Univariate modelling of monthly maximum temperature time series over northeast India: Neural network versus Yule-Walker equation based approach

Meteorological Applications

Volumn 18, Issue 1, 2011, Pages 70-82

  Chattopadhyay, Surajit ^a   Jhajharia, Deepak ^b   Chattopadhyay, Goutami ^c  

^a PAILAN COLLEGE OF MANAGEMENT AND TECHNOLOGY   (India)

^b NORTH EASTERN REGIONAL INSTITUTE OF SCIENCE AND TECHNOLOGY   (India)

^c UNIVERSITY OF CALCUTTA   (India)

Author keywords

Autocorrelation; Autoregressive model; Autoregressive neural network; Modular neural network; Stationary; Trend; Yule Walker equation

Indexed keywords

AUTOCORRELATION; FEEDFORWARD NEURAL NETWORKS; FORECASTING; TIME SERIES; TIME SERIES ANALYSIS;

AUTO REGRESSIVE MODELS; AUTOREGRESSIVE NEURAL NETWORKS; MODULAR NEURAL NETWORKS; STATIONARY; TREND; YULE-WALKER EQUATIONS;

MULTILAYER NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; ERROR ANALYSIS; PARAMETERIZATION; POLLUTANT REMOVAL; TIME SERIES; TREND ANALYSIS; VECTOR AUTOREGRESSION;

EID: 79951847521     PISSN: 13504827     EISSN: 14698080     Source Type: Journal    
DOI: 10.1002/met.211     Document Type: Article

References (105)

