Improved -Nearest Neighbor Weather Generating Model
Publication: Journal of Hydrologic Engineering
Volume 12, Issue 1
Abstract
A major limitation of -nearest neighbor based weather generators is that they do not produce new values but merely reshuffle the historical data to generate realistic weather sequences. In this paper, a modified approach is developed that allows nearest neighbor resampling with perturbation of the historic data. A strategy is introduced that resamples the historical data with perturbations while preserving the prominent statistical characteristics, including the interstation correlations. The approach is similar in spirit to traditional autoregressive models except that the new values are obtained by adding a random component to the individual resampled data points. An advantage of the approach is that unprecedented precipitation amounts are generated that are important for the simulation of extreme events. The approach is demonstrated through application to the Upper Thames River Basin in Ontario. Daily weather variables (maximum temperature, minimum temperature, and precipitation) were simulated at multiple stations in and around the basin. Analysis of the simulated data demonstrated the ability of the model to reproduce important statistical parameters of the observed data series while allowing perturbations to the observed data points. Additionally, no site-specific assumptions regarding the probability distribution of variables are required.
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Acknowledgments
The research presented in this paper was funded by the Canadian Foundation for Climatic and Atmospheric Sciences. The writers gratefully acknowledge the cooperation received from Professor S. P. Simonovic and Dr. J. M. Cunderlik, University of Western Ontario. The writers also thank Mr. Mark Helsten for providing meteorological data for the Upper Thames River Basin. This paper was improved by helpful comments from two anonymous reviewers. The writers are particularly grateful for useful suggestions that led to revisions to the approach used to perturb the data from the basic K-NN model.
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Published In
Journal of Hydrologic Engineering
Volume 12 • Issue 1 • January 2007
Pages: 42 - 51
Copyright
© 2007 ASCE.
History
Received: Jul 15, 2004
Accepted: Jun 7, 2006
Published online: Jan 1, 2007
Published in print: Jan 2007
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