Statistical downscaling using K‐nearest neighbors

Abstract

Statistical downscaling provides a technique for deriving local‐scale information of precipitation and temperature from numerical weather prediction model output. The K‐nearest neighbor (K‐nn) is a new analog‐type approach that is used in this paper to downscale the National Centers for Environmental Prediction 1998 medium‐range forecast model output. The K‐nn algorithm queries days similar to a given feature vector in this archive and using empirical orthogonal function analysis identifies a subset of days (K) similar to the feature day. These K days are then weighted using a bisquare weight function and randomly sampled to generate ensembles. A set of 15 medium‐range forecast runs was used, and seven ensemble members were generated from each run. The ensemble of 105 members was then used to select the local‐scale precipitation and temperature values in four diverse basins across the contiguous United States. These downscaled precipitation and temperature estimates were subsequently analyzed to test the performance of this downscaling approach. The downscaled ensembles were evaluated in terms of bias, the ranked probability skill score as a measure of forecast skill, spatial covariability between stations, temporal persistence, consistency between variables, and conditional bias and to develop spread‐skill relationships. Though this approach does not explicitly model the space‐time variability of the weather fields at each individual station, the above statistics were extremely well captured. The K‐nn method was also compared with a multiple‐linear‐regression‐based downscaling model.