TPTNet: A Data-Driven Temperature Prediction Model Based on Turbulent Potential Temperature

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 2 m 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 (TPT) data at irregularly distributed stations were used as input for predicting the TPT at forecast hours through three trained networks based on convolutional neural network, Swin Transformer, and a graph neural network. By comparing the prediction performance of our network with that of persistence and NWP, we found that our model can make predictions comparable to NWP for up to 12 hr. We developed a new model called TPTNet to predict local temperatures using only temperature measurements from weather stations in South Korea. This model aims to reduce the heavy computational demands of traditional numerical weather prediction (NWP) methods. By analyzing 20 years of data, we separated the regular temperature patterns that change yearly and daily from the more unpredictable fluctuations. We also adjusted the model for the altitude of each weather station. We trained three different types of neural networks for this purpose: a convolutional neural network, a Swin Transformer, and a graph neural network. When we tested our model with new data from 2020, it made reliable temperature predictions for up to 12 hr, performing as well as, or even better than, traditional NWP methods. This new approach could help improve weather forecasting by making it faster and less computationally intensive. Only 2 m temperature data measured at the weather station is used as input for the prediction of temperature at finite forecast hours Turbulent fluctuation temperature relative to the climatological yearly and daily periodic variations with the altitude adjustment using potential temperature is considered in data-driven learning For 12 hr of forecast hour, TPTNet produces forecasts comparable to those of the numerical weather prediction, with the less scattered errors over stations