Testing Automated CME Detection Algorithms for Space Weather Forecasting

Transcription

1 Testing Automated CME Detection Algorithms for Space Weather Forecasting Melissa Payer Plymouth State University Space Environment Center Mentor: Curt A. de Koning August 2,

2 Outline Introduction to Space Weather Background of Space Weather Forecasting Project Objectives Method Results of Study Next Steps Summary Acknowledgements/References 2

3 Space Environment Center (SEC) Part of the National Weather Service One of the nine National Centers for Environmental Prediction - Other examples: Storm Prediction Center and Tropical Prediction Center (includes the National Hurricane Center) The nation s first defense against the effects of space weather and the official source of space weather alerts and warnings 3

4 Coronal Mass Ejections Coronal Mass Ejections (CMEs) are bubbles of gas and magnetic fields released from the solar atmosphere When the solar material hits the Earth s magnetosphere it can result in a geomagnetic storm Large Angle and Spectrometric Coronagraph (LASCO) on board the Solar and Heliospheric Observatory (SOHO) mission is used to observe CMEs Classification: Limb CME- occurs on the edge of the solar disk Partial halo CME- angular width of at least 120 and crosses over one pole Full halo CME- angular width of 360 and traveling directly towards or away from Earth Partial Full Limb Halo CME 4

5 The Importance of CME Detection CME s (Coronal Mass Ejections) cause the biggest space weather storms CME detection is critical in space weather forecasting. Forecasters must know: Did a CME occur? Will the CME hit the Earth, thus causing a geomagnetic storm? When will the storm begin? 1-4 days warning Storm strength and duration? WHO CARES? Radiation Storm Products Radiation Storm Warnings Forecasts NASA Briefings Satellite Orbit Ops Satellite Launch Ops Airlines & FAA NASA/ESA DoD Communications DOE Nuclear Reg Comm Schlumberger NY/PJM Grid Ball NESDIS/SOCC Digital Globe Loral Boeing Lockheed Aerospace Echostar Space Shuttle ISS astronauts Science Missions Deep Space American United Airlines Continental Northwest Geomagnetic Storm Products Geomagnetic Storm Watches Warnings and Alerts Forecasts NASA Briefings Satellite Orbit Ops Electric Power Grid Airlines & FAA Navigation Surveying/Drilling Communications Space weather models also rely on CME imagery 5

6 Effects of CME related storms Aircraft communications blacked out polar routes re-routed - Hundreds of flight routes altered since 2001 GPS degraded: Oil and gas industry, surveying, drilling, construction, farming - GPS is critical for the oil and natural gas exploration and production - Impact costs in the $50,000 to $1,000,000 range Satellite Operations spacecraft and instrument damage, launches delayed - $4 billion in satellite losses can be traced to space weather damage - With good warning lead time, space operators can protect spacecraft and instruments Electric power grid: reduction in ability to transport electricity, blackouts - March 1989 geomagnetic storm - Hydro Quebec blackout for 9 hours (6 mil people) - North American Electric Reliability Corp. distributes geomagnetic storm messages to power systems throughout the USA - A blackout from a geomagnetic storm could cost ~$10 bil Many National Security Systems impacted 6

7 Project Motivation Need for automated real-time CME detection in SEC forecast center eliminates subjective detection possibly issue warnings sooner Software with the ability to report CME characteristics would replace manual measurements and allow forecasters to assess potential for geomagnetic storms faster 7

8 Project Plan Evaluate various automated CME detection packages for operational use in the SEC forecast center Determine the ability of the software to detect CMEs, especially geoeffective CMEs, while minimizing false alarms Determine the ability of the software to describe the morphology, speed, and direction of propagation of the CME Make a recommendation of the best software package for forecast center s needs Hypothesis: We suspect that no single algorithm will be the best at everything. As a result, it may be necessary to use one algorithm for CME detection and another for characterization of the CME. 8

9 Method Research three CME detection algorithms: CACTus SEEDS ARTEMIS For each algorithm determine: - How does algorithm work? - Determine any thresholds used by algorithm - Compare validation studies Perform statistical analyses on 2002 dataset by comparing to online CDAW catalog created by manual detection, which we considered to be ground truth - Correct detections, missed events, false detections - Detection of full halos and partial halos - Evaluate characterization of CMEs 9

10 Polar Transformation r θ 0 r All three algorithms involve the technique of polar transformation Note: position angle is the central angle of the CME measured counterclockwise from solar north (0 ) θ 10

11 CACTus (Computer Aided CME Tracking) r r t S W N E θ t Do a polar transformation for each image in time and take a time-height (t,r) slice for every angle Lay time-height slices side by side. If a slice cuts through a CME, a bright ridge is seen. Detection of a bright ridge is done by a process called the Hough transform (below) 11

12 SEEDS (Solar Eruptive Event Detection System) 2002/09/12 Detect CME by summing total brightness in a column and plotting as a function of the angle Determine width and outline of CME by region growing and thresholding segmentation Output animation of CME evolution 12

