp. 451–460.

    Article  Google Scholar 

  192. Makitov, V.S., Inuhin, V.S., Kushchev, S.A, and Liev, K.B., Hail cloud formation as a result of the merging of convective cells, Izv., Atmos. Ocean. Phys., 2022, vol. 58, no. 4, pp. 384–390.

    Article  Google Scholar 

  193. Malkarova, A.M., Activities on active influences on hydrometeorological processes in the Hydrometeorological Service of Russia, Russ. Meteorol. Hydrol., 2022, no. 7, pp. 5–10.

  194. Malygina, N.S., Eirikh, A.N., Agbalyan, E.V., and Papina, T.S., Isotope composition and source regions of winter precipitation in the Nadym Lowland, Led Sneg., 2020, vol. 60, no. 1, pp. 98–108.

    Google Scholar 

  195. Mikhailov, E.F. and Vlasenko, S.S., High-humidity tandem differential mobility analyzer for accurate determination of aerosol hygroscopic growth, microstructure, and activity coefficients over a wide range of relative humidity, Atmos. Meas. Tech., 2020, vol. 13, pp. 2035–2056. https://doi.org/10.5194/amt-13-2035-2020

    Article  CAS  Google Scholar 

  196. Mikhailov, E.F., Ivanova, O.A., Nebosko, O.A., et al., Subpollen particles as atmospheric cloud condensation nuclei, Izv., Atmos. Ocean. Phys., 2019, vol. 55, no. 4, pp. 357–364.

    Article  Google Scholar 

  197. Mikhailov, E.F., Pöhlker, M.L., Reinmuth-Selzle, K., et al., Water uptake of subpollen aerosol particles: Hygroscopic growth, cloud condensation nuclei activation, and liquid-liquid phase separation, Atmos. Chem. Phys., 2021, vol. 21, pp. 6999–7022. https://doi.org/10.5194/acp-21-6999-2021

    Article  ADS  CAS  Google Scholar 

  198. Mikhailovskii, Yu.P., Popov, V.B., Sin’kevich, A.A., et al., Physicostatistical empirical model of lightning activity of convective clouds, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2019, no. 595, pp. 83–105.

  199. Mikhailovskii, Yu.P., Sin’kevich, A.A., Abshaev, A.M., and Toropova, M.L., On methods of impact on electrical processes in clouds, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2021a, no. 602, pp. 6–22.

  200. Mikhailovskii, Yu.P., Toropova, M.L., Veremei, N.E., et al., Dynamics of the electrical structure of cumulonimbus clouds, Radiophys. Quantum Electron., 2021b, vol. 64, no. 5, pp. 309–320.

    Article  ADS  Google Scholar 

  201. Mikhailushkin, S.Yu., Glibchuk, S.A., Zamorin, I.S., et al., Mesoscale features of the distribution of radar characteristics of cumulonimbus clouds and their relationship with surface meteorological parameters, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2021, no. 603, pp. 130–144.

  202. Mityaev, M.V., Gerasimova, M.V., Ryzhik, I.V., and Ishkulova, T.G., Insoluble fractions of aerosols and heavy metals in fresh snow in the northwest of the Kola Peninsula in 2018., Led Sneg., 2019, vol. 59, no. 3, pp. 307–318.

    Google Scholar 

  203. Mokhov, I.I. and Parfenova, M.R., Relationships between satellite-derived snow cover extent in the Northern Hemisphere and surface air temperature, Russ. Meteorol. Hydrol., 2022, vol. 47, no. 2, pp. 98–106.

    Article  Google Scholar 

  204. Morozov, V.N., Interaction of cloud charge structures with the surrounding conducting atmosphere with inhomogeneous electrical conductivity, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2019, no. 592, pp. 23–79.

  205. Morozov, V.N., The influence of clouds and aerosol particles on the distribution of electrical conductivity in the atmosphere, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2022, no. 606, pp. 78–93.

  206. Moskovchenko, D., Pozhitkov, R., Lodygin, E., and Toptygina, M., Polycyclic aromatic hydrocarbons in the snow cover in the city of Tyumen (Western Siberia, Russia), Toxics, 2022, vol. 10, p. 743. https://doi.org/10.3390/toxics10120743

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  207. Mostamandi, S., Predybaylo, E., Osipov, S., et al., Sea breeze geoengineering to increase rainfall over the Arabian Red Sea coastal plains, J. Hydrometeorol., 2022, vol. 23, no. 1, pp. 3–24. https://doi.org/10.1175/JHM-D-20-0266.1

    Article  ADS  Google Scholar 

  208. Murav’ev, A.V., Kiktev, D.B., Smirnov, A.V., and Zaichenko, M.Yu., Operational nowcasting technology for precipitation using radar data and comparative results of point verification for warm and cold periods of the year, Gidrometeorol. Issled. Prognozy, 2019, no. 2, pp. 12–40.

  209. Murav’ev, A.V., Bundel’, A.Yu., Kiktev, D.B., and Smirnov, A.V., Experience in spatial verification of radar nowcasting of precipitation: Definition and statistics of objects, situations, and conditional samples, Gidrometeorol. Issled. Prognozy, 2022a, no. 2, pp. 6–52.

  210. Murav’ev, A.V., Bundel’, A.Yu., Kiktev, D.B., Smirnov, A.V., Verification of radar nowcasting of large precipitation areas using generalized Pareto distribution. Part 1: Elements of theory and methods for parameter estimation, Gidrometeorol. Issled. Prognozy, 2022b, no. 3, pp. 6–41.

