LAUSR

High temperature is one of the most severe disasters in the world, which causes the death of millions of people each year. Accurate temperature forecasting, as a key member of weather prediction, is of great application value. Many recent contributions treat weather forecasting as a spatio-temporal learning task, in which these methods mainly capture the motion of a rigid body while neglecting the generation and dispersion of fluid elements. However, typical spatio-temporal prediction is quite different from meteorological forecasting. To resolve this key issue, this letter proposes MetPGNet, a meteorological prior guided network for hourly temperature forecasting. Specifically, under the framework of atmospheric theory, three simple but effective multidimensional attention branches, i.e., the advection branch, the vertical branch, and the temporal branch, are elaborately designed to depict the temperature variation of spatial atmospheric advection, vertical atmospheric movement, and temporal heat exchange, respectively. Experiments compared with state-of-the-art spatio-temporal learning and weather prediction methods demonstrate the superiority of the proposed MetPGNet. Specifically, MetPGNet gets an improvement of 0.52 on mean absolute error (MAE) compared with vanilla ConvGRU.