Many recent studies in modeling environmental flows
are centered around the idea of data-based modeling (e.g, machine learning, etc.)
with applications to many areas of the geosciences. A promising new direction
is combining these data-based approaches with more traditional dynamical
modeling. An example is the use of atmospheric dynamical models where
the small-scale processes are represented by data-based models.
This session is aimed at contributions on such hybrid
approaches to model environmental flows, in particular with
applications in climate (ocean/atmosphere) and hydrology.