Broadband radiation calculations are computationally expensive, and climate and weather models require fast parameterizations for computing the flow of energy through Earth's atmosphere. Data-driven quadrature is an alternative to traditional gas-optics parameterizations consisting of an optimal, sparse sample of representative spectral points (frequencies) and weights such that the weighted sum of monochromatic calculations approximates the broadband quantity, offering flexibility while maintaining the accuracy and efficiency of state-of-the-art schemes. Data-driven quadrature was originally developed in cloudless present-day conditions for longwave (thermal) radiation. In this work, we update the optimization algorithm to support shortwave (sunlight) calculations, which must be robust to variations in solar zenith angle and surface reflectivity. We additionally expand both the longwave and shortwave schemes to capture variability in major greenhouse gas concentrations, with potential application to different climate scenarios. The schemes are validated using ERA5 data with clear and cloudy skies and compared to a state-of-the-art radiation parameterization, showing comparable accuracy at lower computational cost. Furthermore, implementation in a single-column radiative-convective equilibrium model with interactive ozone chemistry demonstrates the versatility of the scheme and potential for online operationalization in dynamical models. Here we release and describe these optimized sets of quadrature points and associated weights, along with a tutorial to guide the optimization of new point and weight configurations for other applications.