noaa space weather prediction center space weather forecasting overview solar flare forecasting...

NOAA SPACE WEATHER PREDICTION CENTER

Space Weather ForecastingSpace Weather Forecasting

• Overview

• Solar Flare Forecasting

• Geomagnetic Forecasting

• Solar Energetic Particle Forecasting

2008 Asian-Pacific Region International Heliophysical Year SchoolChristopher BalchNOAA Space Weather Prediction Center29 October 2008

General PointsGeneral Points• Physical Models have not developed to

the point of being useful operationally• There are efforts to improve this

situation– Center for Integrated Space weather

Modeling (CISM)– Coordinated Community Modeling Center

(CCMC)– Center for Space Environment Modeling

(CSEM at University of Michigan)


Forecasting TodayForecasting Today• Key inputs

–Conceptual models–Observational data–Empirical models

• Much depends on the human forecaster to analyze and synthesize the information


Forecaster BrainsForecaster BrainsI’m going to need those…

Forecaster

From www.explodingdog.com


The Weather Analysis andThe Weather Analysis and Forecasting Process Forecasting Process

What happened?What happened?

Why did it happen?Why did it happen?

What is happening?What is happening?

Why is it happening?Why is it happening?

What is going to happen?What is going to happen?

Why is it going to happen?Why is it going to happen?

How did/is/will it affect(ing) my customers?How did/is/will it affect(ing) my customers?

Bosart, 2002

Diagnosis

Nowcasting

Prognosis - Forecasting


Types of ForecastersTypes of Forecasters

• Intuitive Scientists

• Rules-Based Scientists

• Procedure-Based Observers

• Procedure-Based Mechanics

• Disengaged

Pliske et. al., 1997


Good Forecasters…Good Forecasters…• maintain situational

awareness

• are organized and can multitask

• deal well with pressure

• are decisive

• are flexible

• develop good visualization and conceptualization skills

• are passionate about [their work]

• are able to deal with failure

• are continuous learners

• have good “people” skills

• have good communication skills

• can adapt to shift work

Doswell, 2003


Good Forecasters…Good Forecasters…• cultivate increasing technical

proficiency

• synthesize knowledge into useful wx info

• recognize customers needs, knowledge level and expectations

• learn from peers & past events

• distinguish between mechanical and diagnostic prowess

• are interested and passionate about [their work] – professionally dedicated

• have good management/people skills (delegation, prioritization, mentoring)

• acknowledge other perspectives and can tolerate criticism/disagreement

• are honest & accountable

• maintain a productive rapport with researchers / modelers

• scrutinize model output

• have stamina for shift work

Stuart et. al., 2006


ChallengesChallenges• Time pressure

• Too much, too little, bad or conflicting data

• Conflicting, or bad model output

• Incomplete conceptual models

• Human Factors (IT, Rust, Policy, Staffing, Face-Threat)

NOAA POES

NOAA GOES

NASA ACE

ESA/NASA SOHO

L1•ACE (NASA)

–Solar wind speed, density, temperature and energetic particles–Vector Magnetic field

•SOHO (ESA/NASA)–Solar EUV Images–Solar Corona (CMEs)

•GOES (NOAA)–Energetic Particles–Magnetic Field–Solar X-ray Flux–Solar EUV Flux–Solar X-Ray Images

•POES (NOAA)–High Energy Particles–Total Energy Deposition–Solar UV Flux

•Ground Sites–Magnetometers (NOAA/USGS)–Thule Riometer and Neutron monitor (USAF)–SOON Sites (USAF)–RSTN (USAF)–Telescopes and Magnetographs–Ionosondes (AF, ISES, …)–GPS (CORS)

Key Data SourcesKey Data Sources

•STEREO (NASA)– Solar EUV Images– Solar Corona &

Heliosphere (CMEs)– In-situ plasma & fields– In-situ energetic particles– SWAVES

Solar flaresSolar flares• What is a solar flare ?

