Recent predictors of Indian summer monsoon based on Indian and Pacific Ocean SST

  • Namendra Kumar Shahi
  • Shailendra Rai
  • Nishant Mishra
Original Paper


This study investigates the relationship between sea surface temperature (SST) of various geographical locations of Indian and Pacific Ocean with the Indian summer monsoon rainfall (ISMR) to identify possible predictors of ISMR. We identified eight SST predictors based on spatial patterns of correlation coefficients between ISMR and SST of the regions mentioned above during the time domain 1982–2013. The five multiple linear regression (MLR) models have been developed by these predictors in various combinations. The stability and performance of these MLR models are verified using cross-validation method and other statistical methods. The skill of forecast to predict observed ISMR from these MLR models is found to be substantially better based on various statistical verification measures. It is observed that the MLR models constructed using the combination of SST indices in tropical and extra tropical Indian and Pacific is able to predict ISMR accurately for almost all the years during the time domain of our study. We tried to propose the physical mechanism of the teleconnection through regression analysis with wind over Indian subcontinent and the eight predictors and the results are in the conformity with correlation coefficient analysis. The robustness of these models is seen by predicting the ISMR during recent independent years of 2014–2017 and found the model 5 is able to predict ISMR accurately in these years also.



The authors would like to thanks Ministry of Earth Sciences (MoES) (Grant no. MM/SERP/Univ-Allahabad/2013/IND-6/002) and Department of Science and Technology (DST) (Grant no. EMR/2015/000861), Government of India for the financial support to carry out this research. The authors thank the IMD, New Delhi, India for providing the daily gridded rainfall data for this study. We also thank NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, for providing the NCEP/NCAR reanalysis wind dataset (available online at We gratefully acknowledge the providers of HadISST v.1.1 data: UK Meteorological Office, Hadley Centre (available online at SR would like to thank USIEF for granting him Fulbright-Nehru Academic and Professional Excellence Fellowship to visit Columbia University, NY, USA. NKS would like to thank ICTP/IAEA for granting him STEP fellowship to visit ICTP, Trieste, Italy.


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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.K. Banerjee Centre of Atmospheric and Ocean StudiesUniversity of AllahabadAllahabadIndia
  2. 2.M. N. Saha Centre of Space StudiesUniversity of AllahabadAllahabadIndia
  3. 3.Urban Development DepartmentGNCTD, Delhi SecretariatNew DelhiIndia

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