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Virginia Tech1 Overview of Changes and Developments in the SuperDARN Upper Atmosphere Facility Raymond A. Greenwald, J. Michael Ruohoniemi, Joseph.

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Presentation on theme: "Virginia Tech1 Overview of Changes and Developments in the SuperDARN Upper Atmosphere Facility Raymond A. Greenwald, J. Michael Ruohoniemi, Joseph."— Presentation transcript:

1 Virginia Tech1 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 Virginia Tech

2 2 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.

3 Virginia Tech3 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.

4 Virginia Tech4 New Organizational Staffing  Virginia Tech  J. Michael Ruohoniemi:Associate Professor in Department of Electrical and Computer Engineering (ECE)  Joseph B. H. Baker:Assistant Professor in ECE  Raymond A. Greenwald:Part-time Research Professor in ECE  JHU/APL  Elsayed TalaatJHU/APL Science Lead  Robin BarnesSoftware Development

5 Virginia Tech5 Organizational Responsibilities  Virginia Tech  Radar operations and maintenance  Scientific research  Community support  Education and outreach  JHU/APL  Scientific research  Software development  Community support  Outreach  Data distribution

6 Virginia Tech6 Development of SuperDARN Northern Hemisphere Viewgraph from 2005 UAF Meeting Situation Today

7 Virginia Tech7 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.

8 Virginia Tech8 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

9 Virginia Tech9 TTFT Antenna Performance

10 Virginia Tech10 Technology Innovation Forward and Reverse Optimal Golomb Sequences  In 1972, Farley was the first to apply the concept of Golomb rulers to radar measurements in the Earth’s ionosphere.  Within the radar community, this technique is commonly referred to as multipulse sequences.  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.  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 = = 15  Length = 17Missing: 10,15

11 Virginia Tech11 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.

12 Virginia Tech12 Technology Innovation Forward and Reverse Optimal Golomb Sequences Sample types occurring during a 6-pulse Golomb sequence preceded by a single pulse. Range Gates have >10 db signal

13 Virginia Tech13 Technology Innovation Forward and Reverse Optimal Golomb Sequences 7-Pulse Sequence: 15,1,7,4,2,3 Cross-range noise: Range Gates Bad Lags due to Transmitter Pulses and Cross-Range Noise on First 100 Ranges Gates Using Farley Sequence.

14 Virginia Tech14 Technology Innovation Forward and Reverse Optimal Golomb Sequences Bi-Directional - 13-Pulse Sequence Value=0: Data Sample Value=1: Tx Pulse Sample No. Sample Type Farley SequenceFarley Sequence Reversed What happens if we have a choice between two potential solutions for each tau?

15 Virginia Tech15 Technology Innovation Forward and Reverse Optimal Golomb Sequences Bad lags due to transmitter pulses for 13-pulse forward and reverse sequence.

16 Virginia Tech16 Technology Innovation Forward and Reverse Optimal Golomb Sequences (>10 dB Signals at range gates 10-14) (>10 dB Signals at range gates 15-19) Bad lags due to Tx pulse and cross-range noise is highly variable and depends on interplay between two independent processes.

17 Virginia Tech17 Improved Phase Vs. Lag Measurements Allow Doppler Velocities to be Determined from Individual Pulse Sequences

18 Virginia Tech18 Doppler Velocity Vs. Time 200 ms Temporal Resolution

19 Virginia Tech19 14-sec Doppler Velocity Pulsation Observed With Wallops Island Radar (Greenwald et al., 2008) Note Similar period on Ottawa magnetometer

20 Virginia Tech20 Science: Extended Observations of Sub-Auroral Plasma Streams (Oksavik et al., 2006)

21 Virginia Tech21 Science: Identification of Temperature Gradient Instability Onset (Greenwald et al., 2006) Sequence of Events UT: Poleward motion of ocean scatter footprint following sunset UT: Irregularities form in post-sunset ionosphere. Possibly associated with F-region gradient-drift instability as reported previously UT onwards: Temperature gradient reverses and steepens. Backscatter intensifies. Onset of TGI UT

22 THEMIS-SuperDARN Substorm Studies Virginia Tech22  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)

23 THEMIS-SuperDARN Substorm Studies February 22, 2008 Virginia Tech23 Beam-8: normal scan data (2-minutes) Beam-7: camping beam data (8-second) 0430 UT0450 UT0440 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?).

24 Science: Upper Atmosphere Variability at Mid-Latitudes Virginia Tech24

25 Virginia Tech25 Education and Training Advanced Degree Virginia Tech StudentAdvanced Degree Nathaniel FrissellPhD Yin Yan PhD Kevin SterneMS Frederick Wilder (Bob Clauer)PhD Lyndell Hockersmith (Bob Clauer)MS

26 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 Virginia Tech26

27 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? Virginia Tech27


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