Lightning is an electrical discharge -a'spark' or 'flash' as charged regions in the atmosphere instantly balance themselves through this discharge. It is a beautiful and deadly naturally occurring phenomenon. In June 2020, more than a hundred people died in the state Bihar of India only in three days' span due to lightning events. In this work, Lightning Imaging Sensor (LIS) information from the Tropical Rainfall Measuring Mission (TRMM) satellite with a very high spatial resolution of 0.1 X 0.1 degree has been utilized to create the climatology of India for 16 years from 1998 to 2013. Diurnal, monthly, and seasonal variations in the occurrence of lightning flash rate density have also been analyzed. TRMM satellite low-resolution monthly time series (LRMTS) with 2.5-degree resolution datasets have been used for lightning trend analysis.The diurnal lightning event mainly occurs in the afternoon/evening (1400-1900 Hrs) time duration around 0.001 flashes/km 2 /hr. The highest lightning occurred in May (0.04 flashes/km 2 /day) and the least in December (0.005 flashes/km 2 /day). The distribution of lightning flash counts by season over India landmass is mainly in pre-monsoon (MAM) ranges from 0.248 -0.491 flashes/km 2 /day, and monsoon (JJA) ranges from 0.284 -0.451 flashes/km 2 /day and decreases afterward. Spatially, the distribution of lightning flashes mainly at North-Eastern region along with Bangladesh, Bihar, Jharkhand, Orissa, and Jammu& Kashmir region. The CAPE and K Index have positively correlated with the flash rate density seasonally but CAPE is more significantly correlated. This study also focused on finding of lightning hotspots region of India district wise and Rajouri district in Jammu and Kashmir got the highest lightning with 121 flashes/km 2 /yr.
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.
Cloudbursts are powerful precipitation events that cause flash floods and landslides over a 20 km2 area. Three cloudburst events on July 16, 17, and 19 in three Uttarakhand locations are examined in this study. The Weather Research and Forecasting (WRF) model was used to simulate three cloudburst occurrences at a timestep of 18s on a nested domain of 9 km and 3 km resolutions from 1° by 1° spatial resolution NCEP-FNL data. IMDAA and NGFS data were compared. In the three studied scenarios, all simulations forecast 100 mm rainfall and considerable vertical velocity. Maximum rainfall and vertical velocity are somewhat shifted in two situations. Based on GPM-IMERG rainfall observations, each configuration was evaluated using deterministic and categorical metrics. For each timestep's 5 cm rainfall threshold, Equitable Threat Score and False Threat Score were calculated. The analysis indicated that the third configuration had the most skill.
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