  211. Murav’ev, A.V., Bundel’, A.Yu., Kiktev, D.B., and Smirnov, A.V., Verification of radar nowcasting of large precipitation areas using generalized Pareto distribution. Part 2: Appendix to forecasts for the warm and cold periods of 2017–2018, Gidrometeorol. Issled. Prognozy, 2022c, no. 3, pp. 42–77.

  212. Nagorskii, P.M., Zhukov, D.F., Kartavykh, M.S., et al., Properties and structure of mesoscale convective systems over Western Siberia according to remote observations, Russ. Meteorol. Hydrol., 2022, vol. 47, no. 12, pp. 938–945.

    Article  Google Scholar 

  213. Nechepurenko, O.E., Gorbatenko, V.P., Pustovalov, K.N., Gromova, A.V., Thunderstorm activity over Western Siberia, Geosfer. Issled., 2022, no. 4, pp. 123–134.

  214. Noskova, T.V., Lovtskaya, O.V., Panina, M.S., et al., Organic carbon in atmospheric precipitation in the urbanized territory of the south of Western Siberia, Russia, Pure Appl. Chem., 2022, vol. 94, no. 3, pp. 309–315. https://doi.org/10.1515/pac-2021-0321

    Article  CAS  Google Scholar 

  215. Okamoto, H., Sato, K., Borovoi, A., et al., Interpretation of lidar ratio and depolarization ratio of ice clouds using spaceborne high-spectral-resolution polarization lidar, Opt. Express, 2019, vol. 27, pp. 36587–36600. https://doi.org/10.1364/OE.27.036587

    Article  ADS  PubMed  CAS  Google Scholar 

  216. Okamoto, H., Sato, K., Borovoi, A., et al., Wavelength dependence of ice cloud backscatter properties for space-borne polarization lidar applications, Opt. Express, 2020, vol. 28, pp. 29178–29191. https://doi.org/10.1364/OE.400510

    Article  ADS  PubMed  Google Scholar 

  217. Opekunov, A.Y., Opekunova, M.G., Kukushkin, S.Y., et al., Mineralogical–geochemical characteristics of the snow cover in areas with mining and ore-processing facilities, Geochem. Int., 2021, vol. 59, pp. 711–724. https://doi.org/10.1134/S0016702921060070

    Article  CAS  Google Scholar 

  218. Permyakov, M.S., Kleshcheva, T.I., Potalova, E.Yu., et al., Regional features of lightning activity in the south of the Russian Far East, Russ. Meteorol. Hydrol., 2022, vol. 47, no. 8, pp. 629–636.

    Article  Google Scholar 

  219. Petrov, V.V., Microphysical and thermodynamic characteristics of tropical convective clouds (according to experiments in Cuba), Russ. Meteorol. Hydrol., 2021, vol. 46, no. 9, pp. 616–623.

    Article  Google Scholar 

  220. Petrov, V.V., Bazanin, N.V., Kirin, D.V., et al., Relationship between microphysical characteristics and turbulence in winter clouds, in Physics of the Atmosphere, Climatology and Environmental Monitoring. Modern Problems of Atmospheric Physics, Springer, 2022, pp. 269–275.

    Google Scholar 

  221. Piskunov, V.N., Gainullin, K.G., Petrov, A.M., et al., Simulation of the kinetics of precipitation formation in a mixed-phased cloud, Izv., Atmos. Ocean. Phys., 2022, vol. 58, no. 4, pp. 376–383.

    Article  Google Scholar 

  222. Podlesnyi, S.V., Devyatova, E.V., Saunkin, A.V., Vasil’ev, R.V., Comparing methods to estimate cloud cover over the Baikal Natural Territory in December 2020, Sol.-Terr. Phys., 2022, vol. 8, no. 4, pp. 95–102.

    Google Scholar 

  223. Pol’kin, V.V., Panchenko, M.V., and Terpugova, S.A., Condensation activity of different-size particles of atmospheric aerosol using photoelectric counter measurements, Atmos. Oceanic Opt., 2022, vol. 35, no. 2, pp. 133–141

    Article  ADS  Google Scholar 

  224. Poliukhov, A.A., Chubarova, N.E., and Volodin, E.M., Impact of inclusion of the indirect effects of sulfate aerosol on radiation and cloudiness in the INMCM model, Izv., Atmos. Ocean. Phys., 2022, vol. 58, no. 5, pp. 486–493.

    Article  Google Scholar 

  225. Popov, V.B., Sin’kevich, A.A., Yang, D., et al., Characteristics and structure of the cumulonimbus cloud with waterspout over the Gulf of Finland, Russ. Meteorol. Hydrol., 2020, vol. 45, no. 9, pp. 607–614.

    Article  Google Scholar 

  226. Pressman, D.Ya., Approximation of equations for a model of the cloud atmosphere, Russ. Meteorol. Hydrol., 2021, vol. 46, no. 11, pp. 723–734.

    Article  Google Scholar 

  227. Pripachkin, D.A. and Budyka, A.K., Influence of aerosol particle parameters on their scavenging from the atmosphere by raindrops, Izv., Atmos. Ocean. Phys., 2020, vol. 56, no. 2, pp. 173–178.

    Article  Google Scholar 

  228. Romanskii, S.O., Verbitskaya, E.M., and Sulyandziga, P.B., A numerical study of intense convection that caused the tornado in Blagoveshchensk on July 31, 2011, Russ. Meteorol. Hydrol., 2020, vol. 45, no. 6, pp. 403–410.

