Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, Vol. 20, No. 3, pp. 271-281

Convective processes manifestation in integral water vapor series of the atmosphere based on long-term data of tropospheric monitoring by satellite navigation systems signals in Kazan

O.G. Khutorova 1 , M.V. Mamaslova 1 , V.E. Khutorov 1 
1 Kazan Federal University, Kazan, Russia
Accepted: 19.05.2023
DOI: 10.21046/2070-7401-2023-20-3-271-281
Global navigation satellite systems (GNSS) make it possible to obtain estimates of atmospheric integral water vapor with high temporal and spatial resolution. This is a promising technology for all-weather monitoring of mesoscale convective processes due to the growing number of hazardous phenomena associated with atmospheric convection. The paper shows solution to the problem of identifying the relationship between integral water vapor of the atmosphere measured by GNSS receivers and convective processes characteristics, on the basis of monitoring data in Kazan. Kazan University has received long series of 2009–2021 GNSS monitoring data for the atmosphere in Kazan with a time resolution of 5 minutes. The results of GNSS monitoring were compared with convective processes intensity indicators for the entire observation period. To estimate these convective processes, we used physical and statistical parameters of instability calculated from meteorological parameters presented as ERA5 reanalysis data obtained by ECMWF model. We used such convective processes indicators as convective available potential energy, convective inhibition, amount of precipitation, upward vertical velocity, vortex generation parameter, and indices Vertical Totals, Total Totals, K-Index, WMAXSHEAR. It is shown that statistical characteristics of atmospheric integral water vapor vary significantly depending on CAPE, WMAXSHEAR, upward vertical velocity, and vortex generation parameter. It was found that the maximum integral water vapor is reached 30–60 minutes earlier than the maximum convective indices variation for the characteristic variation time scales of 2–4 hours.
Keywords: GNSS, atmospheric convection, atmospheric integral water vapor content, ERA5 reanalysis
Full text

References:

  1. Bykov A.V., Vetrov A.L., Kalinin N.A. Forecast of hazardous convective phenomena in the Perm region using global forecasting models, Proceedings of the Hydrometeorological Center of Russia. 2017, No. 363. pp. 101–119. (in Russian)
  2. 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 phenomena in the Ural region in warm period of 2020, Atmosphere and ocean Optics, 2021, Vol. 34. No. 01. S. 46–56. (in Russian) doi: 10.15372/AOO20210106.
  3. Kalinnikov V.V., Khutorova O.G. Validation of the integral content of water vapor according to ground-based measurements of GNSS signals, Izvestiya Rossiiskoi Akademii Nauk. Physics of the atmosphere and ocean, 2019, Vol. 55, No. 4, pp. 58-63. (in Russian) doi: https://doi.org/10.31857/S0002-351555458-63
  4. Khutorova O.G., Maslova M.V., Khutorov V.E. On monitoring of convective processes with the help of receivers of satellite navigation systems, Optics of the atmosphere and ocean, 2022, Vol. 35, No. 6 (401), pp. 505-509. (in Russian) doi: 10.56820/OAOPA.2022.65.78.002
  5. Chernokulsky A.V., Eliseev A.V., Kozlov F.A., Korshunova N.N., Kurgansky M.V., Mokhov I.I., Semenov V.A., Shvets N.V., Shikhov A.N., Yarynich Yu.I. Hazardous atmospheric phenomena of a convective nature in Russia: observed changes according to various data, Meteorology and Hydrology, 2022, No. 5, pp. 27-41. (in Russian) doi: 10.52002/0130-2906-2022-5-27-41
  6. Shikhov A. N., Chernokulskii A. V., Sprygin A. A., Evaluation of the convective instability of the atmosphere in cases with squalls, tornadoes and large hail from satellite observations and ERA5 reanalysis, Atmospheric and Oceanic Optics, 2022, Vol. 35, No. 6 (401), pp. 429-435. (in Russian) doi: 10.15372/AOO20220601
  7. 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 Spac. 2018, Vol. 70, No. 1, pp. 1-18.doi: https://doi.org/10.1186/s40623-018-0795-7
  8. Bar-Sever Y. E., Kroger P. M., Borjesson, J. A., Estimating horizontal gradients of tropospheric path delay with a single GPS receiver, JGR, 1998, 103(B3), pp.5019-5035. doi: https://doi.org/10.1029/97JB03534
  9. Bevis M S. Businger, T. A. GPS meteorology: Remote sensing of atmospheric water vapor using the Global Positioning System, J. Geophys. Res. 1992. Vol. 97, No. D14, pp. 15787-15801. doi:10.1029/92JD01517 doi:10.1029/92JD01517
  10. Blanchard, D. O. Assessing the Vertical Distribution of Convective Available Potential Energy, Weather and Forecasting. 1998 Vol.13(3), pp.870–877. doi: https://doi.org/10.1175/1520-0434(1998)013<0870:ATVDOC>2.0.CO;2
  11. Brooks, H. B., C. A. Doswell III and R. B. Wilhelmson,The role of midtropospheric winds in the evolution and maintenance of low-level mesocyclones. Mon. Wea. Rev. 1994, Vol.122, pp. 126–136. doi: https://doi.org/10.1175/1520-0493(1994)122<0126:TROMWI>2.0.CO;2
  12. Burgess D. W., Lemon L. R., Severe thunderstorm detection by radar, Radar in Meteorology. – American Meteorological Society,1990. pp. 619-647.
  13. 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. Atmospheric and Solar-Terrestrial Physics, 2020, Vol. 197. pp. 1-12. doi: 10.1016/j.jastp.2019.105143
  14. ECMWF. IFS Documentation CY47R3 -Part IV: Physical Processes, https://www.ecmwf.int/, Sept, 2021. 232 p. https://www.ecmwf.int/en/elibrary/81271-ifs-documentation-cy47r3-part-iv-physical-processes
  15. Ferguson, E. W., Ostby F. P, Leftwich P. W., Jr., Carle W. E., Corfidi S. F., Cundy R. G., Hirt W. D., The tornado season of 1983, Mon. Wea. Rev., 1985, Vol. 113, pp. 395-404.
  16. George, J.J. Weather Forecasting for Aeronautics, W:MA, Academic Press 1960 673p.
  17. Gracier.J Convection parameters. 2012 http://www.juergen-grieser.de/ConvectionParameters/ConvectionParameters.pdf
  18. Guerova G., Dimitrova T., Georgiev S. Thunderstorm Classification Functions Based on Instability Indices and GNSS IWV for the Sofia Plain, Remote Sensing, 2019, Vol. 11, No. 24. pp. 2988-3005. doi: 10.3390/rs11242988
  19. Hersbach H., Bell B., Berrisford P., Hirahara S., Horányi A., Muñoz-Sabater J., Nicolas J., Peubey C., Radu R., Schepers D., Simmons A., Soci C., Abdalla S., Abellan X., Balsamo G., Bechtold P., Biavati G., Bidlot J., Bonavita M., De Chiara G., Dahlgren P., Dee D., Diamantakis M., Dragani R., Flemming J., Forbes R., Fuentes M., Geer A., Haimberger L., Healy S., Hogan R. J., Hólm E., Janisková M., Keeley S., Laloyaux P., Lopez P., Lupu C., Radnoti G., de Rosnay P., Rozum I., Vamborg F., Villaume S., Thépaut J.-N. The ERA5 global reanalysis // Quarterly J. Royal Meteorological Society. 2020. Vol. 146. Iss. 730. P. 1999–2049. doi: 10.1002/qj.3803.
  20. 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, Atmosphere. 2021, Vol.12, No. 7. pp. 908-934. doi: 10.3390/atmos12070908
  21. Kalinnikov, V. V. and Khutorova, O. G.: Diurnal variations in integrated water vapor derived from a GPS ground network in the Volga–Ural region of Russia, Ann. Geophys., 2017 Vol.35, pp. 453-464, doi:10.5194/angeo-35-453-2017,
  22. Khutorova O.G, Blizorukov A.S, Dementiev V.V, Mesoscale tropospheric structure sensing during weather front passage, Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa. 2019, Vol.16, No.6. pp.254-262.
  23. Lindskog M., Ridal M., Thorsteinsson S., and Ning, T.: Data assimilation of GNSS zenith total delays from a Nordic processing centre/, Atmos. Chem. Phys. 2017, No. 17, pp. 13983–13998, https://doi.org/10.5194/acp-17-13983-2017.
  24. Litta A. J., Mohanty U. C., Das S., and Mary Indicula S. Numerical simulation of severe local storms over east India using WRF-NMM mesoscale model, Atmos. Res, 2012. Vol. 116. pp. 161–184. doi:10.1016/j.atmosres.2012.04.015
  25. Miller R.C. Notes on analysis and severe storm forecasting procedures of the Air Force Global Weather Center, Tech. Report No 200. Illinois: Scott AFB. 1972. 190 p.
  26. 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. Atmospheric and Solar-Terrestrial Physics, 2019. Vol. 193.pp.1–26. doi:10.1016/j.jastp.2019.105082
  27. Rasmussen E.N., Blanchard D.O. A baseline climatology of sounding-derived supercell and tornado forecast parameters, Weather and Forecasting,1998. No. 13, pp. 1148–1164. doi:10.1175/1520-0434(1998)013<1148:ABCOSD>2.0.CO;2
  28. Santerre R. GPS satellite sky distribution: impact on the propagation of some important errors in precise relative positioning. Brunswick: UNB, 1989.
  29. Taszarek, M., Brooks, H. E., & Czernecki, B., Sounding-Derived Parameters Associated with Convective Hazards in Europe, Monthly Weather Review. 2017. Vol. 145, No. 4, pp. 1511-1528, https://doi.org/10.1175/MWR-D-16-0384.1.
  30. Torrence G., Compo G.P. A Practical Guide to Wavelet Analysis, Bull. Am. Meteorol.Soc, 1998. Vol.79, No. 1, pp. 61-78. doi:https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2
  31. 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 Vapor, and CAPE in the Eastern Central Andes, Remote Sensing, 2021, Vol. 13, No. 18. pp. 1-19. doi: 10.3390/rs13183788