A New Tool for Forecasting Solar Drivers of Severe Space … · A New Tool for Forecasting Solar...
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A New Tool for Forecasting Solar Drivers of Severe Space Weather Severe Space Weather J.H. Adams 1 , D. Falconer 1,2,3 , A.F. Barghouty 1 , I. Khazanov 3 , and R. Moore 1 1 Space Science Office, VP62, NASA Marshall Space Flight Center, Huntsville, AL 35812 USA 2 Physics Department, University of Alabama in Huntsville, Huntsville, AL 35899 USA 3 Center for Space Plasma and Aeronomic Research, University of Alabama in Huntsville, Huntsville, AL 35899 USA ►Most severe space weather is driven by Solar flares and CMEs – the strongest of these originate in active regions and are driven by the release of coronal free magnetic energy ►There is a positive correlation between an active region’s free magnetic energy and the likelihood of flare and CME production – this positive Motivation lux) + free energy/size data point free-energy/size as event is occurring Flares, CMEs, and SPEs tend to originate from active regions with the most free energy Events in Phase Space Expression “Free Energy” “McIntosh” Best Possible Score The first four scores are based on yes/no forecast, with a forecasted event rate of 0.5/day or greater being counted as a yes forecast, while an event rate of less than 0.5 events/day is counted as a no forecast. The quadratic (Brier) score and SS are a comparison between the forecasted rates (f i ) and the observed flare rate (σ i ), while QR* is the quadratic score if the average flare rate is used (Balch, 2008, Spaced Weather Vol 6). Skill Scores • The tool starts with a full-disk MDI magnetograms, identifies and encloses all strong magnetic field areas. •Each strong magnetic field area is numbered and identified with one or more NOAA active regions. The active regions magnetogram is extracted and the proxy of the free magnetic energy is measured Sample Forecast Output energy and the likelihood of flare and CME production – this positive correlation is the basis of our empirical space weather forecasting tool ►The new tool takes a full disk MDI magnetogram, identifies strong magnetic field areas, identifies these with NOAA active regions, and measures a free-magnetic-energy proxy. It uses an empirically derived forecasting function to convert the free-magnetic-energy proxy to an expected event rate. It adds up the expected event rates from all active regions on the disk to forecast the expected rate and probability of each class of events – X-class flares, X&M class flares, CMEs, fast CMEs, and solar particle events (SPEs) Size (Total Magnetic Fl Proxy of Free Magnetic Energy Score Probability of Detection A/(A+C) 0.45 0.26 1 False Alarm Rate B/(A+B) 0.49 0.70 0 Percent correct (A+D)/N 0.95 0.93 1 Heidke Skill (A+D-E)/(N-E) 0.45 0.24 1 Quadratic Score 1/N)∑(f i -σ i ) 2 0.14 0.16 0 SS (QR*-QR)/QR* 0.20 0.06 1 E=((A+B)(A+C)+(B+D)*(C+D))/N QR*=(1/N)∑(σ-σ i ) 2 =0.17 Our skill scores are always significantly better than McIntosh s kill scores. the free magnetic energy is measured ( L WL SG ). (Blue Box) •The proxy of the free magnetic energy is converted to expected rates of events (Green box). Threat level of each active region is color coded, based on expected M and X flare rate. •Full disk expected event rates and All Clear probabilities are given below the blue and green boxes. •A threat gauge shows the forecasted chance of event, yellow spanning the uncertainty range of the forecast rate. + - s • We bin our sample of 40,000 magnetograms, from 1,300 active regions into 40 equally populated bins based on our free magnetic energy proxy. • For each bin we determine an average of the free magnetic energy proxy, an average event rate(*), and the statistical uncertainty of event rate (I). • A power-law fit (red line) is done for all bins that have an event rate of greater