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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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CSIR-4PI welcomes corporates to invest CSR funds in R&D of institute – Please contact Group Leader

CSIR-4PI welcomes corporates to invest CSR funds in R&D of institute – Please contact Group Leader

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

by Salwa Naushin, Viren Sardana, Rajat Ujjainiya, Nitin Bhatheja, Rintu Kutum, Akash Kumar Bhaskar, Shalini Pradhan, Satyartha Prakash, Raju Khan, Birendra Singh Rawat, Karthik Bharadwaj Tallapaka, Mahesh Anumalla, Giriraj Ratan Chandak, Amit Lahiri, Susanta Kar, Shrikant Ramesh Mulay, Madhav Nilakanth Mugale, Mrigank Srivastava, Shaziya Khan, Anjali Srivastava, Bhawna Tomar, Murugan Veerapandian, Ganesh Venkatachalam, Selvamani Raja Vijayakumar, Ajay Agarwal, Dinesh Gupta, Prakash M Halami, Muthukumar Serva Peddha, Gopinath M Sundaram, Ravindra P Veeranna, Anirban Pal, Vinay Kumar Agarwal, Anil Ku Maurya, Ran Vijay Kumar Singh, Ashok Kumar Raman, Suresh Kumar Anandasadagopan, Parimala Karuppanan, Subramanian Venkatesan, Harish Kumar Sardana, Anamika Kothari, Rishabh Jain, Anupma Thakur, Devendra Singh Parihar, Anas Saifi, Jasleen Kaur, Virendra Kumar, Avinash Mishra, Iranna Gogeri, Geethavani Rayasam,

by Jagat Dwipendra Ray, M. Sithartha Muthu Vijayan,  and Walyeldeen Godah

 

The area of North-East India and Nepal Himalaya undergoes seasonal deformations due to the variation of surface mass loads induced mainly by annual monsoon precipitation. The present study focuses on comparing seasonal horizontal deformations of the Earth’s surface obtained over the area of North-East India and Nepal Himalaya using Global Positioning System (GPS) and the corresponding ones obtained from Gravity Recovery and Climate Experiment (GRACE) satellite mission data. Seasonal deformations of the Earth’s surface in horizontal components were determined using daily observations from 36 GPS stations located in North-East India and Nepal Himalaya and Release-05 GRACE-based Global Geopotential Models (GGMs). The consistency between these seasonal horizontal deformations was investigated using three statistical metrics, namely: the correlation, Weighted Root Mean Square (WRMS) reduction and Nash–Sutcliffe model Efficiency (NSE). The results obtained indicate that at nearly 89% of GPS stations investigated, positive correlation can be determined between seasonal deformations of the Earth’s surface in the north component obtained from GPS and the corresponding ones from GRACE data. The percentage of WRMS reductions computed from seasonal horizontal deformations of the Earth’s surface obtained using GPS and GRACE data reach ~ 18% and 0.71% in north and east components, respectively. Moreover, we obtain the median value of NSE almost 0.28 for north and − 0.01 for east components. The study finds that seasonal horizontal deformations in the area investigated are controlled by local tectonics, and realizes the need of a realistic Earth model comprising local crustal inhomogeneities and tectonic features for better constraining the surface deformations in this region.

 

Source: https://link.springer.com/article/10.1007/s40328-020-00331-3

by Govindan Kutty, Rekha Gogoi, V Rakesh & M Pateria 

This study compares the performance of hybrid ensemble transform Kalman filter – three dimensional variational data assimilation (HYBRID) system and three dimensional variational (3DVAR) data assimilation system in Weather Research and Forecasting Model (WRF) in simulating tropical cyclones (TC) formed over the Bay of Bengal. An Ensemble Transform Kalman Filter (ETKF) system updates the ensemble system that provides flow-evolving background error covariance for HYBRID data assimilation system. Results indicate that use of flow-evolving ensemble error covariance in 3DVAR system has systematically reduced the TC position and intensity errors in the analysis; however, adding more weights to the ensemble error covariance term in 3DVAR cost function has not made any significant impact. The 3DVAR analysis depicts a stronger TC vortex with a well pronounced warm core structure as compared to that in HYBRID analysis. The forecasts from HYBRID analysis outperform that from 3DVAR in reducing TC track forecast error. The relative improvement in TC landfall position is 43% and 49% for variously configured HYBRID experiments. The forecasts initiated from HYBRID analysis has higher skill in quantitative precipitation forecasts during TC landfall compared to 3DVAR, which may be attributed to improved track prediction in the HYBRID experiments.

Source: https://link.springer.com/article/10.1007/s12040-020-01497-8

More Articles ...

  1. Impact of Urbanization on Heavy Rainfall Events: A Case Study over the Megacity of Bengaluru, India
  2. GNSS and its impact on position estimates
  3. Crustal deformation rates in Kashmir valley and adjoining regions from continuous GPS measurements from 2008 to 2019
  4. Atmospheric opacity using 220 GHz (1.36 mm) radiometer data and water vapor trends over Indian Astronomical Observatory (IAO), Hanle
  5. Future changes in rice yield over Kerala using climate change scenario from high resolution global climate model projection

Vision and Mission

Our Vision: To provide modelling, simulation and data-intensive capability powered by high-performance computing and informatics research.

Our Mission: To develop knowledge products in the earth system and information sciences for societal benefit by exploiting modelling, simulation and data science capabilities. The mission statement thus encompasses the continuation of existing modelling and simulation work in earth sciences and places emphasis on exploiting data science capabilities across domains.

Our Mandate: To develop reliable knowledge products for decision support in Earth, Engineering and Information Sciences. To be the national leader in High-Performance Computing as service that will power modelling and informatics across CSIR.

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Student Programme for Advancement in Research Knowledge (SPARK)

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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