The IPS Extreme Space Weather
ServiceR. Marshall, M. Terkildsen, G. Steward, D. Neudegg, Z. Bouya
Ionospheric Prediction Service (IPS)
Australian Bureau of Meteorology

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ESW Motivation• Growing awareness of impacts of space weather has lead to increased
interest from critical infrastructure groups within Australia• Australian Government TISN, includes Energy, Communications, andTransport Sector groups• Energy sector includes power network asset owners and operators, gas
pipeline owners and operators, mining companies• Communications sector includes, Telstra, Optus, major TV
broadcasters• Transport sector includes major airlines (Qantas, Virgin), ASA, shippingand rail

Satellite Systems

CRESS Electon and Proton Fluxes(Brautigan, 2002)

Chilean Ash Cloud, June 2011

Aviation

Getley et al., Space Weather,
2004SW000110

International guidelines:• 100 mSv/yr over 5 yrs• < 50 mSv max any year
• < 1 mSv pregnant women• Australian airline
aircraft altered course
during significant SPE
event earlier this year

LAX-JFK QF107 29th Oct 2003 'A' Total Equivalent Dose 3.6 + 8.4 = 12.0 Micro Sieverts

Oct 29, 2003 Lax - JFK
Kp = 8, 954.5

4GLE

3µSv
per Hr
2.5Total Dose 12 µSv

21.5

Alt 37,000’

1Alt to 39,000’

0.5Time UTC

22:0
6

21:5
3

21:4
1

21:2
8

21

21:1
5

21:0
2

20
:4
920
.3
6

20:2
420

20:1
1

19:5
8

19:4
519

19:2
119:0
7

18:5
4

18:4
1

18:2
818

18:1
618:0
3

17:5
1

0

19:3
2

Dose MicroSieverts/hr

3.522 UT

M. Duldig, adapted from Getley
et al., Space Weather,
2004SW000110

Low-End GPS Systems(from http://www.nap.edu/catalog/12507.html)

High-End GPS Systems(Terkildsen, 2010, IPS-CR-09-01-P, April 2010)[from Pullen et al, 5th International GBAS Working
Group Meeting (GWG/5), Nov 2006][from Ene et al (2005), Proceedings of ION 2005 National Technical Meeting]Power Networks
Threat Level Model from Global network observations

Marshall, Smith, Francis, Waters and Sciffer, Space Weather, 2011Australian/NZ Power Networks
GICy-index Occurrences > 50300

Marshall et al., Space Weather,
2011SW000685

250200180

GICy Index

140200120100150

80601004020501985

1990
HBTCNB1995GNATime

ASP

2000

LRM

02010

2005
CTAKDU

Sunspot

Marshall et al., Space Weather,
2012SW000806© Copyright, Transpower,
New ZealandSunspot Number160

Extreme Space Weather (ESW) Model• Most space weather impacts in Australian region
associated with extreme events• Generalised Linear Model (GLM) techniques• Event-based analysis• Requirements:
LATENCY– ‘Long range’ warning (> 12 hours)
Based on solar data only
– ‘Short range’ alert (~ 1 hour)
Based on solar data + ACE
ACCURACY– Long range: Optimise to minimise missed events– Short range: Optimise for forecast accuracy
SIMPLICITY– Design for active use in space weather forecast environment

ESW Model Events
Event- based analysis≥M1X-rayflarespecifies
IMF
Bz stormsouth
AssociatedCME
Majorevent
Solarwindshock
solar disturbance event

Flares / CMEs

solar wind(shocks)
IMF(Bz events)
Dst(storm events)
day of year

ESW Model Parameters
Model covariates (the ‘input data’)
X-RAY FLARE– Solar flare magnitude

SOLAR CYCLE– Solar Cycle Phase– Solar flare duration
LOCATION OF SOLAR ACTIVE REGION– Latitude of solar active region– Longitude of solar active region
CME CHARACTERISTICS– Presence of Halo CME (CME width)– CME speed

SOLAR WIND / IMF– IMF Bz
– Solar wind shock

ESW Model GLM
Generalised Linear Model
Response variable (what is being modelled)0dstN =  Dst + 49

Dst ≥ −50else

Model (a GLM)
ln ( µ i ) = α 0 + α1 xi1 + α 2 xi 2 + L + α m xim

Training data (for fitting model coefficients)
15 years data (1996 – 2010)

Prediction (a binary output)
0, pi ( y | µ ) < pthreshyi = 1, pi ( y | µ ) ≥ pthresh

xi1 K xim  Input variables
a0 K am  Coefficients (to fit)pi ( y | µ )  Response distributionpthresh Threshold on event
probability used to produce
binary prediction (ESWevent/no-event). Optimised
for required modelperformance.

ESW Model Validation
Model validation: Solar data only
• Optimising for no missed events (false negatives = 0)
false positives (25)true positives (6)Leave-One-Out Cross Validation (LOOCV)

Modelled event probability

true positives
false positives
false negatives
total events

false negatives (0)6250644

Probability threshold(to determine binary outcome)Observed dstN

ESW event threshold (dst < -250)

ESW Model Implementation• Operational GUI in ASFC
• Uses Dst-based ESW models
as a back-end, proving bothbinary and probabilistic
forecasts for ESW• Accepts a range of covariates
for added flexibility• Simple text warning message

Future Developments• ACE solar wind parameters• CME symmetry parameter / CME “mass”• Active region magnetic characteristics (proxy forIMF Bz events?)• CME travel time (flare-shock interval) to replace
CME plane-of-sky speed (a poor proxy for true CMEspeed)• Type II / Type IV radio bursts• Direct modelling of ESW parameters as response– GIC index– Ionospheric gradient index

Thank you.

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