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Dr. Frank Herr
I am going to describe some of the research that Office of
Naval Research (ONR) is either conducting or planning for the
next 5–10 years. We have made strategic decisions based on
Dr. Frank Herr has been the Head of the Office of Naval Research
(ONR) Ocean Battlespace Sensing Department since 2001. The Ocean
Battlespace Department is responsible for the Navy’s and Marine
Corps’ science and technology in ocean and meteorological science,
undersea warfare, mine warfare, space technology, and marine mammals. It comprises two divisions and 14 programs spanning sensing,
systems, and geophysical processes and prediction. The department
also has built and cares for six oceanographic research vessels. From
1996 to 2001, Dr. Herr was director of the Sensing and Systems
Division within ONR. His division’s work spanned undersea warfare, mine warfare, and space technology. Dr. Herr currently is the
U.S. National Representative for the Maritime Systems Group of The
Technical Cooperation Program (TTCP), where he coordinates technology among the United States, the United Kingdom, Canada, Australia,
and New Zealand. Dr. Herr was appointed to the Senior Executive
Service in August 1998. Dr. Herr joined federal service in 1977 as a
research chemist at the Naval Research Laboratory (NRL) and conducted research until 1982 when he joined ONR. Dr. Herr became
the Program Manager for Remote Sensing in 1988. From 1992 to
1994, Dr. Herr served on the staff of Admiral Frank B. Kelso, Chief of
Naval Operations, as Assistant for Science and Technology to the CNO
Executive Panel, N00K. Dr. Herr graduated from Hamilton College in
Clinton, New York, with a B.A. degree. He also holds a Ph.D. from
the University of Maryland, College Park, Maryland. Dr. Herr was a
National Research Council post-doctoral research associate. Dr. Herr
is the author of 22 scientific and technical publications. Dr. Herr
received the Department of the Navy Superior Civilian Service Award
in 1994 and again in 2008, a Presidential Rank Award for Meritorious
Executives in 2005, and the NRL Research Publication Award in 1981.
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conversations that we have had with Rear Admiral David Titley and
the work that he has initiated from the Oceanographer’s Office
and with Rear Admiral Jonathan White, the Commander Navy
Meteorology and Oceanography. Our job at ONR is science and
technology. We are supposed to have the long-term view, but in
this crowd, we are actually the short-term folks because we are
more interested in weather forecasting than in climate change. As
Rear Admiral Titley said, those of us in the DoD and the Navy have
come programmatically a bit late to the climate change issue. I am
leaving it to my colleagues here to get into the climate issues.
Actually, my view of the time spans involved in forecasting
and climate change is changing. I have started thinking of weather
as the government shutdown and climate as an appropriation
for FY2011.
One of the key things that we are starting to work on is what we
are calling Global Seamless Prediction (Figure 1), which is essentially the set of next-generation, coupled ocean–atmosphere–ice
models. Currently, we have 28 environmental prediction systems
to include prediction system models with assimilation of data that
are running at the Naval Oceanographic Office (NAVOCEANO)
and at the Fleet Numerical Meteorology and Oceanography
Center (FNMOC). As Rear Admiral Titley has rightfully pointed
out, our large-scale models that are used for atmosphere and
ocean prediction are not coupled well enough and they are getting a little bit long in the tooth in terms of the way the code was
developed and the model architecture that was used. It is time
for a new generation of these systems, and our goal is to build the
research that will allow us to put together a fully coupled system
that incorporates a higher resolution down to 1/25th of a degree
at worst. We also intend to add the Arctic to this model. The coupling will be sufficient over broad scales to include the effects
of storms, specifically tropical storms. We have had some good
results so far on forecasting the initiation of storms. The effects
of internal waves in the upper ocean and the acoustic environment are things that we are particularly interested in getting into
these systems as well. So, Global Seamless Prediction overall is
a major strategic goal for us.
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Figure 1. Seamless Global Prediction
Figure 2. Establishment of an Arctic Research Program
Conducting additional research in the Arctic is also one of our
major goals. Now, some of you may remember that Navy science
and technology was in the Arctic back in the Cold War. At that time,
we did not have a lot of partners up there, but we had a pretty big
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program. Over the past 25 years, we have let that Arctic work drop
down and focused on other areas to the point that we have been
spending a little less than $1 million on a few projects, but we are
going to ramp that back up again. We now have more partners in
the Arctic than we had before (see Figure 2), and so we are looking
forward to a good, robust Arctic program. The issue that we have
for the Arctic is to be able to build an Arctic Prediction System that
will couple with the Global Seamless Prediction that I described
earlier. In particular, we want to be able to establish boundary conditions that are important for the more temporal latitudes.
