A Physics-Informed Deep Learning Method for Quantitative Evaluation of Artificial Precipitation Enhancement Effects

Quantitative evaluation of artificial precipitation enhancement effects remains a critical challenge in meteorological science. Traditional methods rely on idealized assumptions and control area selection, with limited applicability under complex terrain conditions. To address this problem, this paper proposes physics-informed simple video prediction (SimVP) (PiSim), a hybrid physics-informed disentangled framework designed to disentangle precipitation evolution. By employing the advection–diffusion equation (ADE) as an inductive bias within the latent space, we transform spatial comparative evaluation into a precise spatiotemporal sequence prediction task. The method adopts a dual-branch architecture: The physics branch explicitly models deterministic macroscopic motion (advection and diffusion), whereas the data-driven branch learns complex nonlinear residuals, effectively compensating for microphysical processes and local variations. Experimental results on the Hubei Province Swan radar dataset demonstrate that PiSim achieves a 5.5% improvement in MSE compared to the SimVP baseline, with pronounced advantages in heavy precipitation forecasting. Evaluation of 10 typical artificial precipitation enhancement operations shows hourly net rainfall increments ranging from 0.16 to 4.60 mm, which are highly consistent with historical records, validating the method’s effectiveness.