Assimilating visible (VIS) and near-infrared (NIR) satellite observations into numerical weather prediction (NWP) systems demands radiative transfer (RT) models that combine high accuracy and structural flexibility. This study introduces an enhanced Optical Depth in Pressure Space (ODPS) transmittance model within the Advanced Radiative Transfer Modeling System (ARMS), focusing on the optimization of water vapor predictors to address critical limitations in VIS/NIR applications. The improved algorithm, referred to as $\mathrm{O}\mathrm{D}\mathrm{P}{\mathrm{S}}_{\mathrm{n}\mathrm{e}\mathrm{w}}$, demonstrates substantial accuracy gains across Fengyun (FY)-4B Advanced Geostationary Radiation Imager (AGRI) channels, reducing mean bias and root mean square error by up to 97% and 98%, respectively. By accounting for height-dependent zenith angles, $\mathrm{O}\mathrm{D}\mathrm{P}{\mathrm{S}}_{\mathrm{n}\mathrm{e}\mathrm{w}}$ effectively mitigates angular-dependent errors caused by Earth’s curvature, outperforming the default Optical Depth in Absorber Space (ODAS) algorithm at large viewing angles while maintaining comparable accuracy under nadir conditions. Relative to ODAS, $\mathrm{O}\mathrm{D}\mathrm{P}{\mathrm{S}}_{\mathrm{n}\mathrm{e}\mathrm{w}}$ lowers RMSEs by 63%–96% in line-by-line validations, achieving parity or better accuracy in five channels. Validations using UMBC (University of Maryland at Baltimore County) profiles and real atmosphere profiles confirm enhanced robustness across diverse atmospheric conditions and spatial coherence. Updated water vapor Jacobians exhibit ODAS-level smoothness in 5 of 6 channels and elimi-nate large layer-to-layer jumps, leaving only a minor residual oscillation near 1 hPa. The smoother Jacobians strengthen suitability for variational data-assimilation framework. Overall, $\mathrm{O}\mathrm{D}\mathrm{P}{\mathrm{S}}_{\mathrm{n}\mathrm{e}\mathrm{w}}$ offers a balanced solution that unifies regression accuracy, geometric adaptability, and physical interpretability, providing a promising pathway for assimilating next-generation satellite VIS/NIR radiance.

The ocean surface emissivity model plays a pivotal role in satellite data assimilation and the retrieval of ocean physical parameters. In our previous research, we developed a physical emissivity model featuring a polarized Bidirectional Reflectance Distribution Function (pBRDF-E). This model effectively ensures the consistency between surface emission and reflection parameters. However, it suffers from low computational efficiency. In this study, we introduce a fast ocean emissivity model, OceanEM. Leveraging the emissivity data output from the pBRDF-E model, OceanEM is developed by using a multilayer perceptron neural network. It can compute the polarization emissivity vector across a wide range of conditions: incidence angles from 0° to 80°, wind speeds from 2 to 50 m s⁻¹, sea surface temperatures from −2° to 30°C, sea surface salinities from 0 to 40 psu, and frequencies from 1.4 to 410 GHz. Alongside the FAST Microwave Emissivity Model (FASTEM6) and SURface Fast Emissivity Model for Ocean (SURFEM-ocean), OceanEM is integrated into the Advanced Radiative Transfer Modeling System (ARMS) as a user-selectable option. To validate the accuracy of OceanEM, we compare it with FASTEM6 and SURFEM-ocean using data from WindSAT, a polarimetric radiometer onboard the Coriolis satellite. The results show that the three models generally yield consistent simulations of WindSAT brightness temperatures. Specifically, for channels at 6.8 GHz, 10.7 GHz (both horizontal and vertical polarization), and 18.7 GHz (vertical polarization), OceanEM demonstrates higher accuracy than FASTEM6 but lower than SURFEM-ocean. Conversely, for channels of 18.7 GHz (horizontal polarization), 23.8 GHz, and 37.0 GHz (both horizontal and vertical polarization), OceanEM outperforms both FASTEM6 and SURFEM-ocean.

The original vector discrete ordinate radiative transfer (VDISORT) model takes into account Stokes radiance vector but derives its solution assuming azimuthal symmetric surface reflective matrix and atmospheric scattering phase matrix, such as the phase matrix derived from spherical particles or randomly oriented non-spherical particles. In this study, a new VDISORT is developed for general atmospheric scattering and boundary conditions. Stokes vector is decomposed into both sinusoidal and cosinusoidal harmonic modes, and the radiance at arbitrary viewing geometry is solved directly by adding two zero-weighted points in the Gaussian quadrature scheme. The complex eigenvalues in homogeneous solutions are also taken into full consideration. The accuracy of VDISORT model is comprehensively validated by four cases: Rayleigh scattering case, the spherical particle scattering case with the Legendre expansion coefficients of 0th–13th orders of the phase matrix (hereinafter L13), L13 with a polarized source, and the random-oriented oblate particle scattering case with the Legendre expansion coefficients of 0th–11th orders of the phase matrix (hereinafter L11). In all cases, the simulated radiances agree well with the benchmarks, with absolute biases less than 0.0065, 0.0006, and 0.0008 for Rayleigh, unpolarized L13, and L11, respectively. Since a polarized bidirectional reflection distribution function (pBRDF) matrix is used as the lower boundary condition, VDISORT is now able to handle fully coupled atmospheric and surface polarimetric radiative transfer processes.