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Modeling and forecasting rainfall patterns of southwest monsoons in North–East India as a SARIMA process

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Abstract

Weather forecasting is an important issue in the field of meteorology all over the world. The pattern and amount of rainfall are the essential factors that affect agricultural systems. India experiences the precious Southwest monsoon season for four months from June to September. The present paper describes an empirical study for modeling and forecasting the time series of Southwest monsoon rainfall patterns in the North–East India. The Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) methodology has been adopted for model identification, diagnostic checking and forecasting for this region. The study has shown that the SARIMA (0, 1, 1) (1, 0, 1)4 model is appropriate for analyzing and forecasting the future rainfall patterns. The Analysis of Means (ANOM) is a useful alternative to the analysis of variance (ANOVA) for comparing the group of treatments to study the variations and critical comparisons of rainfall patterns in different months of the season.

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References

  • Brocklebank JC, Dickey DA (2003) SAS system for forecasting time series, 2nd edn. SAS Institute Inc, Cary

    Google Scholar 

  • Chang X, Gao M, Wang Y, Hou X (2012) Seasonal autoregressive integrated moving average model for precipitation time series. J Math Stat 8(4):500–505

    Article  Google Scholar 

  • Chartfield C (1980) Inverse Autocorrelations. J R Stat Soc A142:363–377

    Google Scholar 

  • Chonge Metrine, Nyongesa Kennedy, Mulati Omukoba, Makokha Lucy, Tireito Frankline (2015) A time series model of rainfall pattern of Uasin Gishu County. IOSR J Math 11(5):77–84

    Google Scholar 

  • Cryer JD, Chan KS (2008) Time series analysis with application in R, 2nd edn. Springer, New York, p 491. ISBN 0387759581

    Google Scholar 

  • Dobler A, Ahrens B (2008) Precipitation modelling in Europe and South Asia. Meteorol Z 17:499–509

    Article  Google Scholar 

  • Ebell K, Bachner S, Kapala A, Simmer C (2008) Sensitivity of summer precipitation simulated by the CLM with respect to initial and boundary conditions. Meteorol Z 17:421–431

    Article  Google Scholar 

  • Eni D, Adeyeye FJ (2015) Seasonal ARIMA modeling and forecasting of rainfall in Warri Town, Nigeria. J Geosci Environ Prot 3:91–98. doi:10.4236/gep.2015.36015

    Google Scholar 

  • Guo ZW (2009) the adjustment method and research progress based on the ARIMA model. Chin J Hosp Stat 161:65–69

    Google Scholar 

  • He SY (2004) Applied time series analysis, 1st edn. Peking University Press, Beijing

    Google Scholar 

  • Kantz H, Schreiber T (2004) Nonlinear time series analysis, 2nd edn. Cambridge University Press, Cambridge, p 369. ISBN 0521529026

    Google Scholar 

  • Men B, Xiejing Z, Liang L (2004) Chaotic analysis on monthly precipitation on hills region in Middle Sichuan of China. Nature Sci 2:45–51

    Google Scholar 

  • Momani M, Naill PE (2009) Time series analysis model for rainfall data in Jordan: case study for using time series analysis. Am J Environ Sci 5:599–604. doi:10.3844/ajessp.2009.599.604

    Article  Google Scholar 

  • Stoffer DS, Dhumway RH (2010) Time series analysis and its application, 3rd edn. Springer, New York, p 596. ISBN 1441978658

    Google Scholar 

  • Wang J, Du YH, Zhang XT (2008) Theory and application with seasonal time series, 1st edn. Nankai University Press, Chinese

    Google Scholar 

  • Wang HR, Wang C, Lin X, Kang J (2014) An improved ARIMA model for precipitation simulations, Nonlin. Processes Geophys 21:1159–1168

    Article  Google Scholar 

  • Weesakul U, Lowanichchai S (2005) Rainfall forecast for agricultural water allocation planning in Thailand Thammasat International. J Sci Technol 10(3):18–27

    Google Scholar 

  • Zaw WT, Naing TT (2008) Empirical statistical modeling of rainfall prediction over Myanmar. World Acad Sci Eng Technol 46:565–568

    Google Scholar 

  • Zhou ZJ, Hu CH (2008) An effective hybrid approach based on grey and ARMA for forecasting gyrodrifts. Chaos Solitons Fractals 35(3):525–529

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Mathematics, Madanapalle Institute of Technology and Science, Madanapalle, India

    K. V. Narasimha Murthy & R. Saravana

  2. Department of Statistics, S.G.S. Arts College, Tirupati, India

    K. Vijaya Kumar

Authors
  1. K. V. Narasimha Murthy
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  2. R. Saravana
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  3. K. Vijaya Kumar
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Corresponding author

Correspondence to K. V. Narasimha Murthy.

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Responsible Editor: M. Kaplan.

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Narasimha Murthy, K.V., Saravana, R. & Vijaya Kumar, K. Modeling and forecasting rainfall patterns of southwest monsoons in North–East India as a SARIMA process. Meteorol Atmos Phys 130, 99–106 (2018). https://doi.org/10.1007/s00703-017-0504-2

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  • DOI: https://doi.org/10.1007/s00703-017-0504-2

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