PhD Defence Davide Selvatici | Clouds, atmospheric turbulence and their interaction with wind farms

Clouds, atmospheric turbulence and their interaction with wind farms

Davide Selvatici is a PhD student in the Department of Physics of Fluids. Promotors are dr. R.J.A.M. Stevens and prof.dr. D. Lohse from the Faculty of Science & Technology.

Offshore wind farm sites often lie beneath marine clouds. Globally, the annual cloud coverage reaches approximately 65% of the Earth's surface, with a higher prevalence over maritime environments than over land. Clouds play a major role in the Earth's radiation budget and thus are a crucial component of the atmosphere. They reflect sunlight back to space, making them appear white when seen from above, and significantly contribute to the planetary albedo. Averaged globally, the sunlight reflection is strong enough to produce a net planetary cooling that nearly equals five times the warming associated with a doubling of carbon dioxide. By reflecting sunlight and emitting longwave radiation, low-level clouds drive strong air motions that enhance the transport of heat and water within the lower atmosphere, and are able to modulate key atmospheric conditions, such as temperature, moisture and atmospheric turbulence. Given their atmospheric coupling, clouds can have a strong impact on wind farms' performance.

Due to the rising demand for renewable energy, wind farms are rapidly expanding across the world. Considering the European Union only, wind energy reached a total capacity of 291 GW in the first half of 2025, with an average increase of approximately 12 GW per year in the last 5 years. In 2024, wind energy produced 19% of the EU electricity demand, and is set to reach up to 50% in the next 20 years. This target is crucial to reduce the emission of greenhouse gases from the energy sector and contribute to the mitigation of climate change. Improving the efficiency of wind energy generation makes this transition more rapid and affordable, and requires a more fundamental understanding of wind farm-atmosphere interactions.

To date, wind energy studies and models usually rely on simplified assumptions and focus on dry-air conditions. As a result, the interaction between wind farms and cloud dynamics has received little attention, and this thesis aims to address this knowledge gap.