    Article  Google Scholar 

  229. Rostokin, I.N., Rostokina, E.A., Fedoseeva, E.V., and Shchukin, G.G., Multifrequency microwave radiometric studies of radiothermal radiation from convective clouds under formation and development of hazardous atmospheric weather phenomena, Tr. VKA im. Mozhaiskogo, 2019, no. 670, pp. 140–145.

  230. Rubinshtein, K.G., Gubenko, I.M., Ignatov, R.Yu., et al., Experiments on thunderstorm direction data assimilation, Opt. Atmos. Okeana, 2019, vol. 32, no. 11, pp. 936–941.

    Google Scholar 

  231. Samokhvalov, I.V., Bryukhanov, I.D., Shishko, V.A., et al., Estimation of microphysical characteristics of contrails by polarization lidar data: Theory and experiment, Atmos. Oceanic Opt., 2019, vol. 32, no. 4, pp. 400–409.

    Article  Google Scholar 

  232. Santolaria-Otín, M. and Zolina, O., Evaluation of snow cover and snow water equivalent in the continental Arctic in CMIP5 models, Clim. Dyn., 2020, vol. 55, pp. 2993–3016. https://doi.org/10.1007/s00382-020-05434-9

    Article  Google Scholar 

  233. Savorskii, V.P., Correction of estimates for cloud water reserve from satellite monitoring data, Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2022, vol. 19, no. 1, pp. 78–86.

    Article  Google Scholar 

  234. Semenets, E.S. and Pavlova, M.T., Acidity of atmospheric precipitation falling on the territory of the Northwestern Federal District, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2019, no. 593, pp. 99–115.

  235. Shakina, N.P., Gorlach, I.A., and Skriptunova, E.N., Significance of satellite data on convective clouds for flight accident analysis and prevention, Russ. Meteorol. Hydrol., 2021, vol. 46, no. 12, pp. 866–871.

    Article  Google Scholar 

  236. Shamin, S.I. and Sanina, A.T., Main trends in the emergence of dangerous hydrometeorological phenomena that caused damage on the territory of the Russian Federation, Tr. Vseross. Nauchno-Issled. Inst. Gidrometeorol. Inf., 2021, no. 188, pp. 154–166.

  237. Shapovalov, A.V., Shapovalov, V.A., Stasenko, V.N., and Lesev, V.N., Application of radar, lightning location, and numerical simulation data to study the relationship between total lightning activity and severe weather events, Russ. Meteorol. Hydrol., 2022, vol. 47, no. 8, pp. 613–619.

    Article  Google Scholar 

  238. Shatalina, M.V., Il’in, N.V., and Mareev, E.A., Characteristics of hydrometeorological hazards in Nizhny Novgorod according to in-situ observations of electric field, Meteorol. Gidrol., 2021, no. 6, pp. 107–111.

  239. Shatunova, M.V., Khlestova, O.Yu., and Chubarova, N.E., Forecast of microphysical and optical characteristics of large-scale cloud cover and its radiative effect using the COSMO mesoscale weather prediction model, Atmos. Oceanic Opt., 2020, vol. 33, no. 2, pp. 154–160.

    Article  CAS  Google Scholar 

  240. Shepetov, A., Antonova, V., Kalikulov, O., et al., The prolonged gamma ray enhancement and the short radiation burst events observed in thunderstorms at Tien Shan, Atmos. Res., 2021, vol. 248, p. 105266. https://doi.org/10.1016/j.atmosres.2020.105266

    Article  CAS  Google Scholar 

  241. Shestakova, A.A. and Toropov, P.A., Orographic and lake effect on extreme precipitation on the Iranian coast of the Caspian Sea: A case study, Meteorol. Atmos. Phys., 2021, vol. 133, pp. 69–84. https://doi.org/10.1007/s00703-020-00735-4

    Article  ADS  Google Scholar 

  242. Shikhov, A.N., Chernokul’skii, A.V., Sprygin, A.A., Azhigov, I.O., Identification of mesoscale convective cloud systems with tornadoes using satellite data, Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2019, vol. 16, no. 1, pp. 223–236

    Article  Google Scholar 

  243. Shikhov, A.N., Kalinin, N.A., Bykov, A.V., et al., Tornadoes under weak convective instability of the atmosphere: Analysis of two cases in the east of European Russia, Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2020a, vol. 17, no. 5, pp. 255–268.

    Article  Google Scholar 

  244. Shikhov, A.N., Chernokulsky, A.V., Azhigov, I.O., and Semakina, A.V., A satellite-derived database for stand-replacing windthrow events in boreal forests of European Russia in 1986–2017, Earth Syst. Sci. Data, 2020b, vol. 12, pp. 3489–3513. https://doi.org/10.5194/essd-12-3489-2020

    Article  ADS  Google Scholar 

  245. Shikhov, A.N., Abdullin, R.K., Chernokulsky, A.V., et al., A cartographic database and web service “Convective hazardous meteorological phenomena on the territory of the Central Federal District”, InterKarto. InterGIS, 2021a, vol. 27, no. 3, pp. 120–135.

    Article  Google Scholar 

  246. Shikhov, A., Chernokulsky, A., Kalinin, N., et al., Climatology and formation environments of severe convective windstorms and tornadoes in the Perm region (Russia) in 1984–2020, Atmosphere, 2021b, vol. 12, p. 1407. https://doi.org/10.3390/atmos12111407

    Article  ADS  Google Scholar 

  247. Shikhov, A.N., Chernokulsky, A.V., and Azhigov, I.O., Spatial and temporal distribution of windthrows in the forest zone of Western Siberia in 2001–2020, Cosmic Res., 2022, vol. 60, no. suppl. 1, pp. S91–S103.

