lessons learned in transitioning solar-interplanetary...
TRANSCRIPT
Lessons Learned in Transitioning
Solar-Interplanetary Research
models into Operational Services
Siqing Liu1, Bingxian Luo1, Jiancun Gong1, WengengHuang,1 Jingjing Wang1, Yuming Wang2, Chuanbing
Wang2
1National Space Science Center, CAS2University of Science and Technology of China
Outlines
Motivations
Progress
Lessons Learned
Motivations
Co-rotating Interacting Regions (CIRs) Forecast
– General information of background solar wind
– Time of CIR arrival at Earth
Oncoming CMEs Forecast
– Geoeffectiveness of CMEs (will arrive or not)
– Time of CME arrival at Earth
– Geomagnetic storm intensity
CME-driven Shock-associated SEP forecast
– SEP intensity forecast
– SEP spectrum
Pave the way for future space weather model
transitions
Motivations
Solar Magnet-ogram
Solar Observations
CME’s Corona-graph
Kinematic
Models
Empirical Models
PFSS + WSACoronal B Field
Solar Wind speed
CME detection and fitting ModelCME detection, CME size,
initial speed and direction
HAF + SEPMSolar Wind speed,
density, B Field, and energetic particles
Earth
Sun Near the sun Interplanetary Earth
Solar Interplanetary Forecasting System
Researchers of modelling: Yuming Wang, Chuanbing Wang,
Chenglong Shen, etc., from University of Science and
Technology of China
NSSC support staff (V&V, transition, IT, mamager, etc)
– Managers: Siqing Liu, Jiancun Gong
– Forecasters and model users: Jingjing Wang, Wengeng
Huang, Bingxian Luo, etc., at NSSC, CAS
– Engineers: Yanxia Cai, Guorui Lu, Zhaofeng Chen, Lei
Zhang, Lili Bao, etc., at NSSC, CAS
Supported by National programs.
Progress
Transition Team
Progress
01 02 03
06 05 04
Forecast needs
CIRs arrival
CME arrival
CME-driven shock SEP
Models/tools we have Input data we can get
Background SW models (PFSS+HAF)
CME detection and fitting models
(SEEDS+Cones)
SW propagation models (HAF)
Particle acceleration models (PATH)
Solar magnetic field map
Coronagraph images
System integration
Background solar wind model
CME-detection/fitting model
CME propagation model
SEP simulation model
Model improvement Model verification
Focusing on halo-CMEs (new
CME detection models)
Human-computer interaction
(CME fitting accuracy)
CME detection percentage
CME parameters accuracy
CME propagation evaluation
SEP spectrum
Chart of R2O process
Progress
Solar Interplanetary Forecasting System
1 2 3 4
PFSS modelSCC/HCCS/CSSS
WSA modelHAF model
Auto-detection(SEEDS)
Manned-detectionCone model
Ice-cream Cone
HAF model Diffusive Shock acceleration
model
Background solar wind (CIR)
CME detection and fitting
CME propagation
SEP Simulation
Photosphericfield
Source Surface field
Derivedcoronal holes
Source SurfaceSW speed
Part 1 : background solar wind - PFSS+WSA+HAF model
Problem: There are four different functions to relate the solar wind speed to the flux tube expansion factor.
Part 2 : CME detection and fitting – auto detection
Corona graphic observations
J-map and Hough transform
CME front traceCME collection and identification
Problem: The SEEDS algorithm yields poor performance for Halo-CME detections.
Part 2 : CME detection and fitting – manned detection
Find CME Draw line
Getting pointsDeriving CME trace
Part 2 : CME detection and fitting – Ice-cream cone model
CME front trace Ice-cream Cone model assumption
Fitting
SW density and Speed
SW pressure and magnetic field
Part 3 : CME Propagation Model - HAF
Source Surface field
Source Surface SW speed
Part 4 : SEP simulation (Diffusive Shock accelerative Code)
Progress: System Integration
CME Searching Trace CME front
Output from CME fittingInput for propagationDownload results
Case study : March 15 ,2015
CME observation
C9.1 flare erupted from AR2297 (S24W38)on 00:45UTC, March 15
Cone model fitting
Propagation direction : N01E07Angular width : 126 degreePropagation velocity: 1130 km/s
Case study : March 15 ,2015
IMF at Earth
DVE at Earth