1. Barnett TP, Preisendorfer R. 1987. Origins and levels of monthly and seasonal forecast skill for United States surface air temperatures determined by canonical correlation analysis. Monthly Weather Review 115: 1825-1850.
2. Besse PC, Cardot H, Stephenson DB. 2000. Autoregressive forecasting of some functional climatic variations. Scandinavian Journal of Statistics 27: 673-687.
3. Bouzerdoum A, Mueller R. 2003. Genetic and Evolutionary Computation-GECCO 2003. Springer: Berlin, and Heidelberg, Germany.
4. Box GEP, Jenkins GM, Reinsel GC. 2007. Time Series Analysis: Forecasting and Control, 3rd edn. Dorling Kindersley (India) Pvt. Ltd.: New Delhi, India.
5. Calvo JC, Gregory JD. 1994. Predicting monthly and annual air temperature characteristics in North Carolina. Journal of Applied Meteorology 33: 490-499.
6. Ceschia M, Linussio A, Micheletti S. 1994. Trend analysis of mean monthly maximum and minimum surface temperatures of the 1951-1990 period in Friuli-Venezia Giulia. Il Nuovo Cimento 17: 511-521.
7. Chattopadhyay S. 2007. Feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India. Acta Geophysica 55: 369-382.
8. Chattopadhyay S, Chattopadhyay G. 2008. Comparative study among different neural net learning algorithms applied to rainfall time series. Meteorological Applications 15: 273-280.
9. Chattopadhyay G, Chattopadhyay S. 2009a. Predicting daily total ozone over Kolkata, India: skill assessment of different neural network models. Meteorological Applications 16: 179-190.
10. Chattopadhyay G, Chattopadhyay S. 2009b. Autoregressive forecast of monthly total ozone concentration: A neurocomputing approach. Computers and Geosciences 35: 1925-1932.
11. Chattopadhyay S, Jain R, Chattopadhyay G. 2009. Estimating potential evapotranspiration from limited weather data over Gangetic West Bengal, India: a neurocomputing approach. Meteorological Applications 16: 403-411.
12. Chaudhuri S, Chattopadhyay S. 2001. Measure of CINE-a relevant parameter for forecasting pre-monsoon thunderstorms over GWB. Mausam 52: 679-684.
13. Chaudhuri S, Chattopadhyay S. 2005. Neuro-computing based short range prediction of some meteorological parameters during the pre-monsoon season. Soft Computing 9: 349-354.
14. Chou C-M. 2007. Efficient nonlinear modeling of rainfall-runoff process using wavelet compression. Journal of Hydrology 332: 442-455.
15. Colman AW, Davey MK. 2003. Statistical prediction of global sea-surface temperature anomalies. International Journal of Climatology 23: 1677-1697.
16. Comrie AC. 1997. Comparing neural networks and regression models for Ozone forecasting. Journal of Air and Waste Management Association 47: 653-663.
17. Cracknell AP, Varotsos CA. 2007. The IPCC fourth assessment report and the fiftieth anniversary of Sputnik. Environmental Science and Pollution Research 14: 384-387.
18. Das PJ. 2004. Rainfall regime of northeast India: a hydrometeorological study with special emphasis on the Brahmaputra basin, Unpublished PhD thesis, Gauhati University, Assam, India.
19. Das PJ, Goswami DC. 2003. Long-term variability of rainfall over northeast India. Indian Journal of Landscape Systems and Ecological Studies 26(1): 1-20.
20. Davies TD, Milionis AE. 1994. Box-Jenkins univariate modelling for climatological time series analysis: an application to the monthly activity of temperature inversions. International Journal of Climatology 14: 569-579.
21. Delleur JW, Kavvas ML. 1978. Stochastic models for monthly rainfall forecasting and synthetic generation. Journal of Applied Meteorology 17: 1528-1536.
22. th century and its Relationship with large-scale circulation. International Journal of Climatology 23: 978-1010.
23. Dorffner G. 1996. Neural network for time series processing. Neural Network World 6: 447-468.
24. El-Fandy MG, Ashour ZH, Taiel SMM. 1994. Time series models adoptable for forecasting Nile floods and Ethiopian rainfalls. Bulletin of the American Meteorological Society 75: 83-94.
25. Elliott WP, Angell JK. 1987. The relation between Indian monsoon rainfall, the southern oscillation, and hemispheric air and sea temperature: 1884-1984. Journal of Climate and Applied Meteorology 26: 943-948.
26. Frias MD, Fernandez J, Saenz J, Rodriguez-Puebla C. 2005. Operational predictability of monthly average maximum temperature over the Iberian Peninsula using DEMETER simulations and downscaling. Tellus 57A: 448-463.