  248. Shikhov, A.N., Chernokulsky, A.V., Sprygin, A.A., and Yarynich, Yu.I., Estimation of convective atmospheric instability during squalls, tornadoes, and large hail events from satellite observations and ERA5 reanalysis data, Atmos. Oceanic Opt., 2022b, vol. 35, no. 6, pp. 793–801.

    Article  ADS  Google Scholar 

  249. Shikhovtsev, A.Y., Kovadlo, P.G., Khaikin, V.B., and Kiselev, A.V., Precipitable water vapor and fractional clear sky statistics within the big telescope alt-azimuthal region, Remote Sens., 2022, vol. 14, p. 6221. https://doi.org/10.3390/rs14246221

    Article  ADS  Google Scholar 

  250. Shilin, A.G., Study of the effectiveness of autonomous pyrotechnic generators of ice-forming aerosol under various conditions, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2021a, no. 602, pp. 79–91.

  251. Shilin, A.G., Study of ice-forming agents of active influences in real conditions, Izv. Vyssh. Uchebn. Zaved., Sev.-Kavk. Reg., Estestv. Nauki, 2021b, no. 4, pp. 69–73.

  252. Shilin, A.G. and Khuchunaev, B.M., Possibilities for increasing the efficiency of pyrotechnic ice-forming aerosol generators, Nauka. Innov. Tekhnol., 2022a, no. 1, pp. 87–110.

  253. Shilin, A.G. and Khuchunaev, B.M., Features of ice-forming aerosol generation during the combustion of pyrotechnic composition in the path of the De Laval Nozzle, Russ. Meteorol. Hydrol., 2022b, vol. 47, no. 7, pp. 548–552.

    Article  Google Scholar 

  254. Shilin, A.G., Khuchunaev, B.M., and Budaev, A.Kh., Influence of soluble iodine compounds on the efficiency of ice-forming aerosol, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2021, no. 602, pp. 92–103.

  255. Shilin, A.G., Shilina, A.S., Andreev, Yu.V., et al., Investigation of adsorption modes of molecular iodine and a possibility of modifying ice-forming characteristics of silicate and aluminosilicate aerosol with iodine compounds, Russ. Meteorol. Hydrol., 2022, vol. 47, no. 7, pp. 542–547.

    Article  Google Scholar 

  256. Shishko, V., Konoshonkin, A., Kustova, N., et al., Coherent and incoherent backscattering by a single large particle of irregular shape, Opt. Express, 2019, vol. 27, pp. 32984–32993. https://doi.org/10.1364/OE.27.032984

    Article  ADS  PubMed  Google Scholar 

  257. Shishko, V.A., Konoshonkin, A.V., Kustova, N.V., and Timofeev, D.N., Light scattering by spherical particles for data interpretation of mobile lidars, Opt. Eng., 2020, vol. 59, no. (8), p. 083103. https://doi.org/10.1117/1.OE.59.8.083103

  258. Shishov, A.E. and Gorlach, I.A., Algorithm for recognition and monitoring of deep convection clouds from meteorological artificial Earth satellite data using integer programming, Gidrometeorol. Issled. Prognozy, 2020, no. 2, pp. 39–59.

  259. Shuvalova, J., Chubarova, N., and Shatunova, M., Impact of cloud condensation nuclei reduction on cloud characteristics and solar radiation during Covid-19 lockdown 2020 in Moscow, Atmosphere, 2022, vol. 13. https://doi.org/10.3390/atmos13101710

  260. Shvets’, N.V., Razuvaev, V.N., and Katina, S.P., A specialized dataset of the number of days with precipitation ≥ 1 mm, Tr. Vseross. Nauchno-Issled. Inst. Gidrometeorol. Inf., 2019, no. 185, pp. 67–76.

  261. Shvets’, N.V., Precipitation intensity: Measurement methods, observational databases, use of precipitation intensity data in climate research and for solving applied problems, Tr. Vseross. Nauchno-Issled. Inst. Gidrometeorol. Inf., 2020, no. 186, pp. 69–89.

  262. Sin’kevich, A.A., Mikhailovskii, Yu.P., Matrosov, S.Yu., et al., Relationships between the structure of convective clouds and lightning frequency derived from radiophysical measurements, Russ. Meteorol. Hydrol., 2019, vol. 44, no. 6, pp. 394–403.

    Article  Google Scholar 

  263. Sin’kevich, A.A., Boe, B., Mikhailovskii, Yu.P., and Bogdanov, E.V., Changes in the electrical state of convective clouds when exposed to a crystallizing reagent from an aircraft, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2020a, no. 596, pp. 131–147

  264. Sin’kevich, A.A., Popov, V.B., Mikhailovskii, Yu.P., et al., Characteristics of cumulonimbus with waterspout over Ladoga Lake from remote measurements, Atmos. Oceanic Opt., 2020b, vol. 33, vol. 33, no. 2, pp. 387–392.

  265. Sin’kevich, A.A., Boe, B., Pawar, S.D., et al., Investigation of radar and electrical characteristics of thunderclouds seeded with a glaciogenic reagent in Karnataka, India, Russ. Meteorol. Hydrol., 2021a, vol. 46, no. 8, pp. 545–552.

