Location-Refining neural network: A new deep learning-based framework for Heavy Rainfall Forecast

Abstract

Precipitation nowcasting aims to predict the rainfall distribution within a short-term period. However, it pays the same attention to all locations instead of emphasizing those regions with heavy rainfall that has more threats to human activity. Therefore, we develop an important task named Heavy Rainfall Forecast (HRF), which mainly focuses on the movement and change of heavy rainfall areas. It sets aside one hour to give meteorological administration sufficient time to issue warning information. To tackle this task, firstly, we rebuild the meteorological radar dataset based on three criteria to obtain the samples involving heavy rainfall. Secondly, we propose the Location-Refining (LR) neural network to combine the advantages of the optical flow-based and deep learning-based methods in predicting higher intensity and more accurate position, respectively. LR neural network consists of a location network and a refining network. The former is responsible for the accurate predictions of position and trend of rainfall, and the later accounts for more accurately estimating the intensity. To make the model pay more attention to the high echo region, we design new loss functions and introduce auxiliary information of high echo values. A series of experiments show that our model has a significant improvement on this task. Specifically, compared with existing methods, we improve the valid mean square error by 6.4% for the threshold being 20 and 15.1% for the threshold being 30. The critical success indexes are improved by 12.8% for the threshold being 20 and 24.8% for the threshold being 30. We also improve the heidke skill score by 9.9% for the threshold being 20 and 21.4% for the threshold being 30. Furthermore, the proposed framework can be well transferred to other deep learning-based models, and improves their performance.