A Hybrid Neural Network/Analog Model for Climate Downscaling

Synoptic downscaling models are used in climatology to model local-scale climate variables from synoptic-scale atmospheric circulation variables. This paper presents a hybrid method for multi-site downscaling that combines an artificial neural network and an analog, i.e., k-nearest neighbor, model. The method can resolve complicated synoptic-to local-scale relationships while preserving spatial relationships between sites. Performance on both synthetic and real-world datasets indicates that the hybrid model is capable of outperforming other forms of analog models used in synoptic downscaling.