    Article  Google Scholar 

  266. Sin’kevich, A.A., Toropova, M.L., Mikhailovskii, Yu.P., et al., Features of the relationship between electrical and radar parameters of thunderclouds in India (field studies), Russ. Meteorol. Hydrol., 2021b, vol. 46, no. 6, pp. 410–415.

    Article  Google Scholar 

  267. Sin’kevich, A., Boe, B., Pawar, S., et al., Investigation of thundercloud features in different regions, Remote Sens., 2021c, vol. 13, p. 3216.

    Article  ADS  Google Scholar 

  268. Sin’kevich, A.A., Popov, V.B., Abshaev, A.M., et al., Radar characteristics of convective clouds during transition to the cumulonimbus stage in different regions of the world, Atmos. Oceanic Opt., 2021d, vol. 34, no. 1, pp.134–139.

    Article  ADS  Google Scholar 

  269. Sin’kevich, A.A., Kurov, A.B., Mikhailovskii, Yu.P., et al., Study of thundercloud characteristics in northwest Russia using neural networks, Atmos. Oceanic Opt., 2023, vol. 36, no. 1, pp. 137–143.

    Article  ADS  Google Scholar 

  270. Sinitsyn, A.V. and Gulev, S.K., Comparison of field and satellite data on the total cloudiness for the Atlantic Ocean in the period 2004–2014, Oceanology (Engl. Transl.), 2022, vol. 62, no. 1, pp. 1–7.

  271. Sivkov, B.A. and Kalinin, N.A., Features of the thermodynamic state of the atmosphere during heavy precipitation in the Perm region, Gidrometeorol. Issled. Prognozy, 2020, no. 1, pp. 83–95.

  272. Skakun A.A., Chikhachev K.B., Ekaikin A.A., et al., Isotopic composition of atmospheric precipitation and natural waters in the Barentsburg area (Spitsbergen), Led Sneg., 2020, vol. 60, no. 3, pp. 379–394.

    Google Scholar 

  273. Skorokhodov, A.V., Variability of cloud parameters from satellite data, Russ. Meteorol. Hydrol., 2019, vol. 44, no. 7, pp. 452–458.

    Article  Google Scholar 

  274. Skorokhodov, A.V., Research into the variability of characteristics of cloud manifestations of internal gravity waves during their lifetime based on Himawari-8 satellite data, Izv., Atmos. Ocean. Phys., 2020a, vol. 56, no. 2, pp. 156–164.

    Article  Google Scholar 

  275. Skorokhodov, A.V., Classification of nighttime cloudiness using VIIRS satellite data, Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2020b, vol. 17, no. 3, pp. 240–251.

    Article  Google Scholar 

  276. Skorokhodov, A.V. and Konoshonkin, A.V., Statistical analysis for parameters of specularly reflective layers in high-level clouds over Western Siberia based on MODIS data, Atmos. Oceanic Opt., 2022, vol. 35, no. 1 Suppl., pp. S58–S63.

    Article  ADS  Google Scholar 

  277. Skorokhodov, A.V. and Kur’yanovich, K.V., CALIOP data to estimate cloud base heights in MODIS satellite imagery, Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2022, vol. 19, no. 2, pp. 43–56.

    Article  Google Scholar 

  278. Sorokin, A.G. and Dobrynin, V.A., Method of studying infrasound waves from thunderstorms, J. Sol.-Terr. Phys., 2022, vol. 8, no. 1, pp. 62–68.

    Google Scholar 

  279. Sosnin, E.A., Kuznetsov, V.S., and Panarin, V.A., Energy release in a thundercloud necessary for the formation of middle atmosphere transient light phenomena, Atmos. Oceanic Opt., 2021, vol. 34, no. 8, pp. 722–725.

    Article  ADS  Google Scholar 

  280. Sozaeva, L.T., Backscatter of radio waves by spheroidal rain drops, Radiophys. Quantum Electron., 2022a, vol. 64, no. 8–9, pp. 659–664.

    Article  ADS  Google Scholar 

  281. Sozaeva, L.T., On the possibility of using the spheroidal particle model to calculate the characteristics of radio wave scattering from elongated cloud crystals, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2022b, no. 606, pp. 133–144.

  282. Sozaeva, L.T. and Zhaboeva, M.M., Backscattering of radar radiation by clouds and raindrops, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2020, no. 599, pp. 140–150.

  283. Sozaeva, L.T. and Zhaboeva, M.M., Assessment of droplet deformation influence on precipitation intensity using the radar method, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2021, no. 602, pp. 104–115.

  284. Spivak A.A., Rybnov Yu.S., Ryabova S.A. A complex prognostic feature of dangerous atmospheric events, Dokl. Earth Sci., 2022, vol. 504, no. 1, pp. 291–295.

    Article  ADS  CAS  Google Scholar 

  285. Sprygin, A.A. and Vyazilov, A.E., Study of the mesoscale convective system in central regions of European Russia on August 7, 2021, Gidrometeorol. Issled. Prognozy, 2022, no. 2, pp. 69–91.

  286. Sterlyadkin, V.V., Some aspects of the scattering of light and microwaves on non-spherical raindrops, Atmosphere, 2020, vol. 11, p. 531. https://doi.org/10.3390/atmos11050531

    Article  ADS  Google Scholar 

  287. Strunin, M.A., Metody issledovaniya termodinamicheskogo sostoyaniya atmosfery s pomoshch’yu samoleta-laboratorii (Methods for Studying the Thermodynamic State of the Atmosphere Using a Laboratory Aircraft), Moscow: Shans, 2020a.

