Vol. 35, issue 06, article # 12

Abstract:

The problem of revealing relationships between atmospheric parameters measured with GNSS receivers and characteristics of convective processes based on monitoring data in Kazan for 2010–2020 is solved in the work. Coherence and synchrony of variations in the precipitation intensity and potentially available convective energy with variations in the zenith tropospheric delay are most often detected on time scales smaller than 4 hours, with the wavelet correlation level higher than 0.8 in most cases.

Keywords:

GNSS, atmospheric convection, precipitation, zenith tropospheric delay

References:

1. Kokhanenko G.P., Balin Yu.S., Klemasheva M.G., Penner I.E., Samoilova S.V., Terpugova S.A., Banakh V.A., Smalikho I.N., Falits A.V., Rasskazchikova T.M., Antokhin P.N., Arshinov M.Yu., Belan B.D., Belan S.B. Struktura aerozol'nyh polej pogranichnogo sloya atmosfery po dannym aerozol'nogo i doplerovskogo lidarov v period prohozhdeniya atmosfernyh frontov // Optika atmosf. i okeana. 2016. V. 29, N 8. P. 679–688; Kokhanenko G.P., Balin Yu.S., Klemasheva M.G., Penner I.E., Samoilova S.V., Terpugova S.A., Banakh V.A., Smalikho I.N., Falits A.V., Rasskazchikova T.M., Antokhin P.N., Arshinov M.Yu., Belan B.D., Belan S.B. Structure of aerosol fields of the atmospheric boundary layer according to aerosol and doppler lidar data during passage of atmospheric fronts // Atmos. Ocean. Opt. 2017. V. 30, N 1. P. 18–32.
2. Kalinin N.A., Shikhov A.N., Bykov A.V., Pomortseva A.A., Abdullin R.K., Azhigov I.O. Usloviya formirovaniya i kratkosrochnyj prognoz konvektivnyh opasnyh yavlenij pogody v Ural'skom regione v teplyj period 2020 year // Optika atmosf. i okeana. 2021. V. 34, N 1. P. 46–56; Kalinin N.A., Shikhov A.N., Bykov A.V., Pomortseva A.A., Abdullin R.K., Azhigov I.O. Formation conditions and short-term forecast of convective hazardous weather events in the Ural region in the warm period of 2020 // Atmos. Ocean. Opt. 2021. V. 34, N 3. P. 250–262.
3. Kalinin N.A., Shihov A.N., Bykov A.V. Prognoz mezomasshtabnyh konvektivnyh sistem na Urale s pomoshch'yu modeli WRF i dannyh distantsionnogo zondirovaniya // Meteorol. i gidrol. 2017. N 1. P. 16–28.
4. Rivin G.S., Rozinkina I.A., Vil'fand R.M., Kiktev D.B., Tudrij K.O., Blinov D.V., Varentsov M.I., Zaharchenko D.I., Samsonov T.E., Repina I.A., Artamonov A.Yu. Razrabotka operativnoj sistemy chislennogo prognoza pogody i uslovij vozniknoveniya opasnyh yavlenij s vysokoj detalizatsiej dlya Moskovskogo megapolisa // Meteorol. i gidrol. 2020. N 7. P. 5–19.
5. Aloyan A.E., Arutyunyan V.O., Ermakov A.N. Matematicheskoe modelirovanie konvektivnoj oblachnosti v polyarnyh regionah // Optika atmosf. i okeana. 2017. V. 30, N 3. P. 222–226.
6. Shakina N.P. Prognoz pogody dlya aviatsii na osnove produktsii chislennyh modelej atmosfery // Gidrometeorologicheskie issledovaniya i prognozy. 2019. N 4. P. 241–256.
7. Kurbatova M.M., Rubinshtejn K.G. Gibridnyj metod prognoza poryvov vetra // Optika atmosf. i okeana. 2018. V. 31, N 7. P. 523–529.
8. Taszarek M., Brooks H.E., Czernecki B. Sounding-derived parameters associated with convective hazards in Europe // Mon. Weather Rev. 2017. V. 145, N 4. P. 1511–1528.
9. Kalinin N.A., Shihov A.N., Chernokul'skij A.V., Kostarev S.V., Bykov A.V. Usloviya vozniknoveniya sil'nyh shkvalov i smerchej, vyzyvayushchih krupnye vetrovaly v lesnoj zone Evropejskoj chasti Rossii i Urala // Meteorol. i gidrol. 2021. N 2. P. 35–49.
