Abstract
The study was carried out to developed stochastic model for weekly temperature, humidity and precipitation in Solapur (Latitude 17°40′N, Longitude 75°54′E and altitude 483.50 m amsl) station of western part of Maharashtra, India. In the present study, 42 years data (1969–2010) of daily temperature, relative humidity and precipitation of Solapur station have been used for time series analysis. Weekly mean temperature, relative humidity and monthly precipitation values were used to fit the ARIMA class of models for different orders. ARIMA models of first and second orders were selected based on autocorrelation function (ACF) and partial autocorrelation function (PACF) of the time series. The parameters of the selected models were obtained with the help of maximum likelihood method. The diagnostic checking of the selected models was then performed with the help of three tests (i.e. standard error, ACF and PACF of residuals and AIC) to know the adequacy of the selected models. The ARIMA models that passed the adequacy test were selected for forecasting. One year ahead forecast (i.e. for 2010) of temperature, relative humidity and precipitation values were obtained with the help of these selected models and compared with the values of temperature, relative humidity and precipitation obtained from the climatological data of 2010 by root mean square error (RMSE). According to the Seasonal ARIMA model, ACF, PACF and evaluation of all eventual parameters, the results from analysis show that the model fitted is weekly temperature: ARIMA (111) (011) 52 , weekly relative humidity: ARIMA (111) (111) 52 and monthly precipitation: ARIMA (211)(201) 12 and hence are the best stochastic model for generating and forecasting of weekly temperature, relative humidity and monthly precipitation values for Solapur station, Maharashtra, India.
The studies reveal that if sufficient spread and depth of data are used in model building, frequent updating of model may not be necessary. The study also showed the utility of forecast of climatic parameter values in estimating the irrigation quantity and monitoring the insect pest and disease 1 year ahead for pomegranate orchards. It is concluded that seasonal ARIMA model is a viable tool which can successfully be used for generation and forecasting of climatic parameters having inbuilt seasonal patterns.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control AC 19(6):716–723
Bender M, Simonovle S (1994) Time–series modeling for long-range stream flow forecasting. J Water Resour Plan Manag 120(6):857–870
Box GEP, Jenkins GM (1994) Time series analysis, forecasting and control, Revised edn. Holden-Day, San Francisco
Chhajed N (2004) Stochastic modeling for forecasting Mahi river inflows. M.E. thesis, MPUAT, Udaipur
Davis JM, Rappoport PN (1974) The use of time series analysis techniques in forecasting meteorological drought. Mon Weather Rev 102:176–180
Gorantiwar SD (1984) Investigating applicability of some operational hydrology models to WB streams. M.Tech. thesis, IIT, Kharagpur
Gorantiwar SD, Majumdar M, Pampattiwar PS (1995) Application of autoregressive models of different orders to annual stream flows of Barkar river with their logarithmic transformation. J Appl Hydrol 8:33–39
Gupta RK, Kumar R (1994) Stochastic analysis of weekly evaporation values. Indian J Agric Eng 4(3-4):140–142
Hipel KW, McLeod AI (1994) Time series modeling of water resources and environmental systems. Elsevier, Amsterdam
Hipel KW, McLeod AI, Lennox WC (1976) Advances in Box Jenkins modeling: 1. Model construction. Water Resour Res 13:567–575
Kamte PP, Dahale SD (1984) A stochastic model on drought. Mausam 35:387–390
Katimon A, Demun AS (2004) Water use trend at university technology Malaysia: application of ARIMA model. J Technol 41(B):47–56
Katz RW, Skaggs RH (1981) On the use of autoregressive moving average processes to model meteorological time series. Mon Weather Rev 109:479–484
Maier HR, Dandy GC (1995) Comparison of Box-Jenkins procedure with artificial neural network methods for univariate time series modeling, Research report no. R 127. Department of Civil and Environmental Engineering, University of Adelaide, Adelaide
Montanari A, Rosso R, Taqqu MS (1997) Fractional differenced ARIMA models applied to hydrological time series: identification, estimation and simulation. Water Resour Res 33(5):1035–1044
Montanari A, Rosso R, Taqqu MS (2000) A seasonal fractional ARIMA model applied to the Nile river monthly flows at Aswan. Water Resour Res 36(5):1249–1259
Mutua FM (1998) Transfer function hydrologic modeling: a case study. J Appl Hydrol 11(2):11–15
Narulkar MS (1995) Optimum real time operation of multi-reservoir systems for irrigation scheduling. Ph.D. thesis, IIT, Bombay
Patil RM (2003) Stochastic modeling of water deficit for Rahuri region. M.E. thesis, MPUAT, Udaipur
Reddy KM, Kumar D (1998) Time series analysis of monthly rainfall for Bimo watershed of Ramganga river. J Agric Eng 36(4):19–29
Salas JD, Dellur JW, Yevjevich V, Lane WL (1980) Applied modeling of hydrological time series. Water Resources Publication, Littleton
Singh CV (1998) Long term estimation of monsoon rainfall using stochastic models. Int J Climatol 18:1611–1624
Srinivasan K (1995) Stochastic modeling of monsoon river flows. J Appl Hydrol 8:51–57
Subbaiah R, Sahu DD (2002) Stochastic model for weekly rainfall of Junagadh. J Agrometeor 4:65–73
Trawinski PR, Mackay DS (2008) Meteorologically conditioned time series predictions of West Nile virus vector mosquitoes. Vector Borne Zoonotic Dis 8(4):505–522
Verma A (2004) Stochastic modeling on monthly rainfall of Kota, Rajasthan. M. E. thesis, GBPUAT, Pantanagar, India
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer India
About this chapter
Cite this chapter
Meshram, D.T., Jadhav, V.T., Gorantiwar, S.D., Chandra, R. (2015). Modeling of Weather Parameters Using Stochastic Methods. In: Singh, A., Dagar, J., Arunachalam, A., R, G., Shelat, K. (eds) Climate Change Modelling, Planning and Policy for Agriculture. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2157-9_8
Download citation
DOI: https://doi.org/10.1007/978-81-322-2157-9_8
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2156-2
Online ISBN: 978-81-322-2157-9
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)