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Abstract 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 3 days’ span due to lightning events. In this study, Lightning Imaging Sensor (LIS) from the Tropical Rainfall Measuring Mission (TRMM) satellite with a very high spatial resolution of 0.1° has been utilized to create the lightning climatology of India for 16 years during the period 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° resolution datasets have been used for lightning trend analysis.The diurnal lightning event mainly occurs in the afternoon/evening time duration (1400–1900 h) around 0.001 flashes/km2/h. The highest lightning occurred in the month of May (0.04 flashes/km2/day) and the least in December (0.005 flashes/km2/day). The distribution of lightning flash counts by season over India landmass is mainly in pre-monsoon months (MAM) which ranges from 0.248 to 0.491 flashes/km2/day, and monsoon (JJA) ranges from 0.284 to 0.451 flashes/km2/day and decreases afterward. Spatially, the distribution of lightning flash density is seen over the North-Eastern region along with Bangladesh, Bihar, Jharkhand, Orissa, and Jammu and Kashmir region. The pre-monsoon (MAM) season shows a positive trend of lightning around 0.04 flashes in the North-Eastern region and the post-monsoon (SON) shows a negative trend of − 0.021 in central India. The CAPE and K-Index have positively correlated with the flash rate density seasonally but CAPE is more significantly correlated around 0.83 during pre-monsoon and monsoon season. This study also focused on finding out the district wise lightning hotspots regions of India. Rajouri and Reasi district in Jammu and Kashmir has the highest lightning with 121 and 115 flashes/km2/yr, respectively.

Climate change’s impact on lightning and thunderstorms is uncertain. This study evaluates the sensitivity of multiple Planetary Boundary Layer (PBL) parameterisation schemes within the WRF-ELEC model for simulating a severe lightning and thunderstorm event in Bihar on 25 June 2020. The aim is to understand how these schemes affect lightning and thunderstorm intensity. The model was integrated for 54 h at 0000 UTC on 24 June 2020 using 6-hourly NCEP FNL Operational Global Analysis data at 1° × 1° resolution over Bihar with double nested domains of 9 km (D1) and 3 km (D2). It effectively captures the peak lightning and thunderstorm activity from 0000 to 0900 UTC on 25 June 2020, significantly impacting certain regions. The study utilised ERA5 and IMDAA reanalysis datasets and NASA GPM IMERG daily data to analyse the event and assess the model’s performance. Among the PBL schemes tested, ACM2, BouLac, SHsa, and MRF exhibit robust performance. Flash Origin Density (FOD) patterns broadly match observations, although occasional discrepancies occur in southern Bihar. Convective Available Potential Energy (CAPE) and precipitation (mm) analysis reveals anticipated trends. Statistical scores highlight strong performance by ACM2, UW, and GBM schemes in POFD/FAR. The MRF scheme excels in POD/Hit Rate, and the UW scheme achieves the highest score for 24-h accumulated total precipitation. HSS and GSS/ETS underscore the superior performance of the UW and GBM schemes. This study offers insights into lightning and thunderstorm simulations over Bihar with diverse PBL parameterisation schemes in the WRF-ELEC model.

The modelling of rainfall-runoff simulation is of great importance for assessment of runoff quantities from rainfall data which is essential for optimizing water storage and mitigating flood hazards. One of the contemporary challenges is to simulate streamflow using the integrated hydro-meteorological approach, which depends on different synoptic conditions and model parameters. HEC-HMS model is run by the rainfall data generated from output of Weather Research Forecasting (WRF) model in the catchment of Mahi dam. Through this approach, the integration of atmospheric and hydrological models is established to evaluate the predictive capabilities of streamflow forecasts. To perform geospatial analysis of the catchment area, ArcGIS extension i.e. HEC-GeoHMS was used to generate the hydrologic elements for the HEC-HMS model. Here, we integrate hydrometeorological approach, in which two WRF simulated (Kain-Goddard-ACM2 parameterization scheme) extreme rainfall events i.e. 25 to 29 July 2015 and 22 to 26 August 2020 were integrated into calibrated HEC-HMS model. Model calibration was done from 1 to 31 August 2019. Daily rainfall, stream flow, and curve number were also used. The catchment is divided into five sub-basins with CWC gauging station over sub-basin 3. The performance and accuracy assessment of the models is calculated using several statistical indices such as the NSE, RMSE, and the coefficient of determination. The results showed that NSE, and R 2 values 0.89, and 0.89 for calibration, and 0.85, and 0.88 for validation of simulated event 2, were found in good agreement with the observed values. The novelty of this study lies in assessing the performance of the HEC-HMS model using rainfall data simulated by the WRF model, which demonstrated good accuracy in streamflow simulation. The findings of the study indicate that the calibrated HEC-HMS model can be applied for flow forecasting at short time intervals under future climate scenarios in similar environments. This model may serve as a valuable tool for water resource managers to predict and mitigate flood risks.

