This talk will cover two major obstacles when considering high-frequency temporal and space-time data. As the power grid moves to a more renewable future, energy sources from weather-driven phenomena such as solar power will form an increasingly large portion of electricity generation. The variability, non-Gaussianity and intermittency of solar resources challenge current grid operation paradigms, and realistic data scenarios are required for grid planning and operational studies. However, such data are not available at the space-time resolution needed for realistic grid models. Given sparse spatial samples that are high-resolution in time, we introduce a framework for spatiotemporal prediction in a functional data analysis framework when data exhibit nonstationary phase misalignment. The approach is illustrated on a challenging irradiance dataset and compares favorably against existing methods.