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Unobserved component modeling for seasonal rainfall patterns in Rayalaseema region, India 1951-2015

机译:1951 - 2015年雷阿拉斯马省地区季节降雨模式的未观察组件建模

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摘要

Rainfall is the significant parameter for climate change, meteorological and hydrological process. The present paper describes the seasonal rainfall patterns in the Rayalaseema region of Andhra Pradesh state, India using the unobserved component model (UCM) with the hidden components like trend, seasonal, cyclical and irregular. The seasonal rainfall data were provided by India Meteorological Department (IMD), using daily gridded rainfall data with 10 automatic weather stations spread over the Rayalaseema region and the study deals with four seasons of rainfall as classified by IMD, namely winter, pre-monsoon, southwest monsoon and northeast monsoon. Basic Structural Model (BSM) with the components of constant trend, deterministic trigonometric seasonal, deterministic cycle and irregular is selected from the parsimonious models of UCM based on Akaike's information criteria (AIC), Bayesian information criteria (BIC), significant tests and statistical fit. The model parameters are obtained using maximum likelihood method; the adequacy of the selected model is determined through correlation and normal diagnostics. The forecast of the seasonal rainfall patterns during the years 2016-2018 has been noticed with the help of selected UCM. From the model forecast, it is observed that the pre-monsoon season receives rainfall of 96.7 mm in the years 2016 and 2018, whereas 71.3 mm in the year 2017; the southwest monsoon season receives the rainfall of 396.0 mm in the year 2016, 424.2 mm in 2017 and 419.2 mm in 2018; the northeast monsoon season receives the rainfall of 286.8 mm in 2016, 261.3 mm in 2017 and 286.8 mm in 2018.
机译:降雨是气候变化,气象和水文过程的重要参数。本文描述了Andhra Pradesh State,India的Rayalaseema地区的季节降雨模式,使用未观察的组件模型(UCM),隐藏组件如趋势,季节性,周期性和不规则。印度气象部门(IMD)提供的季节性降雨数据,使用10种自动气象站的日常喷射数据,分布在雷拉塞萨马州地区,研究涉及由IMD分类的四季降雨,即冬季,冬季季前翁,西南季风和东北季风。基本结构模型(BSM)与恒定趋势的组件,确定性三角季节性,确定性周期和不规则选自基于Akaike的信息标准(AIC),贝叶斯信息标准(BIC),显着的测试和统计拟合的UCM 。使用最大似然法获得模型参数;通过相关性和正常诊断确定所选模型的充分性。在选定的UCM的帮助下,已经注意到2016 - 2018年季节降雨模式的预测。从模型预测中,据指出,季季度季季节在2016年和2018年收到了96.7毫米的降雨,而2017年的71.3毫米;西南季风季节在2016年的降雨量为396.0毫米,2017年424.2毫米,2018年419.2毫米;东北季风季节于2016年收到了286.8毫米的降雨,2017年261.3毫米,2018年286.8毫米。

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