首页> 外文会议>URSI General Assembly and Scientific Symposium >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, 2011
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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, 2011

机译:一种Markov链条方法,通过人工神经网络通过人工神经网络预测,每天总臭氧作为预测XXXTH URSI大会和科学研讨会在土耳其举行,2011年8月13日至2011年8月13日至2011年

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摘要

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 premonsoon season (March-May) of the year 2005 as the dichotomous time series{ } t X that realizes 0 and 1 for nonoccurrence and occurrence of TS respectively, a first order two state (FOTS) Markov dependence is revealed within this time series.
机译:本文的目的是检查对甘甘湾严重季隆雷暴发生的可预测性。代替考虑各种气象预测因子,由于对流层以及流程层臭氧对气象现象的成因的影响,因此选择每日总臭氧浓度作为预测因子。考虑到2005年的前大多数季节(3月5月)作为二分时序列的发生/不发生的雷暴,这是一个分别实现0和1的二分钟和1的X.分别是第一个阶两个状态( FOTS)Markov依赖性在此时间序列中揭示。

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