Thunderstorm Classification Functions Based on Instability Indices and GNSS IWV for the Sofia Plain

Abstract

Bulgaria is a country with a high frequency of hail and thunderstorms from May to September. For the May–September 2010–2015 period, statistical regression analysis was applied to identify predictors/classification functions that contribute skills to thunderstorm forecasting in the Sofia plain. The functions are based on (1) instability indices computed from radiosonde data from Sofia station F1, and (2) combination of instability indices and Integrated Water Vapor (IWV), derived from the Global Navigation Satellite System (GNSS) station Sofia-Plana, F2. Analysis of the probability of detection and the false alarm ratio scores showed the superiority of the F2 classification function, with the best performance in May, followed by June and September. F1 and F2 scores were computed for independent data samples in the period 2017–2018 and confirmed the findings for the 2010–2015 period. Analysis of IWV and lightning flash rates for a multicell and supercell thunderstorm in June and July 2014 showed that the monthly IWV thresholds are reached 14.5 and 3.5 hours before the thunderstorm, respectively. The supercell IWV peak registered 40 min before the thunderstorm, followed by a local IWV minimum corresponding to a peak in the flash rate. In both cases, an increase of IWV during severe hail was registered, which is likely related to the hydrometeor contribution to GNSS path delay. The results of this study will be integrated into the Bulgarian Integrated NowCAsting tool for thunderstorm forecasting in the warm/convective season.

GNSS-Based Machine Learning Storm Nowcasting

“…The IWV values during storm days (ST) are significantly higher. Thus our analysis confirmed the separation between ST and NST IWV values in Southeast Europe (Sofia, Bulgaria) reported by [13].…”

“…Another feature reported by [13] is the distinction between daily IWV values for storms with and without thunder registrations in the period May-September 2010-2015. A monthly IWV threshold was derived for Sofia (Bulgaria).…”

“…By determining the lambda as differences between maximum and minimum of PWV values for every hour they found that about 69% of the samples with the lambda above 2 mm and with the minimum period of three consecutive hours were the lightning days. A study in Bulgaria [13] covered the thunderstorm period from May to September (2010-2015) and focused on predictors identification to improve storm forecasting. Analysis of integrated water vapour (IWV) values compared with lightning occurrence showed that there are significant differences between IWV mean for days with and without a storm.…”

A comparison of stability indices and precipitable water derived from radiosonde data collected at two nearby locations and weather radar data in Brazil

“…The stability indices K, LI, TT, SWEAT, CAPE and CIN, and PW were calculated from the vertical profiles of temperature, pressure, and relative humidity collected by the radiosondes (Dong et al, 2018;do Carmo et al, 2019;Guerova et al, 2019;Ajilesh et al, 2020). In the following formulas, the notation used is: T = environmental temperature, TD = dew point temperature, TP = temperature of the parcel, θ = virtual temperature of the environment, θP = virtual temperature of the parcel, f = wind speed, w = direction of the wind, and g = acceleration of gravity.…”

Estimación de parámetros troposféricos en tiempo casi real para Sudamérica mediante técnicas GNSS

“…Desde entonces hasta la fecha, se desarrollaron infinidad de investigaciones y aplicaciones en esta área que se encuentra completamente cimentada. Su capacidad operativa y la expansión permanente de las estaciones de monitoreo continuo, la han transformado en una de las técnicas más elegidas para analizar el vapor de agua (Guerova et al, 2019;Ferrando et al, 2018a;Zhao et al, 2018;Wang et al, 2018;Huelsing et al, 2017) Las estimaciones GNSS de IWV , han sido comparadas con valores obtenidos mediante otros dispositivos, como la radiosonda, el radiómetro de vapor de agua, o el LIDAR. La diferencia promedio entre GNSS y las otras técnicas es de unos dos milímetros (Li et al, 2003;Liou et al, 2001;Sakai et al, 2007).…”