A Transformer model for predicting near-surface temperature and relative humidity

It is a well-known fact that numerical weather prediction (NWP) models exhibit systematic errors, especially for near-surface variables. Reasons for this are, among other, the inability of these models to successfully handle sub-grid phenomena and shortcomings in the physical formulation of the model dynamics. Even though high-resolution regional NWP models usually have a spatial resolution of a few kilometers (or even finer) they generally exhibit local biases due to unresolved topography and obstacles. In order to obtain more local and site-specific forecasts post-processing methods can be used. Here, we have implemented a Transformer Neural Network model for post-processing 48-hour forecasts of 2 m temperature and relative humidity. The observational data used in this study consist of observations of 2 m air temperature and relative humidity from a network of private weather stations (PWSs). All in all, data from more than 1,000 locations are used. Forecast data from the Global Forecast System (GFS) model – such as temperature, relative humidity, wind speed and direction, radiation fluxes and upper level model fields – are also used as input to the model. The model is trained on 1.5 years of observational and forecast data and the performance is evaluated using an independent validation dataset of PWSs. We find that the Transformer post-processing model reduces the bias and standard deviation compared to the raw NWP forecast for a majority of stations. Furthermore, the model is validated on completely independent data from the Danish Meteorological Institute’s (DMI’s) observational network, where good results were obtained. Overall, the Transformer model produces forecasts that better match the locally observed weather.