Vol. 37, issue 06, article # 13
Copy the reference to clipboard
Abstract:
Atmospheric monitoring from global satellite navigation systems is usually used to estimate the integral water vapor of the atmosphere. In addition, such parameters as the zenith tropospheric delay of satellite radio signals and its gradient parameters characterizing atmospheric mesoscale irregularities measured with high temporal resolution. The work shows a significant variability of these atmospheric characteristics associated with sever convective weather phenomena. A sample of several hundred events of severe weather phenomena corresponding to available observations of the nearest satellite stations in the Republic of Tatarstan and Moscow region located at latitudes 55–56° N is used. It was found that under the conditions of severe weather phenomena, the inhomogeneity of the zenith tropospheric delay field of satellite signals strongly increases which manifested in the increase in its gradient parameters and their fluctuations, as well as in the growth of the integral water vapor. The intensity of fluctuations of integral water vapor most strongly changes if the station is located not further than 20 km from a hazardous phenomenon, which is explained by the size of convective cells. However, even at the station location at distances of up to 200 km from hazardous phenomena, an increase in the atmospheric integral water vapor and the effect of amplification of inhomogeneity as compared to mean multiyear data are observed.
Keywords:
GNSS monitoring, atmospheric convection, severe weather phenomena, mesoscale inhomogeneity
References:
1. Rukovodstvo po praktike meteorologicheskogo obslujivaniya naseleniya. 2000. ВМО N 834 // World meteorological organization. URL: https://library.wmo.int/records/item/43384------?offset=1 (data obrashcheniya: 03.11.2023).
2. Gensini V.V. Severe convective storms in a changing climate // Clim. Change Extreme Events. 2021. P. 39–56. DOI: 10.1016/B978-0-12-822700-8.00007-X.
3. Kharyutkina E.V., Loginov S.V., Moraru E.I., Pustovalov K.N., Martynova Yu.V. Dinamika harakteristik ekstremal'nosti klimata i tendentsii opasnyh meteorologicheskih yavlenii na territorii Zapadnoi Sibiri // Optika atmosf. i okeana. 2022. V. 35, N 2. P. 136–142. DOI: 10.15372/AOO20220208; Kharyutkina E.V., Loginov S.V., Moraru E.I., Pustovalov K.N., Martynova Yu.V. Dynamics of extreme climatic characteristics and trends of dangerous meteorological phenomena over the territory of Western Siberia // Atmos. Ocean. Opt. 2022. V. 35, N 4. P. 394–401. DOI: 10.1134/S1024856022040078.
4. Sumak E.N., Semenova I.G. Tsiklonicheskaya aktivnost' i povtoryaemost' opasnyh yavlenii pogody nad territoriei Belarusi // Jurn. Belorus. gos. un-ta. Geografiya. Geologiya. 2019. N 2. P. 79–93. DOI: 10.33581/2521-6740-2019-2-79-93.
5. Davis I., Li F., Chavas D. Future changes in the vertical structure of severe convective storm environments over the US central Great Plains // arXiv preprint arXiv:2310.11631.
6. Rivin G.S., Rozinkina I.A., Vil'fand R.M., Kiktev D.B., Tudrii K.O., Blinov D.V., Varentsov M.I., Zaharchenko D.I., Samsonov T.E., Repina I.A., Artamonov A.Yu. Sistema COSMO-Ru negidrostaticheskogo mezomasshtabnogo kratkosrochnogo prognoza pogody Gidromettsentra Rossii: vtoroi etap realizatsii i razvitiya // Meteorol. i gidрол. 2015. N 6. P. 58–70.
7. Rivin G.S., Rozinkina I.A., Vil'fand R.M., Kiktev D.B., Tudrii K.O., Blinov D.V., Varentsov M.I., Zaharchenko D.I., Samsonov T.E., Repina I.A., Artamonov A.Yu. Razrabotka operativnoi sistemy chislennogo prognoza pogody i uslovii vozniknoveniya opasnyh yavlenii s vysokoi detalizatsiei dlya Moskovskogo megapolisa // Meteorol. i gidрол. 2020. N 7. P. 5–19.
8. Alekseeva A.A. Prognoz uragannyh vetrov vnetropicheskih tsiklonov na territorii Rossii // Meteorol. i gidрол. 2017. N 1. P. 5–15.
