by Sridevi Jade & T. S. Shrungeshwara
The 2500 km Himalayan arc spanning Kashmir, Ladakh, in the west to Eastern syntaxis in the east is a tectonically complex and seismically active northern subduction boundary of the Indian plate. Three decades of GPS data give well-constrained surface convergence rates ranging from 10 to 26 mm/yr in the various segments of the Himalayas (Kashmir, Ladakh, Himachal, Garhwal, Kumaun, Nepal, Sikkim, Bhutan, Arunachal and Eastern syntaxis). Arc parallel rates of 3–10 mm/yr is the manifestation of locked curvature of the central Himalayan arc and the E–W extension rate of Tibet. Inverse modelling of surface convergence rates is used to estimate oblique slip rate of 13–20 mm/yr along Main Himalayan Thrust (MHT) at a depth of 15–20 km and locking width of 100–150 km from the frontal Himalayas suggesting that each segment of the Himalaya is unique in nature. Geodetic strain rates derived from the GPS-derived surface convergence rates suggest that the Himalayan region is predominantly under compression with high strain rate coinciding with the northern boundary of sub surface basal decollement (MHT) along which Indian plate subducts below Tibet. Seismic strain rates for each segment of the Himalaya are computed using the instrumental and historical earthquake catalogue. Seismic potential of each segment of the Himalaya is estimated by a combined analysis of geodetic and seismic strain rates and the corresponding moment rates. Further, strain budget and accumulated slip since the last devastating earthquake was used to estimate the recurrence interval and probable magnitude of impending earthquake in the Himalayan segments.
Source: https://doi.org/10.1007/978-981-97-7658-0_3
by Ajay Bankar, V. Rakesh & Smrati Purwar
This study explores the impact of assimilating diverse observational data on forecasting extreme rainfall events (EREs) using a three dimensional variational (3D-Var) assimilation approach. It focuses on 38 EREs across three meteorological divisions in Karnataka, India, using a high-resolution (03-km) Weather Research and Forecasting (WRF) model with three nested domains. Five distinct experiments were conducted, including a Control experiment without assimilation, and subsequent experiments integrating observations from various sources like atmospheric profiles from Atmospheric InfraRed Sounder (AIRS) and Moderate resolution Imaging Spectroradiometer (MODIS) satellites and radiosondes, ocean surface wind observations from Advanced Scatterometer (ASCAT), Special Sensor Microwave Imager (SSMI), and WindSAT satellites and buoys, ground observations from Karnataka State Natural Disaster Monitoring Centre (KSNDMC), as well as a combined assimilation experiment with all available observations. The accuracy of rainfall forecasts is evaluated by comparing model outputs with high-resolution telemetric rain-gauge (TRG; 6480 stations) data and other meteorological parameters against telemetric weather station (TWS; 860 stations) data from KSNDMC. Assimilation experiments show positive improvements over control experiment in predicting rainfall. Results consistently indicate underprediction of rainfall in the intricate topographical region of the Western Ghats (WG) across all experiments, contrasting with overprediction along the coastal areas of Karnataka. The experiment involving Ocean Winds showcased a substantial 40 % reduction in rainfall overprediction (above 2 mm threshold). Both Ocean Winds and Station Data assimilation notably enhanced rainfall prediction accuracy over most of the regions in Karnataka, with Ocean Winds exhibiting the highest improvement (53 %), closely followed by Station Data (50 %). Importantly, assimilating Ocean Winds and Station Data aided in reducing overprediction, while assimilating Satellite Profiles reduced underprediction in the interior part of Karnataka but increased overprediction over the coastal region compared to the control experiment. Frequency of occurrence of rainfall is considerably enhanced along the coastline in all 3D-Var experiments. Bias score indicates maximum improvement in assimilation using Ocean Winds and Station Data. Simulation of basic meteorological parameters also improved with assimilation particularly during the day hours. The results underscore the crucial role of assimilation of satellite and in-situ observations in improving forecast accuracy of EREs during the monsoon season.
Source: https://doi.org/10.1016/j.atmosres.2024.107777
