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Meteotsunami Signature Found in
New Jersey CODAR Data
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Storm system that induced a Meteotsunami off the New Jersey coastline
13 June 2013.
Image credit: G. Carbin U.S. National Weather Service / SPC.
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A strange series of blogs and anecdotal community gossip described people
getting swept off breakwaters and out to sea in New Jersey on June 13. It
received only local attention for several days. Later someone put these
together with an unusual storm system that had raced Eastward across the
country, commonly called a "derecho" and proposed it may have launched a
"meteotsunami". Meteotsunamis occur regularly in the Mediterranean region
(Adriatic, Aegean, and Black Seas), but are rarely mentioned in the U.S. The
National Oceanographic & Atmospheric Administration (NOAA) stepped in to
investigate this 13 June event, and put forward another possible origin besides
meteorological: an undersea landslide in the Hudson Canyon.
The fact that it was indeed a tsunami was confirmed by 30 tide gages along the
East coast up through New England, and as far away as Puerto Rico. The NOAA
DART buoy 4402 confirmed it, as well as another bottom-pressure sensor-ofopportunity
in the region, a Sonardyne BPR. All of these outputs give a measure
of the tsunami wave height.
A panel of scientists was convened by NOAA to study this event, with the first
goal to pinpoint the nature of the source (meteorological or undersea landslide).
Examination of New Jersey SeaSonde data by scientists at Rutgers University and
CODAR company headquarters revealed not only the meteotsunami signature
but also helped the team confirm its origin. Examination focused first on the 13-
MHz SeaSonde at Brant Beach, NJ. |
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How a coastal HF radar sees the tsunami and what it sees, compared to other sensors.
• A tsunami is a shallow-water wave, meaning the depth determines its properties and how they evolve. The radar measures its
orbital velocity, not its height. The orbital velocity of any wave at its crest moves in the direction of wave propagation; at its
trough, velocity opposes that direction.
• All other sensors provide point measurements. The radar maps the velocity with distance from shore. Mapping distance
depends on bathymetry and tsunami strength.
• For the orbital velocity observed by the radar as a current, a single radar is adequate (unlike normal 2D current mapping that
requires two or more with overlapping coverage). However, multiple sites producing 2D maps will give a more complete picture
of the near-field dynamics.
What did the single radar see at Brant Beach?
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Left Panel:
Brant Beach NJ orbital velocity seen by CODAR
SeaSonde with distance offshore. Data have been detrended and
filtered around tsunami bandwidths. Negative velocity means
water is moving offshore. Dash line tracks the first trough
minimum of the tsunami event.
Right Panel:
Water level seen by nearby coastal tide gage at
Atlantic City. Bottom panel is detrended (detided) height. Heavy
arrows delineate common period when tsunami occurred. |
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Here are the salient features:
• The first tsunami signal deviation was a "trough". The SeaSonde-observed velocity was offshore, and the closest tide gage at Atlantic City also saw a trough (depression).
•Nonetheless, the wave itself was approaching the coast. The SeaSonde observed the event at the most distant ranges first, confirming this statement.
• The panels on the left highlight the velocity minimum at the trough as a dash line, as it progresses toward shore.
• Adjacent lower Figure to the right the time vs. range of the SeaSonde trough detection, along with the water depth vs. distance. New Jersey has a wide continental shelf (typical of the East and Gulf coasts) where shallow water extends to 120 km with depths less than 100 m.
• The SeaSonde observed the tsunami 33 minutes before it arrived at the coast, 23 km offshore.
• The tide-gage height reading provides both qualitative and quantitative confirmation of the radar observations. These results were presented by Belinda Lipa and Don Barrick to a NOAA science group convened on July 18, the first panel session to study this event. |
Blue Curve: Arrival time of first tsunami trough in hours from 00:00 June 13, 2013 vs. distance from shore seen by CODAR SeaSonde at Brant Beach, NJ.
Red Diamond at top left shows time of trough arrival recorded by coastal tide gage at Atlantic City.
Magenta Curve: Average water depth with distance from shore off Brant Beach. |
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Conclusions
A number of tentative conclusions were reached, supported by the SeaSonde data as well as model backtracking of the tsunami genesis:
• The tsunami was definitely spawned by the "derecho" atmospheric event depicted in the first figure, not an undersea landslide. Although the atmospheric pressure anomalies moved offshore toward the East, the first observed tsunami wave advanced onshore. How is this possible? This counterintuitive behavior was explained as follows:
• A pressure anomaly above the sea causes its level to rise or fall depending on whether it is a low or high (pulling water up or pushing it down).