• H-alpha classification system

• X-ray classification system

June 6, 2000 at 1616 UTC Holloman Solar Observatory

June 6, 2000 at 1715 UTC SXT

GOES XRS 5-7 June 2000

• Two letter classification based on H-alpha

• Importance: based on area of brightening

• Brightness: Line width of intensity increase

• Example: 1B means area of 100-250, brightness such that ≥20 millionths is at ≥ 50 % above bkgnd± 1.0 Å of H-alpha center

BLOCK I – Flares

•Flare Classification•Observatories report H imagery and report their brightness and area of coverage

•Optical brightness •intensity of a solar flare is classified as faint (F), normal (N), or brilliant (B)

•Area importance •indicates the actual coverage area of the flare on the solar surface

BLOCK I – Flares

•Flare Classification•Observatories report H imagery and report their brightness and area of coverage

•Optical brightness •intensity of a solar flare is classified as faint (F), normal (N), or brilliant (B)

•Area importance •indicates the actual coverage area of the flare on the solar surface

H-alpha flare classification system

X-ray flare classification system• Based on total (spatially integrated) x-ray flux from Sun in 1-8 Å band

• Continuous observations provided by GOES satellites

• Letter, number system: letter for ‘decade’, number for level in decade

Peak Flux

Class

≥10-8 A≥10-7 B≥10-6 C≥10-5 M≥10-4 XExample:

if peak flux is 2.3 x 10-5 then x-ray class is M2.3

Solar Active Regions and Flare PredictionSolar Active Regions and Flare Prediction

• C, M, X, Proton probabilities 1-3 days

• Images used: white light, surface magnetic fields, H-alpha, X-ray

• Focused on active regions and magnetic field structure

• Baseline – climatology• Persistence

June 7, 2000 at 1430 UTCMt Wilson Solar Observatory

June 5, 2000 at 1714 UTCBig Bear Solar Observatory

Analysis

• Why is a given region flaring ?

• Evaluate complexity, dynamics, rate of growth/decay, ‘hot spots’

• Looking for shear, proper motion, differential rotation effects

• E/W vs N/S inversion lines


Sunspot Classification System: Optical


Solar magnetic fieldSolar magnetic field• Sunspots and magnetic fields

• Observations

• Magnetic classification system

• Vector magnetic fields

• Conceptual model for magnetic loops– ‘Potential fields’– ‘Non-potential’ fields– Relationship to flare probability

• Role of growth, decay, differential rotation, proper motions


Sunspot Classification Systems: Magnetic


Extension of magnetic fields into the corona

Storing energy in the coronal magnetic field


Forecast these values using the formula

RTOTAL = 1-[(1-R1)

*(1-R2)*… *(1-Rn )]where

R1, R2 ,… ,Rn are C, M or X flare probabilities for the individual regions on the disk.

Statistical probabilities

Observed flares

Forecaster- entered values

Flare Prediction ToolFlare Prediction Tool


LimitationsLimitations• Usually do not have vector field observations

• No one really knows what triggers a flare

–Concept of self-organized criticality

• Do not know when new flux will emerge or old flux will decay

• No physical model guidance


Developmental ActivitiesDevelopmental Activitiesthat may improve flare forecastingthat may improve flare forecasting

• Helicity as a driver of eruptive events

• Parameters derived from vector magnetograms

–Also exist studies deriving parameters from line-of-sight magnetograms

• Bayesian methods to combine multiple inputs (e.g. persistence and sunspot classification)

• Solar dynamics observatory

• Helioseismology (to detect emerging flux…)

Geomagnetic ForecastingGeomagnetic Forecasting• Physical Drivers of Geomagnetic Activity

–Transients from CME’s–Recurrent High Speed Streams from Coronal Holes

• Additional Factors to consider

–Seasonal effects (climatology)–Continuation of current levels (persistence)


