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© 2019 by SciPy.

Geoffrey Cureton

Sunglint-Polyspectra: The Generation of Simulated Random Data for Numerical Experiments

Modeling geophysical processes requires simulation of random input data of known quality. Simulating the required data is often done in an ad-hoc manner, with little attention paid to repeatability. To address this need in the modelling of optical scattering phenomena from the ocean surface, the python tool `sunglint-polyspectra` has been developed, which generates random ocean slopes, and applies sunlight scattering via Snell's law to simulate sunglint. The user may specify the power spectra and correlation behavior of the random sequences, and the results are output in the form of CF-compliant NetCDF4 files.