Forecasting models of daily maximum and minimum temperature – A comparative study for Dumdum airport

Authors

  • K. SEETHARAM Meteorological Office, Kolkata – 700 027, India

DOI:

https://doi.org/10.54302/mausam.v56i3.991

Keywords:

Albedo, Exponential smoothing, Markovian probability, Neural networks, Smoothing constant, Damping constant

Abstract

In this paper, statistical techniques namely, Single Exponential Smoothing and Optimized Single Exponential Smoothing, Double Exponential Smoothing and Optimized Double Exponential Smoothing were used in the field of meteorology as forecasting models for prediction of daily as well as 3-years average (2001-03) maximum and minimum temperatures and the results were compared to judge applicability of these statistical models for operational  use The modifications in algorithms of the two known techniques, two new Optimized techniques are obtained. The results established that these methods are advantageous in comparison with persistence and climatology. Further the accurate prediction of daily rainfall improves forecast of the maximum and minimum temperatures.

 

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Published

01-07-2005

How to Cite

[1]
K. SEETHARAM, “Forecasting models of daily maximum and minimum temperature – A comparative study for Dumdum airport”, MAUSAM, vol. 56, no. 3, pp. 609–616, Jul. 2005.

Issue

Vol. 56 No. 3 (2005): MAUSAM

Section

Research Papers

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