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Introduction: The current research investigates into the application of various thunderstorm indices to predict severe thunderstorm occurrences during the monsoon season across four distinct regions in India. Methods: The study assesses the prediction model's efficacy using various skill scores and the Weather Research and Forecasting (WRF) model has been integrated for 30 h with double moment microphysics scheme NSSL-17 which accurately reproduces vertical and meteorological measures. Objective: Furthermore, it investigates fifteen thunderstorm indices derived from the ERA5 dataset to identify the most effective index for forecasting severe thunderstorms. Results: The results indicate that combining thunderstorm indices with skill scores, such as the Heidke Skill Score and True Skill Statistic, enhances the accuracy of severe thunderstorm predictions in the Indian monsoon season. The accurate predictions rely on determining optimal thresholds for each index. The study emphasizes the importance of using multiple indices rather relying solely on single measure for predicting severe thunderstorms. Advanced indices like the Energy Helicity Index (EHI) and Supercell Composite Parameter (SCP) perform well in forecasting extreme severe thunderstorms due to their strong reliance on wind shears. The EHI (> 1), and SCP (≥ 3.5), STP (≥ 1.2) along with low SRH at 3 km (100 m2/s2), indicated no evidence of helicity or tornado activity during the event. On the other hand, the CAPE, K Index, and VT Index demonstrate robust predictive capabilities for non-severe category thunderstorms. Conclusions: Integrating numerous thunderstorm indices improves meteorologists' forecasts, ensuring public safety. Based on this work, future research can improve severe weather forecasting models' accuracy and reliability. [ABSTRACT FROM AUTHOR]

The present study was conducted to predict the current and future potential distribution of a tree species Pterocarpus marsupium Roxb. in Ranchi, Eastern India using ecological niche modeling. Nine environmental variables comprising of isothermality, precipitation of wettest and warmest quarter, annual temperature range, soil type, human influence index, elevation, slope and land use cover were used to model the distribution of the species. Climatic variables governed the predicted distribution of the species as they contributed 56.7% as compared to the other nonclimatic variables (43.3%). Northern parts exhibited the most suitable niche of the species as compared to south‐east and central parts that showed low probability of occurrence due to high disturbances caused by rapid urbanization as well as over exploitation of the species for timber, edible and medicinal uses. The average test area under the receiver operating curve (AUC) (0.921) as well as the partial AUC indicated good model performance. The projected change scenarios of RCP2.6, RCP4.5 and RCP6.0 for the year 2050 reveal that the climatically suitable areas will be drastically reduced in Ranchi. The population of the species is declining due to its exploitation from natural habitats for timber and medicinal uses and is listed as near‐threatened by the current IUCN‐Red lists. The findings of this paper will help to identify the potential habitats for further conservation of this near‐threatened species in the changing climatic conditions and increasing anthropogenic pressure. [ABSTRACT FROM AUTHOR]

Widespread event of lightning occurred in two states of eastern India on June 25, 2020, killing 83 people in Bihar and 24 people in Uttar Pradesh (UP). Lightning event is studied using ground-based lightning observational data and Weather Research and Forecasting (WRF) model. A Specialized forecast system for lightning and thunderstorm is needed to minimize the loss of lives. The updraft and ice particle concentration play essential roles in the initialization of lightning inside the thundercloud. The charge generation and separation inside the clouds play an essential role in the severity of lightning. The Lightning Potential Index (LPI) measures the potential of charge generation and separation inside the clouds. It plays a significant role in determining lightning-prone zones. As the monsoon intensified over the Indo Gangetic Plains, lightning simulation is carried out using an ensemble of different WRF model configurations. The model has been integrated from 24th June 0000 UTC to 26th June 0600 UTC with an ensemble of 9 members. 8 members are simulated with a single domain (3 km resolution), and only the second member is simulated with a triple nested domain (9, 3, and 1 km resolution) over Bihar. The ensemble consists of 1 control +8 members with different configurations of the WRF model. LPI is calculated using the model-derived parameters. Furthermore, the model explicitly simulated lightning flash count using a lightning parameterization scheme. The results are analyzed using the Indian Institute of Tropical Meteorology (IITM) ground-based observation data. Every ensemble member's results are distinct from each other. This shows the sensitivity of every configuration. Member 2 (M2) is the triple nested member; it uses the same physics option as member 1 (M1). Moreover, M2 shows comparatively good results towards the observation. Simulated radar reflectivity is compared and analyzed using Indian National Satellite System (INSAT-3D) 3D Brightness Temperature (BT). To check the results of all ensemble members model skill score has been calculated. Ensemble probability is calculated to measure the spatial pattern and threat of lightning based on LPI, and it recorded the high lightning threat (90–100) % probability over northwest Bihar (Gopalganj), and it also recorded the heavy rainfall (300 mm). The skill scores of simulated lightning flash count in M2, M8, and M9 showed reasonably good POD values (0.6, 0.5, and 0.69, respectively). • Sensitivity test of different configurations of WRF model to simulated Lightning event with an ensemble technique. • Performance of Lightning Potential Index and lightning flash count has been checked to understand Bihar lightning event. • Ensemble mean, ensemble probability and model skill scores are used to simulate the Bihar Lightning event. • M2 performs better than M1 despite of using same physical configurations, however their resolutions are different. • LPI performs better than lightning flash count to simulate lightning activity [ABSTRACT FROM AUTHOR]

Advancements have been made in the field of lightning research. A study on lightning frequency in India, a tropical hotspot, has not been conducted despite the existence of a global climatology on mean flash rate lightning. Lightning climatology in India is determined using high-resolution datasets from MicroLab-1 and Tropical Rainfall Measuring Mission (TRMM) satellites and Lightning Imaging Sensor (LIS) diurnal datasets spanning from 1996 to 2013. The study analyzed the difference in density of lightning flashes during two time periods, 1996–2001 and 2008–2013, on a daily, monthly, and seasonal level. Three regions with high lightning activity have been selected: East Khasi Hills in the northeast with 101.25 flashes/Km2/yr, Rajouri in Jammu & Kashmir with 121.40 flashes/Km2/yr, and Kottayam with 92.94 flashes/Km2/yr. The study focuses on the rainfall, convective available potential energy (CAPE), and lightning density. Due to localized climatological factors, lightning events vary in diurnal and seasonal patterns across the three hotspots. Lightning strikes are predominantly distributed in certain regions of India, including Jammu, Kashmir, the foothills of the Himalayan belt, and the North-Eastern region, encompassing Bangladesh, Bihar, Jharkhand, and Orissa. The frequency of lightning strikes ranges from 0.2 to 0.45 flashes/Km2/day. Lightning activity experienced a slight rise between 2008 and 2013. There was a rise in lightning activity (0.04 flashes/Km2/day) in India and its three hotspots in 2010. The study aims to investigate the daily changes in lightning activity. A significant portion of daily lightning activity occurs at approximately 1000 UTC and generally decreases in the afternoon across all seasons. The study examines the seasonal variation in lightning activity across India, specifically during pre-monsoon, monsoon, post-monsoon, and winter. The results are significant for developing lightning climatology in the region. Lightning density in India experiences a slight increase during both pre-monsoon and monsoon seasons, with a noticeable variation observed across India and three hotspot locations. • Diurnal variation of lightning flash rate density over the lightning hotspots of India. • Diurnal variation: Lightning in India peaks around 1000 UTC, then gradually declines in the afternoon. • Seasonal variation: Lightning rates rise in pre-monsoon and monsoon, with a slight overall increase in India. [ABSTRACT FROM AUTHOR]