Geomagnetic Specification Using Geomagnetic Specification Using IndicesIndices• A summary of activity:

– The indices are intended to provide a summary of variations of the Earth’s magnetic fields

• An interpretation:– Helps to extract a particular type of magnetic variation or an ensemble of

variations (as related to one or more magnetospheric or ionospheric current systems)

• Help users and non-specialists– Enable users to distinguish between times of high risk and low risk– Non-specialists can know the the ‘level’ of activity without having to

interpret magnetograms or satellite data• Facilitate comparative studies

– Compare activity level with related phenomena– Study cause-effect relationships– Investigate long time series behavoir– Simplify predictions of activity levels which are based on solar or solar

wind observations


List of IndicesList of IndicesIndex Year Application/Interpretation

K 1939 3-hourly range of irregular variations (quasi-log)

Kp1951 Planetary average of K

A/Ap1951 Based on K/Kp, Equivalent amplitude, daily average

R 1963 Hourly range (auroral/polar variations – substorms)

am 1969 Globally averaged 3-hourly range (linear)

AE 1969 Global Auroral Electrojet (substorms)

Dst 1969 Equatorial variations (‘ring current’)

aa 1975 Antipodal average, 3-hourly, equivalent amplitude

PC 1979* Polar variations (related to SW merging electric field)


K-index & A-indexK-index & A-index• Measure of 3 hourly range of irregular variations:

– Designed for sub-auroral and mid-latitude stations– Time interval optimized for 45-180 minute timescales:

• magnetic bays – typical signature of an electrojet injection

– Subtraction of SR: daily regular variation from the magnetometer data

– Maximum of range of horizontal components: • scaled from 0 to 9 using quasi-log scale (e.g. from Boulder): Range: 0-4 5-9 10-19 20-39 40-69 70-119 120-199 200-329 330-499 500 K: 0 1 2 3 4 5 6 7 8 9

– Normalization by geomagnetic latitude: (e.g. K9 threshold in Boulder is 500, College is 2500), to get similar K frequency distributions

– K9 occurs about 0.1% of the time (~30 times per solar cycle)• 24 hour A-index – average of 3-hourly equivalent amplitutes, ak:

K 0 1 2 3 4 5 6 7 8 9ak 0 3 7 15 27 48 80 140 240 400


Planetary Kp/ApPlanetary Kp/Ap• Kp: a type of planetary average of station K-indices

– A specific set of sub-auroral stations is used

– The calculation includes a ‘standardization’ for each station K-index

– Kp is discretized into 28 levels from 0 to 9

• Kp-est: USAF estimate of Kp based on real-time data– Different observatory network

– Network has a Northern American bias

– Currently updated on one-hour cadence

• Ap/Ap-est: 24 hourly index of activity– Calculated just like the 24-hour A-index for a single station

– Based on Kp/Kp-est


Berthelier, 1993


Activity CategoriesActivity Categories• Qualitative activity level

descriptor based on K & AK=0,1,2A є [0,7]

Quiet

K=3A є [8,15]

Unsettled

K=4A є [16,29]

Active

K=5A є [30-49]

Minor Storm

K=6A є [50,99]

Major Storm

K=7,8,9A > 100

Severe Storm

Geomagnetic Geomagnetic ForecastsForecasts

Sources:• Earthward-directed

CME’s• Coronal-hole high speed

streams

Prediction inputs – CME’s• CME properties

• Properties of associated x-ray event

• Location of associated activity

• Radio signatures (Type II/Type IV)

• Medium energy particles (ACE)

Prediction inputs – CH’s• Size & Location or CH

• Polarity of CH

• Comparison with previous rotation


Solar Wind & Coronal HolesSolar Wind & Coronal Holes

• Open field regions

– Source of high speed solar wind • High Speed Stream (HSS)

– Evolve slowly from rotation to rotation

• Plasma characteristics:

– elevated T

– low density

– presence of Alfven waves

Solar Wind & Coronal HolesSolar Wind & Coronal Holes• Co-rotating Interaction

Region (CIR)

