“Testing Automated CME

Detection Algorithms for

Space Weather Forecasting”

Melissa Payer

Plymouth State University

Space Environment Center

Mentor: Curt A. de Koning

August 2, 2007 1

2

Outline

• Introduction to Space Weather

• Background of Space Weather Forecasting

• Project Objectives

• Method

• Results of Study

• Next Steps

• Summary

• Acknowledgements/References

3

• 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

Space Environment Center

(SEC)

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 toobserve 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

Limb CMEPartial Halo CMEFull Halo CME

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• 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?

Space weather models also rely on CME imagery

Geomagnetic StormProducts

Satellite Orbit OpsSatellite Launch Ops

Airlines & FAANASA/ESA

DoDCommunications

DOENuclear Reg Comm

SchlumbergerNY/PJM Grid

BallNESDIS/SOCCDigital Globe

Loral

BoeingLockheedAerospaceEchostar

Space ShuttleISS astronauts

Science MissionsDeep Space

AmericanUnited Airlines

ContinentalNorthwest

Radiation Storm WarningsForecasts

NASA Briefings

Satellite Orbit OpsElectric Power Grid

Airlines & FAANavigation

Surveying/DrillingCommunications

Geomagnetic Storm Watches

Warnings and AlertsForecasts

NASA Briefings

Radiation StormProducts

The Importance of CME Detection

WHO CARES?

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• 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. distributesgeomagnetic storm messages to power systems throughoutthe USA

- A blackout from a geomagnetic storm could cost ~$10 bil

• Many National Security Systems impacted

Effects of CME related storms

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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

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.

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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

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• 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

r

Polar Transformation

θ0°

90° 270°

180°

r

270°180°90°0°

11

r

t

θ

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)

r

t

CACTus(Computer Aided CME Tracking)

ENWS

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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

SEEDS(Solar Eruptive Event Detection System)

2002/09/12

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time

po

siti

on

an

gle

Create synoptic map by placing the slices

side by side. CMEs appear as vertical,

narrow streaks on the streamer belt

ARTEMIS(Automatic Recognition of Transient Events and

Marseille Inventory from Synoptic Maps)

Take a slice at 3 solar radii

for each image in time

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Results

• ARTEMIS validation study only done for 5 days during the period

November 9 -14, 2003

• 16 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

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Results

• 2002 dataset:

CDAW Catalog --- 1687 events (considered ground truth)

CACTus --- 3591 detections

SEEDS --- 2992 detections

Conclude: No single algorithm in its present state is

ready for operational use

Correct

Detections

Missed

Events

False

Detections

CACTus 73% 27% 192%

SEEDS 73% 27% 143%

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0

200

400

600

800

1000

1200

0 20 40 60 80 100 120 140 160 180

0

200

400

600

800

1000

1200

0 20 40 60 80 100 120 140 160 180

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

Fre

qu

en

cy

Detection

Angular width (deg)

CACTus SEEDS

70% of false detections

had aw < 25°

91% of false detections

had aw < 25°

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CME Characteristics

Start

Time

Position

Angle

Angular

Width

Velocity Accel.

CACTus

SEEDS

ARTEMIS

Output of detection into catalog along with characteristics

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-30

0

30

60

90

120

150

180

210

240

270

300

330

360

-30 0 30 60 90 120 150 180 210 240 270 300 330 360

-30

0

30

60

90

120

150

180

210

240

270

300

330

360

-30 0 30 60 90 120 150 180 210 240 270 300 330 360

CharacterizationCentral Position Angle

Both CACTus and SEEDS report central position angle with fairly good

accuracy

Detected position angle (deg)

CD

AW

po

sit

ion

an

gle

(d

eg

)

CACTus SEEDS

Line of perfect

detection

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CharacterizationAngular Width

1

10

100

1000

1 10 100 1000

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

Detected Angular width (deg)

CD

AW

An

gu

lar

wid

th (

deg

)

CACTus SEEDS

1

10

100

1000

1 10 100 1000

20

CharacterizationCME Speed

0

500

1000

1500

2000

2500

0 500 1000 1500 2000 2500

0

500

1000

1500

2000

2500

0 500 1000 1500 2000 2500

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

Detected speed (km/s)

CD

AW

speed (

km

/s)

CACTus SEEDS

0

500

1000

1500

2000

2500

0 500 1000 1500 2000 2500

Median

Maximum

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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

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Proposed System of Detection

Run SEEDS

and CACTus

SEEDS

detect Yes

SEEDS

detect No

CACTus detect

No

CACTus detect

Yes

CACTus detect

No

CACTus detect

Yes

Report

Characteristics

Send CME Alert

3 images total

(~30 min)

3 images total

(~30 min)

8 images total

(~1 hr, 45 min)

8 images total

(~1 hr ,45 min)

Store SEEDS

Characteristics

Store CACTus

Characteristics

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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

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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

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• 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)

Acknowledgements

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References

Boursier, Y., et al. (2005). Online ARTEMIS Catalog of LASCO CME.

Retrieved June 2007 from http://lascor.oamp.fr/lasco/

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 http://cdaw.gsfc.nasa.gov/CME_list/

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

http://solar.scs.gmu.edu/research/autocme/

Robbrecht, E. (2007). CACTus homepage. Retrieved June 2007 from

http://sidc.oma.be/cactus/

Robbrecht, E. & Berghmans, D. (2004). Automated recognition of coronal

mass ejections (CMEs) in near-real-time data. Astronomy &

Astrophysics, 425, 1097-1106.

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Questions?