A Pattern‐Referencing Model for Hourly Temperature Forecasting in Coastal Regions

ABSTRACT This study proposes a pattern‐referencing model for hourly temperature forecasting in coastal regions, specifically designed for scenarios with missing data. The Chiayi–Tainan coastal plain in Taiwan exhibits pronounced spatiotemporal temperature variations driven by sea–land breezes, topography, and solar radiation, impacting real‐time decision‐making in industries such as aquaculture, agriculture, and tourism. The proposed model directly utilizes all available input data without requiring prior imputation or specialized pretraining. In a multistation study involving 14 weather stations, the model employs a weighted K‐nearest neighbors (WKNN) approach, using a masked Euclidean distance and the Dudani weighting scheme. The optimal configuration (look‐back length = 1, number of neighbors = 18) achieved mean absolute errors of 0.35°C–0.59°C and root‐mean‐square errors of 0.45°C–0.86°C across diverse weather scenarios, outperforming both persistence forecasts and an autoregressive integrated moving average (ARIMA) model. The model performs best under low‐temperature conditions but shows a slight tendency to underestimate at high temperatures; nighttime forecasts are the most stable, while daytime errors are larger. Even with missing station data, the model maintains its predictive capability, offering decision‐makers more reliable hourly forecasts in resource‐limited networks with unstable data availability, and enabling policymakers to build early‐warning systems that help coastal communities and industries respond to extreme temperature events.