Speaker
Description
This talk will explore how computational science and evolving network
and storage capabilities, together with ongoing improvements in remote
and in-situ sensing, may be poised, possibly like never before, to
have significant impacts on global ocean research. Simultaneous improvements
across network, storage, computation and sensing technologies are beginning
to create a new lens through which to view, explore and understand some
of the key mathematics and observations used to describe and reason about
physical, chemical and biological aspects of the Earth's oceans.
Specifically this presentation examines a global one-kilometer horizontal
resolution numerical ocean computation that embraces network and storage
enabled computational science based approaches. The computation and some
of its applications will be described. Some of the key network, storage
and computational science technology ingredients that enable the work
will be outlined.
The computation examined is work that was recently undertaken using the
NASA Pleadies computer. It is one of a new generation of ocean computations
that include representations of tidal forcings and realistic synoptic
meterology. Including these aspects, at kilometer scale resolution, captures
more of the rich dynamics present and observed in the real ocean. This
qualitatively increases fidelity of the spatial and temporal variability
represented numerically.
Our calculation is initialized from a data constrained estimate of the
real-world, large-scale global ocean state. It is driven with boundary
conditions taken from high-resolution, data assimilating weather models. The
domain is fully global. Interestingly, from a network and storage enabled
computational science perspective, we chose to take a uniquely ambitious
approach to storing and distributing the simulation solution. We sampled
and archived computation state to a storage subsystem at hourly frequency
and at full global resolution for a full year. This created a new and
novel resource for ocean research. It is multi-petabyte in size and has
global coverage.
The resulting set of more than 10^15 spatially and temporally varying
numerical values is supporting a variety of interesting and insightful
studies. Many of these would not be easily possible without the underlying
network and storage cyberinfrastructure. Advanced cyberinfrastructure
underlies archive creation, enables distribution of sizable sub-samples from
the archive, and provides tools used in multiple subsequent research studies.
High spatial and temporal storing of the computation more readily
reveals an ocean that is teeming with turbulent vorticies and wave
motions globally. A series of eye catching visualizations illustrate
this. They show what the ocean would look like to eyes that could discriminate
components vorticity and density surfaces, instead of visible light!
Examining local regions in frequency wave number space, the stored solution
provides notably more complete comparison with theoretical predictions and
historical observations than previous generation ocean models. This increased
fidelity, combined with the rich sampling archive, is allowing the effort to
help guide and support focussed observational field campaigns both at specific
locations and globally.
High spatial and frequency capture also allows us to explore new directions in
developing statistical relations between readily observable ocean fields and
features of interest that are not as directly observable. One example of
this, is trying to reduce the stochastic uncertainty due to the ocean internal
wave field that impacts acoustic travel time estimates. Underwater acoustics
is a potentially powerful tool for measuring the ocean and for creating fully
mobile sub-surface networks. It is notoriusly challenging in part because of
inherent low bandwidth, but also in part because of the complicated time
dependent nature of the ocean as a transmission media. We will illustrate how
network and storage enabled approaches can be leveraged in this context.
Leveraging these approaches allows us to develop new ways to determine aspects
of the internal wave field statistics in a more complete manner. This work
draws on the application of statistical methods prevalent in machine
learning/big-data communities. Using those methods we can develop
various semi-empirical regressions between observable fields and
internal wave statistics. Application of these sorts of methods is
fundamentally enabled by increasingly robust storage and network
cyberinfrastructure technologies.
Another example application looks at the role of high spatio-temporal frequency
processes in shaping marine microbial patterns in the ocean. Microbial
communities in the ocean form the base of the food chain and play a major, but
uncertain, role in Earths carbon, oxygen and nitrogen balance. Marine microbial
community structure and ecosystem dynamics remain an area of active research. A
highly sampled global fluid solution with spatial and temporal resolution down
to scales of kilometers and hours support new ways to explore possible ideas on
governing mechanisms for these communties. Recent work in this context will be
illustrated.
Finally, we will also sketch briefly the network and storage technologies
employed. We will describe approaches for storing data at adequate rates and
for disseminating the solution across national networks. The approaches are
allowing us to begin to share solutions widely, to local/regional facilities and
to cloud services including Dropbox, AWS and Azure. The technical lessons from
this exercise show great promise. They provide an illustration of the potential
that future ongoing hyperconnected cyberinfrastructure investments could
unleash - especially if key technologies are made more routine and
implemented generally in a sufficiently interoperable, capable and
cost-effective manner.