• The HSS builds up a leading CIR

• Plasma characteristics:

– Compressed

– Enhanced Density

– Enhanced Magnetic Field

– Possible shock formation

• In some cases the CIR shock will accelerate particles

• Solar wind signatures look similar to a transient

Recent 27 day plot of solar wind data, showing the high-speed stream structure

Svalgaard et al, 2002

A spiral field will have components Bx and By in the solar-equatorial coordinate system (GSEQ)

The Solar Magnetospheric coordinate system is rotated about X, so the By component in GSEQ will have By and Bz components in GSM (e.g. the Earth’s ‘dipole’ tilts into or away from the spiral field)

Away sector more geoeffective in fallTowards sector more geoeffective in springRussell & McPherron 1973

Long-known semi-annual variation of geomagnetic activity

Russell McPherron EffectRussell McPherron Effect


Coronal Hole ForecastingCoronal Hole Forecasting• Recurrence – what happened 27 days ago?

– Recurrence can help to identify co-rotating disturbances; the best view is obtained by looking at a 27 day plot of solar wind data

– Look for changes in coronal hole morphology for this rotation relative to last rotation – this should be factored in as a small modification to recurrence

– Remember semi-annual variation of geomag activity

– Positive holes are more geoeffective in the fall, negative holes are more geoeffective in the spring

– CIR’s lead coronal holes and can look similar to CME’s (Temperature provides the key clue)

Coronal Hole Coronal Hole Forecasting Forecasting ChallengesChallenges

• Timing can be difficult

– Depends on SW speed

– Coronal hole boundaries don’t provide full information about expansion in the heliosphere

– Evolution is slow but occurs

– Can miss timing by 12-24 hours easily

Coronal Hole Coronal Hole Forecasting Forecasting ChallengesChallenges

• Timing can be difficult

– Depends on SW speed

– Coronal hole boundaries don’t provide full information about expansion in the heliosphere

– Evolution is slow but occurs

– Can miss timing by 12-24 hours easily

• Stereo-B can help

– There are subtle effects in addition to co-rotation that have to be accounted for


Solar Wind - TransientsSolar Wind - Transients• Coronagraph data are critical

• POS speed can be estimated

– lower constraint on transit time

• Interaction of CME with ambient solar wind is important

• Back-to-back CME’s – second CME won’t slow down very much

• Size of CME as it moves out is difficult to know


Solar Wind - TransientsSolar Wind - Transients• CME’s in the solar wind

–Shock, sheath, driver–Transit times for CMEs–Using EPAM

HAF model

ENLIL model


Geomagnetic Forecasting: TransientsGeomagnetic Forecasting: Transients

• CME-driven disturbances– As the CME moves out it develops three key

components: shock, sheath (swept up solar wind), and driver (simple conceptual picture)

– What direction is CME going, how fast, what kind of solar wind is it ‘plowing’ through ?

– Will Earth go through the center, through the side, maybe we will only go through the sheath, or maybe it will miss earth altogether

– Have to rely on grey-matter fusion to estimate CME trajectory: use location on disk and visual appearance

– STEREO –it will help although we don’t have an objective technique at this point


Example: X3/4B flare with CMEExample: X3/4B flare with CME

Fast moving CME, estimated POS velocity 1500 km/s

d/v calculation ~ 28 hours

Shock at 14/1356Z

Response to X3/CME at 13/0240Z - ~35 hour transit time

Sheath

Driver/Cloud


• CME-driven disturbances

– EPAM also provides clues (Energetic Particles on ACE)

– Absence of EPAM signatures is a decent indicator that we won’t get hit, at least not by a strong CME

– Timing: distance/velocity provides a lower limit on the arrival time (the fast CME’s tend to decelerate). However, if the solar wind has been previously swept up by a preceding CME, deceleration of the successive CME is usually small