27. Gardner MW, Dorling SR. 1998. Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmospheric Environment 32: 2627-2636.
28. Ghosh Hajra N, Kumar R. 1999. Seasonal variation in photosynthesis and productivity of young tea. Experimental Agriculture 35: 71-85.
29. Gilbert CG. 1953. An aid for forecasting the minimum temperature at Denver, Colo. Monthly Weather Review 81: 233-245.
30. Goon AM, Gupta MK, Dasgupta B. 1994. Fundamentals of Statistics, Vol. 2. The World Press Private Limited: Kolkata, India.
31. Griffiths GM, Chambers LE, Haylock MR, Manton CMJ, Nicholls N, Beak BH, Choi DY, Della-Marta PM, Gosai FA, Iga AN, Lata GR, Laurent HV, Maitrepierre IL, Nakamigawa JH, Ouprasitwong KN, Solofa LD, Tahani ML, Thuy NDT, Tibig OL, Trewin PB, Vediapanq BK, Zhir P. 2005. Change in mean temperature as a predictor of extreme temperature change in the Asia-pacific region. International Journal of Climatology 25: 1301-1330.
32. Hanssen-Bauer I, Førland EJ. 1998. Long-term trends in precipitation and temperature in the Norwegian Arctic: can they be explained by changes in atmospheric circulation patterns? Climate Research 10: 143-153.
33. Hingane LS, Kumar KR, Murty BVR. 1985. Long term trends of surface air temperature in India. International Journal of Climatology 5: 521-528.
34. Hirsch RM, Alexander RB, Smith RA. 1991. Selection of methods for the detection and estimation of trends in water quality. Water Resources Research 27: 803-814.
35. Hsieh WW, Tang B. 1998. Applying neural network models to prediction and data analysis in meteorology and oceanography. Bulletin of American Meteorological Society 79: 1855-1870.
36. Hussain M. 1984. Estimation of global and diffuse irradiation from sunshine duration and atmospheric water vapour content. Solar Energy 33: 217-20.
37. Intergovernmental Panel on Climate Change (IPCC). 2007. In Climate Change 2007: Mitigation of Climate Change, Metz B, Davidson O, Bosch P, Dave R, Meyer L (eds). Cambridge University Press: New York, NY.
38. Jhajharia D, Shrivastava SK, Sarkar D, Sarkar S. 2009. Temporal characteristics of pan evaporation trends under the humid conditions of northeast India. Agricultural and Forest Meteorology 149: 763-770.
39. Kamarthi SV, Pittner S. 1999. Accelerating neural network training using weight extrapolations. Neural Networks 12: 1285-1299.
40. Kangieser PC. 1959. Forecasting minimum temperatures on clear winter nights in an arid region. Monthly Weather Review 87: 19-28.
41. Khan S, Ganguly AR, Saigal S. 2005. Detection and predictive modeling of chaos in finite hydrological time series. Nonlinear Processes in Geophysics 12: 41-53.
42. Klein WH, Lewis F. 1970. Computer forecasts of maximum and minimum temperatures. Journal of Applied Meteorology 9: 350-359.
43. Klein WH, Lewis F, Hammons GA. 1971. Recent developments in automated max/min temperature forecasting. Journal of Applied Meteorology 10: 916-920.
44. Klein WH, Marshall F. 1973. Screening improved predictors for automated max/min temperature forecasting. Preprints of 3rd Conference on Probability and Statistics. American Meteorological Society: Boulder, CO; 36-43.
45. Koscielny AJ, Duchon CE. 1984. Autoregressive modelling of the tropical stratospheric quasi-biennial oscillation. International Journal of Climatology 4: 347-363.
46. Kothawale DR, Rupa Kumar K. 2005. On the recent changes in surface temperature trends over India. Geophysical Research Letters 32: L18714.
47. Kumar KK, Kumar KR, Pant GB. 1997. Pre-monsoon maximum and minimum temperatures over India in relation to the summer monsoon rainfall. International Journal of Climatology 17: 1115-1127.
48. Kumar KK, Rajagopalan B, Cane MA. 1999. On the weakening relationship between the Indian Monsoon and ENSO. Science 284: 2156-2159.
49. Leite SM, Peixoto JP. 1996. The autoregressive model of climatological time series: an application to the longest time series in Portugal. International Journal of Climatology 16: 1165-1173.
50. Lettenmaier DP, Wood EF, Wallis JR. 1994. Hydro-climatological trends in the continental United States, 1948-1988. Journal of Climate 7: 586-607.
51. Lianchun S, Cannon AJ, Whitfield PH. 2007. Changes in seasonal patterns of temperature and precipitation in China during 1971-2000. Advances in Atmospheric Sciences 24: 459-473.
52. Ludlam F. 1980. Clouds and Storms. Pennsylvania State University Press: Pennsylvania.
