CFP last date
20 December 2024
Reseach Article

A Comprehensive Review of Numerical Weather Prediction Models

by Rashi Aggarwal, Rajendra Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 74 - Number 18
Year of Publication: 2013
Authors: Rashi Aggarwal, Rajendra Kumar
10.5120/12989-0246

Rashi Aggarwal, Rajendra Kumar . A Comprehensive Review of Numerical Weather Prediction Models. International Journal of Computer Applications. 74, 18 ( July 2013), 44-48. DOI=10.5120/12989-0246

@article{ 10.5120/12989-0246,
author = { Rashi Aggarwal, Rajendra Kumar },
title = { A Comprehensive Review of Numerical Weather Prediction Models },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 74 },
number = { 18 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 44-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume74/number18/12989-0246/ },
doi = { 10.5120/12989-0246 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:42:41.047811+05:30
%A Rashi Aggarwal
%A Rajendra Kumar
%T A Comprehensive Review of Numerical Weather Prediction Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 74
%N 18
%P 44-48
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
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
  1. 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
  2. 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
  3. 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.
  4. 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.
  5. 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
  6. 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
  7. 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
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. Raihane Mechgoug, A. Taleb Ahmed, Lakhmissi Cherroun (2012), Optimization of neural predictor for air pollution, Preceedinds of the World Congress on Engineering vol II
  15. 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
  16. 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
  17. 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.
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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.
  23. Y. Radhika and M. Shashi (2009), Atmospheric Temperature Prediction using Support Vector Machines, International Journal of Computer Theory and Engineering, Vol. 1, 1793-8201
  24. 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
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Networks weather forecasting time series analysis