Development and Application of K-Nearest Neighbour Weather Generating Model

A generic weather generator, based on the K-nearest neighbour algorithm, is presented for producing synthetic weather sequences that can be used in conjunction with hydrological models. Application of the model to the Upper Thames River basin in Ontario has clearly demonstrated the practicality of the approach in generating plausible climate change scenarios for the basin. Daily weather variables (maximum temperature, minimum temperature, and precipitation) were simulated at multiple stations in the basin. Statistical analysis of the synthetic series generated by the model clearly demonstrated the ability of the model to reproduce important statistical parameters of the observed data series such as the mean, variance, and skewness. A distinct practical advantage of the approach presented here over the traditional Richardson and serial type weather generator is that the spatial correlation of the variables can be adequately reproduced. Cross-correlations of the variables at a station, and autocorrelations of variables, are also strongly preserved. Site-specific assumptions regarding the probability distributions of the variables are not required thereby permitting transportability of the model to other basins with very few modifications