UDL-RT: A unified differentiable all-sky microwave radiative transfer model with learnable parameterizations

Abstract

Radiance-based retrievals and data assimilation require radiative transfer models (RTMs) to simulate radiances and quantify their sensitivities to atmospheric and surface conditions. Conventional RTMs often rely on separately maintained forward, tangent-linear, and adjoint codes, complicating model development and extension. Here, we present the Unified Differentiable and Learnable Radiative Transfer model (UDL-RT), a PyTorch-based framework for all-sky microwave radiative transfer that couples neural-network (NN) parameterizations of gaseous absorption, hydrometeor bulk scattering properties (BSPs), and ocean surface emissivity with an explicit radiative transfer solver in a unified differentiable computational graph. Automatic differentiation directly computes Jacobians with respect to input variables, reducing the model maintenance burden. The NN parameterizations offer flexible representations of complex nonlinear relationships. For example, the NN gas-absorption parameterization reduces the standard deviation of brightness-temperature (TB) errors by approximately 60-85% in the water-vapour channels compared with linear regression. Moreover, UDL-RT propagates loss gradients to trainable parameters to optimize physical parameterizations against observations, with GPU-accelerated batched computation supporting efficient iterative learning. Using Microwave Humidity Sounder-2 (MWHS-2) observations, we optimize the snow BSP parameterization through gradient-based learning. The optimized parameterization improves agreement between simulated and observed TB distributions and reduces the systematic bias and skewness of observation-minus-background (O-B) departures. Overall, UDL-RT integrates radiance simulation, sensitivity analysis, and parameterization optimization within a unified framework, supporting future remote sensing retrieval and data assimilation applications.