13 position angle ARTEMIS (Automatic Recognition of Transient Events and Marseille Inventory from Synoptic Maps) time Take a slice at 3 solar radii for each image in time Create synoptic map by placing the slices side by side. CMEs appear as vertical, narrow streaks on the streamer belt 13

14 Results ARTEMIS validation study only done for 5 days during the period November 9-14, events listed in CDAW catalog (considered ground truth) Correct Detections Missed Events False Detections ARTEMIS 100% 0% 219% While detection looks promising, this algorithm still needs to be tested on a larger dataset No further investigation into ARTEMIS at this time 14

15 Results 2002 dataset: CDAW Catalog CACTus SEEDS events (considered ground truth) detections detections Correct Detections Missed Events False Detections CACTus 73% 27% 192% SEEDS 73% 27% 143% Conclude: No single algorithm in its present state is ready for operational use 15

16 Frequency Detection CACTus SEEDS % of false detections had aw < % of false detections had aw < Angular width (deg) Looked at various CME angular width threshold values Choose aw >= 25 for detection threshold greatly reduces false detections without affecting correct detections too much Narrow CMEs are not geoeffective 16

17 CME Characteristics Output of detection into catalog along with characteristics Start Time Position Angle Angular Width Velocity Accel. CACTus SEEDS ARTEMIS 17

18 CDAW position angle (deg) Characterization Central Position Angle CACTus SEEDS Line of perfect detection Detected position angle (deg) Both CACTus and SEEDS report central position angle with fairly good accuracy 18

19 CDAW Angular width (deg) Characterization Angular Width 1000 CACTus 1000 SEEDS Detected Angular width (deg) SEEDS greatly underestimates angular extent of CMEs while CACTus does a better job of estimating angular width of CMEs Note: SEEDS underestimate may be helpful (actual greater than or equal to value) Both underestimate angular width of full halo CMEs

20 CDAW speed (km/s) Characterization CME Speed Median CACTus SEEDS Maximum Detected speed (km/s) CACTus calculates speed for every 1 within CME and reports median, max and min speeds. SEEDS reports speed averaged over the position angles SEEDS linear speed underestimate CME speed CACTus maximum speed overestimates CACTus median speed does a better job but has a lot of scatter 20

21 Results If we run CACTus and SEEDS simultaneously: Use CME aw >= 25 for threshold and consider only detections within 120 minutes of each other as an event Both detected 60% of all CMEs Missed 40% of all CMEs False detection rate reduced to 4% Important to SEC forecasters: Full halos Both detected 81% and missed 19% Partial halos Both detected 79% and missed 21% Note: They detect these CMEs but do not properly classify them as partial and full halos due to underestimates of CME angular width Propose running CACTus and SEEDS algorithms together 21

22 Proposed System of Detection 3 images total (~30 min) 8 images total (~1 hr,45 min) SEEDS detect Yes CACTus detect No CACTus detect Yes Store SEEDS Characteristics Report Characteristics Send CME Alert Run SEEDS and CACTus 3 images total (~30 min) SEEDS detect No 8 images total (~1 hr, 45 min) CACTus detect Yes CACTus detect No Store CACTus Characteristics 22

23 Next Steps More research into ARTEMIS algorithm Longer validation study How well does it characterize CMEs? Get codes and try running algorithms on expanded dataset Implement algorithms into test bed to evaluate real-time performance Focus on improving software s ability to describe CME characteristics 23

24 Summary Researched three automated CME detection algorithms Compared validation of each and determined that no single algorithm in its present state is ready for operational use Algorithm characterization of CMEs needs to be improved greatly Proposed running CACTus and SEEDS algorithms together to detect CMEs 24

25 Acknowledgements Curt A. de Koning (Space Environment Center) Doug Biesecker (Space Environment Center) Bill Murtagh (Space Environment Center) CACTus Team Eva Robbrecht, David Berghmans, Gareth Lawrence (Royal Observatory of Belgium) SEEDS Team Jie Zhang, Oscar Olmedo (George Mason University) ARTEMIS Team Yannick Boursier, Phillippe Lamy, Antoine Llebaria (Laboratoire D Astrophysique De Marseille) 25

26 References Boursier, Y., et al. (2005). Online ARTEMIS Catalog of LASCO CME. Retrieved June 2007 from Boursier, Y., et al. (2007). The Marseille-Artemis catalog of LASCO CMEs. SOHO 17. CDAW Data Center (2007). SOHO LASCO CME Catalog. Retrieved June 2007 from Oscar, O., et al. (2006). Development of an automatic Solar Eruptive Event Detection System (SEEDS). American Astronomical Society, SPD Meeting #37. Oscar, O., et al. (2006). SEEDS Monthly Catalog. Retrieved June 2007 from Robbrecht, E. (2007). CACTus homepage. Retrieved June 2007 from Robbrecht, E. & Berghmans, D. (2004). Automated recognition of coronal mass ejections (CMEs) in near-real-time data. Astronomy & Astrophysics, 425,

27 Questions? 27