Thunderstorm frequency distribution and associated convective mechanisms over Pakistan

Ahmed, Rafay; Latif, Muhammad; Adnan, Shahzada; Abuzar, Muhammad Khubaib

doi:10.1007/s00704-018-2619-x

Cited by 13 publications

(5 citation statements)

References 31 publications

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“…This region showed good correlation between specific humidity at 850 and 1,000 mbar and lightning activities during pre‐monsoon season (Figure 13n). CAPE, however, did not show any strong influence during pre‐monsoon probably because of the convective inhibition in this region (Ahmad et al ., 2019). On the contrary, both CAPE and humidity seemed to be well‐correlated with lightning activity in this region during monsoon (Figure 13m).…”

Section: Resultsmentioning

confidence: 99%

A machine learning approach for prediction of seasonal lightning density in different lightning regions of India

Chatterjee

Mandal

Das

2023

Intl Journal of Climatology

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Lightning is one of the most severe weather events causing significant loss of human lives and resources. Increasing number of lightning fatalities due to recent climatic changes is emerging out to be a serious concern for India during last few years. Proper characterization and parameterization of the same, therefore, is extremely crucial. However, lightning is an extremely dynamic phenomenon having enormous spatio‐temporal inhomogeneity especially over such a vast country like India with varied topographic and climatological features. Therefore, proper parameterization of lightning activity over India needs consideration of different lightning climatologies. This study has attempted to resolve the issue by regionalizing Indian subcontinent in different lightning climatologies based on lightning density and associated atmospheric variables that is, CAPE, specific humidity at different pressure levels, temperature, k index and cloud particle size and identified seven distinct lightning climatologies over India. A regression model is proposed for estimating the annual and seasonal (monsoon and pre‐monsoon) lightning activities over the seven resulting lightning zones based on the said atmospheric variables using machine learning techniques. Four machine learning models have been tested among which Random forest has shown the best accuracy. The regression model has shown an R‐squared score of 0.81 during monsoon season and 0.71 during the pre‐monsoon. The atmospheric features based on their influences on the lightning activity in these seven climatologies has been ranked which presented the evidences of largely varied interplay between different atmospheric variables and lightning over different parts of the country and during different seasons.

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Section: Resultsmentioning

confidence: 99%

A machine learning approach for prediction of seasonal lightning density in different lightning regions of India

Chatterjee

Mandal

Das

2023

Intl Journal of Climatology

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“…Thus, the air parcel possesses more potential energy over the area and the air parcel may be lifted high enough in the atmosphere to produce convective systems, such as severe thunderstorms and precipitation. As described by Ahmed et al (2019), the highest rate of increase in CAPE over the southern upper half and lower northern parts of the country has been observed over the core precipitation region of Pakistan, which is vulnerable to thunderstorm development. Moreover, as demonstrated by Donner and Phillips (2003), CAPE can also be used as an indicator of the potential intensity of deep convection and their relationship with heavy downpour intensity area.…”

Section: Cape Analysismentioning

confidence: 94%

Dynamics of Unusual Extreme Rainfall Events Over Different Altitudes Using Richardson Number

Ullah

Ikram²

2022

Earth and Space Science

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To delineate the dynamic stability of a moist atmosphere and to measure stability within a statically stable atmosphere, local Richardson number (Ri) and Brunt‐Väisälä (BV) frequency are used in this study. The BV frequency is an important parameter that is widely used in dynamic meteorology and is calculated by replacing potential temperature with equivalent potential temperature in a moist atmosphere. For this purpose, extreme rainfall events were simulated by the WRF model. These events occurred on 9 September 2012 and 28 July 2010 during the summer monsoon in areas of different altitudes (ranging approximately from 100 to 3,500 m) which resulted in flash floods, loss of lives, and property damages in southern and northern parts of Pakistan. In the present case Ri (local Richardson number based on potential temperature) and Rie (local Richardson number based on equivalent potential temperature) are applied to investigate instability (Rie < 1) in a moist atmosphere. The results show that the values of Rie < 1 are mainly located in the lower‐to‐upper troposphere over the rainfall areas. The analysis shows that Rie is more suitable index for describing the convective instability in a moist saturated atmosphere. The results of Ri and Rie were justified with the European Centre for Medium‐Range Weather Forecasts fifth generation reanalysis (ERA5 hourly) available from Climate Data Store. Moreover, negative values of vertical velocity (hPa/s) clearly show large‐scale dynamical uplifting associated with instability resulting in a severe thunderstorm. Further, strong synoptic forcings also play a key role to develop deep convection over the region. In addition, maximum convective available potential energy (3,000 J/kg) and K‐index (>40) were observed over the areas of high instability that dominate its role in enhancing convection during the events.

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“…Kahraman and Markowski (2014) stated that tornadoes generally occur in the afternoon and early evening hours in Turkey. Studies conducted in the South Asia (Ahmed et al, 2019) and China (Chen et al, 2013;Ma et al, 2021;Tian et al, 2015) have also shown that TSs are most frequent during the warm season (May-September). It was also found that the highest number of observations in TS-induced hail events peaked in May and June (Kahraman et al, 2016).…”

Section: Introductionmentioning

confidence: 97%

Performance Analyzes of Thermodynamic Indices and Atmospheric Parameters in Thunderstorm and Non-thunderstorm Days in Istanbul, Turkey

Yavuz

2024

Pure Appl. Geophys.

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This study aims to analyze the thunderstorm (TS) events in the megacity Istanbul by using thermodynamic indices and atmospheric stability parameters for the period of 2001–2022. It was determined that TS events did not show any trend on an annual basis, mostly (%69) occurred in the warm season (May–September), and mostly (%93) lasted for a few hours (0–3 h). The thermodynamic indices and atmospheric stability parameters used in the study are Showalter Index (SI), Lifted Index (LI), Severe Weather Threat Index (SWEAT), K-Index (KI), Totals Totals Index (TTI), Convective Available Potential Energy (CAPE), Convective Inhibition (CIN), and Bulk Richardson Number (BRN). Annual and seasonal analyzes of all indices and parameters were performed for TS and non-TS events. Significant differences were found in both average, maximum, and minimum values. The Probability of Detection (POD), False Alarm Ratio (FAR), Miss Rate (MR), Critical Success Index (CIS), Hiedke Skill Score (HSS), and True Skill Score (TSS) were used to analyze the success of the threshold values presented in the literature in detecting TS events. Then, the seasonal successes of these threshold values were tested. It was observed that the performance of the selected indices varied across seasons. The highest predictive skill was generally observed during the summer season, with the POD value ranging between 0.58 and 0.97 and the TSS value varying between 0.32 and 0.57. Conversely, the lowest predictive skill was typically observed during the winter season, where the POD value ranged from 0.00 to 0.75 and the TSS value varied between 0.00 and 0.40. The ideal threshold values were determined for indices and parameters by increasing or decreasing the existing threshold values at certain rates. Success increases of up to 15% in skill scores for the proposed threshold values.

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Thunderstorm frequency distribution and associated convective mechanisms over Pakistan

Cited by 13 publications

References 31 publications

A machine learning approach for prediction of seasonal lightning density in different lightning regions of India

A machine learning approach for prediction of seasonal lightning density in different lightning regions of India

Dynamics of Unusual Extreme Rainfall Events Over Different Altitudes Using Richardson Number

Performance Analyzes of Thermodynamic Indices and Atmospheric Parameters in Thunderstorm and Non-thunderstorm Days in Istanbul, Turkey

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