  288. Strunin, M.A., Estimation of accuracy of temperature and wind measurements in the AMDAR system using the Yak-42D “Roshydromet” research aircraft data, Russ. Meteorol. Hydrol., 2020b, vol. 45, no. 8, pp. 102–117.

    Article  Google Scholar 

  289. Stulov, E.A., Sosnikova, E.V., Kirin, D.V., Monakhova, N.A., and Pozdeev, V.N., Study of the characteristics of cloud condensation nuclei in the Moscow region, in Vserossiiskaya konferentsiya po fizike oblakov i aktivnym vozdeistviyam na gidrometeorologicheskie protsessy, Sb. nauchn. tr. (Proceedings of All-Russian Conference on Cloud Physics and Active Impacts on Hydrometeorological Processes), Nalchik, 2021, pp. 63–68.

  290. Svechnikova, E.K., Ilin, N.V., Mareev, E.A., and Chilingarian, A.A., Characteristic features of the clouds producing thunderstorm ground enhancements, J. Geophys. Res.: Atmos., 2021, vol. 126. https://doi.org/10.1029/2019JD030895

  291. Sviashchennikov, P. and Drugorub, A., Long-term trends in total cloud cover in the arctic based on surface observations in 1985–2020, Bull. Geogr., Phys. Geogr. Ser., 2022, vol. 22, pp. 33–43. https://doi.org/10.12775/bgeo-2022-0003

    Article  Google Scholar 

  292. Sviyazov, E.M. and Vetrov, A.L., Numerical modeling of heavy summer precipitation for various regular grid spacing options, Geogr. Vestn., 2021, no. 4, pp. 73–83.

  293. Tarabukina, L. and Kozlov, V., Seasonal variability of lightning activity in Yakutia in 2009–2019, Atmosphere, 2020, vol. 11, p. 918. https://doi.org/10.3390/atmos11090918

    Article  ADS  Google Scholar 

  294. Tarasenkov, M.V., Engel, M.V., Zonov, M.N., and Belov, V.V., Assessing the cloud adjacency effect on retrieval of the ground surface reflectance from MODIS satellite data for the Baikal region, Atmosphere, 2022, vol. 13, p. 2054. https://doi.org/10.3390/atmos13122054

    Article  ADS  Google Scholar 

  295. Tarasov, A.V., Estimating the accuracy of cloud mask extraction algorithms from Sentinel-2 and PlanetScope data, Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2020, vol. 17, no. 7, pp. 26–40.

    Article  Google Scholar 

  296. Tentyukov, M.P., Gabov, D.N., Simonenkov, D.V., and Yazikov, E.G., Contamination of the snow surface with polycyclic aromatic hydrocarbons during frost, Led Sneg., 2019, vol. 59, no. 4, pp. 483–493

    Google Scholar 

  297. Timofeev, D.N., Konoshonkin, A.V., Kustova, N.V., et al., Estimation of the absorption effect on light scattering by atmospheric ice crystals for wavelengths typical for problems of laser sounding of the atmosphere, Atmos. Oceanic Opt., 2019, vol. 32, no. 5, pp. 564–568.

    Article  CAS  Google Scholar 

  298. Timofeev, D.N., Konoshonkin, A.V., Kustova, N.V., and Shishko, V.A., Light Backscattering properties of distorted hexagonal atmospheric ice particles within the physical optics Approximation, Atmos. Oceanic Opt., 2022, vol. 35, no. 1, pp. 158–163.

    Article  ADS  Google Scholar 

  299. Tishchenko, V.A., Khan, V.M., Kruglova, E.N., and Kulikova, I.A., Monthly and seasonal prediction of precipitation and air temperature in the Amur River basin, Russ. Meteorol. Hydrol., 2019, vol. 44, no. 3, pp. 169–179.

    Article  Google Scholar 

  300. Tkachev, I.V., Timofeev, D.N., Kustova, N.V., and Konoshonkin, A.V., Databank of matrices of light backscattering on atmospheric ice crystals of 10–100 microns for interpretation of laser sensing data, Opt. Atmos. Okeana, 2021, vol. 34, no. 3, pp. 199–206.

    Google Scholar 

  301. Topchaya, V.Yu. and Chechko, V.A., Study of insoluble atmospheric material of the snow cover of the coastal zone of the southeastern Baltic Sea, Reg. Stud. Mar. Sci., 2022, vol. 52, p. 102399. https://doi.org/10.1016/j.rsma.2022.102399

    Article  Google Scholar 

  302. Topchaya, V.Yu. and Kotova, E.I., Composition of rainfall in the coastal zone of the Kaliningrad region of the Russian Federation (based on data from 2019), Pure Appl. Chem., 2022, vol. 94, no. 3, pp. 285–290. https://doi.org/10.1515/pac-2021-0302

    Article  CAS  Google Scholar 

  303. Toropova, M.L. and Rusin I.N., Reproducing atmospheric stratification to predict convective phenomena using the WRF-ARW mesoscale model, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2019, no. 593, pp. 160–176.

  304. Toropov, P.A., Shestakova, A.A., Yarynich, Yu.I., and Kutuzov, S.S., Modeling the orographic component of precipitation: Test case of Elbrus, Led Sneg., 2022a, vol. 62, no. 4, pp. 485–503.

    Google Scholar 

  305. Toropova, M.L., Mikhailovskii, Yu.P., Veremei, N.E., et al., Ensemble forecast for the development of thunderclouds in the northwest of European Russia and verification of modeling results, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2022b, no. 606, pp. 7–31.

