1. Forecasting the arrival time of the CME’s shock at the Earth
Evangelos Paouris and Helen Mavromichalaki
Athens Space Weather Forecasting Center,
Cosmic Ray Group,
Faculty of Physics,
National and Kapodistrian University of Athens
28 November 2017, Ostend, Belgium

2. Evangelos Paouris ESWW14 28 November 2017
Overview
New ICMEs database
Effective Acceleration Model – EAMv1
Projection effects – New EAMv2
Statistics – bootstrap method
Summary – Future work
Evangelos Paouris ESWW November 2017

3. Evangelos Paouris ESWW14 28 November 2017
The new ICMEs database
Jan 1996 – Dec 2009
266 ICMEs
51 columns
Available at:
Evangelos Paouris ESWW November 2017

4. Effective Acceleration Model
Arrival time of the CME
r = 0.95
Pred. levels: 99%
r = 0.98
Slow CMEs accelerating while the fast ones decelerating
(Paouris and Mavromichalaki, 2017)
Evangelos Paouris ESWW November 2017

5. Effective Acceleration Model
Almost 47.7% of the examined events had the maximum 3-hour value of geomagnetic Ap-index during the sheaths
(Paouris and Mavromichalaki, 2017)
Replacing the velocity inside the ICME with the velocity inside the sheath
(Paouris and Mavromichalaki, 2017b)
«Community Coordinated Modeling Center – CCMC»
NASA (
Evangelos Paouris ESWW November 2017

6. Effective Acceleration Model for CME’s shock
Arrival time of the CME’s shock
Total 266 events
with Shock 222
(214 without any data gaps)
r = 0.98
Evangelos Paouris ESWW November 2017

7. Effective Acceleration Model v1
CME linear speed u
(from e.g. CACTUS or our IDL code)
How it works?
Credit: SOHO/LASCO
1 AU
Credit: NASA
Outputs:
Arrival time of the CME’s shock
Acceleration / Deceleration
Speed of the shock at 1 AU
Inputs:
onset time
linear speed
Code IDL
(Paouris and Mavromichalaki, 2017b)
Evangelos Paouris ESWW November 2017

8. Projection effects
The speed of a CME (u) which is calculated by white light coronagraph data is possible to be a slight different than the actual radial speed of the CME. This is due to the projection effects as this speed is measured on the plane of the sky.
(Gopalswamy et al., 2001; Leblanc et al., 2001; Xie et al., 2006; Vrsnak et al., 2007)
Taking into account projection effects
ur : radial speed
u0 : speed of CME from coronagraphs
α : cone angle (half angular width)
φ : calculated from the coordinates of the active region
(Leblanc et al., 2001)
(Sheeley et al., 1999)
Credit: SOHO/LASCO
Where ψ and λ are the longitude and latitude of the active region
(Paouris and Mavromichalaki, 2017b)
Evangelos Paouris ESWW November 2017

9. Radial speed vs Linear speed
From the 118 events the 87 were associated with solar flares = known coordinates!
Excluding halo CMEs (148)
Total 266 events
r ≈ 0.99
(Paouris and Mavromichalaki, 2017b)
Evangelos Paouris ESWW November 2017

10. Using CME radial speed CME radial speed CME speed from LASCO
Improved version: Taking into account projection effects!
(Paouris and Mavromichalaki, 2017b)
Evangelos Paouris ESWW November 2017

11. Effective Acceleration Model v2
Evangelos Paouris ESWW November 2017

12. Statistics for two versions
EAMv1
EAMv2
<T> = hours
Standard error = h
Median = h
min T = h
max T = h
MAE = h
RMSE = h
<T> = hours
Standard error = h
Median = h
min T = h
max T = h
MAE = h
RMSE = h
MAE: Mean Absolute Error
RMSE: Root Mean Squared Error
(Paouris and Mavromichalaki, 2017b)
Evangelos Paouris ESWW November 2017

13. Statistics for two versions Statistical parameter
Simple Bootstrap method with replacement for 1×107 runs
Table 5 Summary of the statistical parameters for both models using a sample of 214 ICMEs of the duration (T) between the predicted arrival time and the actual arrival time of the shock T = tpred – tshock. The error of the average is on the mean of all events. The error bars for the other parameters are estimated using a simple bootstrap method with replacement for 107 times. All results are in hours.
Statistical parameter
EAMv1
EAMv2
Original Sample
Bootstrapped results with errors
Average
+3.03
+3.03±1.52
-0.28
-0.28±1.47
Median
+4.17
+3.90±1.83
+0.35
+0.47±1.88
Standard deviation
+22.32
+22.24±0.98
+21.60
+21.53±0.96
MAE
+18.58
+18.58±0.86
+17.65
+17.65±0.84
RMSE
+22.47
+22.45±0.95
+21.55
(Paouris and Mavromichalaki, 2017b)
Evangelos Paouris ESWW November 2017

