首页> 外文会议>2011 30th 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:通过马尔可夫链方法通过人工神经网络预测季风前雷暴,以每日总臭氧量作为预测因子:第XXX届URSI大会和科学研讨会将于2011年8月13日至20日在土耳其伊斯坦布尔举行

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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 occurrenceon-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:本文的目的是检验恒河西孟加拉邦严重的季风前雷暴发生的可预测性。由于对流层以及平流层臭氧对气象现象发生的影响,因此没有考虑各种气象预测因素,而是选择每日总臭氧浓度作为预测因素。将2005年季风前季节(3月至5月)的雷暴发生/不发生视为二分时间序列{X t },实现了0和1的不发生以及在分别发生TS的情况下,在此时间序列内揭示了一阶二态(FOTS)马尔可夫依赖性。

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