Our skill scores are always significantly better than McIntosh s kill scores. • Event rates are additive, so we can add the forecasted event rates of all active regions on the disk. From Active Regions to Full Disk Empirical Forecasting Relation Front CME Side CME Right: A flare appears in both of the green SOHO/EIT, and in both cases a CME is seen in the blue SOHO/LASCO images. The front CME also produces a shock- wave that accelerates particles, producing a Solar Particle Event, also seen in the Flares, CMEs & Severe Space Weather uncertainty range of the forecast rate. Active-Region’s Magnetic Quantities Free Magnetic + + - More Less • A power-law fit (red line) is done for all bins that have an event rate of greater than 0.01 events/day (left dashed blue line). This is the forecasting function, to convert any given free magnetic energy proxy measure to an empirical event rate. Each event type has its own forecasting function. R=a( L WL SG ) b Event Type log 10 a b Reduced Chi- squared X and M Flares -9.75±0.03 1.95±0.14 0.21 X Flares -10.77±0.11 2.00±0.58 0.39 CMEs -7.81±0.04 1.50±0.16 0.31 Power-Law Fits • Forecasted event rates from poorly observed active regions (active regions near the limb) are less certain, because our proxy of free magnetic energy is more uncertain. These measurements will improve with SDO vector magnetograms. Active regions at the west limb could have forecast made based on previous history. • Event rates for active regions or full disk can be converted to event (or All-Clear) probabilities • All-Clear event probability: Prob(t)=100%(e -Rt ) ELECTROMAGNETIC RADIATION (IMMEDIATE: TIME 8 MIN) HIGH ENERGY PARTICLES (DELAYED HOUR) ENHANCED SOLAR WIND (DELAYED ~3 Days) VISIBLE LIGHT RADIO WAVES SIMULTANEOUS EFFECTS DELAYED EFFECTS ATOMIC NUCLEI IONS AND ELECTRONS INCREASED D-LAYER IONIZATION OUTBURST OF RADIO NOISE GEOMAGNETIC RADIO INTERFERENCE ENHANCED ENERGETIC PARTICLES IONIZATION GEOMAGNETIC STORM MANNED FLIGHT RADIATION HAZARD(I) ANOMALOUS IONOSPHERIC STORM AND INDUCED X-EUV ULTRA- VIOLET Solar Energetic Particle Event (SPE or SEP Event) Flare CME Arrival e Weather a Solar Particle Event, also seen in the LASCO image. Conclusion Free Magnetic Twist Size Energy ~(Twist x Size) From SDO/HMI Active-Region’s Magnetic Measures CMEs -7.81±0.04 1.50±0.16 0.31 Fast CMEs -8.36±0.07 1.55±0.29 0.43 SPEs -8.84±0.12 1.57±0.59 0.44 • We bin our sample of 40,000 magnetograms, from 1,300 active regions into 40 • All-Clear event probability: Prob(t)=100%(e -Rt ) Transition to SDO Performance of Forecasting Relation DISTURBANCE INTERFERENCE ANOMALOUS RADIO PROPAGATION SPACECRAFT RADIATION HAZARD (C 3 ) ANOMALOUS RADIO PROPAGATION RADAR CLUTTER INDUCED CURRENTS RADIO EFFECTS AURORA SATELLITE DRAG Space Outline of Method From SDO/HMI Size: Φ=∫|B z | da (|B z | >100G) Free Magnetic Energy: WL SG = ∫(B z ) dl (potential B h >150G) where B z is the vertical field, B is the horizontal field, and the line In the line-of-sight approximation (MDI): • We bin our sample of 40,000 magnetograms, from 1,300 active regions into 40 equally populated bins based on our free magnetic energy proxy. • For each bin we determine an average of the free magnetic energy proxy, an average event rate(*), and the statistical uncertainty of event rate (I). • A power-law fit (red line) is done for all bins that have an event rate of greater than 0.01 events/day (left dashed blue line). This is the forecasting function, to convert any given free magnetic energy proxy measure to an empirical event rate. Each event type has its own forecasting function. R=a( L WL SG ) b Power-Law Fits ► SDO\HMI will produce full-disk vector magnetograms, at higher cadence, higher resolution, and with less latency than MDI. ► The vector magnetograms will allow accurate assessment of our free magnetic energy proxy further from disk center than MDI line-of-sight