International Journal of Mathematical, Engineering and Management Sciences

ISSN: 2455-7749

Artificial Neural Network (ANN) Based Empirical Interpolation of Precipitation

Rajesh Joshi
G. B. Pant National Institute of Himalayan Environment and Sustainable Development, Kosi-Katarmal, Almora-263643, Uttarakhand, India.

DOI https://dx.doi.org/10.33889/IJMEMS.2016.1.3-011

Received on June 17, 2016
  ;
Accepted on August 18, 2016

Abstract

Various climate impact studies need to generate estimates of climate variables at a given location based on values from other locations. It is well established fact that there are strong sensible physical linkages between global climate and local scale weather phenomenon. Therefore, empirical interpolation or downscaling has emerged as a prospective tool to relate atmospheric circulation patterns to surface variables for forecasting regional climate from GCM and RCM output dataset. In this paper, application of Artificial Neural Networks (ANNs) based soft computing model for empirical interpolation of precipitation in Himalayan region is attempted. This method uses ANNs to generate precipitation estimates for 11 districts of Uttarakhand state (India) given information from a lattice of surrounding locations. In the present paper, we have used Feed Forward Back Propagation (FFBP) algorithm to develop a Multilayer Perceptron ANN model for empirical downscaling of precipitation in Himalayan region. The model is developed using climate data of Climate Research Unit (CRU) and observed data for past 110 years (1901-2010). The robustness and suitability of the developed ANN model is verified by testing its applicability for 11 districts of Uttarakhand state. 80% of the data are used for training of the model and remain 20% are used for testing of the model. The performance evaluation of the model is tested by RMSE value. The study show that the model works quite well for climatic records of most of the district after bias correction.

Keywords- Empirical downscaling, Artificial neural networks (ANNs), Feed forward back propagation (FBBP) algorithm, Precipitation, Climate change, Himalaya.

Citation

Joshi, R. (2016). Artificial Neural Network (ANN) Based Empirical Interpolation of Precipitation. International Journal of Mathematical, Engineering and Management Sciences, 1(3), 93-106. https://dx.doi.org/10.33889/IJMEMS.2016.1.3-011.

Conflict of Interest

Acknowledgements

Author is thankful to the director, GBPNIHESD for providing necessary facilities and support during course of this study. The climate dataset provided to the scientific community by the Tyndall Centre for Climate Change Research, UK is duly acknowledged.

References

Biau, G., E. Zorita, H. von Storch and H. Wackernagel (1999). Estimation of precipitation by kriging in EOF space. Journal of Climate, 12, 1070-1085.

Bogardi, I., I. Matyasovsky, A. Bardossy, and L. Duckstein (1993). Application of a space–time stochastic model for daily precipitation using atmospheric circulation patterns. Journal of Geophysical Research, 98 (D9), 16 653–16 667.

Cavazos, T. (1997). Downscaling large-scale circulation to local rainfall in North-Eastern Mexico. International Journal of Climatology, 17, 1069-1082.

Clark, W., (1985). Scales of climate change. Climatic Change, 7, 5–27.

Crane, R. G., & Hewitson, B. C. (1998). Doubled CO2 precipitation changes for the Susquehanna basin: down-scaling from the GENESIS general circulation model. International Journal of Climatology, 18(1), 65-76.

Dickinson, R. E., Errico, R. M., Giorgi, F., & Bates, G. T. (1989). A regional climate model for the western United States. Climatic Change, 15(3), 383-422.

Fischer, M. M., & Gopal, S. (1994). Artificial neural networks: a new approach to modeling interregional telecommunication flows. Journal of Regional Science, 34(4), 503-527.

Forest Survey of India (2011). India State of Forest Report-2011

Gardner, M. W., & Dorling, S. R. (1998). Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric Environment, 32(14), 2627-2636.

Giorgi, F., & Mearns, L. O. (1991). Approaches to the simulation of regional climate change: a review. Reviews of Geophysics, 29(2), 191-216.

Gopal, S., & Scuderi, L. (1995). Application of artificial neural networks in climatology: A case study of sunspot prediction and solar climate trends. Geographical Analysis, 27(1), 42-59.

Hewitson, B. C., & Crane, R. G. (1992). Large‐scale atmospheric controls on local precipitation in tropical Mexico. Geophysical Research Letters, 19(18), 1835-1838.

Hewitson, B. C., & Crane, R. G. (1996). Climate downscaling: techniques and application. Climate Research, 7(2), 85-95.

Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359-366

Imbert, A., & Benestad, R. E. (2005). An improvement of analog model strategy for more reliable local climate change scenarios. Theoretical and Applied Climatology, 82(3-4), 245-255.

Katz, R. W., & Parlange, M. B. (1996). Mixtures of stochastic processes: application to statistical downscaling. Climate Research, 7(2), 185-193.

Matyasovszky, I., Bogardi, I., Bardossy, A., & Duckstein, L. (1994). Local temperature estimation under climate change. Theoretical and Applied Climatology, 50(1-2), 1-13.

McGinnis, D. L. (1994). Predicting snowfall from synoptic circulation: a comparison of linear regression and neural network methodologies. In Neural Nets: Applications in Geography (pp. 79-99). Springer Netherlands.

Mitchell, T. D., & Jones, P. D. (2005). An improved method of constructing a database of monthly climate observations and associated high‐resolution grids. International Journal of Climatology, 25(6), 693-712.

Rumelhart, D. E., G. E. Hinton, and Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.

Russo, J. M., & Zack, J. W. (1997). Downscaling GCM output with a mesoscale model. Journal of Environmental Management, 49(1), 19-29.

Snell, S. E., Gopal, S., & Kaufmann, R. K. (2000). Spatial interpolation of surface air temperatures using artificial neural networks: Evaluating their use for downscaling GCMs. Journal of Climate, 13(5), 886-895.

Von Storch, H. (2000). On the use of "inflation" in downscaling. Journal of Climate (in press).

Von Storch, H. and Zwiers, F.W. (1999). Statistical Analysis in Climate Research. Cambridge University Press, ISBN 0 521 45071 3, 528 pp.

Weichert, A., & Bürger, G. (1998). Linear versus nonlinear techniques in downscaling. Climate Research, 10(2), 83-93.

Werbos, P. (1974). Beyond regression: new tools for prediction and analysis in the behavioral sciences. Ph.D. dissertation, Dept. of Applied Mathematics, Harvard University, 453 pp.

Wilby, R. L., & Wigley, T. M. L. (1997). Downscaling general circulation model output: a review of methods and limitations. Progress in Physical Geography, 21(4), 530-548.

Wilby, R. L., T. Wigley, D. Conway, P. Jones, B. Hewitson, J. Main, and D. Wilks, (1998). Statistical downscaling of general circulation model output: A comparison of methods. Water Resources Research, 34 (11), 2995–3008.

Zhang, M., & Scofield, R. A. (1994). Artificial neural network techniques for estimating heavy convective rainfall and recognizing cloud mergers from satellite data. Remote Sensing, 15(16), 3241-3261.

Zorita, E., & Von Storch, H. (1999). The analog method as a simple statistical downscaling technique: comparison with more complicated methods. Journal of Climate, 12(8), 2474-2489.

Privacy Policy| Terms & Conditions