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CSIR Fourth Paradigm Institute

(Formerly CSIR Centre for Mathematical Modelling and Computer Simulation)

A constituent laboratory of Council of Scientific & Industrial Research (CSIR).

Ministry of Science and Technology, Government of India.

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Modelling for science, for a better future - some recent outcomes

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 

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.

Source: https://doi.org/10.1016/j.dynatmoce.2024.101504 

by K. Silpa and Anil Earnest

This study investigates the seismic behavior and deformation characteristics in the outer-arc region off the Nicobar trench using stress inversion and teleseismic finite-fault modeling. The spatial and depth-wise variations in stress field in this region indicate variations in post-seismic deformation scenarios along the arc. The lateral shear, similar to the north Wharton basin, is the major deformational field in the Nicobar segment, favoring earthquake faulting along ∼ESE or ∼NNE nodal planes here. Shallow oblique-normal faulting and deeper oblique-reverse regimes are observed in the Nias region. These observations suggest that the plate bending effects directly influence the ongoing tectonics in the Nias region. Moreover, variations in the stress fields across the arc could be indicative of an uncoupled plate interface. Our finite-fault modeling analysis indicates dominant ∼ESE-WNW fault plane orientations for selected events from this region. This suggests the possibility of faulting within similarly oriented active shear structures within the northern Wharton Basin, contrasting the prevailing ∼N-S faulting pattern in that area. Besides, the centroid depths of most of these earthquakes are typically within the 600°C isotherm. However, the seismic slip may extend deeper by rupturing the crust, occasionally reaching upper mantle depths.

Source: https://doi.org/10.1016/j.jseaes.2024.106299 

More Articles ...

  1. A comprehensive study of atmospheric dynamics associated with cloudburst events in 2022 over Indian Himalayan Region
  2. Morphology of Equatorial F-region Irregularities over the Indian Longitude sector using GPS-derived ROTI Observations
  3. Assessment of long-term spatio-temporal variability of hot extremes and associated physical mechanism over India
  4. Homogeneity Adjustment of Surface Temperature Data and Study of the Climate Variability Over Coastal Odisha by a Climate Departure Index
  5. Assessment of Country-Specific GHG Emissions from Road Transport Sector in India
  6. Decoding low-frequency climate variations: A case study on ENSO and ocean surface warming
  7. Intensity duration and frequency of Heat wave in different phases of MJO over India
  8. Impact of two severe geomagnetic storms on the ionosphere over Indian longitude sector during March-April 2023
  9. Impact of COVID-19 vaccination: a global perspective
  10. Precipitation Scaling in Extreme Rainfall Events and the Implications for Future Indian Monsoon: Analysis of High-Resolution Global Climate Model Simulations

Vision: 

To synergize the Transdisciplinary Pan-CSIR expertise and build a unified platform that embodies a rich set of big data enabling technologies and services with optimized performance to facilitate data intensive scientific discovery in the country.

Mission:

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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SPARK is intended to provide a unique opportunity to bright and motivated students of reputed Universities to carry out their major project/thesis work and advance their research knowledge in mathematical modelling and simulation of complex systems. The programme is intended to increase the interaction between scientists and faculty members of academic institutes along with their students towards a long term research collaboration. Click here to apply for SPARK.

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