magnetograms. ► The lower latency would allow forecasting with more recent observations. ► It will take a solar cycle to develop a database comparable to that available from MDI. To make forecasts based on HMI observations, in the meantime we will have to use the empirical conversion function determined from MDI. ► Since our free-magnetic-energy proxies is based on measuring gradients in the line- of-sight fields, where the line-of-sight field is approximately the vertical field, and MDI unlikely fully resolves these gradients, we will need to develop a conversion Full Disk Magnetogram Identify Active Regions Evaluate Active Regions NOAA presently classifies active regions by the McIntosh classification scheme, and then uses previous history of that class of active regions to forecast event rates, modified by observers experience. Our method uses a measured proxy of an active region’s free magnetic energy, and uses a forecasting curve determined from a sample of 40,000 magnetograms from 1,300 active regions with known event histories to convert the free B h is the horizontal field, and the line integral is on the polarity inversion line. (MDI): B z is replaced by the line-of-sight field and B h is replaced by the potential transverse field which is calculated from the line-of-sight field SG Event Type log 10 a b Reduced Chi- squared X and M Flares -9.75±0.03 1.95±0.14 0.21 X Flares -10.77±0.11 2.00±0.58 0.39 CMEs -7.81±0.04 1.50±0.16 0.31 Fast CMEs -8.36±0.07 1.55±0.29 0.43 SPEs -8.84±0.12 1.57±0.59 0.44 MDI unlikely fully resolves these gradients, we will need to develop a conversion function, f(WL SDO ), using overlapping observations , magnetograms of the same active regions from both MDI and HMI R=a f(WL SDO ) b ► We will likely use the SDO tiles for each active region, and thus eliminate the automated active region identification step. ☼ Predict expected rates and magnitude of events, from past behavior of similar active regions Produce Full Sun Event Rate with known event histories to convert the free magnetic energy proxy into the active region’s expected event rate. All magnetograms are from SOHO/MDI and were of active regions observed during 1996-2004, during central disk passage. https://ntrs.nasa.gov/search.jsp?R=20100033126 2018-07-28T05:36:13+00:00Z
Transcript of A New Tool for Forecasting Solar Drivers of Severe Space … · A New Tool for Forecasting Solar...
A New Tool for Forecasting Solar Drivers of Severe Space WeatherSevere Space Weather
J.H. Adams1 , D. Falconer1,2,3, A.F. Barghouty1, I. Khazanov3, and R. Moore1
1Space Science Office, VP62, NASA Marshall Space Flight Center, Huntsville, AL 35812 USA2Physics Department, University of Alabama in Huntsville, Huntsville, AL 35899 USA
3Center for Space Plasma and Aeronomic Research, University of Alabama in Huntsville, Huntsville, AL 35899 USA
►Most severe space weather is driven by Solar flares and CMEs – the strongest of these originate in active regions and are driven by the release of coronal free magnetic energy
►There is a positive correlation between an active region’s free magnetic energy and the likelihood of flare and CME production – this positive
Motivation
Size
(Tot
al M
agne
tic F
lux)
+ free energy/size data point free-energy/size as event is occurring
Flares, CMEs, and SPEs tend to originate from active regions with the most free energy
Events in Phase Space
Expression “Free Energy” “McIntosh” Best Possible Score
The first four scores are based on yes/no forecast, with a forecasted event rate of 0.5/day or greater being counted as a yes forecast, while an event rate of less than 0.5 events/day is counted as a no forecast. The quadratic (Brier) score and SS are a comparison between the forecasted rates (fi) and the observed flare rate (σi), while QR* is the quadratic score if the average flare rate is used (Balch, 2008, Spaced Weather Vol 6).
