  While many vibrational Raman spectroscopy studies of liquid water have
investigated the temperature dependence of the high-frequency O-H stretching
region, few have analyzed the changes in the Raman spectrum as a function of
temperature over the entire spectral range. Here, we obtain the Raman spectra
of water from its melting to boiling point, both experimentally and from
simulations using an ab initio-trained machine learning potential. We use these
to assign the Raman bands and show that the entire spectrum can be well
described as a combination of two temperature-independent spectra. We then
assess which spectral regions exhibit strong dependence on the local
tetrahedral order in the liquid. Further, this work demonstrates that changes
in this structural parameter can be used to elucidate the temperature
dependence of the Raman spectrum of liquid water and provides a guide to the
Raman features that signal water ordering in more complex aqueous systems.
