Transcript Dynamics of Large-Scale Plasma Convection on Inner

Overview of Changes and Developments in the SuperDARN Upper
Atmosphere Facility
Raymond A. Greenwald, J. Michael Ruohoniemi, Joseph B. H. Baker
Bradley Department of Electrical and Computer Engineering
Virginia Tech
Elsayed Talaat and Robin Barnes
Johns Hopkins University Applied Physics Laboratory
Presented at the 2008 NSF Upper Atmosphere Facilities Workshop
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Organizational Changes
 Virginia Tech is now the Principal Investigator Institution of
the U.S. SuperDARN Upper Atmosphere Facility.
 Transition brought about by:
 Retirement of Ray Greenwald from JHU/APL.
 Academic appointments of Mike Ruohoniemi and Joseph Baker at
Virginia Tech.
 JHU/APL remains a collaborating partner within the
SuperDARN UAF.
 Effort carried out by Elsayed Talaat and Robin Barnes.
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Motivations for Change
 Virginia Tech offers significantly greater opportunities for
student training and development.
 Virginia Tech has provided considerable institutional support
for the development of the SuperDARN research effort.
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New Organizational Staffing
 Virginia Tech
 J. Michael Ruohoniemi:
 Joseph B. H. Baker:
 Raymond A. Greenwald:
Associate Professor in Department of
Electrical and Computer Engineering (ECE)
Assistant Professor in ECE
Part-time Research Professor in ECE
 JHU/APL
 Elsayed Talaat
 Robin Barnes
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JHU/APL Science Lead
Software Development
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Organizational Responsibilities
 Virginia Tech
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Radar operations and maintenance
Scientific research
Community support
Education and outreach
 JHU/APL
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Scientific research
Software development
Community support
Outreach
Data distribution
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Development of SuperDARN
Northern Hemisphere
Viewgraph from 2005 UAF Meeting
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Situation Today
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SuperDARN – Northern Hemisphere
Future Development
The right-hand map includes all of the radars shown at the left plus
eight radars extending from the Azores to the Aleutians that constitute
an NSF MSI proposal and a single radar in violet located in the U.K.
Also, shown are additional radars identified by faint dashed lines that
have been proposed by other countries to various funding agencies.
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Technology Innovation
Greenwald Twin-Terminated Folded Dipole Antenna
 The TTFD antenna has proven to be a major improvement in SuperDARN
antenna usage.
 Reduced cost
 Improved azimuthal coverage
 Improved front-to-back ratio
 More rugged due to fewer electrical connections and lower wind loading
 Used at Wallops Island, Blackstone, Rankin Inlet, Inuvik, and Antarctica
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TTFT Antenna Performance
VSWR Values: Blackstone Main Array
4
VSWR
3
2
1
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Frequency (MHz)
Ant 1
Ant 7
Ant 13
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Ant 2
Ant 8
Ant 14
Ant 3
Ant 9
Ant 15
Ant 4
Ant 10
Ant 16
Ant 5
Ant 11
Model
Ant 6
Ant 12
Wallops
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Technology Innovation
Forward and Reverse Optimal Golomb Sequences
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In 1972, Farley was the first to apply the concept of Golomb
rulers to radar measurements in the Earth’s ionosphere.
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Within the radar community, this technique is commonly
referred to as multipulse sequences.
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Multipulse sequences provide a means of resolving the range-time
ambiguities that are common to radar Doppler measurements when
there are spread targets with significant Doppler velocities.
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However, multipulse techniques are notorious for adding noise due to
other transmitter pulses and their returns to the analysis process.
6-pulse optimal ruler
Possible distances = 5+4+3+2+1 = 15
Length = 17
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Missing: 10,15
1
7
4
2
3
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Technology Innovation
Forward and Reverse Optimal Golomb Sequences
 The pattern above is a 13-pulse sequence consisting of a single
pulse followed by forward and reverse 6-pulse optimal
Golomb sequences.
 This pattern is resistant to bad lags due to transmitter pulses
and strong cross range noise.
 In most instances there is at least one good option for each lag.
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Technology Innovation
Forward and Reverse Optimal Golomb Sequences
Farley, 1972
Sample types occurring during a 6-pulse Golomb sequence
preceded by a single pulse.
Value=0: Data Sample
Value=1: Tx Pulse Value=2: Data Sample>10dB
3
Sample Type
Range Gates 10-14 have >10 db signal
2
1
0
0
50
100
150
200
250
300
350
Sample No.
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Technology Innovation
Forward and Reverse Optimal Golomb Sequences
Bad Lags due to Transmitter Pulses and Cross-Range Noise on
1972Sequence.
