title: Nonlinear analog predictor analysis: a coupled neural network/analog model for climate downscaling.
abstract: Synoptic downscaling models are used in climatology to predict values of weather elements at one or more stations based on values of synoptic-scale atmospheric circulation variables. This paper presents a hybrid method for climate prediction and downscaling that couples an analog, i.e., k-nearest neighbor, model to an artificial neural network (ANN) model. In the proposed method, which is based on nonlinear principal predictor analysis (NLPPA), the analog model is embedded inside an ANN, forming its output layer. Nonlinear analog predictor analysis (NLAPA) is a flexible model that maintains the ability of the analog model to preserve inter-variable relationships and model non-normal and conditional variables (such as precipitation), while taking advantage of NLPPA's ability to define an optimal set of analog predictors that maximize predictive performance. Performance on both synthetic and real-world hydroclimatological benchmark tasks indicates that the NLAPA model is capable of outperforming other forms of analog models commonly used in synoptic downscaling.
authors.affiliation: Meteorological Service of Canada, Environment Canada, Vancouver, BC, Canada. alex.cannon@ec.gc.ca
authors.forename: Alex J
authors.initials: AJ
authors.lastname: Cannon
mesh_terms: D001185:Artificial Intelligence; D002980:Climate; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D021281:Quantitative Structure-Activity Relationship