A feasibility of six-hourly rainfall forecast over central India using model output and remote sensing data

International Journal of Hydrology Science and Technology

Volumn 2, Issue 2, 2012, Pages 238-252

  Prakash, Satya ^a   Mahesh, C ^a   Gairola, Rakesh Mohan ^a   Buyantogtokh, Batjargal ^b  

^a INDIAN SPACE RESEARCH ORGANISATION   (India)

^b Institute of Meteorology   (Mongolia)

Author keywords

ANN; Artificial neural network; Central India; GFS; Global forecast system; Hydrology; Multi layer perceptron; Rainfall forecasting

Indexed keywords

EID: 84875875111     PISSN: 20427808     EISSN: 20427816     Source Type: Journal    
DOI: 10.1504/IJHST.2012.047409     Document Type: Article

References (31)

1. Abdel-Aal, R.E. and Elhadidy, M.A. (1995) 'Modeling and forecasting the daily maximum temperature using abductive machine learning', Weather and Forecasting, Vol. 10, No. 2, pp.310-325.
2. Abedi-Koupai, J., Amiri, M.J. and Eslamian, S.S. (2009) 'Comparison of artificial neural network and physically based models for estimating of reference evapotranspiration in greenhouse', Australian Journal of Basic and Applied Sciences, Vol. 3, No. 3, pp.2528-2535.
3. AghaKouchak, A., Behrangi, A., Sorooshian, S., Hsu, K. and Amitai, E. (2011) 'Evaluation of satellite-retrieved extreme precipitation rates across the central United States', Journal of Geophysical Research, Vol. 116, D02115, doi:10.1029/2010JD014741.
4. Cannon, A.J. and McKendry, I.G. (1999) 'Forecasting all-India summer monsoon rainfall using regional circulation principal components: a comparison between neural network and multiple regression models', International Journal of Climatology, Vol. 19, No. 14, pp.1561-1578.
5. Dumais, R.E. and Young, K.C. (1995) 'Using a self-learning algorithm for single-station quantitative precipitation forecasting in Germany', Weather and Forecasting, Vol. 10, No. 1, pp.105-113.
6. Dutta, D., Sarma, D.K., Konwar, M., Sharma, S., Viswanathan, G., Gairola, R.M., Das, J., Kannan, B.A.M. and Venkateswaralu, S. (2010) 'Nowcasting of yes/no rain situations at a station using soft computing technique to the radar imagery', Indian Journal of Radio and Space Physics, Vol. 39, No. 2, pp.92-102.
7. Eslamian, S.S., Gohari, A., Biabanaki, M. and Malekian, R. (2008) 'Estimation of monthly pan evaporation using artificial neural networks and support vector machines', Journal of Applied Sciences, Vol. 7, No. 19, pp.2900-2903.
8. Eslamian, S.S., Abedi-Koupai, J., Amiri, M.J. and Gohari, A.R. (2009) 'Estimation of daily reference evapotranspiration using support vector machines and artificial neural networks in greenhouse', Research Journal of Environmental Sciences, Vol. 3, No. 4, pp.439-447.
9. Eslamian, S.S. and Lavaei, N. (2009) 'Modelling nitrate pollution of groundwater using artificial neural network and genetic algorithm in an arid zone', International Journal of Water, Special Issue on Groundwater and Surface Water Interaction (GSWI), Vol. 5, No. 2, pp.194-203.
10. Fletcher, D. and Goss, E. (1993) 'Forecasting with neural networks: an application using bankruptcy data', Information and Management, Vol. 24, No. 3, pp.159-167.
11. Gholizadeh, M.H. and Darand, M. (2009) 'Forecasting precipitation with artificial neural networks (case study: Tehran)', Journal of Applied Sciences, Vol. 9, No. 9, pp.1786-1790.
12. Goswami, B.N., Venugopal, V., Sengupta, D., Madhusoodanan, M.S. and Xavier, P.K. (2006) 'Increasing trend of extreme rain events over India in a warming environment', Science, Vol. 314, No. 5804, pp.1442-1445, doi:10.1126/science.1132027.
13. Hall, T., Brooks, H.E. and Doswell, C.A., III (1999) 'Precipitation forecasting using neural network', Weather and Forecasting, Vol. 14, No. 3, pp.338-345.
