A Markov chain approach in the prediction of severe pre-monsoon thunderstorms through artificial neural network with daily total ozone as predictor: XXXth URSI general assembly and scientific symposium to be held in Istanbul, Turkey, August 13–20,

Author

Chattopadhyay, Goutami ; De, S.S.

Author_Institution

Centre of Adv. Study in Radio Phys. & Electron., Univ. of Calcutta, Kolkata, India

fYear

2011

fDate

13-20 Aug. 2011

Firstpage

1

Lastpage

4

Abstract

Purpose of the present paper is to examine the predictability of the occurrence of the severe pre-monsoon thunderstorm over Gangetic West Bengal. Instead of considering various meteorological predictors, the daily total ozone concentration is chosen as the predictor because of the influence of tropospheric as well as stratospheric ozone on the genesis of meteorological phenomena. Considering the occurrence/non-occurrence of thunderstorm in the pre-monsoon season (March-May) of the year 2005 as the dichotomous time series{X_t} that realizes 0 and 1 for non-occurrence and occurrence of TS respectively, a first order two state (FOTS) Markov dependence is revealed within this time series.

Keywords

Markov processes; atmospheric composition; atmospheric humidity; atmospheric techniques; neural nets; ozone; stratosphere; thunderstorms; time series; troposphere; AD 2005 03 to 05; AD 2011 08 13 to 08 20; Gangetic West Bengal; Istanbul; Markov chain approach; O₃; Turkey; URSI General Assembly; artificial neural network; dichotomous time series; meteorological phenomena; meteorological predictor method; ozone concentration analysis; premonsoon thunderstorms; stratospheric ozone; tropospheric effect; Artificial neural networks; Atmospheric modeling; Correlation; Markov processes; Meteorology; Terrestrial atmosphere; Time series analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

General Assembly and Scientific Symposium, 2011 XXXth URSI

Conference_Location

Istanbul

Print_ISBN

978-1-4244-5117-3

Type

conf

DOI

10.1109/URSIGASS.2011.6050840

Filename

6050840