  Wind speed retrieval at sea surface is of primary importance for scientific
and operational applications. Besides weather models, in-situ measurements and
remote sensing technologies, especially satellite sensors, provide
complementary means to monitor wind speed. As sea surface winds produce sounds
that propagate underwater, underwater acoustics recordings can also deliver
fine-grained wind-related information. Whereas model-driven schemes, especially
data assimilation approaches, are the state-of-the-art schemes to address
inverse problems in geoscience, machine learning techniques become more and
more appealing to fully exploit the potential of observation datasets. Here, we
introduce a deep learning approach for the retrieval of wind speed time series
from underwater acoustics possibly complemented by other data sources such as
weather model reanalyses. Our approach bridges data assimilation and
learning-based frameworks to benefit both from prior physical knowledge and
computational efficiency. Numerical experiments on real data demonstrate that
we outperform the state-of-the-art data-driven methods with a relative gain up
to 16% in terms of RMSE. Interestingly, these results support the relevance of
the time dynamics of underwater acoustic data to better inform the time
evolution of wind speed. They also show that multimodal data, here underwater
acoustics data combined with ECMWF reanalysis data, may further improve the
reconstruction performance, including the robustness with respect to missing
underwater acoustics data.
