Enhancing Nowcasting With Multi-Resolution Inputs Using Deep Learning: Exploring Model Decision Mechanisms

Abstract

Nowcasting methods based on deep learning typically rely solely on radar data. However, effectively leveraging multi-source data with diverse spatio-temporal resolutions remains a significant challenge in the field. To address this challenge, we propose and validate a novel deep learning model for nowcasting, termed Nowcastformer. This model utilizes radar data and upper-air atmospheric variables, and has been pretrained on satellite data from non-target regions. Quantitative statistical assessments demonstrate that both the integration of multi-source data and the implementation of pre-training strategies enhance the model's performance. Additionally, we conduct a comprehensive analysis of predictor importance, revealing a trend where atmospheric variables become increasingly important as the forecast horizon increases. To illustrate the model's interpretability, we employ the integrated gradients method, which highlights critical areas in representative cases and provides insights into the model's decision-making process.