by Payoshni Samantray & Krushna Chandra Gouda
Cloudbursts are brief, intense rainfall occurrences that present significant risks to human life, infrastructure, and ecosystems, especially in the Indian Himalayan Region (IHR). Despite their damaging characteristics, predicting these events remains challenging due to their localized nature, the influence of complex terrain, and a lack of high-resolution observational data. Therefore, enhancing the simulation and comprehension of such occurrences is crucial for improved forecasting and disaster readiness. This research introduces a high-resolution computational modeling technique utilizing the Weather Research and Forecasting (WRF) model to replicate cloudburst events in the Indian Himalayan Region (IHR) during the monsoon season of 2022. These extreme weather phenomena, marked by intense and localized rainfall, are hard to forecast because of the region's complex terrain and convective dynamics. In this study six representative cloudburst events from Himachal Pradesh, Uttarakhand, and Jammu & Kashmir were simulated at a resolution of 3 km using six-hourly NCEP-FNL reanalysis data. The analysis concentrated on critical atmospheric mechanisms such as vorticity, convective instability (CAPE, CINE), moisture availability, and orographic lifting. The model outputs were validated against daily rainfall data from the India Meteorological Department (IMD), Global Precipitation Measurement Mission (GPM), and the CPC MORPHing technique (CMORPH). The model demonstrated a strong correlation with IMD data (correlation coefficient of 0.84 and RMSE of 45.07 mm), although performance varied with satellite-based datasets. This study contributes to understanding the simulation of cloudburst events using high-resolution regional modeling and multiple observational validations.
Source: https://doi.org/10.1007/s40808-025-02680-w
by Siva Sai Kumar Rajana, Sambit Prasanajit Naik, Sampad Kumar Panda, Chiranjeevi G. Vivek, Sridevi Jade
Myanmar, located at the convergence of the Indian and Sunda plates, is one of the most seismically active and tectonically complex regions in Southeast Asia. Despite the high seismic hazard, limited ionospheric research has been conducted to understand earthquake-related atmospheric and ionospheric disturbances in this region. The Mw 7.7 Myanmar earthquake that occurred near Mandalay on 28 March 2025 has been considered in this work to examine seismo-ionospheric coupling along the active Sagaing Fault. Using GNSS-derived Total Electron Content (TEC) data, this study identifies both co-seismic and pre-seismic ionospheric anomalies. Multiple Receiver-Satellite (R-S) pairs reveal coherent, temporally correlated perturbations peaking around 0635 UT, indicative of upward-propagating seismic-induced Acoustic Gravity Waves (AGWs). The disturbance amplitudes decreased with distance from the epicenter, with dominant frequencies (3–5 mHz) consistent with seismically induced Traveling Ionospheric Disturbances (TIDs). Horizontal propagation speed of 1.19 km/s confirm the acoustic wave origin of these TEC perturbations. Prominent ionospheric anomalies are detected in the western side of the epicenter, corresponding to the fault rupture zone. These localized, directional perturbations indicate the clear coupling between the strike-slip fault dynamics and ionospheric disturbances. A significant regional TEC reduction detected three days prior to the event under geomagnetically quiet conditions suggests Lithosphere-Atmosphere-Ionosphere (LAI) coupling, likely linked to stress-induced radon emissions or electric field changes. The timing and characteristics of these anomalies reinforce the potential of TEC monitoring as an effective earthquake precursor. The findings not only demonstrate the sensitivity of TEC data to both pre- and post-seismic disturbances but also highlight the potential of TEC-based monitoring as a viable early warning tool, particularly in inter-plate settings where conventional seismic networks may be sparse or underdeveloped. Such ionospheric precursor detection can play a crucial role in enhancing seismic hazard forecasting and preparedness in geologically complex and high-risk regions like Myanmar.
