Spatio-temporal variability of lightning climatology and its association with thunderstorm indices over India

Mondal, Unashish; Panda, Subrat Kumar; Das, Someshwar; Sharma, Devesh

doi:10.1007/s00704-022-04032-5

Cited by 26 publications

(14 citation statements)

References 63 publications

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“…1). A similar study was done by Mondal et al (2022), examining the hotspots region in India with the highest ash rate density that will help provide early warning and arrangements for the safety of the people, livestock, and property.…”

Section: Introductionmentioning

confidence: 92%

Spatio-Temporal Climatology and Trends of Convective Available Potential Energy (CAPE) over Bangladesh, including three lightning hotspots during 40 years (1982-2021)

Kader

Panda

Wasson

et al. 2023

Preprint

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This study investigates the climatology of Convective Available Potential Energy (CAPE) over Bangladesh and its eight administrative divisions, along with three lightning hotspots (Sherpur, Shahjadpur, and Bajitpur), using monthly, seasonal, and annual data for 40 years (1982–2021). The monthly CAPE data at 0000 UTC and 1200 UTC has been collected from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis data (ERA5) at 0.25° resolution. The study reveals that the increasing CAPE trend over Bangladesh may be responsible for the increased frequency of extreme events. Significant CAPE values were observed in Bangladesh’s south-west and southern parts from March to May. In April, there was a notable increasing trend in CAPE values, particularly in the north-western region. The average CAPE values for Bangladesh’s three lightning hotspot regions (Sherpur, Shahjadpur, and Bajitpur) is higher than 1500 J/kg during the pre-monsoon at 0000 UTC, directly correlating with the lightning and thunderstorm. The Mann-Kendall test has been employed to follow yearly and seasonal trends. Overall, this study provides valuable insights into the spatial distribution of CAPE and its association with thunderstorms in Bangladesh, which can inform the development of effective strategies to manage weather-related hazards in the country.

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Section: Introductionmentioning

confidence: 92%

Spatio-Temporal Climatology and Trends of Convective Available Potential Energy (CAPE) over Bangladesh, including three lightning hotspots during 40 years (1982-2021)

Kader

Panda

Wasson

et al. 2023

Preprint

Self Cite

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“…Reviewing several articles, I found that many researchers have employed different machine learning methods. It also highlights the importance of machine learning in forecasting outcomes [5]. Various machine learning models have been developed to forecast thunderstorms: Linear Regression, Decision Tree, Support Vector Machine, etc.…”

Section: Machine Learningmentioning

confidence: 99%

Thunderstorms Prediction Using Satellite Images

Amjad¹,

Malik²,

Ghous³

et al. 2023

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Human life and property are put at risk by lightning, heavy hail, and strong winds caused by thunderstorms. Accurate prediction of thunderstorms has long been a difficult problem. In difference from conventional methods, the modern methods for predicting thunderstorms utilize a combination of computer models, observation (via the use of radars and satellite photos), and modern knowledge and patterns employed by thunderstorms stations. Observations: Using these methods, it is possible to make reasonably accurate predictions. Prediction models based on complex formulas run the majority of forecasting computer models. Artificial neural networks, machine learning, deep learning, and statistics are all examples of adaptive systems that alter their structure in response to information flowing through the network. In this reviewed paper, different Artificial intelligence techniques are used such as machine learning, deep learning, and ANN (Artificial neural network). Different statistical models were presented to predict thunderstorm systems. Various researchers achieved technical milestones by presenting statistical methods on thunderstorm systems using satellite data.

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“…As per the record around nine percent of the death due to natural hazard is caused by the lightning in India (Siingh et al, 2014;Yadava et al, 2020). The highest number of thunderstorms occur in the pre-monsoon months of (March, April, May, and early June) (Albrecht et al, 2016;Das, 2017;Mondal et al, 2022;Saha et al, 2017). The intensity of the thunderstorms are severe (Hoddinott, 1986) over India due to the topography (Barthlott et al, 2006;Mushtaq et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Study of Thunderstorm Indices Threshold Favorable for Thunderstorms During Monsoon Season Using WRF-ARW Model and ERA5 Over India

Mondal

Kumar

Panda

et al. 2023

Preprint

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This study investigates the use of various thunderstorm indices in predicting severe thunderstorms events during the monsoon season in four different regions in India. The research evaluates the performance of the prediction model using a model skill score and utilizes the Weather Research and Forecasting (WRF) model with the double moment microphysics scheme to simulate model cases. It also compares fifteen thunderstorm indices derived from the ERA5 dataset to identify the most effective index for predicting severe thunderstorms events. The results of this study show that incorporating thunderstorm indices with model skill scores improves severe thunderstorms forecasting in the monsoon season in India. The result revealed that determining the optimal threshold for each index is crucial in achieving accurate predictions. The study also highlights the importance of considering multiple indices rather than relying on a single index to predict severe thunderstorms events. The advance indices such as Energy Helicity Index (EHI), Supercell Composite Parameter (SCP), mainly works well with extreme severe thunderstorms. The simplistic indices can predict the weak or severe thunderstorm easily. The use of multiple thunderstorm indices can also help meteorologists to make more accurate predictions, which can further enhance public safety. In conclusion, this study demonstrates the potential of incorporating thunderstorm indices with model skill scores like HSS and TSS and combinations of different skill scores in severe thunderstorms forecasting during the monsoon season in India. Future research can build upon the findings of this study to develop more accurate and reliable severe weather forecasting models.

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Spatio-temporal variability of lightning climatology and its association with thunderstorm indices over India

Cited by 26 publications

References 63 publications

Spatio-Temporal Climatology and Trends of Convective Available Potential Energy (CAPE) over Bangladesh, including three lightning hotspots during 40 years (1982-2021)

Spatio-Temporal Climatology and Trends of Convective Available Potential Energy (CAPE) over Bangladesh, including three lightning hotspots during 40 years (1982-2021)

Thunderstorms Prediction Using Satellite Images

Comprehensive Study of Thunderstorm Indices Threshold Favorable for Thunderstorms During Monsoon Season Using WRF-ARW Model and ERA5 Over India

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