• The resulting sea-level peaks/troughs move in the direction of the weather fronts, i.e., to the East.
• The average speed was ~40 knots, according to NOAA/NWS. The pressure anomalies are most effective in
generating the tsunami when this velocity matches the phase speed of a shallow-water tsunami wave (a "resonance" condition); the latter velocity is equal to the square root of depth times the acceleration of gravity.
• This happens in the region off New Jersey where the depth is about 47 meters at a distance about 65 km.
• The NOAA tsunami models show that a strong reflection occurs at the shelf edge, where the water quickly gets deeper, referred to as the beginning of the continental slope. This happens about 120 km offshore, where depth drops from 100 m to 1000 m over about 10 km.
• Both the NOAA and our own tsunami models show (again counterintuitively) that a stronger reflection happens when the water drops to the seafloor than when it rises (i.e., the latter would be a Westward moving wave hitting the slope).
• This model prediction was confirmed by the SeaSonde HF radar observations presented above, where the wave was first observed farther offshore, moving toward the coast to the West.
• Hence, the SeaSonde and the models were key in deducing the genesis of the meteotsunami. Future Investigations
Where do we go from here? Since this event is considered a "first" in the U.S., supported by extensive observations and modeling (also a "first" for any HF radars), there will be additional panel sessions to further refine the science and initiate potential development of real-time warning systems. CODAR and Rutgers scientists intend to examine data from several other SeaSondes along the New Jersey coast, producing 2D animations of this and another event seen four hours later. It is indeed rare that any tsunami is ever observed on the U.S. East coast where the water is shallow. This has offered a treasuretrove of invaluable data from which to discern the nature and evolution of these events. Already, special AGU and Ocean Sciences sessions are being planned to present our collective findings.
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13 MHz SeaSonde at Brant Beach, New Jersey |
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Tech Tip: Batch Reprocessing Tool |
 Reprocess your SeaSonde Remote Unit data sets in convenient batches using the new application tool called SpectraOfflineProcessing. Part of SeaSonde Release 7 Radial Suite® Update 1 and above (both online and offline versions), this tool allows you to easily run multiple instances of processing at same time. Ideal if you want to reprocess using different settings and compare results, while having no adverse affect upon any real-time processing taking place on that same computer.
The tool can process spectra to other radial outputs like short-time radials, radial metrics, radial filler, and images. SpectraOfflineProcessing has a friendly GUI, and comes with its own help document which also can be opened from GUI adding further to the convenience. It also gives you ability to automatically load and watch processing progress in the log file. |
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SeaSonde Current Vector Uncertainties
The Start of QA/QC
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With hundreds of SeaSondes in worldwide operation, most within large networks, increasing emphasis is being paid to data Quality Assurance & Quality Control (QA/QC). How good are the real-time vectors? Can one define metrics that will flag time-changing error or uncertainty variations to use in diagnoses or applications? Small-scale current-pattern variations in time and space are generally not considered useful (in fact, noise), in terms of current patterns deemed meaningful for most applications. Flagging these for user consideration has always been our goal at CODAR. This is one part of a comprehensive QA/QC process. To this end, we have provided "uncertainties" outputted to radial and total vector files for well over a decade. Yet many attempt, with good intentions, to contrive QA/QC methods, with a goal of identifying and removing "bad vectors" rather than using these CODAR-provided uncertainties. Without solid physical or statistical foundation, this could do more harm than good. With this in mind, we ask the question: why not start with the uncertainties provided by CODAR? Here we explain what they are what they tell us, and what are their limitations.
SeaSonde Output Uncertainties Defined
There are no models nor assumptions involved in calculating our two uncertainties. They are based on fluctuations in the data themselves: i.e., standard deviations. This is why having a lot of data over time (rather than five minutes out of the hour) provides more substantial and meaningful estimates of these uncertainties.
Spatial Uncertainties in Radials.
Radial velocities are calculated from the data on a polar grid. For example, every ten minutes, there are generally many estimates of radial velocity for every range cell and bearing grid cell (typically 5°). These
are averaged to produce a mean radial velocity. In the same calculation, the standard deviation about this mean is calculated and outputted, for that grid cell. Why do we call this a "spatial uncertainty"? Because if there were no random fluctuations in the data, these radial vector estimates would all be the same, i.e., their standard deviation would be zero. The fact that they are not tells us that they actually came from different bearings, where they probably did not belong. Hence the term "spatial uncertainty". These are written to a temporary file (in our example case, every 10 minutes), and then averaged over the (typical) hour "merging period", after which a "Radial (or Elliptical) File" is created and stored/transmitted to a central site.
