Wed, Nov 25, 2020
Text Size

 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.

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

by Sulochana Gadgil, K Rajendran and D S Pai

Abstract: Most of the studies of the observed variability of the Indian summer monsoon rainfall (ISMR), its prediction and of its impact have involved analysis of an index for ISMR derived by Parthasarathy et al. (1995) or the all-India rainfall during the summer monsoon, available from the India Meteorological Department (IMD) website. Both these indices are based on the average rainfall over the meteorological subdivisions of India. Rajeevan et al. (2006) first derived a gridded rainfall data set for the Indian region which was at a resolution of 1° and subsequently, Pai et al. (2014) have derived a finer resolution (0.25°) rainfall data set for the same region. At present, these data sets are widely used by modelers to generate the ‘observed’ ISMR for assessment of the skill of their models. However, in different studies, different regions are used for averaging the grid data to obtain the ‘observed’ ISMR. For proper assessment and comparison of the skill of the simulations/predictions by different models/versions, it is important that the same region be used for averaging the rainfall to obtain the observed ISMR in each case. Here, we suggest what we consider as the appropriate regions for averaging the rainfall in terms of the 1° and 0.25° to derive/represent ISMR, on the basis of the present understanding of the monsoonal regions and the Indian summer monsoon. We show that the interannual variation of the ISMR thus derived (by averaging rainfall over the regions identified in this study) from gridded data sets is largely consistent with the indices derived as the area weighted sub-divisional rainfall data used in the indices used earlier.

Citation: Sulochana Gadgil, K Rajendran and D S Pai (2019): A new rain-based index for the Indian summer monsoon rainfall, MAUSAM, 70 (3), 485-500


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.

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.

A FAQ on SPARK is available here.



Articles View Hits