Trapped lee waves, and resultant turbulent rotors downstream, present a hazard for aviation and land-based transport. While high resolution numerical weather prediction models can represent such phenomena, there is currently no simple and reliable automated method for detecting the extent and characteristics of these waves in model output. Spectral transform methods have traditionally been used to detect and characterise regions of wave activity in model and observational data, however these methods can be slow and have their limitations. Machine learning (ML) techniques offer a new and potentially fruitful method of tackling this problem.
This paper demonstrates that a deep learning model can be trained to accurately recognise and label coherent regions of lee waves from vertical velocity data on a single level from a high-resolution NWP (numerical weather prediction) model. Using transfer learning, wave characteristics (wavelength, orientation and amplitude) can be extracted from the trained segmentation model. The use of synthetic wave fields with prescribed wave characteristics makes this transfer learning possible without the need to characterise real complex wave fields. Addition of noise to the synthetic data makes the models more robust when applied to more complex and noisy NWP data. The collection of trained models produced provides a valuable tool to investigate the prevalence and nature of lee wave activity, as well as a new way for forecasters to detect resolved waves. The deep learning model was more capable and quicker at detecting and characterising lee waves compared to a spectral technique.
This work is just one example of how already established machine learning techniques can be used to detect and characterise complex weather phenomena from NWP model output and observational data, and how the careful use of synthetic data can reduce the requirements for large volumes of hand-labelled training data for ML models.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)