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Physics Informed Shallow Machine Learning for Wind Speed Prediction
We train supervised learning algorithms using the past history of wind to predict its value at a future time (horizon).
Increasing the skill of short-term wind speed ensemble forecasts combining forecasts and observations via a new dynamic calibration
All numerical weather prediction models used for the wind industry need to produce their forecasts starting from the main synoptic hours 00, 06, 12, and 18 UTC, once the analysis becomes available.
Transition from shear-dominated to Rayleigh-Taylor turbulence
Turbulent mixing layers in nature are often characterized by the presence of a mean shear and an unstable buoyancy gradient between two streams of different velocity.
Fluid Dynamics