53. Luterbacher J, Dietrich D, Xoplaki E, Grosjean M, Wanner H. 2004. European seasonal and annual temperature variability, trends, and extremes since 1500. Science 303: 1499-1503.
54. Maier HR, Dandy GC. 2000. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling and Software 15: 101-124.
55. Mandal M, Mohanty UC, Sinha P, Ali MM. 2007. Impact of sea surface temperature in modulating movement and intensity of tropical cyclones. Natural Hazards 41: 413-427.
56. Mantis HT, Dickey WW. 1945. Objective methods of forecasting the daily minimum and maximum temperature. Report Number 4, U.S. Army Air Force, Weather Station, New York University.
57. Massie DR, Rose MA. 1997. Predicting daily maximum temperatures using linear regression and Eta Geopotential thickness forecasts. Weather and Forecasting 12: 799-807.
58. Mearns LO, Katz RW, Schneider SH. 1984. Extreme high-temperature events: changes in their probabilities with changes in mean temperature. Journal of Applied Meteorology 23: 1601-1613.
59. Meehl GA, Karl T, Easterling DR, Changnon S, Pielke R Jr, Changnon D, Evans J, Groisman PY, Knutson TR, Kunkel KE, Mearns LO, Parmesan C, Pulwarty R, Root T, Sylves RT, Whetton P, Zwiers F. 2000. An introduction to trends in extreme weather and climate events: observations, socio-economic impacts, terrestrial ecological impacts, and model projections. Bulletin of the American Meteorological Society 81(3): 413-416.
60. Melin P, Castillo O. 2005. Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing: An Evolutionary Approach for Neural Networks and Fuzzy Systems (Studies in Fuzziness and Soft Computing). Springer-Verlag, New York, Inc.: Secaucus, NJ.
61. Mirza MMQ, Warrick RA, Ericksen NJ, Kenny GJ. 1998. Trends and persistence in precipitation in the Ganges, Brahmaputra and Meghna basins in South Asia. Hydrological Sciences Journal 43: 845-858.
62. Namias J. 1948. Evolution of monthly mean circulation and weather patterns. Transactions of American Geophysics U29: 777-788.
63. Onate JJ, Pou A. 1996. Temperature variations in Spain since 1901: A preliminary analysis. International Journal of Climatology 16: 805-815.
64. Onoz B, Bayazit M. 2003. The power of statistical tests for trend detection. Turkish Journal of Engineering and Environmental Science 27: 247-251.
65. Ortín S, Gutiérrez JM, Pesquera L, Vasquez H. 2005. Nonlinear dynamics extraction for time-delay systems using modular neural networks synchronization and prediction. Physica A 351: 133-141.
66. Pant GB, Rupa Kumar K. 1997. Climates of South Asia. John Wiley and Sons: New York, NY; 320.
67. Pattanaik DR. 2007. Variability of convective activity over the North Indian Ocean and its associations with monsoon rainfall over India. Pure and Applied Geophysics 164: 1527-1545.
68. 2.5 concentrations several hours in advance using neural networks in Santiago, Chile. Atmospheric Environment 34: 1189-1196.
69. Peterson RE, Mehta KC. 1995. Tornadoes of the Indian subcontinent. Paper presented at 9th International Conference on Wind Engineering, International Association of Wind Engineering, New Delhi, Indiaxs.
70. Rehman S, Husain T, Halawani TO. 1990. Application of one-dimensional planetary boundary layer model to the regional transport of pollutants-a case study. Atmospheric Research 25: 521-38.
71. Said SAM. 1992. Degree-day base temperature for residential building energy prediction in Saudi Arabia. ASHRAE Transactions 98: 346-53.
72. Sarkar D. 1995. Methods to speed up error back-propagation learning algorithm. ACM Computing Surveys 27: 519-544.
73. Shrestha AB, Wake CP, Mayewski PA, Dibb JE. 1999. Maximum temperature trends in the Himalaya and its vicinity: an analysis based on temperature records from Nepal for the period 1971-1994. Journal of Climate 12: 2775-2786.
74. Sivakumar B. 2005. Chaos in rainfall: variability, temporal scale and zeros. Journal of Hydroinformatics 7: 175-184.
75. Snell SE, Gopal S, Kaufmann RK. 2000. Spatial interpolation of surface air temperatures using artificial neural networks: evaluating their use for downscaling GCMs. Journal of Climate 13: 886-895.
76. Spreen WC. 1956. Empirically determined distributions of hourly temperatures. Journal of Atmospheric Sciences 13: 351-355.
77. Storch HV, Zwiers FW. 1999. Statistical Analysis in Climate Research. Cambridge University Press: Cambridge, UK.