Skill Scores
• The tool starts with a full-disk MDI magnetograms, identifies and encloses all strong magnetic field areas.•Each strong magnetic field area is numbered and identified with one or more NOAA active regions. The active regions magnetogram is extracted and the proxy of the free magnetic energy is measured
Sample Forecast Output
energy and the likelihood of flare and CME production – this positive correlation is the basis of our empirical space weather forecasting tool
►The new tool takes a full disk MDI magnetogram, identifies strong magnetic field areas, identifies these with NOAA active regions, and measures a free-magnetic-energy proxy. It uses an empirically derived forecasting function to convert the free-magnetic-energy proxy to an expected event rate. It adds up the expected event rates from all active regions on the disk to forecast the expected rate and probability of each class of events – X-class flares, X&M class flares, CMEs, fast CMEs, and solar particle events (SPEs)
Size
(Tot
al M
agne
tic F
lux)
Proxy of Free Magnetic Energy
ScoreProbability of
Detection A/(A+C) 0.45 0.26 1
False Alarm Rate B/(A+B) 0.49 0.70 0
Percent correct (A+D)/N 0.95 0.93 1
Heidke Skill (A+D-E)/(N-E) 0.45 0.24 1
Quadratic Score 1/N)∑(fi-σi)2 0.14 0.16 0
SS (QR*-QR)/QR* 0.20 0.06 1
E=((A+B)(A+C)+(B+D)*(C+D))/N QR*=(1/N)∑(σ-σi)2=0.17
Our skill scores are always significantly better than McIntosh skill scores.
the free magnetic energy is measured (LWLSG). (Blue Box)•The proxy of the free magnetic energy is converted to expected rates of events (Green box). Threat level of each active region is color coded, based on expected M and X flare rate.•Full disk expected event rates and All Clear probabilities are given below the blue and green boxes.•A threat gauge shows the forecasted chance of event, yellow spanning the uncertainty range of the forecast rate.
+-
Mor
e
L
ess
• We bin our sample of 40,000 magnetograms, from 1,300 active regions into 40 equally populated bins based on our free magnetic energy proxy.
• For each bin we determine an average of the free magnetic energy proxy, an average event rate(*), and the statistical uncertainty of event rate (I).
• A power-law fit (red line) is done for all bins that have an event rate of greater
Our skill scores are always significantly better than McIntosh skill scores.
• Event rates are additive, so we can add the forecasted event rates of all active regions on the disk.
From Active Regions to Full DiskEmpirical Forecasting Relation
Front CME Side CMERight: A flare appears in both of the green SOHO/EIT, and in both cases a CME is seen in the blue SOHO/LASCO images. The front CME also produces a shock-wave that accelerates particles, producing a Solar Particle Event, also seen in the
Flares, CMEs & Severe Space Weather
uncertainty range of the forecast rate.
Active-Region’s Magnetic Quantities
Free Magnetic
+-
+-
Mor
e
L
ess
• A power-law fit (red line) is done for all bins that have an event rate of greater than 0.01 events/day (left dashed blue line). This is the forecasting function, to convert any given free magnetic energy proxy measure to an empirical event rate. Each event type has its own forecasting function.
R=a(LWLSG)b
Event Type log10 a b Reduced Chi-squared
X and M Flares
-9.75±0.03 1.95±0.14 0.21
X Flares -10.77±0.11 2.00±0.58 0.39
CMEs -7.81±0.04 1.50±0.16 0.31
Power-Law Fits
rates of all active regions on the disk. • Forecasted event rates from poorly observed active regions
(active regions near the limb) are less certain, because our proxy of free magnetic energy is more uncertain. These measurements will improve with SDO vector magnetograms. Active regions at the west limb could have forecast made based on previous history.
• Event rates for active regions or full disk can be converted to event (or All-Clear) probabilities
• All-Clear event probability: Prob(t)=100%(e-Rt)
ELECTROMAGNETIC RADIATION(IMMEDIATE: TIME 8 MIN) HIGH ENERGY PARTICLES
(DELAYED HOUR)
ENHANCED SOLAR WIND(DELAYED ~3 Days)
VISIBLELIGHT
RADIOWAVES
SIMULTANEOUS EFFECTS DELAYED EFFECTS
ATOMIC NUCLEI
IONS ANDELECTRONS
INCREASEDD-LAYERIONIZATION
OUTBURSTOF RADIO NOISE
GEOMAGNETIC RADIOINTERFERENCE
ENHANCEDENERGETIC PARTICLES
IONIZATION GEOMAGNETICSTORM
MANNED FLIGHTRADIATION HAZARD(I) ANOMALOUS
IONOSPHERICSTORM AND
INDUCED
X-EUV ULTRA-VIOLET
Solar Energetic Particle Event (SPE or SEP Event)
Flare CME Arrival
Spa
ce W
eath
er
a Solar Particle Event, also seen in the LASCO image.