This study addresses the critical need for accurate lightning prediction in northeastern India, a region frequently impacted by severe convective storms. Despite advancements in forecasting, there remains a gap in predicting lightning activity, especially during extreme weather. This research evaluates the Weather Research and Forecasting (WRF) model’s performance in simulating lightning events on June 16 and 17, 2022. The study employs the Morrison double-moment 6-class microphysics scheme and the Price and Rind A simple lightning parameterization for calculating global lightning distributions lightning parameterization method to model cloud-to-ground (CG) and intra-cloud (IC) lightning. Model predictions are validated against data from the Indian Institute of Tropical Meteorology Lightning Location Network (IITM-LLN) and the World-Wide Lightning Location Network (WWLLN), alongside INSAT-3D cloud brightness temperature and NASA-IMERG rainfall data. The research findings show that the lightning parameterization scheme with a 20 dBZ top threshold provides accurate predictions, with spatial autocorrelation metrics of 67.41% and 85.75% for June 16 and 17, respectively. Further analysis through skill scores, such as the Equitable Threat Score and Probability of Detection, supports the model’s reliability. Hotspot analysis identifies regions of heightened lightning activity, offering valuable insights into areas prone to frequent strikes. This research emphasizes the importance of advanced modeling techniques and comprehensive data integration in enhancing weather forecasts, contributing to improved safety and mitigation strategies for extreme weather events in northeastern India.

Cloudbursts are meteorological phenomena characterized by intense precipitation, resulting in the occurrence of flash floods and landslides within a spatial extent of around 20 km 2 . This study examines three cloudburst events that occurred on July 16, 17, and 19 of 2018 in three distinct sites within Uttarakhand. The Weather Research and Forecasting (WRF) model was employed to replicate three instances of cloudburst events, with a temporal resolution of 18 s, on a nested domain including resolutions of 9 km and 3 km. The model utilized spatial data from the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL), which had a spatial resolution of 1° by 1°. A comparison was made between the Indian Monsoon Data Assimilation and Analysis (IMDAA) data. In all three examined situations, the simulations predict a precipitation amount of 100 mm and significant vertical velocity. There is a slight displacement observed in the maximum rainfall and vertical velocity between the two scenarios. The evaluation of each configuration was conducted using deterministic and categorical metrics, based on the rainfall observations from Integrated Multi-Satellite Retrievals for GPM (IMERG). Equitable Threat Score (ETS) and False Alarm Rate (FAR) skill score were computed for each timestep’s rainfall threshold of 5 cm. The results of the investigation demonstrated that the third configuration had the highest level of skills.

The study analysed the changes in the rainfall, extreme indices and their future projections over Rajasthan state based on observed gridded datasets (1976–2005) and simulated climate models. The climate projections from two global circulation models (HadCM3 and GFCM21) are used in statistical downscaling tool LARS-WG5 (Long Ashton Research Station-Weather Generator) to generate future precipitation. Further, the changes in precipitation pattern are investigated for the baseline period and the future periods based on seven extreme precipitation indices. Three future periods are used for the analysis i.e., early century period 2011–2040 (2025s), a mid-century period of 2041–2070 (2055s) and a late-century period of 2071–2100 (2085s). The study area is classified in three regions based on elevation range i.e., region 1 (< 250 m), region 2 (251–350 m) and region 3 (350–1700 m). Based on results, it is observed that there is a possible decrease in monsoon precipitation at many grid points for all the three future periods. The maximum decrease in rainfall (−142 mm) is observed in Banswara for the period 2041–2070, while the maximum increase (37 mm) is found in Alwar along with Churu 1 and Ganganagar during the period 2071–2100. Consecutive dry days (CDD) is predicted to increase in the west and south-west direction, while it shows decrease values in eastern and central part of the study area with the maximum value in Ajmer district. The pattern in PRCPTOT revealed maximum negative change (− 90 mm) in southern parts, and maximum positive change in the northern regions (62.2 mm) in Churu 1. Further, R20 and RX5day are projected to decrease in all three regions in future with several magnitudes. For RX1day, a maximum positive change is observed in eastern parts (Jhalawar, Sawai Madhopur) and negative changes in the southern part of the study area. In case of R95p index, both positive and negative changes are observed. Similarly, the SDII indicates a positive change in 2011–2040 and negative changes for the remaining two future periods. Finally, SDII shows maximum positive changes in the south and southeastern regions (Jhalawar, Chittaurgarh) and positive changes in various parts with spatial and temporal changes. The results will help water resources planner to understand the change pattern in various precipitation indices in water scarce state of India.