9. Alekseeva A.A. Metod prognoza sil'nyh shkvalov // Meteorol. i gidрол. 2014. N 9. P. 5–15.
10. Krinitskiy M., Sprygin A., Elizarov S., Narizhnaya A., Shikhov A., Chernokulsky A. Towards the accurate automatic detection of mesoscale convective systems in remote sensing data: From data mining to deep learning models and their applications // Remote Sens. 2023. V. 15, N 14. P. 3493. DOI: 10.3390/rs15143493.
11. Bevis M.S., Businger T.A. GPS meteorology: Remote sensing of atmospheric water vapor using the Global Positioning System // J. Geophys. Res. 1992. V. 97, N D14. P. 15787–15801. DOI: 10.1029/92JD01517.
12. Hofmann-Wellenhof B., Lichtenegger H., Collins J. Global Positioning System. Theory and Practice. Wien, New York: Springer-Verlag, 1994. 356 p.
13. Xu G. GPS. Theory, algorithms and applications. Berlin: Springer, 2007. 340 p.
14. Barindelli S., Realini E., Venuti G., Fermi A., Gatti A. Detection of water vapor time variations associated with heavy rain in northern Italy by geodetic and low-cost GNSS receivers // Earth, Planets and Space. 2018. V. 70, N 1. P. 1–18. DOI: 10.1186/s40623-018-0795-7.
15. Lasota E., Slavchev M., Guerova G., Rohm W., Kapłon J. Combined space- and ground-based GNSS monitoring of two severe hailstorm cases in Bulgaria // J. Atmos. Oceanic Technol. 2022. V. 39. P. 649–665. DOI: 10.1175/JTECH-D-21-0100.1
16. Łoś M., Smolak K., Guerova G., Rohm W. GNSS-based machine learning storm nowcasting // Remote Sens. 2020. V. 12. 2536. P. 1–13. DOI: 10.3390/rs12162536.
17. Nykiel G., Figurski M., Baldysz Z. Analysis of GNSS sensed precipitable water vapour and tropospheric gradients during the derecho event in Poland of 11th August 2017 // J. Atmos. Sol.-Terr. Phys. 2019. V. 193. DOI: 10.1016/j.jastp.2019.105082.
18. Aichinger-Rosenberger M., Aregger M., Kopp J. Detecting signatures of convective storm events in GNSS-SNR: Two case studies from summer 2021 in Switzerland // Geophys. Res. Lett. V. 50, N 212023. P. 1–11. DOI: 10.1029/2023GL104916.
19. Dotzek N., Groenemeijer P., Feuerstein B., Holzer A.M. Overview of ESSL’s severe convective storms research using the European Severe Weather Database ESWD // Atmos. Res. 2009. V. 93. P. 575–586. DOI: 10.1016/j.atmosres.2008.10.020.
20. Hutorova O.G., Maslova M.V., Hutorov V.E. Proyavlenie konvektivnyh protsessov v ryadah integral'nogo vlagosoderjaniya atmosfery po mnogoletnim dannym monitoringa troposfery signalami sputnikovyh navigatsionnyh sistem v g. Kazani // Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2023. V. 20, N 3. P. 271–281. DOI: 10.21046/2070-7401-2023-20-3-271-281.
21. Hutorova O.G., Maslova M.V., Hutorov V.E. Vliyanie sil'noi konvektsii v letnii period na harakteristiki atmosfery poluchennye po dannym GNSS-monitoringa // Optika atmosf. i okeana. 2024. V. 37, N 2. P. 163–168. DOI: 10.15372/AOO20240211.
22. IGS: International GNSS Service. 2020. URL: https://igs.org/ (05.11.2023).
23. Kobzar' A.I. Prikladnaya matematicheskaya statistika. M.: Fizmatlit, 2006. 468 p.
24. Vel'tishchev N.F., Stepanenko V.M. Mezometeorologicheskie protsessy: ucheb. posobie M.: MGU, 2007. 126 p.
25. Orlanski I. A rational subdivision of scales for atmospheric processes // Bull. Am. Meteorol. Soc. 1975. V. 56, N 5. P. 527–530.
26. Schumacher R.S., Rasmussen K.L. The formation, character and changing nature of mesoscale convective systems // Nat. Rev. Earth Environ. 2020. V. 1, N 6. P. 300–314. DOI: 10.1038/s43017-020-0057-7.