Temporal Uncertainties in Radials.
This is the very first uncertainty that we created and outputted. It is the standard deviation of the short-term radials (e.g., every 10 minutes) for each polar (or elliptical) grid cell. Over an hour, then, there could potentially be 7 that go into estimating this standard deviation. Because the statistic is calculated over an hour's time, it is called a "temporal uncertainty".
Uncertainties in Total Vectors.
With two or more sets of radials (and/or ellipticals) combined to get the u/v components of a total vector, the radial uncertainties are "propagated" mathematically to get u/v uncertainties also (as well as a covariance between u/v). The temporal uncertainties (not spatial) are used for this calculation, and outputted as columns in the total vector files. The mathematics of this propagation calculation is found in papers by Lipa in our Web library.
Are They Meaningful?
This is the first question a user should ask, because the fact that something is calculated and outputted does not guarantee it is meaningful or useful. The answer is yes, it is. A comprehensive study was done by an independent investigator, Kip Laws, in 2006 on the temporal uncertainty. Using many years of continuous data from Monterey Bay, he compared and presented at AGU the temporal uncertainties with baseline differences seen between two radars looking at the same spot from opposite sides of the bay. (With no fluctuations or errors, baseline differences should be zero, so this is a good metric for establishing a correlation with outputted uncertainties.) He found a coefficient of determination to be 92%, meaning a correlation coefficient of 96%. Yes indeed, it is meaningful.
Sample CODAR Mapped Uncertainties: In addition to ASCII outputs, CODAR provides a simple, quick way to
display and average uncertainties through its 'SeaDisplay' tool.
• Example Radial Uncertainties Overlain on Vectors. The figure below shows spatial (left) and temporal (right)
uncertainties for a 13 MHz SeaSonde (from August 9, 2013) that has been operating at Bodega Marine Laboratories for 13
years. This is from an hourly merged Radial file. Many options in SeaDisplay for coloring and displaying uncertainties are
available for viewer interpretation, both with and without vectors. Points to note here:
• The colorbar scale shows uncertainties for the most part fall below 5-10 cm/s.
• There are gaps in both the vectors and the uncertainties. Gaps are due to the fact that vectors were not found at that grid point. Calculation of uncertainties require (by definition) at least two points; if not a gap is shown.
• Patterns for the spatial (left) and temporal (right) uncertainties are generally similar.
• High uncertainties do not one-for-one always appear to be associated with wild vectors, and vice versa.
SeaSonde at the same time.
To get these, radials from the Bodega Marine Lab site in the middle (shown earlier) were combined with simultaneous radials from the Southern site near Point Reyes (not shown here). Plotted at the left are the u (Eastward) uncertainties, with v (Northward) uncertainties at the right. Points to note:
• The colorbar scale has been changed, with a max now at 20 cm/s.
• Eastward (u) uncertainties (typically 2-3 cm/s) are lower than those Northward (v). The latter are greatest at the outermost coverage to the Northwest.
• Both vector and uncertainty maps for 2D vectors are completely filled in with no gaps, unlike radials. This is due to the broader averaging circle used for combination into totals.
What Is the Role of These Uncertainties in QA/QC?
Now we can make several points that put uncertainties from the data into perspective.
• Can we weed out bad points using radial uncertainties? Laws (cited earlier) has shown that -- taken in aggregate
over long time periods -- uncertainties are a solid indicator of quality (or errors), with a correlation of 96%. Our plots
above show that a high uncertainty does not always mean a bad vector, and vice versa. Thus it is dangerous to attempt to
use a single radial uncertainty as an indicator that the vector at that grid point is good or bad. Why is this true? A couple
examples:
• Suppose a ship target got through. It would give a "wild" vector, because it would have been processed as though it were a current. But as a solid target, its variance (thence standard deviation) would yield a very low uncertainty.
• Suppose two or three points went into creating a standard deviation, but one was a very large outlier. It w ldgive a high standard deviation or uncertainty. But a merged radial vector created from the median of the three would be very reasonable.
• Suppose second-order sea echo got mixed up with first-order (because of strong currents and/or high waves). This would give a very large "wild" vector. It would be great if it could be flagged and removed. But its uncertainty from its standard deviation could be quite low.