14. Hornik, K., Stinchcombe, M. and White, H. (1989) 'Multilayer feedforward neural networks are universal approximators', Neural Networks, Vol. 2, No. 5, pp.359-366.
15. Hsu, K., Gao., X., Soroshian, S. and Gupta, H.V. (1997) 'Precipitation estimation from remotely sensed information using artificial neural networks', Journal of Applied Meteorology, Vol. 36, No. 9, pp.1176-1190.
16. Huffman, G.J., Adler, R.F., Bolvin, D.T., Gu, G., Nelkin, E.J., Bowman, K.P., Hong, Y., Stocker, E.F. and Wolff, D.B. (2007) 'The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales', Journal of Hydrometeorology, Vol. 8, No. 1, pp.38-55.
17. Hung, N.Q., Babel, M.S., Weesakul, S. and Tripathi, N.K. (2009) 'An artificial neural network model for raining forecasting in Bangkok, Thailand', Hydrology and Earth System Sciences, Vol. 13, No. 8, pp.1413-1425.
18. Kalnay, E., Kanamitsu, M. and Baker, W.E. (1990) 'Global numerical weather prediction at the National Meteorological Centre', Bulletin of American Meteorological Society, Vol. 71, No. 10, pp.1410-1428.
19. Kanamitsu, M., Alpert, J.C., Campana, K.A., Caplan, P.M., Deaven, D.G., Iredell, M., Katz, B., Pan, H.L., Sela, J. and White, G.H. (1991) 'Recent changes implemented into the global forecast at NMC', Weather and Forecasting, Vol. 6, No. 3, pp.425-435.
20. Kuligowski, R.J. and Barros, A.P. (1998) 'Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks', Weather and Forecasting, Vol. 13, No. 4, pp.1194-1204.
21. Maier, H. and Dandy, G. (2000) 'Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications', Environmental Modelling and Software, Vol. 15, No. 1, pp.101-124.
22. Marzban, C. and Stumpf, G.J. (1996) 'A neural network for tornado prediction based on Doppler radar-derived attributes', Journal of Applied Meteorology, Vol. 35, No. 5, pp.617-626.
23. McCann, D.W. (1992) 'A neural network short-term forecasting of significant thunderstorms', Weather and Forecasting, Vol. 7, No. 3, pp.525-534.
24. Mishra, A., Gairola, R.M., Varma, A.K. and Agarwal, V.K. (2010) 'Remote sensing of precipitation over Indian land and oceanic regions by synergistic use of multi-satellite sensors', Journal of Geophysical Research, Vol. 115, D08106, doi:10.1029/2009JD012157.
25. Navone, H.D. and Ceccatto, H.A. (1994) 'Predicting Indian monsoon rainfall: a neural network approach', Climate Dynamics, Vol. 10, Nos. 6-7, pp.305-312, doi:10.1007/BF00228029.
26. Olson, D.A., Junker, N.W. and Korty, B. (1995) 'Evaluation of 33 years of quantitative precipitation forecasting at the NMC', Weather and Forecasting, Vol. 10, No. 3, pp.498-511.
27. Prakash, S., Mahesh, C., Gairola, R.M. and Pal, P.K. (2010) 'Estimation of Indian summer monsoon rainfall using Kalpana-1 VHRR data and its validation using rain gauge and GPCP data', Meteorology and Atmospheric Physics, Vol. 110, Nos. 1-2, pp.45-57, DOI: 10.1007/s00703-010-0106-8.
28. Ramirez, M.C.V., Velho, H.F.C. and Ferreira, N.J. (2005) 'Artificial neural network technique for rainfall forecasting applied to the Sao Paulo region', Journal of Hydrology, Vol. 301, Nos. 1-4, pp.146-162.
29. Tian, Y., Peters-Lidard, C.D., Eylander, J.B., Joyce, R.J., Huffman, G.J., Adler, R.F., Hsu, K., Turk, F.J., Garcia, M. and Zeng, J. (2009) 'Component analysis of errors in satellite-based precipitation estimates', Journal of Geophysical Research, Vol. 114, D24101, DOI:10.1029/ 2009JD011949.
30. Vislocky, R.L. and Fritsch, J.M. (1995) 'Generalized additive models versus linear regression in generating probabilistic MOS forecasts of aviation weather parameters', Weather and Forecasting, Vol. 10, No. 4, pp.669-680.
31. Wilks, D.S. (2006) Statistical Methods in the Atmospheric Sciences, 2nd ed., Academic Press, USA, 648pp.