Abstract Accurate modeling of hydrometeor bulk scattering properties (BSPs) is essential for the effective assimilation of satellite microwave observations in cloudy and precipitating conditions. Nevertheless, current radiative transfer models use oversimplified hydrometeor BSP parameterizations, leading to significant simulation errors and biases that limit the full potential of all‐sky assimilation. To address this challenge, this study developed PhySCAT‐Net, a physics‐informed deep learning (DL) framework that integrates forward and Jacobian operators of a physical radiative transfer model into the neural network training, enabling efficient optimization of the BSP models against satellite observations. Applied to vertically and horizontally polarized radiances from the Global Precipitation Measurement Microwave Imager 166.5 GHz channels, the framework selectively fine‐tunes the snow BSP model while temporarily fixing other hydrometeor types. Results demonstrate that the optimized DL model substantially improves agreement between simulated and observed brightness temperatures. Across most regions globally, mean observation‐minus‐background (O‐B) biases are reduced to within ±1K, and the Jensen‐Shannon divergence decreases by orders of magnitude. The error distributions, which were previously highly skewed and therefore problematic for data assimilation, are now roughly symmetrical. Furthermore, PhySCAT‐Net enables the DL model to extract polarimetric information of non‐spherical ice particles directly from observed radiances, demonstrating superior performance compared to existing empirical schemes. It successfully reproduces the distributions of polarization differences and their non‐monotonic relationship with brightness temperature.
Li et al. (2026) studied this question.