An effort is made to investigate the tropical cyclone (TC) variability over the Bay of Bengal in the warming climate scenario during the satellite era (1979–2018). In this study, the authors investigate interannual variations in the overall capacity of the Bay of Bengal (BoB) basin in the post‐monsoon season to produce either intense or moderate TCs. Globally accepted and widely used metrics accumulated cyclone energy (ACE) and power dissipation index (PDI) are used to examine the energy and damage generated by TCs. A statistical change‐point analysis is conducted to detect the shift in the tropical cyclone frequency and ACE during the study period, indicating the year 1999 as a change point. The results show the increasing trend in ACE in the BoB during the post‐monsoon season with a statistical significance of 95%. On the contrary, the overall frequency is almost unchanged but has shown an increasing trend in the last decade of the study period. Additionally, atmospheric and ocean factors influencing the TC intensity are investigated by applying principal component analysis for three categories of TCs, viz. the very severe cyclonic storm, severe cyclonic storm, and cyclonic storm. Implementation of principal component analysis and correlations of maximum sustained wind speed (MSW²; intensity) with relative vorticity, minimum sea‐level pressure (MSLP), sea‐surface temperature (SST), specific rainwater content, and vertical wind shear revealed relative vorticity as the most dominating parameter amongst all governing factors which determine the final intensity of the BoB TCs. On the contrary, SST shows the different nature of the relationship with different TC categories.

The global retreat in glaciers is considered to be one of the critical indicators of climate change. However, the glaciers of the Karakoram (KR) region of the Karakoram–Himalayas (KH) stand out because of their divergent response, displaying a surge as opposed to glaciers in other regions. This phenomenon is known as the “Karakoram anomaly.” Although many factors control the establishment and sustenance of the anomaly, the present study establishes winter precipitation associated with western disturbances (WDs) over the KH as one of the key drivers behind its emergence. To examine the role of WDs, a tracking algorithm is applied to 39 seasons (November–March) for three separate (ERA5, MERRA-2, and NCEP-CFSR/CFSv2) reanalysis datasets. The associated reanalysis ensemble statistics of WD properties produced in terms of their intensity, precipitation/snowfall volumes, and wind speed suggest a revival in recent years over the core-anomaly regions. However, the frequency has remained steady. The Karakoram has witnessed a rise of ∼10% in precipitation intensity associated with WDs in recent decades. The high percentage of snowfall received by the Karakoram (∼65%) from WDs relative to the total seasonal snowfall suggests a crucial role in modulating the regional mass-balance anomaly. Simultaneously, the amount of snowfall from non-WD sources in the Karakoram has had a statistically significant decline of ∼17% in recent decades, coinciding with the anomaly period. The enhanced intensity of WDs is found to be associated with changes in increased baroclinic instability and a shift of the subtropical westerly jet mean latitudinal position.

This study aims to provide a comprehensive and balanced assessment of recent scientific studies on the evolution, temporal variability and prediction of tropical cyclones (TCs), focusing on the North Indian Ocean (NIO). The involvement of earth’s components in TC genesis and intensification has been elaborated in a confined way. The advancement of multidisciplinary approaches for comprehending the TCs is highlighted after a brief description of the involvement of oceanic, atmospheric, and land surface processes. Only a few studies illustrate how land surface plays a role in TC intensification; however, the role of latent heat flow, moisture, and convection in cyclogenesis is well documented. Despite two to 3 decades of advancement and significant development in forecasting techniques and satellite products, the prediction of TC’s intensity, dissipation, track, and landfall remains a challenge. The most noticeable improvements in NIO TC’s prediction have been achieved in the last couple of decades when concord techniques are utilized, especially the data assimilation methods and dynamical coupled atmosphere-ocean regional models. Through diverse methodologies, algorithms, parameterization, in-situ observational data, data mining, boundary layer, and surface fluxes, significant research has been done to increase the skills of standalone atmospheric models and air-sea coupled models. However, some crucial issues still exist, and it is suggested that they should be addressed in future studies.

In the Himalayan regions of complex terrains, such as Himachal Pradesh, the occurrence of extreme rainfall events (EREs) has been increasing, triggering landslides and flash floods. Investigating the dynamics and precipitation characteristics and improving the prediction of such events are crucial and could play a vital role in contributing to sustainable development in the region. This study employs a high-resolution numerical weather prediction framework, the weather research and forecasting (WRF) model, to deeply investigate an ERE which occurred between 8 July and 13 July 2023. This ERE caused catastrophic floods in the Mandi and Kullu districts of Himachal Pradesh. The WRF model was configured with nested domains of 12 km and 4 km horizontal grid resolutions, and the results were compared with global high-resolution precipitation products and the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis dataset. The selected case study was amplified by the synoptic scale features associated with the position and intensity of the monsoon trough, including mesoscale processes like orographic lifting. The presence of a western disturbance and the heavy moisture transported from the Arabian Sea and the Bay of Bengal both intensified this event. The model has effectively captured the spatial distribution and large-scale dynamics of the phenomenon, demonstrating the importance of high-resolution numerical modeling in accurately simulating localized EREs. Statistical evaluation revealed that the WRF model overestimated extreme rainfall intensity, with the root mean square error reaching 17.33 mm, particularly during the convective peak phase. The findings shed light on the value of high-resolution modeling in capturing localized EREs and offer suggestions for enhancing disaster management and flood forecasting.