Low-level moisture advection as a trigger mechanism for atmospheric convection and a predictor of its forecast

N. M. Mishchenko; Y. L. Panova; O. M. Hrushevskyi

doi:10.31481/uhmj.25.2020.03

N. M. Mishchenko
Y. L. Panova
O. M. Hrushevskyi

DOI: https://doi.org/10.31481/uhmj.25.2020.03

Keywords: atmospheric convection, instability indexes, nonlocal instability, moisture advection, numerical simulation data, thunderstorm activity, trigger mechanisms, duration of thunderstorms

Abstract

The convective phenomena forecast in the operational practice of meteorological units is made on the basis of radiosonde data for 00 and 12 GMT allowing to obtain a number of quantitative characteristics of atmospheric convection. Particularly, the frequency analysis of different gradations of CAPE (Convective Available Potential Energy) index for the cases of thunderstorms, the one that is most commonly used for the forecast of convective phenomena, shows that the highest frequencies are associated with the gradations that do not provide sufficient grounds for the forecast of convective phenomena availability. This approach is of low efficiency due to the inability of tracking the dynamics of changes in the meteorological values' structure across the convection layer. In contrast to the distribution of CAPE index gradation frequencies, the frequency analysis of CIN (Convective Inhibition) index for the cases of thunderstorms shows that its largest values fall within the range characterizing the favourable conditions for convective phenomena. Thus, according to the radiosonde data in 00 GMT, CIN index has a greater predictive significance than CAPE index.

The possibility of increasing the lability energy magnitude after 00 GMT during thunderstorms formation was demonstrated on the basis of radiosonde data with 6-hour interval. This actualizes the need for diagnosis and forecast of the processes contributing to such growth. The article describes the way in which radiosonde data may be combined with the numerical simulation data in order to forecast deep convection. In particular, it shows that such trigger mechanism, a low-level moisture advection, contributes to realization of nonlocal conditional instability of atmosphere NcI.

In the reviewed cases, the moisture advection in the atmospheric boundary layer either precedes or accompanies the thunderstorm activity. The intensity of moisture advection and its duration have a direct impact on the intensity and duration of thunderstorms. The most prominent levels of low-level moisture advection to “start” the thunderstorm are 975 and 925 hPa levels. The initialization of thunderstorm activity through moisture advection in the atmospheric boundary layer is shown on the diagram clearly demonstrating it.

It is important that this mechanism can be diagnosed based on GFS predicted data and used to forecast the intensity and duration of convective phenomena.

References

What Kind of Damage Do Thunderstorms Cause? Sciencing. Available at: https://sciencing.com/kind-damage-do-thunderstorms-cause-7462714.html. (Accessed: 09.05.2019)

Severe Thunderstorms and Tornadoes in the United States. December 2, 2008. May 22, 2013. R40097. Available at: https://www.everycrsreport.com/reports/R40097.html (Accessed: 12. 10. 2019 р.).

Naslidky strashnoi hrozy na Zakarpatti: Maizhe 8 tysiach poshkodzhenykh budynkiv ta 88 milioniv zbytkiv [Consequences of a terrible thunderstorm in Transcarpathia: Nearly 8,000 damaged homes and 88 million damage]. Available at: http://uzhgorod.net.ua/news/97640. (Accessed: 24. 12. 2019). (in Ukr.)

Zvit pro osnovni rezultaty diialnosti Derzhavnoi sluzhby Ukrainy z nadzvychainykh sytuatsii u 2018 rotsi [Report on the Main Results of the State Emergency Service of Ukraine in 2018]. Available at: https://www.dsns.gov.ua/files/2019/1/18/321/%D0%9F%D1%83%D0%B1%D0 %BB%D1%96%D1%87%D0%BD%D0%B8%D0%B9%20%D0%B7%D0%B2%D0% B8%D1%82%20%D0%B7%D0%B0%202018%20%D1%80%D1%96%D0%BA.pdf. (Accessed: 08. 07. 2019). (in Ukr.)

Enrique V., Mattos, Luiz A.T., Machado, Earle R., Williams, Rachel I., Albrecht. (2016). Polarimetric radar characteristics of storms with and without lightning activity. Advancing Earth and space science publications. JGR Atmospheres. Online ISSN: 2169 – 8996, 125, pp. 201 - 220. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2016JD025142.

Vera, Meyer. (2010). Thunderstorm Tracking and Monitoring on the Basis of Three-Dimensional Lightning Data and Conventional and Polarimetric Radar Data, pp. 30-37. Available at: https://edoc.ub.uni-muenchen.de/12102/1/Meyer_Vera.pdf. (Accessed: 29. 01. 2020).

Stull, R. (2016). Practical Meteorology: An algebra-based survey of atmospheric science. University of British Columbia.

Hrushevskyi, O.M. et al. (2018). On the structure of the dynamic features of the lower layer of the atmosphere in the presence of low cloudiness. Ukraïnsʹkij gìdrometeorologìčnij žurnal [Ukrainian hydrometeorological journal], 22, pp. 5-15. https://doi.org/10.31481/uhmj.22.2018.01 (in Ukr.)