A Spatiotemporal Attention Model for Severe Precipitation Estimation

Abstract

Quantitative precipitation estimation (QPE) is an essential task in meteorology and hydrology and is of great significance for disaster prevention and control. The starting point of QPE is to establish a point-by-point mapping relationship between atmospheric observations and rain gauges. Traditional methods called Z-R relationships fit the parameters in a given paradigm to perform QPE under meteorology prior guidance. Methods based on machine learning (ML) construct the QPE models from statistical views, which could benefit from large historical data. However, in operational applications, these methods are challenging to estimate severe precipitation accurately. The reason is that severe precipitation is usually caused by convective systems. The point-by-point QPEs only focus on fixed isolated points and are difficult to characterize convective systems that cause precipitation effectively. In this letter, a spatiotemporal attention model is proposed for one-hour QPE. For each pixel, the spatiotemporal attention guides the model to find and focus on the most worthy attention region at each moment instead of a definite isolated point, making the model view more flexible and insightful. In experiments, the radar data from 2015 to 2016 in North China are used to train and evaluate the model. Compared with other methods, the results show that the spatiotemporal attention model could effectively improve the accuracy of QPE, especially for intense precipitation. The case study also shows that the operation of our model is more consistent with meteorological perspectives.