Deep learning methods dominate short-term high-resolution precipitation nowcasting in terms of prediction error. However, their operational usability is limited by difficulties explaining dynamics behind the predictions, which are smoothed out and missing the high-frequency features due to optimizing for mean error loss functions. We experiment with hand-engineering of the advection-diffusion differential equation into a PhyCell to introduce more accurate physical prior to a PhyDNet model that disentangles physical and residual dynamics. Results indicate that while PhyCell can learn the intended dynamics, training of PhyDNet remains driven by loss optimization, resulting in a model with the same prediction capabilities.

翻译：深度学习方法在短期高分辨率降水临近预报的预测误差方面占据主导地位。然而，其实际可用性受到解释预测背后动力学困难程度的限制，由于针对平均误差损失函数进行优化，这些预测被平滑处理并缺失了高频特征。我们实验性地将平流-扩散微分方程手工工程化为PhyCell，为解耦物理与残余动力学的PhyDNet模型引入更准确的物理先验。结果表明，尽管PhyCell能够学习预期的动力学，但PhyDNet的训练仍由损失优化驱动，导致模型具有相同的预测能力。