CNN-based Surface Temperature Forecasts with Ensemble Numerical Weather Prediction over Medium-range Forecast Periods

In this study, a method that integrates convolutional neural networks (CNNs) with ensemble numerical weather prediction (NWP) models is proposed. This method enables surface temperature forecasting with lead times beyond the short-range, extending up to five days. Due to limited computational resources, operational medium-range temperature forecasts typically rely on low-resolution NWP models, which are prone to systematic and random errors. To resolve these limitations, the proposed method applies CNN-based post-processing (bias correction and spatial super-resolution) to an ensemble NWP system. First, the post-processing is applied to each ensemble member to reduce systematic errors and reconstruct high-resolution temperature fields from low-resolution model outputs. This approach reduces the systematic and random errors in NWP model outputs and outperforms operational post-processing. Second, the CNN is applied to all ensemble members to construct a new ensemble forecasting system, in which deterministic forecast accuracy, probabilistic reliability, and representation of ensemble spread are improved compared with those of the original system. We demonstrate that this CNN-based post-processing is fundamentally different from the artificial error reduction caused by smoothing inherent in ensemble averaging because the post-processing reduces forecast errors without degrading the forecast information. These results indicate that the proposed method provides a practical and scalable solution for improving medium-range temperature forecasts and is particularly valuable for use in operational centers with limited computational resources.