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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 Jayasankar C B, K Rajendran, Sajani Surendran and K V Ajay Anand

In this study, projected changes in mean northeast monsoon (NEM) rainfall and associated extreme rainfall and temperature events, over peninsular India (PI) and its six subdivisions, are quantified. High‐resolution dynamically downscaled simulations of the Weather Research and Forecasting (WRF) regional climate model driven by the boundary conditions from the Community Climate System Model version 4 (CCSM4) model (WRF‐CCSM4) are compared with statistically downscaled simulations of NASA Earth Exchange Global Daily Downscaled Projections (NEX‐GDDP). Over PI, these downscaled simulations show low bias in mean NEM rainfall (≤ − 0.44 mm·day−1) and high pattern correlation coefficient (≥0.75), giving confidence in their future projections. Under future warming over PI, both downscaled simulations project future significant enhancement in NEM rainfall with WRF‐CCSM4 projecting 1.98 mm·day−1 (83.78% change with respect to the present‐day mean) whereas the multimodel ensemble (MME) of eight NEX‐GDDP models project 0.67 ± 0.58 mm·day−1 (19.78%) by the middle of the century and 1.42 ± 0.97 mm·day−1 (42.76%) by the end of the century. Analysis of extreme rainfall events shows that WRF‐CCSM4 projects future enhancement (reduction) in extreme rainfall (R95p) days over 91.4% (8.6%) of grid‐points over PI. In future, coastal areas of Karnataka and Andhra Pradesh will likely experience increased extreme rainfall occurrence by more than 25 days and 15–20 days respectively. Projected future enhancement in the mean and extreme NEM rainfall is attributed to the increased precipitable water under a warming climate. Future projection of extreme temperature indices shows an increase in minimum and maximum temperatures over PI during the NEM season. Over PI, future winter nights and days are found to be warmer than those in the present day and the temperature change in future winter nights is found to be larger than that in winter days. This climate change information would help decision‐makers in evaluating existing policies and devising revised policies to reduce risk due to climate change.

Citation: Jayasankar C B, K Rajendran, Sajani Surendran and K V Ajay Anand 2021: High-resolution Climate Change Projection of Northeast Monsoon Rainfall over Peninsular India.Quarterly Journal of Royal Meteorological Society, doi: 10.1002/qj.4017


Source:https://doi.org/10.1002/qj.4017

by Sumana Sarkar & S. Himesh 

The monsoon-driven river basins are more vulnerable to the flash-floods triggered by intense rainfall activities. Due to the lack of adequate high resolution forecast of real-time hydro-meteorological variables, a reliable forecast of flash floods remains a challenge. One plausible way to generate such a high-resolution forecast of hydro-meteorological variables is to use a coupled atmospheric–hydrologic modelling system. Thus, in this study, a physically-based, fully distributed, multi-scale hydrologic modelling framework, WRF-Hydro with optimized configurations (stand-alone and coupled-mode) is used to simulate the important hydro-meteorological variables like precipitation, runoff, soil moisture, and land surface heat fluxes over Cauvery river basin, India. In stand-alone mode, the model is driven by the high-resolution gridded data from the Global Land Data Assimilation System; while the coupled model is run with the WRF atmospheric model. In this study, the ability of a fully coupled WRF–WRF-Hydro modelling framework , with 3 km grid spacing is used to simulate the hydro-meteorological conditions during an extreme rainfall event (08–09 August 2019). The innermost domain of WRF-Hydro in conjunction with a high resolution hydrological routing grid (300 m) is also utilized, to include the subgrid scale disaggregation–aggregation weighting procedures to generate the land–atmospheric feedbacks on the hydrometeorological variables. The resulting variables have been validated through relevant observations; the overall performance of the coupled WRF-Hydro is shown to be relatively good when compared to the WRF-only simulations.

Source: https://doi.org/10.1007/s00024-021-02684-4

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,

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  5. Revisiting the seismogenic characteristics of stable continental interiors: The case of three Indian events
  6. Comparison of the performance of HYBRID ETKF-3DVAR and 3DVAR data assimilation scheme on the forecast of tropical cyclones formed over the Bay of Bengal
  7. Impact of Urbanization on Heavy Rainfall Events: A Case Study over the Megacity of Bengaluru, India
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  9. Crustal deformation rates in Kashmir valley and adjoining regions from continuous GPS measurements from 2008 to 2019
  10. Site Effects Investigation in Srinagar City of Kashmir Basin Using Microtremor and Its Inversion

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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.

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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.

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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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