– For earthbound CME’s, speed is probably the best indicator of storm severity

Geomagnetic Forecasting: TransientsGeomagnetic Forecasting: Transients

ACE EPAM shows rise in flux ahead of the interplanetary shock


LimitationsLimitations• Arrival time is the biggest challenge today

• Must somehow account for the swept up component of the solar wind

• Geometry of the shock, the sheath, and the driver are all uncertain

• Magnetic field in the driver is unknown

–Not obvious if it can be determined w/o in-situ measurements


Solar Energetic Particles:Solar Energetic Particles:nowcasts, forecasts, productsnowcasts, forecasts, products

• GOES real-time particle flux data– Range of energies 0.6 – 500 MeV

– Event defined as flux of 10 p cm-2 s-1 ster-1 (PFU) at > 10 MeV

• Daily forecast– Three day probabilities for SEP events

• Short term warnings– Based primarily on flare observations– Thresholds: 10 PFU 10 MeV and 1 PFU 100 MeV– Prediction for onset time, maximum flux, time of maximum flux, and expected

event duration• Alerts

– Real-time reports of an observed event– Issued shortly after threshold has been attained

• Additional notices at 100, 1000, and 10000 PFU.• Event Summaries

– Important for users to have an ‘all-clear’ indicator


Solar Energetic Particle Event ForecastingSolar Energetic Particle Event Forecasting

Sources:• Strong CME-driven shocks• Energetic flares in active

regions

Reames, 1999

Available Prediction Inputs• X-ray maximum and integral flux

• Location of associated activity

• Radio signatures (Type II/Type IV)

• CME properties (speed, total mass, direction)

Cane et al, 1988


Measuring SEPsMeasuring SEPsGOES Energetic Particle Sensor (EPS)Monitors the energetic electron, proton, andalpha particle fluxes e: 0.6 to 4.0 MeV p: 0.7 to 700 MeV a: 4 to 3400 MeV

SWPC processes the data to derive integral proton fluxes: ≥ 10 MeV ≥ 30 MeV ≥ 60 MeV ≥ 100 MeV

Proton Event defined: ≥ 10 PFU ≥ 10 MeV


Energetic Storm Particles Energetic Storm Particles (ESP)(ESP)• Prior to shock passage:

particle trapping leads to flat time-intensity profiles at lower energies due to streaming limit

• Leads to “Energetic Storm Particles” (ESP) when shock passes the Earth

• Leads to a ‘broken-power law’ energy spectrum

• Even high energy particles can be trapped in the big events


SEP forecastsSEP forecasts• Three day prediction

– Starts with the flare prediction– Factor in location of the region (west is better)– Factor in age of the region (older is better)– Include persistence of ongoing events

• Warnings– Key inputs: Integrated x-ray flux, type IV, type II,

location, CME speed– Longitude/spectrum dependence– Statistical guidance available but has its limits – If a Earthbound CME is associated there is a possibility

for an ESP event– History of the active region– Comparison with past, similar events


SEP statistical guidanceSEP statistical guidance• Current operational model inputs

– integrated x-ray flux

– peak x-ray flux

– occurrence of type II and type IV radio sweeps

• Statistical: based on event data

• Example:Integrated flux [0.085-0.257]

X-ray class [M3-M8]

Radio sweep type II and type IV:

16 such events historically, 6 of which were associated with proton events => 37.5 % probability (±12%)


1999 Verification Result1999 Verification Result• 86 Proton Flares

– Mean prediction 0.37

• 1334 Control Flares

– Mean prediction 0.04

• Event Criteria: M1 flare 0.01 Integrated flux


Skill score (HSS) optimization is achieved for range of probabilities 20-30%

At optimal point:POD = 56% (75/134)FAR = 55% (91/166)PC = 96% (3888/4038) HSS = 0.48

