A Comprehensive Review of Numerical Weather Prediction Models

Abstract

Weather forecasting has been an area of considerable interest among researchers since long. In particular, precipitation has been found to be interesting because of its chaotic nature and also because of the direct impact it has on the society. Even after the invention of complex Coupled Numerical Weather Prediction Models, the errors in prediction have been found to be of significant magnitude. The present study aims at investigating all the aspects of error dynamics in dynamic and statistical predictions, and reviews these two prediction models on the basis of errors arising due to initial conditions and understanding of physical processes generating with time series.

References

• A. K Sahai, M. K Soman, V. Satyan (2000). All India summer monsoon rainfall prediction using an artificial neural network. Springer-Verlag, Climate Dynamics vol. 16 pg. 291-302
• Abhishek Aggarwal, Vikas Kumar, Ashish Pandey and Imran Khan (2012), An application of time series analysis for weather forecasting, International Journal of Enginnering Research and Applications, ISSN:2248-9622, Vol. 2, issue 2 974-980
• Bengtsson, L. , Schlese U. , Roeckner, E. , Latif, M. , Narnett, T. P. and Graham, N. E. (1993), A two-tiered approach to long-range climate forecasting, Science, 261 1028-1029.
• E Balaguer Ballester, G Camps i Valls, J. L Carrasco-Rodriguez, E Soria Olivas, S del Valle-Tascon (2002) , Effective 1-day ahead prediction of hourly surface ozone concentrates in eastern Spain using linear models and neural networks, Elsevier, Ecological Modelling 15627-41.
• El-Shafie, A. H. , El-Shafie, A. , El Mazoghi, H. G. , Shehata, A. and Taha, M. R. (2011), Artificial neural network technique for rainfall forecasting applied to Alexandria, Egypt, International Journal of the Physical Sciences 6, 1306-1316
• Gheyas, I. A. and Smith, L. S. (2012), A novel neural network ensemble architecture for time series forecasting, Neurocomputing, doi:10. 1016/j. neucom. 2011. 08. 005
• Gyanesh Shrivastava, Sanjeev Karmakar, Manoj Kumar Kowar, Pulak Guhathakurta (2012), Application of Artificial neural networks in Weather Forecasting: A Comprehensive Literature Review, International Journal of Computer Applications (0975-8887), Vol 51- no. 18
• Imran Maqsood, Muhammad Riaz Khan , Ajith Abaraham (2004). An ensemble of neural networks for weather forecasting, Springer-Verlag, Neural Comput & Applic vol. 13 pg. 112-122.
• J. A Velazquez, F. Anctil, M. H Ramos, C. Perrin (2011), Can a multi-model approach improve hydrological ensemble forecasting? A study on 29 French catchments using 16 hydrological model structures, Advances in Geosciences, doi:10. 5194/adgeo-29-33-2011.
• K. C Tripathi, Shailendra Rai, A. C Pandey, I. M. L Das((2008). Southern Indian Ocean SST indices as early predictors of Indian Summer Monsoon. International Journal of Marine Science, Vol. 37(1),pp. 70-76.
• Mason, S. J (2008), From Dynamical Model Predictions to Seasonal Climate Forecasts, (chapter in Seasonal Climate: Forecasting and Managing Risk, edited by Harrison, M. , Troccoli, A. , Anderson, D. L. T. and Mason, S. ,J. ), NATO Science Series, Springer, Dordrecht, Netherlands, 82 205-234.
• Palmer, T. N. and others (2004), Development of a European multi-model ensemble system for seasonal to inter-annual prediction (DEMETER), Bulletin of American Meteorological Society, 85 853-872.
• Rai, S. , A. C. Pandey, K. C. Tripathi and S. Dwivedi (2008), Predictive Skill of DEMETER models for wind prediction near Madagascar, Indian Journal of Marine Sciences, 37, 62-69.
• Raihane Mechgoug, A. Taleb Ahmed, Lakhmissi Cherroun (2012), Optimization of neural predictor for air pollution, Preceedinds of the World Congress on Engineering vol II
• R. P. Shukla, K. C. Tripathi, A. C. Pandey and I. M. L. Das(2011), Prediction of Indian summer monsoon rainfall using Niño Indices: a neural network approach, Atmospheric Research, DOI information: 10. 1016/j. atmosres. 2011. 06. 013, 102, 99–109
• Sahai, A. K. , R. Chattopadhyay and B. N. Goswami (2008), A SST based large multi-model ensemble forecasting system for Indian summer monsoon rainfall, Geophysical Research Letters, 35, L19705, doi:10. 1029/2008GL035461
• S K Roy Bhowmik, V R Durai(2012), Development of multimodel ensemble based district level medium range rainfall, Indian Academy of Science, 121, no. 2 , 273-285.
• Suzuki,R. , S. K. Behera, S. Iizuka, and T. Yamagata (2004),, Indian Ocean subtropical dipole simulated using a coupled general circulation model, Geophysical Research Letters, 1091-18 doi:10. 1029/2003JC001974
• T. N. Krishnamurti, C. M. Kishtawal, Zhan Zhang, Timothy LaRow, David Bachiochi, and Eric Williford (2000), Multimodel Ensemble Forecasts for Weather and Seasonal Climate, journal of climate, American Meteorological Society, vol. 13 pg. 4196-4216
• Vladimir M. Krasnopolsky and Ying Lin (2012), "A Neural Network Nonlinear Multimodel Ensemble to Improve Precipitation Forecasts over Continental US," Advances in Meteorology, Article ID 649450, 11 pages, 2012. doi:10. 1155/2012/649450
• W. T Yun, L. Stefanova, A. K Mitra, T. S. V. Vijaya Kumar, W. Dewar and T. N. Krishnamurti (2004), Multi-Model Synthetic superensemble Algorithm for seasonal climate prediction using DEMETER Forecasts, Tellus, DEMETER
• W. W Hsieh. and Tang B. (1998). Applying neural network models to prediction and data analysis in meteorology and oceanography. Bulletin of American Meteorological Society, Vol. 79 1855-1870.
• Y. Radhika and M. Shashi (2009), Atmospheric Temperature Prediction using Support Vector Machines, International Journal of Computer Theory and Engineering, Vol. 1, 1793-8201
• Zhang, G. P. and V. L. Berardi (2001). Time series forecasting with neural network ensembles: an application for exchange rate prediction. Operational Research Society, vol. 52, pg 652-664

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