This paper provides a validation of a novel sampling, storage, and evaluation method named raytraverse that can quickly and accurately compute glare and visual comfort metrics including vertical illuminance, Daylight Glare Probability (DGP), and Unified Glare Probability (UGP). The motivation is to provide a path towards understanding the spatial and temporal distribution of daylight conditions in an architectural space. Current spatial temporal simulation workflows are constrained by the trade-offs between simulation time, accuracy, generality, and storage requirements. Raytraverse provides a bridge between illuminance sensor calculations, which are fast to calculate but provide limited information, and high-resolution image generation, which provide more information but have long simulation times. To make this bridging possible, it relies on a pair of strategies that yields both high accuracy and high information data. First, an iteratively guided sampling approach based on the discrete wavelet transformation greatly reduces the number of view rays submitted to the renderer. Second, rather than collecting returned values in a raster image or dense matrix, a spatial data structure is used to both sparsely store and re-weight the samples according to their effective solid angle, allowing for the direct integration of any view based lighting metric and the synthesis of interpretable high dynamic range images (HDRi). These strategies can be coupled with existing rendering and climate based daylight modeling (CBDM) methods. Through a comparison with high-quality reference simulations and a best practice CBDM method based on Radiance, the raytraverse methods are shown to significantly reduce the simulation time needed to accurately simulate saturation, contrast, and combined visual comfort metrics for a complete set of annual hourly sky conditions from a range of locations within an office floor plan. The stored simulation data can be quickly re-analyzed for different view directions, metrics or images, and sky conditions.