TPTNet: A Data-Driven Temperature Prediction Model Based on Turbulent Potential Temperature
This work addresses computational efficiency for weather forecasting in South Korea, but it is incremental as it applies existing neural network methods to a specific domain.
The authors tackled the computational burden of numerical weather prediction by developing TPTNet, a data-driven model that uses only 2m temperature measurements from weather stations to predict local temperature up to 12 hours ahead, outperforming NWP in this timeframe.
A data-driven model for predicting the surface temperature using neural networks was proposed to alleviate the computational burden of numerical weather prediction (NWP). Our model, named TPTNet uses only 2m temperature measured at the weather stations of the South Korean Peninsula as input to predict the local temperature at finite forecast hours. The turbulent fluctuation component of the temperature was extracted from the station measurements by separating the climatology component accounting for the yearly and daily variations. The effect of station altitude was then compensated by introducing a potential temperature. The resulting turbulent potential temperature data at irregularly distributed stations were used as input for predicting the turbulent potential temperature at forecast hours through three trained networks based on convolutional neural network (CNN), Swin Transformer, and a graphic neural network (GNN). The prediction performance of our network was compared with that of persistence and NWP, confirming that our model outperformed NWP for up to 12 forecast hours.