• How about general quality vs. map position? This is an area where the uncertainty maps can provide valuable insight. For example, the if the pattern of uncertainties found in the Bodega data above persists over time, it indicates that North/South(alongshore) accuracy is poorer than East/West (onshore), especially to the Northwest.
How Are They Useful in Circulation Models?
Numerical circulation models assimilate either radial or total vectors into them, in order to produce better forecasts; this
improvement has been shown consistently by many investigators. These models need data covariances also. In the past,
these have usually been ignored, or set to a constant because of lack of measured data. Our uncertainties (in both radials
and totals) are actually the covariances needed by the models. Their value at any single space/time point is not always
meaningful (as we have shown), but the models do not need this. They need an average over space and time, which is
exactly the strength of the outputted SeaSonde uncertainties, as Laws and others have shown.
Role of SeaSonde Uncertainties in QA/QC: We have highlighted our uncertainties as a valuable resource that has been
rarely used, in understanding QA/QC performance. They are easy to display and interpret with the SeaDisplay tool
available to all clients. However, they are only one of several procedures and tools needed to ensure total QA/QC, because a bad uncertainty (or high possible error) does not tell the whole story. What else is needed?
• Antenna Pattern Measurement. Lack of proper antenna pattern measurement can bias output radial data. (Included
with these patterns are amplitude and phase settings among the antenna element cables and receiver channels.) Such
biases occur because the algorithms will misplace radial velocity vectors at the wrong bearings. The uncertainties
described herein do not and cannot reflect the use of an improper antenna pattern and could be low, although vectors
could be consistently wrong in certain regions.
• Hardware and System Diagnostic Outputs. If any hardware or system component fails or malfunctions, this will
not necessarily show up as high uncertainties. However, CODAR has provided 10-minute updated diagnostic files on over
120 critical system parameters (e.g., temperatures, voltages, forward/reflected power at the transmit antenna, etc.). In
addition, CODAR supplies a convenient tool -- 'DiagDisplay' --for plotting histories of any user-selectable combination of
these diagnostics. These diagnostics should be routinely reviewed, as they offer QA/QC assistance that the uncertainties
alone cannot provide.
• Proper Attention to First/Second-Order Setup. Site-specific settings are usually required in order to separate firstorder
echoes (used for currents) from second-order (used for waves). If this has been ignored or left at default settings,
second-order echoes can mistakenly be processed, and will give totally erroneous radial vectors that are too large. As
mentioned earlier, such "wild" vectors will not necessarily be associated with large uncertainties.
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Register for the
HF Radar Operation &
Application Workshop
at
34th Asian Conference
on Remote Sensing 2013
An exciting 1-day workshop tutorial on High Frequency (HF) Radar Operation and Application in the context of operational oceanography will take place Tuesday 22 October 2013 as part of the 34th Asian Conference on Remote Sensing 2013 held in Bali, Indonesia.
Speakers include scientists from the Rutgers University Coastal Ocean Observation Laboratory based in New Brunswick, New Jersey and CODAR Ocean Sensors Ltd., based in Mountain View, California.
The tutorial will provide an introduction to the principles and current state of the art technology for HF radar applications.
Presenters will touch upon the following topics:
• What is an HF Radar? Principles of operation, data products & state-of-the-art.
• Operating an HF Radar Network: What does a network look like and what does it take to manage? How does one process, analyze and visualize the surface current data? How are the products quality controlled?
• Applications & Case Studies: How are HF data products currently used in operational oceanography? Case Studies will be shown for recent events including search and rescue operations, the Deepwater Horizon oil spill response and Hurricanes Irene and Sandy.
| Workshop Tutorial Outline: |
First 2-hour unit
(8:40-10:40):
• Principles of operation & data products
• State-of-the-Art in HF radar technology
• Data visualization & QA/QC
• Introduction to US National and
Global HF Radar Networks |
Second 2-hour unit
(11:10-13:10):
• Search and rescue applications
• Pollution floatables tracking
• Deepwater Horizon, model validation |
Third 2-hour unit
(15:10-17:10):
• Storm forecasting
• Fisheries Management
• Tsunami Detection |
This event is part of the 34th ACRS 2013 conference. Information on ACRS
event and registration form is available through the official conference web site
at http://www.acrs2013.com/.
More workshop details are also available by
contacting CODAR Ocean Sensors Ltd. via [email protected] or CODAR’s local
Indonesian partner company PT. Trisari Tigaputra Utama via [email protected]
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