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Theoretical and Applied Climatology

, Volume 138, Issue 3–4, pp 1241–1253 | Cite as

Analysis of NMME system in simulating Indian summer monsoon rainfall

  • Sushant S. PuranikEmail author
  • Makarand A. Kulkarni
  • Jatin Singh
Original Paper
  • 58 Downloads

Abstract

Prediction of the Indian summer monsoon rainfall is essential for the country because the food production depends on the rainfall during the summer monsoon season. India being an agricultural country, its economy also depends on the monsoon rainfall. Various institutes across the world give global prediction. An experimental prediction based on the numerical simulations produced by North American Multi-Model Ensemble (NMME) has been attempted for the initial conditions of May to understand the strength, variability, predictability, and associated changes in the Indian summer monsoon. This study is particularly aimed to find out the efficacy of NMME models over the Indian region during the southwest monsoon season. The analysis shows that all the nine models underestimate the climatology. A mismatch exists between the spatial patterns of model climatology as compared to the observed climatology. It is found that models underestimate the inter-annual variability of the precipitation as compared to the observed ones. This can be attributed to the overestimation of sea surface temperature-Indian summer monsoon rainfall (SST-ISMR) response. This might lead to poor performance of the model in terms of precipitation prediction. The spatial correlation shows varying correlation pattern as compared to the observed one. However, almost all models have positive correlation over the peninsular India. The basic idea of MME approach is to generate a single prediction from the predictions from different models. The MME approach shows positive correlation over the peninsular and central India.

Keywords

Indian monsoon NMME Prediction strength Multi model ensemble 

Notes

Acknowledgments

The authors wish to thank International Research Institute for Climate and Society (IRI), USA, for providing hindcast datasets, NOAA/OAR/ESRL PSD for ERSST data, and government of India for subdivisional rainfall data which have been used in the study. We also thank anonymous reviewer for valuable suggestions that helped in improving the manuscript.

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Skymet Weather Services Pvt. Ltd.NoidaIndia
  2. 2.Go Digit General Insurance Ltd.PuneIndia

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