With the advent of offshore wind farms, the research into the various phenomenon that affects their performance is vast and detailed. But the effect of a particular phenomenon, atmospheric gravity waves (AGWs), on wind farm performance is limited. AGWs are oscillations of the airflow due to an imbalance in the buoyancy and gravity forces, generated by topographical or meteorological obstacles in neutral or stable surface atmospheric conditions.

AGWs are frequent over offshore regions and affect offshore wind farms as the event occurs over a large area. Detecting them through satellite images is easy by an eye test, but not so much when viewed digitally through meteorological data. Weather data can be obtained from reanalysis data which combines past weather forecasts with observational data assimilation.

This project aims to develop machine learning models that detect AGWs in satellite images and detect AGWs from atmospheric conditions, such as temperature and wind speed profile with height. The models learn using the reanalysis data and satellite images. The same satellite images are used to label the reanalysis data so that the model is taught to pick out gravity waves in the case of having no satellite image. Thus the final objective of the project is to train a model to detect an AGW event, based solely on reanalysis data. The trained model is then used to predict the percentage of time an AGW occurs or will occur over a chosen wind farm site.