TPTNet: A Data-Driven Temperature Prediction Model Based on Turbulent
  Potential Temperature
Jun Park Changhoon Lee
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.