FAR falls as the threshold level is increased

However, POD also decreases with increasing threshold

Categorical quality measures for the current model

Balch, 2008


Work in progressWork in progress• New proton event database 1986-2004

–165 events with associated solar activity

• Control event database 1986-2004

–These are events that meet the necessary solar conditions for an event (from the 165 events), but did not result in an SEP event

Event Event ParametersParameters

Onset Time when 10 MeV flux begins to rise

Threshold Time when 10 MeV flux reaches 10 PFU

Maxtime Time of 10 MeV maximum flux

Endtime Time when 10 MeV flux drops below 10 PFU

EventType Characteristics of the event

P10Max Maximum flux at 10 MeV



P100Max Maxmum flux at 100 MeV

P10Fluence Integral of 10 MeV flux from threshold to end




Associated XRS event

Identifies associated XRS event

Associated CME event

Identifies associated CME event

Proton Events parameters

Onset/Max/End XRS event times (End at ½ power point)

Peak Flux Maximum 1-8 Å x-ray flux (from XRS)

OptClass H-alpha optical class of associated flare

OptLocation H-alpha location of associated flare

Type II Identify association of type II radio sweep (Yes or No)

Type IV Identify association of type IV radio sweep (Yes of No)

CME onset Time of onset of associated CME

CME speed Leading edge CME speed (linear fit)

Integrated XRS flux Integral of XRS flux from onset to end

Bkgd Subtracted integrated XRS flux

Integral of background subtracted XRS flux from onset to end (background is taken to be the pre-event level)

Temperature1 Derived Temperature using ratio of two XRS channels

Emission Measure1 Derived Emission Measure using ratio of two XRS channels

Associated SEP event

Identifies associated proton event (or set to none if there is no association)

XRS/CME event parameters

1 Temperature and Emission measure were derived using the SolarSoft library routinesGOES_CHIANTI_TEM.PRO and GOES_MEWE_TEM.PRO

Single Variable Density EstimationSingle Variable Density Estimation

• Density Estimation (Silverman, 1998) is a method of deducing continuous probability density functions from observed data

• We consider the distribution of one of the parameters in our data set, for example integrated x-ray flux

• We expect to have different probability distributions for this parameter, depending on whether it is from the set of proton associated events, or from the set of events not associated with proton events

• Once smooth distributions are found for these two cases, we can deduce a continuous function for the probability for an event as a function of the parameter

Description of the MethodDescription of the Method• Let {Xi} be the set of n observed values of the parameter, i=1, 2, …, n

• The probability density function is defined on the continuous domain of values x for this parameter, such that:

Where we use the normal probability density function for the kernel function K:

and h is a smoothing parameter

• Geometrically, each observed value contributes a ‘bump’ to the overall density estimate, so that the resulting function is a smooth, continuous probability density for the parameter Xi

f̂

n

i

i

h

XxK

nhxf

1

1ˆ

2)(

22zezK

Density estimate for log of background subtracted integrated x-ray flux for proton events using the normal probability density with a window width of 0.15

Density estimate for log of background subtracted integrated x-ray flux for events not associated with proton events (control events)

Example

Probability model for proton events, using the density functions

Probability = np*f1/(np*f1 +nc*f2),

np - number of proton eventsf1 - density estimate - proton associated events nc - number of control eventsf2 - density estimate for the control events

The probability is set to constant once maximum probability is reached

Probability model for proton events, using the density functions

Density Model metrics:

• Accuracy = 0.0235

• Rms error = 0.153

• Skill = 0.269

• Reliability=2.59 x 10-4

• Resolution=8.94 x 10-3

For comparison - metrics for operational model:

• Accuracy = 0.0246

• Rms error = 0.157

• Skill = 0.234

• Reliability=6.0 x 10-4

• Resolution=8.1 x 10-3

Density analysis for one parameter (background subtracted integrated x-ray flux) gives better accuracy, skill, reliability, and resolution !

