Improving Explainability of Deep Learning for Polarimetric Radar Rainfall Estimation.

Machine learning‐based approaches demonstrate a significant potential in radar quantitative precipitation estimation (QPE) applications. In contrast to conventional methods that depend on local raindrop size distributions, deep learning (DL) can establish an effective mapping from three‐dimensional radar observations to ground rain rates. However, the lack of transparency in DL models poses challenges toward understanding the underlying physical mechanisms that drive their outcomes. This study aims to develop a DL‐based QPE system and provide a physical explanation of radar precipitation estimation process. This research is designed by employing a deep neural network consisting of two modules. The first module is a quantitative precipitation estimation network that has the capability to learn precipitation patterns and spatial distribution from multidimensional polarimetric radar observations. The second module introduces a quantitative precipitation estimation shapley additive explanations method to quantify the influence of each radar observable on the model estimate across various precipitation intensities. Plain Language Summary: Ground radars can provide continuous spatial observations over large areas with high spatiotemporal resolutions, so they form the infrastructure for precipitation monitoring and observation in many countries. Recently, deep learning (DL) techniques have shown great potential for use in polarimetric radar‐based precipitation estimates. Nevertheless, the black‐box and turn‐key characteristics of DL models make it difficult for researchers to understand the model decision‐making process and cast doubt on the reliability of the model results. This study introduces a physically explainable polarization radar‐based quantitative precipitation estimation (QPE) system built on DL technology that can explain the causes of the precipitation estimates provided by deep learning models under different rainfall amounts. An experiment indicates that our model achieves better estimates than the conventional methods. Furthermore, the explainability methodology allows for visualization of the microphysical precipitation information. Being the initial attempt to apply explainability learning in the QPE domain, the explainability results may offer valuable guidance for rainfall estimation. Key Points: A polarimetric radar‐based rainfall estimation system is developed using deep neural networksThe deep learning‐based rainfall estimates generally outperform products derived from traditional parametric relationsThe proposed deep learning interpretation method can provide physical and statistical explanations of the model decision‐making process [ABSTRACT FROM AUTHOR]