Deep-convective wind gust forecasting and implications for wind energy applications

Deep convective clouds (or thunderstorms) are associated with extreme weather events, including lightning, abrupt darkening, heavy precipitation, and strong wind ramps. The convectively-induced wind ramps, also known as “gust fronts”, play a critical role in wind-energy applications, particularly in the context of wind turbine performance and longevity. Convective cold pools, which form when cold, denser air produced within thunderstorms spreads out near the surface, can produce strong, local changes in wind speed and direction (the gust front) over short time scales, presenting both opportunities and challenges for turbines and the energy sector. From a power output perspective, gust fronts are associated initially with temporarily increased wind speeds, affecting turbine power generation impacted by these weather events.

Therefore, the ability to accurately predict gust fronts is vital for maximizing the efficiency and safety of wind energy production. However, the short-term prediction of small-scale convective gusts is difficult for operational numerical weather models which have coarse spatiotemporal resolution. Nowcasting, which provides short-term, localized weather predictions from remote-sensing observations, can help wind farm operators anticipate the onset of gust fronts and adjust turbine operations accordingly.

The objective of this PhD thesis is the development of an accurate, robust and physically consistent short-term forecasting tool for boundary-layer gust front events induced by deep moist convection. The focus will be on the recently built state-of-the-art research wind park located in Krummendeich, northern Germany (windenergy-researchfarm.com (https://www.windenergy-researchfarm.com)). The extensive observational network at the wind park provides an excellent opportunity for testing and validating nowcasting models. The project will build on existing tools and longstanding experience at DLR. The model building will benefit from related concepts investigated in ongoing and parallel PhD projects and will exploit synergies. In addition to thorough validation of the model, case studies in wind energy demonstrators are an integral part of this project.