  We performed Out-Of-Distribution (OOD) analysis of 7.8 million Sea Surface
Topography Merged Altimeter L4 cdr grid cutouts in an effort to identify rare
(possibly unknown) physical phenomenon sea surface height (SSH) data. The
algorithm used for the project is Ulmo which is a probabilistic autoencoder
(PAE), originally developed for sea surface temperature data. A PAE is made of
an autoencoder for taking the extracted images and encoding them into a latent
representation of the data, and a normalizing flow which takes the encoding and
maps it to a normal distribution for probabilistic interpretation. A
Log-Likelihood (LL) value for each cutout was calculated from this normal
distribution and we defined the images with the lowest 0.1 percentile of LL
values as anomalies. Ulmo successfully identifies outliers and distinguishes
the ocean's most dynamic regions being Western boundary currents.
