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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，通过人工神经网络预测具有日常臭氧的严重季风雷暴，作为预测因素：XXXth Ursi大会和科学研讨会在土耳其伊斯坦布尔，2011年8月13日至20日

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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 pre-monsoon season (March-May) of the year 2005 as the dichotomous time series{Xt} 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.
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年季风季节（3月）的发生/不发生雷暴作为二分法时间序列{X T }，用于实现0和1的不发生和1在该时间序列中，分别发生了TS的发生，第一阶两个状态（FOT）马尔可夫依赖性。

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