Abstract: How to integrate effectively the synoptic characteristics of severe precipitation events based on the experience of forecasters that is not constrained by physical formulas into the deep learning model as a priori information is one of the important challenges to improve the nowcasting accuracy of severe precipitation. First,we describe briefly the out-of-the-state deep learning models and some studies about the interpretability of deep learning models. Second,several widely-used methods of nowcasting for severe precipitation are briefly introduced. Finally,based on the consideration of the improvement of deep learning algorithm in the nowcasting technology of severe precipitation,we propose the possible way to integrate the prior information into the deep learning models. It includes quantifying or automatically identifying some empirical features as model inputs,taking the pre-extracted features as model labels,embedding the quantitative code of the prior knowledge in the model architectures,and optimizing the model by designing loss functions based on the differentiable test indicators. At the same time,improving the consistency of the subjective and objective evaluation of nowcasting products of severe precipitation can better determine the direction of the improvement of nowcasting technology of severe precipitation.