As you may know, we have no climatology for where the
Arctic is going now; we do not really have a way to say this is
the way the Arctic works. So, we want to build a dynamic model
that can couple with the rest of the world and the ocean atmosphere system in order to fill in that big hole in the way the Global
Seamless Prediction would operate. We have some very interesting
tools that are going to help us that we did not have the last time
we were up in the Arctic. One of those is synthetic aperture radar
(SAR). The number of passes that our global SAR systems are giving
us for the Arctic is actually pretty astounding to me. We want to
be able to assimilate all that data and use that information to determine where the ice is and how it is working. Coupling that with
the mathematics of the dynamics of ice melting and movement will
provide us with a really stupendous model for the Arctic.
Unmanned undersea vehicles (UUVs) are another technology
that can play a key role in the Arctic. ONR has led a number of
these developments; those systems are now sufficiently reliable that
we can begin sending them up into the Arctic to do work under ice
and in the marginal ice zone. We could not do that in the past, so
we are eager to begin. We also have a larger diameter UUV program that will have an endurance of between 30 and 60 days and
that will be particularly useful in the Arctic. Placing remote sensors
on those underwater vehicles will help us to understand how the
halocline is changing, where the marginal ice zone is, and how all
that is moving and thereby help us establish new boundary conditions for our Global Seamless Prediction capability. I realize that
that is a tall order, but we are moving in that direction.
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In my department at ONR, we have established a process called
Department Research Initiatives (DRIs), which are $9–10 million
programs over 5 years that develop teams of researchers and then
move forward on specific topics. The one that we have recently
put in place for FY2012 will kick off part of our overall Arctic program. This specific effort is intended to help us understand the
dynamics of the marginal ice zones and provide the science input
that would go into a new model (Figure 3). We will also be looking at halocline circulation and air–sea coupling and working on
assimilating ice-related information into our models.
Figure 3. DRI: Emerging Dynamics of the Marginal Ice Zone
The importance of this research was made apparent in a recent
conversation I had with researchers from Defense Research and
Development Canada (DRDC)–Atlantic last week. They told me
that they had been making regular trips to areas in the Arctic where
we had worked cooperatively 20 years ago. At that time, these
areas were essentially Arctic deserts; they received little or no precipitation. However, the last several times that they have visited
these places, they have found 5–8 feet of snow. While it is only
anecdotal evidence, it provides a good example of the air–sea
coupling and the development of air–sea interaction that was not
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there in the past but is now occurring in a very strong way. So,
Arctic meteorology is going to be another key element in what we
are doing.
As we move from weather forecasting models, where we can
make reasonably accurate projections out to about 7 days, to
working with the climatologists who have been working on their
own model systems, we find that there is a gap between forecasting 7 days into the future and making predictions that are now
3 months, 6 months, or 1 year ahead. The question invariably
comes up as to whether or not we can pull together the best of
what is going on with climate modeling with the forecasting capability and the physics that we have built into the forecast systems
used by the Navy and the weather services around the world. So,
we have initiated some programs in this area.
Figure 4. DRI: Extended Range Prediction
In particular, one of our Department Research Initiatives is looking into long-term forecasting on the order of 6 months to 1 year
(Figure 4). The task involves a lot of questions that we do not yet
fully understand. For example, how do these very large elements
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of the spherical harmonics of the atmosphere and ocean—such
as El Niño or the Madden–Julian oscillations—how do they operate, how do they wobble, how do they change, and how can we
investigate them in a way that will give us some indication of how
we would want to predict them?
At the same time, we have to begin to understand from a longrange perspective and a climate perspective what sorts of questions we want to be able to answer in a predictive mode. What
the limits of prediction could be is itself an important element, so
we are starting slowly in this area. We are going to look for some
really brilliant proposals and get the community to start thinking
about the idea of extending forecasting by bringing in the best of
what we know on climate and to work new avenues of research in
that area. We know how long it takes to improve the skill level for
our models and prediction systems, about a decade per day of skill
increase. At that rate, it is going to be a long time before we have
even a 1-month prediction capability. So, you can see why this is
an important science and technology issue.