  306. Toropova, M.L., Sin’kevich, A.A., Pawar, S., et al., Characteristics of monsoon and post-monsoon thunderclouds in India, Russ. Meteorol. Hydrol., 2022c, no. 8, pp. 68–79.

  307. Travova, S.V., Tolstykh, M.A., and Shashkin, V.V., Assessment of the forecast of heavy precipitation from the PLAV20 operational global atmospheric model, Gidrometeorol. Issled. Prognozy, 2020, no. 1, pp. 96–112.

  308. Tretii otsenochnyi doklad ob izmeneniyakh klimata i ikh posledstviyakh na territorii Rossiiskoi Federatsii (Third Assessment Report on Climate Changes and Their Effects on the Territory of the Russian Federation), Kattsov, V.M., Ed., St. Petersburg: Naukoemkie tekhnologii, 2022.

  309. Ukraintsev, A.V., Plyusnin, A.M., and Zaikovskii, V.I., Morphology and chemical composition of dispersed particles in the snow cover of burnt forest areas in Western Transbaikalia (Russia), Appl. Geochem., 2020, vol. 122, p. 104723. https://doi.org/10.1016/j.apgeochem.2020.104723

    Article  CAS  Google Scholar 

  310. Vasil’chuk, Y., Chizhova, J., Budantseva, N., et al., Stable isotope composition of precipitation events revealed modern climate variability, Theor. Appl. Climatol., 2022, vol. 147, pp. 1649–1661. https://doi.org/10.1007/s00704-021-03900-w

    Article  ADS  Google Scholar 

  311. Vasil’ev, D.Yu., Kucherov, S.E., Semenov, V.A., and Chibilev, A.A., Reconstruction of precipitation by radial growth of Scots pine in the Southern Urals, Dokl. Earth Sci., 2020, vol. 490, no. 1, pp. 31–35.

    Article  ADS  Google Scholar 

  312. Veremei, N.E., Dovgalyuk, Yu.A., Toropova, M.L., et al., The influence of thermal inhomogeneities of the underlying surface on the formation and development of convective clouds and related hazardous weather phenomena, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2022, no. 606, pp. 32–49.

  313. Veselovskii, I., Hu, Q., Goloub, P., et al., Combined use of Mie-Raman and fluorescence lidar observations for improving aerosol characterization: Feasibility experiment, Atmos. Meas. Tech., 2020, vol. 13, pp. 6691–6701. https://doi.org/10.5194/amt-13-6691-2020

    Article  CAS  Google Scholar 

  314. Vetrov, A.L. and Kostarev, S.V., Applicability of multimodel ensemble prediction of heavy precipitation for the Perm Region: A case study for the summer of 2019, Russ. Meteorol. Hydrol., 2021, vol. 46, no. 7, pp. 443–453.

    Article  Google Scholar 

  315. Vladimirov, S.A., Kirin, D.V., Krutikov, N.O., and Pastushkov, R.S., Approximation of distribution functions of cloud drops and ice crystals according to measurement data from the microphysical complex of the Roshydromet YAK-42D aircraft laboratory for numerical models of clouds and precipitation and active influences on them, in Vserossiiskaya konferentsiya po fizike oblakov i aktivnym vozdeistviyam na gidrometeorologicheskie protsessy, Sb. nauchn. tr. (Proceedings of All-Russian Conference on Cloud Physics and Active Impacts on Hydrometeorological Processes), Nalchik, 2021, pp. 8–12.

  316. Vlasov, D., Vasil’chuk, J., Kosheleva, N., and Kasimov, N., Dissolved and suspended forms of metals and metalloids in snow cover of megacity: Partitioning and deposition rates in western Moscow, Atmosphere, 2020, vol. 11, p. 907. https://doi.org/10.3390/atmos11090907

    Article  ADS  CAS  Google Scholar 

  317. Vlasov, D.V., Kasimov, N.S., Eremina, I.D., et al., Partitioning and solubilities of metals and metalloids in spring rains in Moscow megacity, Atmos. Pollut. Res., 2021, vol. 12, no. 1, pp. 255–271. https://doi.org/10.1016/j.apr.2020.09.012

    Article  CAS  Google Scholar 

  318. Volkov, V.V., Kolokutin, G.E., Strunin, M.A., and Bazanin, N.V., The onboard data-acquisition system of research aircraft for studying atmospheric processes, Instrum. Exp. Tech., 2019, vol. 62, no. 3, pp. 401–407.

    Article  Google Scholar 

  319. Volkov, V.V., Strunin, M.A., and Strunin, A.M., Determination of wind shear and turbulence intensity according to Yak42-D “Roshydromet” research aircraft data, Russ. Meteorol. Hydrol., 2021a, vol. 46, no. 9, pp. 640–649.

    Article  Google Scholar 

  320. Volkov, V.V., Kirin, D.V., Petrov, V.V., and Strunin, A.M., Study of the microphysical characteristics of winter clouds of a warm front from the YAK-42D Roshydromet laboratory aircraft, in Vserossiiskaya konferentsiya po fizike oblakov i aktivnym vozdeistviyam na gidrometeorologicheskie protsessy, Sb. nauchn. tr. (Proceedings of All-Russian Conference on Cloud Physics and Active Impacts on Hydrometeorological Processes), Nalchik, 2021b, pp. 68–73.

  321. Volkov, V.V., Petrov, V.V., and Krutikov, N.O., Measurement of cloud water content from a research aircraft, in Physics of the Atmosphere, Climatology and Environmental Monitoring. Modern Problems of Atmospheric Physics, Springer, 2022, pp. 339–346.

