  The Argo project deploys thousands of floats throughout the world's oceans.
Carried only by the current, these floats take measurements such as temperature
and salinity at depths of up to two kilometers. These measurements are critical
for scientific tasks such as modeling climate change, estimating temperature
and salinity fields, and tracking the global hydrological cycle. In the
Southern Ocean, Argo floats frequently drift under ice cover which prevents
tracking via GPS. Managing this missing location data is an important
scientific challenge for the Argo project. To predict the floats' trajectories
under ice and quantify their uncertainty, we introduce a probabilistic
state-space model (SSM) called ArgoSSM. ArgoSSM infers the posterior
distribution of a float's position and velocity at each time based on all
available data, which includes GPS measurements, ice cover, and potential
vorticity. This inference is achieved via an efficient particle filtering
scheme, which is effective despite the high signal-to0noise ratio in the GPS
data. Compared to existing interpolation approaches in oceanography, ArgoSSM
more accurately predicts held-out GPS measurements. Moreover, because
uncertainty estimates are well-calibrated in the posterior distribution,
ArgoSSM enables more robust and accurate temperature, salinity, and circulation
estimates.
