Improving Visible and Near-Infrared Transmittance Models for Advanced Radiative Transfer Modeling System (ARMS)

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 ODPSnew demonstrates substantial accuracy gains across FY-4B AGRI channels, reducing mean bias and root mean square error by up to 97% and 98%, respectively. By accounting for height-dependent zenith angles, ODPSnew  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, ODPSnew lowers RMSEs by 63%-96% in line-by-line validations, achieving parity or better accuracy in five channels. Validations using UMBC and real atmosphere profiles confirm enhanced robustness across diverse atmospheric conditions and spatial coherence. Updated water-vapor Jacobians exhibit ODAS-level smoothness in five of six channels and eliminate 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, ODPSnew 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.