  Sea surface temperature (SST) is an essential climate variable that can be
measured via ground truth, remote sensing, or hybrid model methodologies. Here,
we celebrate SST surveillance progress via the application of a few relevant
technological advances from the late 20th and early 21st century. We further
develop our existing water cycle observation framework, Flux to Flow (F2F), to
fuse AMSR-E and MODIS into a higher resolution product with the goal of
capturing gradients and filling cloud gaps that are otherwise unavailable. Our
neural network architecture is constrained to a deep convolutional residual
regressive neural network. We utilize three snapshots of twelve monthly SST
measurements in 2010 as measured by the passive microwave radiometer AMSR-E,
the visible and infrared monitoring MODIS instrument, and the in situ Argo
dataset ISAS. The performance of the platform and success of this approach is
evaluated using the root mean squared error (RMSE) metric. We determine that
the 1:1 configuration of input and output data and a large observation region
is too challenging for the single compute node and dcrrnn structure as is. When
constrained to a single 100 x 100 pixel region and a small training dataset,
the algorithm improves from the baseline experiment covering a much larger
geography. For next discrete steps, we envision the consideration of a large
input range with a very small output range. Furthermore, we see the need to
integrate land and sea variables before performing computer vision tasks like
those within. Finally, we see parallelization as necessary to overcome the
compute obstacles we encountered.