10. Gao Sh., Du N., Min J., Yu H. Impact of assimilating radar data using a hybrid 4DEnVar approach on prediction of convective events // Dyn. Meteorol. Ocean. 2021. V. 73, N 1. P. 1–19.
11. Bevis M., Businger S., Herring T.A., Rocken Ch., Anthes R.A., Ware R.H. GPS meteorology: Remote sensing of atmospheric water vapor using the Global Positioning System // J. Geophys. Res. 1992. V. 97, N D14. P. 15787–15801.
12. Hofmann-Wellenhof B., Lichtenegger H., Collins J. Global Positioning System. Theory and Practice. Wien; New York: Springer, 1994. 356 p.
13. Kalinnikov V.V., Hutorova O.G. Validatsiya integral'nogo soderzhaniya vodyanogo para po dannym nazemnyh izmerenij signalov GNSS // Izv. RAN. Fiz. atmosf. i okeana. 2019. V. 55, N 4. P. 58–63.
14. Xu G. GPS. Theory, algorithms and applications. Berlin: Springer, 2007. 340 p.
15. Lindskog M., Ridal M., Thorsteinsson S., Ning T. Data assimilation of GNSS zenith total delays from a Nordic processing centre // Atmos. Chem. Phys. 2017. N 17. P. 13983–13998.
16. Hutorova O.G., Blizrukov A.S., Dement'ev V.V., Hutorov V.E. Zondirovanie mezomasshtabnoj struktury troposfery v periody prohozhdeniya atmosfernyh frontov // Sovremennye problemy distantsionnogo zondirovaniya zemli iz kosmosa. 2019. V. 16, N 6. P. 254–262.
17. ERA5 hourly data on single levels from 1979 to present. URL: https://cds.climate.copernicus.eu/cdsapp#!dataset/reanalysis-era5-single-levels?tab=overview (last access: 20.12.2021).
18. Torrence G., Compo G.P. A practical guide to wavelet analysis // Bull. Am. Meteorol. Soc. 1998. V. 79, N 1. P. 61–78.
19. Bykov A.V., Vetrov A.L., Kalinin N.A. Prognoz opasnyh konvektivnyh yavlenij v Permskom krae s ispol'zovaniem global'nyh prognosticheskih modelej // Tr. Gidromettsentra Russia. 2017. N 363. P. 101–119.
20. Miller R.C. Notes on Analysis and Severe Storm Forecasting Procedures of the Air Force Global Weather Center. Tech. Report No 200. Illinois, 1972. 190 p.
21. Jelić D., Prtenjak M.T., Malečić B., Vozila A.B., Megyeri O.A., Renko T. A new approach for the analysis of deep convective events: Thunderstorm intensity index // Atmos. 2021. V. 12, N 7. P. 908–934.
22. 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.
23. 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 Space. 2018. V. 70, N 1. P. 1–18.
24. Camisaya M.F., Rivera J.A., Mateo M.L., Morichetti P.V., Mackern M.V. Estimation of integrated water vapor derived from Global Navigation Satellite System observations over Central-Western Argentina (2015–2018). Validation and usefulness for the understanding of regional precipitation events // J. Atmos. Sol.-Terr. Phys. 2020. V. 197. P. 1–12.
25. Ziarani M.R., Bookhagen B., Schmidt T., Wickert J., De la Torre A., Deng Z., Calori A. A model for the relationship between rainfall, GNSS-derived integrated water vapour, and CAPE in the Eastern Central Andes // Remote Sens. 2021. V. 13, N 18. P. 1–19.
26. Guerova G., Dimitrova T., Georgiev S. Thunderstorm classification functions based on instability indices and GNSS IWV for the Sofia Plain // Remote Sens. 2019. V. 11, N 24. P. 2988–3005.
27. Litta A.J., Mohanty U.C., Das S., Mary Indicula S. Numerical simulation of severe local storms over east India using WRF-NMM mesoscale model. // Atmos. Res. 2012. V. 116. P. 161–184.