14. Test case – 3 Earth directed CMEs
Table 1 Predicted arrival time of the shock driven by the CME of 23 May :00UT by three models. The difference time between this time and the actual arrival time of the shock (in hours) is also given.
Model
Prediction
Difference (hours)
WSA-ENLIL + Cone (GSFC SWRC)
:00 UT
-20.78
WSA-ENLIL + Cone (Met Office)
:00 UT
-23.78
EAMv1 (ASWFC)
:07 UT
+6.33
Table 2 Predicted arrival time of the shock driven by the CME of 28 June :24UT by three models. The difference between this time and the actual arrival time of the shock (in hours) is also presented.
Model
Prediction
Difference (hours)
WSA-ENLIL + Cone (Met Office)
:00
+28.60
WSA-ENLIL + Cone (GSFC SWRC)
:30
+27.10
EAMv1 (ASWFC)
:50
+36.40
Positive time difference means that the prediction time was later than the actual arrival time of the shock and vice versa for negative values.
Table 3 Predicted arrival time of the shock driven by the CME of 14 July :36UT by different models. The difference between this time and the actual arrival time of the shock (in hours) is also presented.
Model
Prediction
Difference (hours)
WSA-ENLIL + Cone (NOAA/SWPC)
:00
+12.77
DBM (SIDC)
:00
+6.77
WSA-ENLIL + Cone (Met Office)
:00
+9.77
WSA-ENLIL + Cone (GSFC SWRC)
:42
+16.47
SARM
:53
+3.65
Ensemble WSA-ENLIL + Cone (GSFC SWRC)
:51
+11.62
EAMv1 (ASWFC)
:48
+3.57
Visit CME Scoreboard:
(Paouris and Mavromichalaki, 2017b)
Evangelos Paouris ESWW November 2017

15. Comparison between two versions for 3 events
Table 4 Predicted and actual arrival times of the three CMEs shocks with the EAMv1 and EAMv2 models.
The difference between these times (in hours) is also presented.
CME
(date and time UT)
Shock arrival time (date and time UT)
Predicted arrival date and time
EAM-v1
Difference (hours)
EAM-v2
:00
:47
27/05/ :07
+6.33
27/05/ :55
+3.15
:24
:26
03/07/ :50
+36.40
03/07/ :27
+33.03
:36
:14
16/07/ :48
+3.57
16/07/ :33
+0.33
This new version improved all the forecasted events
(Paouris and Mavromichalaki, 2017b)
Evangelos Paouris ESWW November 2017

16. Summary – Future work
A new database of 266 interplanetary coronal mass ejections (ICMEs) with as much information as possible is introduced
A new model for the estimation of the effective acceleration firstly introduced on Paouris and Mavromichalaki (2017)
This model using simple equations of motion estimates the arrival time of the CME (main part)
A new version of this simple model (EAMv1) is introduced for the forecast of the arrival time of the shock that preceds a CME and at the same time this version becomes one of the registered methods-models of the CCMC CME Scoreboard providing predictions for Earth directed CMEs
For the first time, the projection effects of the linear speed of CMEs are taken into account in this empirical model (EAMv2), which significantly improves the prediction of the arrival time of the shock
Future work:
Study the interaction with the ambient solar wind – connection with the background conditions of the interplanetary space before the arrival of the CME
Prediction of Bz component at 1 AU using ICMEs database
Evangelos Paouris ESWW November 2017

17. Paouris, E. and Mavromichalaki, H
Paouris, E. and Mavromichalaki, H., “Interplanetary Coronal Mass Ejections Resulting from Earth-Directed CMEs Using SOHO and ACE Combined Data During Solar Cycle 23”, Solar Physics, (2017) 292: 30.
DOI: /s
Paouris, E. and Mavromichalaki, H., “Effective Acceleration Model for the Arrival Time of Interplanetary Shocks driven by Coronal Mass Ejections”, Solar Physics, (2017) 292: 180.
DOI: /s
Thank you!
Athens Space Weather Forecasting Center,
Cosmic Ray Group,
Faculty of Physics,
National and Kapodistrian University of Athens
Evangelos Paouris ESWW November 2017