Performance of categorical forecasts using probability thresholds

one parameter density model

Parameter is background subtracted integrated x-ray flux

Skill score optimization is at probability threshold of 0.18

At optimal point:POD = 59% (79/134)FAR = 59% (113/192)PC = 96% (3773/4020) HSS = 0.46

White – PODRed – FARGreen – PCYellow – PCHBlue – GSSCyan - HSS


• The method can be applied using more than one variable as a prediction inputs• In this case we consider a set of n observation vectors Xi which consist two

components• Each component represents one of the observed variables (e.g. 1st component

could be integrated x-ray flux, 2nd component could be CME speed)• The density estimate in this two-dimensional parameter space is defined to be:

where we use the standard, multivariate normal density function:

n

i

i

hK

nhf

12

1ˆ Xxx

Two Parameter Density EstimationTwo Parameter Density Estimation

2

exp)( 2

1 zzz

T

K

• In order to use a scalar smoothing parameter, h, it is necessary to normalize the observation vectors – we rescale all of the parameters so that their range is restricted to the interval [0,1]

2D Density Maps for Background Subtracted Integrated X-ray flux combined with H-alpha flare longitude

Red – proton event associated

Green – control event associated

Corresponding Probability Map for Proton Events

Performance Statistics for this 2D probability model(Log Background Subtracted Integrated X-ray Flux & Longitude)

QR = 0.0263RMS = 0.162Skill = 0.308REL = 7.60 x 10-4

RES = 1.34 x 10-2

Performance for Categorical Forecasts – 2D density model

At optimal point, threshold probability = 21%, POD = 53.7%, FAR = 41.5%, PC = 96.6%, HSS = 0.542


2D Density Maps for CME speed combined with Emission Measure

Red – proton event associated

Green – control event associated

Corresponding Probability Map for Proton Events

Performance Statistics for this 2D probability model(CME speed and Emission Measure)

QR = 0.0423RMS = 0.206Skill = 0.316REL = 1.86 x 10-3

RES = 2.14 x 10-2

Performance for Categorical Forecasts – 2D density model

At optimal point, threshold probability = 24%, POD = 51.6%, FAR = 41.8%, PC = 94.3%, HSS = 0.517



Parameter1 Parameter2 QR RMS SS REL RES HSS

Thresh POD FAR PC

BgSub Int Xray Flux longitude 0.0263 0.162 0.325 7.60E-04 1.34E-02 0.543 0.21 53.7 41.5 96.6

Int Xray Flux longitude 0.0269 0.164 0.308 1.19E-03 1.32E-02 0.524 0.18 53.7 45.0 96.3

EM (Mewe) CME speed 0.0423 0.206 0.316 1.86E-03 2.14E-02 0.517 0.24 51.6 41.8 94.3

CME speed longitude 0.0544 0.233 0.313 3.87E-03 2.86E-02 0.495 0.25 43.6 32.5 93.3

EM (Chianti) CME speed 0.0442 0.210 0.286 1.37E-03 1.91E-02 0.490 0.28 45.2 39.1 94.4

Log Max Xray Flux CME speed 0.0443 0.210 0.285 1.45E-03 1.91E-02 0.484 0.27 45.2 40.4 94.3

Top Performers with respect to HSS2D probability density models

All of these pairs of parameters result in a prediction model the HSS better than the existing operational model


SummarySummary• Forecasting today depends on

– Observing data– Empirical/statistical approaches– An experienced person

• Research and Development of practical operational physical models is in progress

– Importance of establishing and using common set of verification measures to compare performance & track progress

• Flare forecasting:– Climatology, persistence, imagery are key inputs– Eruption of magnetic flux is dominant uncertainty

• Geomagnetic forecasting:– CME’s and CH’s are key inputs– Arrival time of disturbed solar wind is dominant uncertainty

• Solar Energetic Particle forecasting– Currently depends on solar observables and statistics of past events to

deduce mostly likely outcomes– Physical modeling is particularly difficult

End of Part IIEnd of Part II

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