Site-specific and time-specific wind field characteristics have a significant impact on the structural response and the lifespan of wind turbines. This paper presents a machine-learning approach towards analyzing and predicting the response of a wind turbine structure to diurnal and nocturnal wind fields. Machine-learning algorithms are applied (1) to better understand the changes of wind field characteristics because of atmospheric conditions and (2) to gain insights into the wind turbine loads being affected by the wind field. Using a Gaussian mixture model, the variations in wind field characteristics are investigated by comparing the joint probability density functions of selected wind field features. The wind field features are constructed from long-term monitoring data taken from a 500-kW wind turbine in Germany that is used as a reference system. Furthermore, employing Gaussian discriminant analysis, representative daytime and nocturnal wind turbine loads are compared and analyzed.