78. Tang B, Hsieh WW, Monahan AH, Tangang FT. 2000. Skill comparisons between neural networks and canonical correlation analysis in predicting the Equatorial Pacific Sea surface temperatures. Journal of Climate 13: 287-293.
79. Tasadduq I, Rehman S, Bubshait K. 2002. Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia. Renewable Energy 25: 545-554.
80. Temizel TT, Caset MC. 2005. A comparative study of autoregressive neural network hybrids. Neural Networks 5-6: 781-789.
81. Tim H, Leorey M, Marcus O, William R. 1994. Artificial neural network models for forecasting and decision making. International Journal of Forecasting 10: 5-15.
82. Trapletti A, Leisch F, Hornick K. 2000. Stationary and integrated autoregressive neural network processes. Neural Computation 10: 2427-2450.
83. Ustaoglu B, Cigizoglu HK, Karaca M. 2008. Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods. Meteorological Applications 15: 431-445.
84. Van Loon H, Jenne RL. 1975. Estimates of seasonal mean temperature, using persistence between seasons. Monthly Weather Review 103: 1121-1128.
85. Varotsos C. 2004. Atmospheric pollution and remote sensing: implications for the southern hemisphere ozone hole split in 2002 and the northern mid-latitude ozone trend. Monitoring of Changes Related to Natural and Manmade Hazards Using Space Technology 33: 249-253.
86. Varotsos C, Assimakopoulos MN, Efstathiou M. 2007. Technical note: Long-term memory effect in the atmospheric CO2 concentration at Mauna Loa. Atmospheric Chemistry and Physics 7: 629-634.
87. Varotsos CA, Cracknell AP. 2004. New features observed in the 11-year solar cycle. International Journal of Remote Sensing 25: 2141-2157.
88. Varotsos CA, Milinevsky G, Grytsai A, Efstathiou M, Tzanis C. 2008. Scaling effect in planetary waves over Antarctica. International Journal of Remote Sensing 29: 2697-2704.
89. Vil'fand RM, Tishchenko VA, Khan VM. 2007. Statistical forecast of temperature dynamics within month on the basis of hydrodynamic model outputs. Russian Meteorology and Hydrology 32: 147-153.
90. Visser H, Molenaar J. 1995. Trend estimation and regression analysis in climatological time series: An application of structural time series models and the Kalman filter. Journal of Climate 8: 969-979.
91. Weston KJ. 1972. The dry-line of Northern India and its role in cumulonimbus convection. Quarterly Journal of Royal Meteorological Society 98: 469-471.
92. Widrow B, Lehr MA. 1990. 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation. Proceedings of the IEEE 78: 1415-1442.
93. Wijeratne MA. 1992. Vulnerability of Sri Lanka tea production to global climate change. Water, Air, and Soil Pollution 92: 87-94.
94. Wilks DS. 2006. Statistical Methods in Atmospheric Sciences, 2nd edn. Academic Press: San Diego, California.
95. Willmott CJ, Matsuura. K. 1995. Smart interpolation of the annually averaged air temperature in the United States. Journal of Applied Meteorology 34: 2577-2586.
96. Woodward WA, Gray HL. 1993. Global warming and the problem of testing for trend in time series data. Journal of Climate 6: 953-962.
97. Woodward WA, Gray HL. 1995. Selecting a model for detecting the presence of a trend. Journal of Climate 8: 1929-1937.
98. Wu Z, Huang N, Long SR, Peng CK. 2007. On the trend, detrending, and variability of nonlinear and nonstationary time series. Proceedings of the National Academy of Sciences 104: 14889-14894.
99. Xoplaki E, Gonzalez-Rouco JF, Luterbacher J, Wanner H. 2003. Mediterranean summer air temperature variability and its connection to the large-scale atmospheric circulation and SSTs. Climate Dynamics 20: 723-739.
100. Yamane Y, Hayashi T. 2006. Evaluation of environmental conditions for the formation of severe local storms across the Indian subcontinent. Geophysical Research Letters 33: L17806, DOI: 10.1029/2006GL026823.
101. Yue S, Pilon P, Phinney B, Cavadias G. 2002. The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrological Processes 16: 1807-1829.
102. Zhang GP, Kline DM. 2007. Quarterly time-series forecasting with neural networks. IEEE Transactions on Neural Networks 18: 1800-1814.
103. Zhang GP, Min Q. 2005. Neural network forecasting for seasonal and trend time series. European Journal of Operations Research 160: 501-514.
104. Zuidema P. 2003. Convective clouds over the Bay of Bengal. Monthly Weather Review 131: 780-798.
105. Zwiers FW, Storch HV. 1995. Taking serial correlation into account in tests of mean. Journal of Climate 8: 336-350.