The study of climate changes in India and search for robust evidences are issues of concern specially when it is known that poor people are very vulnerable to climate changes. Due to the vast size of India and its complex geography, climate in this part of the globe has large spatial and temporal variations. Important weather events affecting India are floods and droughts, monsoon depressions and cyclones, heat waves, cold waves, prolonged fog and snowfall. Results of this comprehensive study based on observed data and model reanalyzed fields indicate that in the last century, the atmospheric surface temperature in India has enhanced by about 1 and 1.1°C during winter and post-monsoon months respectively. Also decrease in the minimum temperature during summer monsoon and its increase during post-monsoon months have created a large difference of about 0.8°C in the seasonal temperature anomalies which may bring about seasonal asymmetry and hence changes in atmospheric circulation. Opposite phases of increase and decrease in the minimum temperatures in the southern and northern regions of India respectively have been noticed in the interannual variability. In north India, the minimum temperature shows sharp decrease of its magnitude between 1955 and 1972 and then sharp increase till date. But in south India, the minimum temperature has a steady increase. The sea surface temperatures (SST) of Arabian Sea and Bay of Bengal also show increasing trend. Observations indicate occurrence of more extreme temperature events in the east coast of India in the recent past. During summer monsoon months, there is a decreasing (increasing) trend in the frequency of depressions (low pressure areas). In the last century the frequency of occurrence of cyclonic storms shows increasing trend in the month of November. In addition there is increase in the number of severe cyclonic storms crossing Indian Coast. Analysis of rainfall amount during different seasons indicate decreasing tendency in the summer monsoon rainfall over Indian landmass and increasing trend in the rainfall during pre-monsoon and post-monsoon months.

This study assess the performance of two versions of Regional Climate Model (RegCM) in simulating the Indian summer monsoon over South Asia for the period 1998 to 2003 with an aim of conducting future climate change simulations. Two sets of experiments were carried out with two different versions of RegCM (viz. RegCM4.2 and RegCM4.3) with the lateral boundary forcings provided from European Center for Medium Range Weather Forecast Reanalysis (ERA-interim) at 50 km horizontal resolution. The major updates in RegCM4.3 in comparison to the older version RegCM4.2 are the inclusion of measured solar irradiance in place of hardcoded solar constant and additional layers in the stratosphere. The analysis shows that the Indian summer monsoon rainfall, moisture flux and surface net downward shortwave flux are better represented in RegCM4.3 than that in the RegCM4.2 simulations. Excessive moisture flux in the RegCM4.2 simulation over the northern Arabian Sea and Peninsular India resulted in an overestimation of rainfall over the Western Ghats, Peninsular region as a result of which the all India rainfall has been overestimated. RegCM4.3 has performed well over India as a whole as well as its four rainfall homogenous zones in reproducing the mean monsoon rainfall and inter-annual variation of rainfall. Further, the monsoon onset, low-level Somali Jet and the upper level tropical easterly jet are better represented in the RegCM4.3 than RegCM4.2. Thus, RegCM4.3 has performed better in simulating the mean summer monsoon circulation over the South Asia. Hence, RegCM4.3 may be used to study the future climate change over the South Asia.

The present study was conducted with an objective of isolation, in vitro expansion, growth kinetics, molecular characterization and in vitro differentiation of fetal adnexa derived caprine mesenchymal stem cells. Mid-gestation gravid caprine uteri (2-3 months) were collected from abattoir to derive mesenchymal stem cells (MSCs) from fetal adnexa {amniotic fluid (cAF), amniotic sac (cAS), Wharton's jelly (cWJ) and cord blood (cCB)} and expanded in vitro. These cultured MSCs were used at the 3rd passage (P3) to study growth kinetics, localization as well as molecular expression of specific surface antigens, pluripotency markers and mesenchymal tri-lineage differentiation. In comparison to cAF and cAS MSCs, cWJ and cCB MSCs showed significantly (P<0.05) higher clonogenic potency, faster growth rate and low population doubling (PDT) time. All the four types of MSCs were positive for alkaline phosphatase (AP) and differentiated into chondrogenic, osteogenic, and adipogenic lineages. These stem cells expressed MSC surface antigens (CD73, CD90 and CD105) and pluripotency markers (Oct4, Sox2, Nanog, KLF, cMyc, FoxD3) but did not express CD34, a hematopoietic stem cell marker (HSC) as confirmed by RT-PCR, immunocytochemistry and flow cytometric analysis. The relative mRNA expression of MSC surface antigens (CD73, CD90 and CD105) was significantly (P<0.05) higher in cWJ MSCs compared to the other cell lines. The mRNA expression of Oct4 was significantly (P<0.05) higher in cWJ, whereas mRNA expression of KLF and cMyc was significantly (P<0.05) higher in cWJ and cAF than that of cAS and cCB. The comparative assessment revealed that cWJ MSCs outperformed MSCs from other sources of fetal adnexa in terms of growth kinetics, relative mRNA expression of surface antigens, pluripotency markers and tri-lineage differentiation potential, hence, these MSCs could be used as a preferred source for regenerative medicine.

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 m 2 /s 2 ), 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.