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Inferring physical parameters of turbulent flows by assimilation of datameasurements is an open challenge with key applications in meteorology, climatemodeling and astrophysics. Up to now, spectral nudging was applied forempirical data-assimilation as a mean to improve deterministic and statisticalpredictability in the presence of a restricted set of field measurements only.Here, we explore under which conditions a nudging protocol can be used for twonovel objectives: to unravel the value of the physical flow parameters and toreconstruct large-scale turbulent properties starting from a sparse set ofinformation in space and in time. First, we apply nudging to quantitativelyinfer the unknown rotation rate and the shear mechanism for turbulent flows.Second, we show that a suitable spectral nudging is able to reconstruct theenergy containing scales in rotating turbulence by using a blind set-up, i.e.without any input about the external forcing mechanisms acting on the flow.Finally, we discuss the broad potentialities of nudging to other keyapplications for physics-informed data-assimilation in environmental or appliedflow configurations.

Inferring flow parameters and turbulent configuration with physics-informed data-assimilation and spectral nudging