  The present study proposed the framework of the spatiotemporal
superresolution measurement based on the sparse regression with dimensionality
reduction using the proper orthogonal decomposition (POD). The
non-time-resolved particle image velocimetry (PIV) and the time-resolved
near-field acoustic measurements using microphones were simultaneously
performed for a Mach 1.35 supersonic jet. POD is applied to PIV and microphone
data matrices and the sparse linear regression model of the reduced-order data
is calculated using the least absolute shrinkage and selection operator
regression. The effects of the hyperparameters of the superresolution
measurement were quantitatively evaluated through randomized cross-validation.
The superresolved velocity field indicated the smooth convection of the
velocity fluctuations associated with the screech tone, while the convection of
the large-scale structures at the downstream side was not observed. The
proposed framework can reconstruct the unsteady fluctuation with multiple
frequency phenomena, although the reconstruction is limited to the phenomena
that are associated with the microphone output.