First 100 Ranges GatesFarley
Using Farley
7-Pulse Sequence: 15,1,7,4,2,3
Cross-range noise: Range Gates 10-14
Bad Lags
15
10
5
0
0
20
40
60
80
100
Range Gate
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Technology Innovation
Forward and Reverse Optimal Golomb Sequences
Bi-Directional - 13-Pulse Sequence
Value=0: Data Sample Value=1: Tx Pulse
Sample Type
3
What happens if we have a choice between two potential solutions for
each tau?
2
Farley Sequence
Farley Sequence Reversed
1
0
0
100
200
300
400
500
600
Sample No.
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Technology Innovation
Forward and Reverse Optimal Golomb Sequences
Bad lags due to transmitter pulses for 13-pulse forward and reverse sequence.
Bad Lags
15
10
5
0
0
20
40
60
80
100
Range Gate
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Technology Innovation
Forward and Reverse Optimal Golomb Sequences
Tauscan
(>10 dB Signals at range gates 10-14)
Bad Lags
15
10
5
0
0
20
40
60
80
100
Range Gate
Tauscan
(>10 dB Signals at range gates 15-19)
Bad Lags
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Bad lags due to Tx pulse and cross-range noise is highly
variable and depends on interplay between two independent
processes.
10
5
0
0
20
40
60
80
100
Range Gate
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Improved Phase Vs. Lag Measurements Allow Doppler
Velocities to be Determined from Individual Pulse Sequences
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Doppler Velocity Vs. Time
200 ms Temporal Resolution
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14-sec Doppler Velocity Pulsation Observed With
Wallops Island Radar (Greenwald et al., 2008)
Note Similar period
on Ottawa
magnetometer
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Science: Extended Observations of Sub-Auroral
Plasma Streams (Oksavik et al., 2006)
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Science: Identification of Temperature Gradient
Instability Onset (Greenwald et al., 2006)
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23
00
01
02
03
04 UT
Sequence of Events
22-00 UT: Poleward motion of ocean scatter
footprint following sunset.
00-0120 UT: Irregularities form in post-sunset
ionosphere. Possibly associated with F-region
gradient-drift instability as reported previously.
0120 UT onwards: Temperature gradient
reverses and steepens. Backscatter intensifies.
Onset of TGI.
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THEMIS-SuperDARN Substorm Studies
 During THEMIS tail conjunctions
SuperDARN radars run a special
THEMIS mode that increase
temporal sensitivity to substorm
dynamics:
 Dwell time reduced from 7 to
4 seconds.
 SD radars returns to a
designated camping-beam
between each successive
scan beam.
THEMIS Mode camping beams (Blue)
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THEMIS-SuperDARN Substorm Studies
February 22, 2008
Beam-8: normal scan data (2-minutes)
Beam-7: camping beam data (8-second)
0430 UT
0440 UT
0450 UT
Substorm expansion phase onset at approximately 0437 UT:
THEMIS spacecraft measure two bursts of Earthward convection in the tail.
Ground-based magnetometers measure the onset of Pi2 oscillations.
Blackstone Radar Measurements:
Pi2 oscillations measured on camping beam at approximately location of
plasmapause (Alfven Waves?).
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Science: Upper Atmosphere Variability at Mid-Latitudes
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Education and Training
Advanced Degree Students @ Virginia Tech
Student
Advanced Degree
Nathaniel Frissell
PhD
Yin Yan
PhD
Kevin Sterne
MS
Frederick Wilder (Bob Clauer)
PhD
Lyndell Hockersmith (Bob Clauer)
MS
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SuperDARN: Issues and Concerns
 The reconstitution of the JHU/APL SuperDARN activity at
Virginia Tech and JHU/APL will still require some time to
bring to completion. At Virginia Tech,
 We have a good group of involved students.
 We hope to add an engineer with SuperDARN experience.
 Goose Bay and Kapuskasing have upgrade/ maintenance
needs:
 Kapuskasing: digital receiver
 Kapuskasing and Goose Bay: new low-loss cables
 Kapuskasing and Goose Bay: potential antenna deterioration
 Serious issues in obtaining maintenance support at Wallops
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SuperDARN: Issues and Concerns
 Air Force infrastructure support for Goose Bay disappearing
 Ionosonde no longer in operation
 No Air Force funds for heat, electricity, or snow plowing
 Death of Dr. Jean-Paul Villain raises concerns about future
support for Stokkseryi radar
 We are working with University of Leicester to identify magnitude of
problem and possible solutions.
 Full SuperDARN network can produce 4+TB of data samples
per year. How do we gather and disseminate data?
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