Modeling and predicting the patterns of seasonal rainfall in Tamil Nadu, India 1951–2017: an UCM approach

The main objective of the paper is to investigate the behavior, modeling, and predicting the patterns of the seasonal rainfall in Tamil Nadu using the unobserved components model (UCM) with the concealed components drift, seasonal, cyclical, and irregular. The India Meteorological Department (IMD) h...

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Veröffentlicht in:Arabian journal of geosciences 2020, Vol.13 (5), Article 226
Hauptverfasser: Viswanath, Narasimha Murthy Kaipa, Ramachandran, Saravana, Reganti, Hemadri Reddy
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Sprache:eng
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Zusammenfassung:The main objective of the paper is to investigate the behavior, modeling, and predicting the patterns of the seasonal rainfall in Tamil Nadu using the unobserved components model (UCM) with the concealed components drift, seasonal, cyclical, and irregular. The India Meteorological Department (IMD) has provided the seasonal rainfall data of Tamil Nadu (TN), and the analysis has been done for winter, pre-monsoon, monsoon, and post-monsoon seasons. The UCM with deterministic components drift, cycle, and trigonometric seasonal and stochastic irregular component is selected for predicting the patterns of seasonal rainfall in TN from the parsimonious models based on the significant tests of free parameters and components, information criteria, and statistical fit. The maximum likelihood estimation is used for the model parameters, and the validity of the chosen model is resolved through normal and correlation diagnostics. The patterns of seasonal rainfall are predicted with 95% confidence interval during the years 2018–2020 in virtue of selected UCM. The identified UCM forecasts the winter rainfall around 11.12 mm in 2018, 8.57 mm in 2019, and 19.3 mm in 2020; pre-monsoon rainfall will be 132.72 mm in 2018, 116.74 mm in 2019, and 107.62 mm in 2020; monsoon rainfall will be 354.16 mm in 2018, 349.52 mm in 2019, and 334.65 mm in 2020; post-monsoon rainfall will be 440.84 mm in 2018, 454.68 mm in 2019, and 457.1 mm in 2020.
ISSN:1866-7511
1866-7538
DOI:10.1007/s12517-020-5216-0