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AMT - Wind speed and direction estimation from wave spectra using deep learning

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AMT - Comparison of methods to derive radial wind speed from a continuous- wave coherent lidar Doppler spectrum

Frontiers A neural network approach to the estimation of in-water attenuation to absorption ratios from PACE mission measurements

Diurnal and Daily Variations of PM2.5 and its Multiple-Wavelet Coherence with Meteorological Variables in Indonesia - Aerosol and Air Quality Research

Wind inflow observation from load harmonics via neural networks: A simulation and field study - ScienceDirect

Wind inflow observation from load harmonics via neural networks: A simulation and field study - ScienceDirect

A Deep Learning–Based Approach for Empirical Modeling of Single-Point Wave Spectra in Open Oceans in: Journal of Physical Oceanography Volume 53 Issue 9 (2023)

Smart multi-step deep learning model for wind speed forecasting

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Frontiers Remotely Sensed Winds and Wind Stresses for Marine Forecasting and Ocean Modeling

AMT - On the estimation of boundary layer heights: a machine learning approach

The pdfs based on the HW and lidar measurements together with the

Deep learning approach for wind speed forecasts at turbine locations in a wind farm - Kou - 2020 - IET Renewable Power Generation - Wiley Online Library