ConclusionFree MagneticTwist Size Energy
~(Twist x Size)
From SDO/HMI
Active-Region’s Magnetic Measures
CMEs -7.81±0.04 1.50±0.16 0.31
Fast CMEs -8.36±0.07 1.55±0.29 0.43
SPEs -8.84±0.12 1.57±0.59 0.44
• We bin our sample of 40,000 magnetograms, from 1,300 active regions into 40
• All-Clear event probability: Prob(t)=100%(e-Rt)
Transition to SDOPerformance of Forecasting Relation
GEOMAGNETICDISTURBANCE INTERFERENCE
ANOMALOUSRADIO PROPAGATION
RADIATION HAZARD(I)
SPACECRAFTRADIATIONHAZARD (C3)
ANOMALOUSRADIO PROPAGATION
RADARCLUTTER
INDUCED CURRENTS
RADIOEFFECTS
AURORA
SATELLITEDRAG
Spa
ce W
eath
er
Outline of Method
From SDO/HMI
Size:
Φ=∫|Bz| da (|Bz| >100G)
Free Magnetic Energy:
WLSG= ∫(Bz) dl (potential Bh>150G)
where Bz is the vertical field,
B is the horizontal field, and the line
In the line-of-sight approximation (MDI):
• We bin our sample of 40,000 magnetograms, from 1,300 active regions into 40 equally populated bins based on our free magnetic energy proxy.
• For each bin we determine an average of the free magnetic energy proxy, an average event rate(*), and the statistical uncertainty of event rate (I).
• A power-law fit (red line) is done for all bins that have an event rate of greater than 0.01 events/day (left dashed blue line). This is the forecasting function, to convert any given free magnetic energy proxy measure to an empirical event rate. Each event type has its own forecasting function.
R=a(LWLSG)b Power-Law Fits
► SDO\HMI will produce full-disk vector magnetograms, at higher cadence, higher resolution, and with less latency than MDI.
► The vector magnetograms will allow accurate assessment of our free magnetic energy proxy further from disk center than MDI line-of-sight magnetograms.
► The lower latency would allow forecasting with more recent observations.
► It will take a solar cycle to develop a database comparable to that available from MDI. To make forecasts based on HMI observations, in the meantime we will have to use the empirical conversion function determined from MDI.
► Since our free-magnetic-energy proxies is based on measuring gradients in the line-of-sight fields, where the line-of-sight field is approximately the vertical field, and MDI unlikely fully resolves these gradients, we will need to develop a conversion
Full Disk Magnetogram
Identify Active Regions
Evaluate Active Regions
NOAA presently classifies active regions by the McIntosh classification scheme, and then uses previous history of that class of active regions to forecast event rates, modified by observers experience.
Our method uses a measured proxy of an active region’s free magnetic energy, and uses a forecasting curve determined from a sample of 40,000 magnetograms from 1,300 active regions with known event histories to convert the free Bh is the horizontal field, and the line
integral is on the polarity inversion line.
(MDI):
Bz is replaced by the line-of-sight field and
Bh is replaced by the potential transverse field which is calculated from the line-of-sight field
R=a( WLSG)Event Type log10 a b Reduced Chi-
squaredX and M
Flares-9.75±0.03 1.95±0.14 0.21
X Flares -10.77±0.11 2.00±0.58 0.39
CMEs -7.81±0.04 1.50±0.16 0.31
Fast CMEs -8.36±0.07 1.55±0.29 0.43
SPEs -8.84±0.12 1.57±0.59 0.44
MDI unlikely fully resolves these gradients, we will need to develop a conversion function, f(WLSDO), using overlapping observations , magnetograms of the same active regions from both MDI and HMI
R=a f(WLSDO)b
► We will likely use the SDO tiles for each active region, and thus eliminate the automated active region identification step.
☼
Predict expected rates and magnitude of events, from past behavior of similar active regions
Produce Full Sun Event Rate
with known event histories to convert the free magnetic energy proxy into the active region’s expected event rate. All magnetograms are from SOHO/MDI and were of active regions observed during 1996-2004, during central disk passage.
https://ntrs.nasa.gov/search.jsp?R=20100033126 2018-07-28T05:36:13+00:00Z
