by Sampad Kumar Panda, Siva Sai Kumar Rajana, Chiranjeevi G. Vivek, Jyothi Ravi Kiran Kumar Dabbakuti, Wangshimenla Jamir and Punyawi Jamjareegulgarn
In this study, we explored the occurrence of near-sunrise equatorial plasma bubbles (EPBs) and inhibition of dusk-time EPBs during the geomagnetic storm (SYM-Hmin= −139 nT) of 19–20 April 2024 using multi-instrument observations over the Indian and Southeast Asian longitude sectors. The initial phase of this storm commenced around 0530 UT on 19 April 2024 and did not manifest any visible alterations in the ionospheric electric fields during the main phase of the storm, which corresponded to a period between post-sunset to midnight over the study region. However, during the recovery phase of the storm, the IMF Bz suddenly flipped northward and was associated with an overshielding of the penetrating electric fields, which triggered the formation of near-sunrise EPBs. Interestingly, the persistence of EPBs was also noticed for more than three hours after the sunrise terminator. Initially, sunrise EPBs were developed in the Southeast Asian region and later drifted toward the Indian longitude region, along with the sunrise terminator. Moreover, this study suggested that the occurrence of EPBs was suppressed due to the altered storm time electric fields at the dip equatorial region across the 70–90°E longitude sector in the recovery period. This study highlighted that even moderate geomagnetic storms can generate near-sunrise EPBs in a broader longitude sector due to penetrating electric fields in overshielding conditions, which can significantly affect trans-ionospheric signals.
by Dhananjay A. Sant, Gunjankumar K. Makwana, Prabhin Sukumaran, Imtiyaz A. Parvez, Govindan Rangarajan and K. Krishnan
We propose a rapid and cost-effective Horizontal-to-Vertical Spectral Ratio (HVSR) method that uses microtremors (ambient noise) for mapping intercalated sedimentary sequences with lateral heterogeneity for a depth of about 100 m in Lower reaches of Narmada Valley, western India. The elastic parameters for various units/layers at a given site (1D) were derived theoretically based on HVSR inversion, built on the concepts of Diffuse Field Assumption and retrieval of the imaginary part of Green's function. This approach enables efficient forward calculation, delves into the relationship between the HVSR curve and elastic parameters, and accelerates the inversion process. A site-specific priori knowledge gathered during the field visits is further integrated. We successfully derive overall shear wave velocity and density models inferring three sedimentary Units and associated Subunits. The key highlight of this study is successfully distinguishing low-velocity sedimentary Units/Subunits within a sedimentary sequence.
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.
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.
by Priyanshi Singhai, Arindam Chakraborty, Kaushik Jana, Kavirajan Rajendran, Sajani Surendran and Kathy Pegion
An ensemble of forecasts is necessary to identify the uncertainty in predicting a non-linear system like climate. While ensemble averages are often used to represent the mean state and diagnose physical mechanisms, they can lead to information loss and inaccurate assessment of the model’s characteristics. Here, we highlight an intriguing case in the seasonal hindcasts of the Climate Forecast System version 2 (CFSv2). While all ensemble members often agree on the sign of predicted El Niño Southern Oscillation (ENSO) for a particular season, non-ENSO climate forcings, although present in some of the individual members, are disparate. As a result, an ensemble mean retains ENSO anomalies while diminishing non-ENSO signals. This difference between ENSO and non-ENSO signals significantly influences moisture convergence and Indian summer monsoon rainfall (ISMR). This stronger influence of ENSO on seasonal predictions increases ENSO–ISMR correlation in ensemble mean seasonal hindcasts. Thus, this discrepancy in the ENSO–ISMR relationship is not present in the individual ensemble members, considered individually or together (without averaging) as independent realizations. Therefore, adequate care should be taken while evaluating physical mechanisms of teleconnection in ensemble mean predictions that can often be skewed due to constructive or destructive superposition of different impacts.
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To Pioneer data driven interdisciplinary research in diverse fields through state-of-the-art data science ecosystem and impactful industrial partnerships for the betterment of Society.
Mandate:
To develop cutting edge data science products as a horizontal across the CSIR Themes and position as an Institute of Excellence in Bigdata and Artificial Intelligence.
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