    Google Scholar 

  322. Volkova, E.V. and Kukharskii, A.V., Automated technology for diagnosing the parameters of cloud cover, precipitation, and hazardous weather phenomena for the European territory of Russia using SEVIRI radiometer data from geostationary weather satellites of the Meteosat MSG series, Gidrometeorol. Issled. Prognozy, 2020, no. 4, pp. 43–62.

  323. Volkova, E.V., Andreev, A.I., Kostornaya, A.A., Cloud Cover and Precipitation Monitoring Based on Data from Polar Orbiting and Geostationary Satellites, Russ. Meteorol. Hydrol., 2021, vol. 46, no. 12, pp. 830–838.

    Article  Google Scholar 

  324. Volodin, E.M., Equilibrium sensitivity of a climate model to an increase in the atmospheric CO2 concentration using different methods to account for cloudiness, Izv., Atmos. Ocean. Phys., 2021, vol. 57, no. 2, pp. 127–132.

    Article  Google Scholar 

  325. Volodin, E., The mechanisms of cloudiness evolution responsible for equilibrium climate sensitivity in climate model INM-CM4-8, Geophys. Res. Lett., 2021, vol. 48. https://doi.org/10.1029/2021GL096204

  326. Volodina, D.A., Talovskaya, A.V., Devyatova, A.Yu., et al., Elemental composition of dust aerosols near cement plants based on the study of samples of the solid phase of the snow cover, Pure Appl. Chem., 2022, vol. 94, no. 3, pp. 269–274. https://doi.org/10.1515/pac-2021-0315

    Article  CAS  Google Scholar 

  327. Voropay, N., Ryazanova, A., and Dyukarev, E., High-resolution bias-corrected precipitation data over South Siberia, Russia, Atmos. Res., 2021, vol. 254, p. 105528. https://doi.org/10.1016/j.atmosres.2021.105528

    Article  Google Scholar 

  328. Voskanyan, K.L., Zamorin, I.S., Kryukova, S.V., et al., Comparison of the efficiency meteorological object detection by two Doppler radars in the Leningrad region, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2019, no. 592, pp. 80–97.

  329. Vyshkvarkova, E. and Sukhonos, O., Compound extremes of air temperature and precipitation in Eastern Europe, Climate, 2022, vol. 10, p. 133. https://doi.org/10.3390/cli10090133

    Article  Google Scholar 

  330. Wang, P., Huang, Q., Tang, Q., et al., Increasing annual and extreme precipitation in permafrost-dominated Siberia during 1959–2018, J. Hydrol., 2021, vol. 603, p. 126865. https://doi.org/10.1016/j.jhydrol.2021.126865

    Article  Google Scholar 

  331. Wang, Z., Shishko, V., Kustova, N., et al., Radar-lidar ratio for ice crystals of cirrus clouds, Opt. Express, 2021, vol. 29, pp. 4464–4474. https://doi.org/10.1364/OE.410942

    Article  ADS  PubMed  CAS  Google Scholar 

  332. Yakovlev, E., Druzhinina, A., Zykova, E., et al., Assessment of heavy metal pollution of the snow cover of the Severodvinsk industrial district (NW Russia), Pollution, 2022, vol. 8, no. 4, pp. 1274–1293. https://doi.org/10.22059/poll.2022.341500.1438

    Article  CAS  Google Scholar 

  333. Yakovleva, V., Zelinskiy, A., Parovik, R., et al., Model for reconstruction of γ-background during liquid atmospheric precipitation, Mathematics, 2021, vol. 9, p. 1636. https://doi.org/10.3390/math9141636

    Article  Google Scholar 

  334. Zharashuev, M., Precipitation measurement with increased water collector area, Russ. Inzh., 2019a, no. 3, pp. 45–48.

  335. Zharashuev, M.V., Statistical analysis of the recurrence of lightning discharges of the cloud–cloud type in the North Caucasus republics and the Stavropol Territory, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2019b, no. 595, pp. 145–152.

  336. Zharashuev, M.V., Comparison of statistical data on thunderstorm and hail activity in the North Caucasus, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2021, no. 603, pp. 145–154.

  337. Zharashuev, M.V., Methodology for automated statistical analysis of cloud–ground discharges for the territory of the North Caucasus, Meteorol. Gidrol., 2022a, no. 4, pp. 111–116.

  338. Zharashuev, M.V., Optimization of the operation of the radar network of the Russian Federation, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2022b, no. 606, pp. 145–151.

  339. Zhurba, O.M., Alekseenko, A.N., Shayakhmetov, S.F., Merinov, A.V., Study of polycyclic aromatic and petroleum hydrocarbons in snow cover in an urbanized area, Gig. Sanit., 2019, vol. 98, no. 10, pp. 1037–1042.

    Article  CAS  Google Scholar 

  340. Zolotukhina, O.I., Thermodynamic conditions for the formation of dangerous convective phenomena in the region of the Vostochny Cosmodrome, Tr. VKA im. A.F. Mozhaiskogo, 2020, no. S674, pp. 181–187.

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    N. A. Bezrukova

  2. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, 119017, Moscow, Russia

    A. V. Chernokulsky

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Bezrukova, N.A., Chernokulsky, A.V. Russian Studies on Clouds and Precipitation in 2019–2022. Izv. Atmos. Ocean. Phys. 59 (Suppl 3), S294–S325 (2023). https://doi.org/10.1134/S0001433823150033

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