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Theoretical and Applied Climatology

, Volume 47, Issue 3, pp 159–173 | Cite as

Optimization of the raingauges for a representative all-India and subdivisional rainfall series

  • N. A. Sontakke
  • N. Singh
  • G. B. Pant
Article

Summary

An objective approach similar to the forward selection of independent variables in the multiple linear regression has been attempted to optimize the network of raingauges for the summer monsoon rainfall (June–September total) series (1871–1984) of India as well as its 29 selected meteorological subdivisions prepared involving the data of 306 raingauges. For the all-India monsoon rainfall series twenty seven gauges entered the selection whose mean showed the correlation coefficient (CC) of 0.9869. Keeping in view the difficulties of getting data from all the 306 gauges, 35 India Meteorological Department (IMD) gauges with mean showing CC of 0.9898 have been identified for updating this series. The constructed all-India monsoon rainfall series for the period 1871–1992 using 35 selected observations is presented. It was interesting to note that the set of 35 gauges selected for the monsoon total has shown equally promising results for the all-India monsoon monthly (June–September) as well as the annual rainfall series.

For the 29 subdivisional monsoon rainfall series, however, in total 188 IMD-gauges (62% of the total of 306 gauges) entered the selection. For 17 subdivisions the CC exceeded 0.98, for 3 subdivisions it varied between 0.97 and 0.98, for 5 subdivisions between 0.96 and 0.97 and for the remaining 4 subdivisions between 0.90 and 0.94. They showed equally encouraging results for the monsoon monthly and annual rainfall series for the different subdivisions.

Limitations and implications of the optimization technique are also briefly discussed.

Keywords

Climate Change Waste Water Water Pollution Multiple Linear Regression Promising Result 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag 1993

Authors and Affiliations

  • N. A. Sontakke
    • 1
  • N. Singh
    • 1
  • G. B. Pant
    • 1
  1. 1.Indian Institute of Tropical MeteorologyPuneIndia

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