Density and magnetic field
Pressure and magnetic field
Predicted Shock Arrival Time Difference (hrs) Confidence (%) Method
03-16T23:00Z -5.08 SEPC model
03-17T18:00Z 13.92 ---- WSA-ENLIL + Cone (NOAA/SWPC)
03-17T18:08Z (-15.0h, +26.3h) 14.05 55.0 COMESEP
03-16T18:00Z (-12.0h, +12.0h) -10.08 ---- Other (SIDC)
03-17T12:00Z (-12.0h, +6.0h) 7.92 60.0 WSA-ENLIL + Cone (Met Office)
03-17T11:39Z (-7.0h, +7.0h) 7.57 ---- WSA-ENLIL + Cone (GSFC SWRC)
03-17T11:48Z (-5.3h, +7.7h) 7.72 100.0 Ensemble WSA-ENLIL + Cone (GSFC SWRC)
03-17T10:55Z 6.83 71.6667 Average of all Methods
Case study : March 15 ,2015
Data from CCMC
Case study : June 18 and 21 ,2015
CME observation
M3 flare erupted from S24W38on 13:30UTC, June 18
Cone model fitting
Propagation direction : S21W32 ,S09W04Angular width : 194 degree,148 degreePropagation velocity: 630 km/s ,850 km/s
CME observation
M2 flare erupted from AR2371 (N13W00)on 01:42UTC, June 21
Case study : June 18 and 21 ,2015
IMF at Earth
DVE at Earth
Density and magnetic field
Pressure and magnetic field
Predicted Shock Arrival Time Difference (hrs) Confidence (%) Method
06-20T20:00Z -19.33 SEPC model
06-21T02:20Z (-4.53h, +7.12h) -13.33 31.0 Ensemble WSA-ENLIL + Cone (GSFC SWRC)
06-21T21:008Z 5.33----
WSA-ENLIL + Cone (NOAA/SWPC)
06-21T09:26Z (-7.0h, +7.0h) -6.23 31.0 WSA-ENLIL + Cone (GSFC SWRC)
06-21T08:00Z (-12.0h, +12.0h) -7.67 40.0 Other (SIDC)
06-21T16:00Z (-12.0h, +12.0h) 0.33 70.0 WSA-ENLIL + Cone (Met Office)
06-21T11:21Z -4.32 43.0 Average of all Methods
Case study : June 18 ,2015
Data from CCMC
Predicted Shock Arrival Time Difference (hrs) Confidence (%) Method
06-22T16:00Z -1.98 SEPC model
06-22T17:00Z (-12.0h, +12.0h) -0.98 90.0 Other (SIDC)
06-22T21:00Z 3.02----
WSA-ENLIL + Cone (Met Office)
06-22T21:43Z (-7.0h, +7.0h) 3.73 100.0 WSA-ENLIL + Cone (GSFC SWRC)
06-22T19:03Z (-5.15h, +3.33h) -1.07 100.0 Ensemble WSA-ENLIL + Cone (GSFC SWRC)
06-22T23:00Z (+7.0h) 5.02 100.0 DBM
06-22T22:50Z (-5.0h, +8.0h) -4.85 ---- ElEvo
06-22T14:00Z -3.98 ---- WSA-ENLIL + Cone (NOAA/SWPC)
06-22T19:48Z 1.82 97.5 Average of all Methods
Case study : June 21,2015
Data from CCMC
Case study : August 12 and 14 ,2015
CME observation
Associated with prominence erupted on 13:42Z, August 12on 13:30UTC, June 18
Cone model fitting
Propagation direction : N28E34 ,N34W00Angular width : 170 degree,34 degreePropagation velocity: 570 km/s ,490 km/s
CME observation
Associated with filament erupted below AR2399 (S07W47) on 06:30Z, August 14
Case study : August 12 and 14 ,2015
IMF at Earth
DVE at Earth
Density and magnetic field
Pressure and magnetic field
Predicted Shock Arrival Time Difference (hrs) Confidence (%) Method
08-14T23:00Z -8.72 SEPC model
08-16T04:00Z (-12.0h, +12.0h) 20.28 20.0 Other (SIDC)
08-16T09:09Z (-7.0h, +7.0h) 25.43----
Ensemble WSA-ENLIL + Cone (GSFC SWRC)
08-16T03:00Z 19.28 ---- WSA-ENLIL + Cone (NOAA/SWPC)
08-16T06:02Z (-7.0h, +6.66h) 22.32 91.0 WSA-ENLIL + Cone (GSFC SWRC)
08-16T00:01Z (-6.0h, +6.0h) 16.3 70.0 WSA-ENLIL + Cone (Met Office)
08-16T04:26Z 20.72 60.33 Average of all Methods
Case study : August 12 ,2015
Data from CCMC
Predicted Shock Arrival Time Difference (hrs) Confidence (%) Method
08-19T00:00Z(SWIDM: Will not arrive! )
---- SEPC model
08-18T12:00Z (-7.0h, +7.0h) ---- 10.0 WSA-ENLIL + Cone (GSFC SWRC)
08-18T05:00Z --------
WSA-ENLIL + Cone (NOAA/SWPC)
08-19T12:00Z (-12.0h, +12.0h) ---- 20.0 Other (SIDC)
08-18T18:00Z (-12.0h, +8.0h) ---- 40.0 WSA-ENLIL + Cone (Met Office)
08-18T17:45Z ---- 22.3333 Average of all Methods
Case study : August 14 ,2015
Data from CCMC
Difference (hrs) SEPC SWPC SWRC MET SIDC
March 15, 2015 -5.08 -13.92 7.57 7.92 -10.8
April 4, 2015 -33.17 -47.97 -28.17 -25.17
April 6, 2015 -24.0 -11.0 -15.7 -9.5 -6.0
May 6, 2015 21.0 -4.42 12.0
May 2, 2015 -11.83 15.17 41.17 3.17
June 9, 2015 -20.0 -1.92
June 18, 2015 -19.33 5.33 -6.23 0.33 -7.67
June 19, 2015 -4.85 16.15 1.22 21.15 3.15
June 21, 2015 -1.98 -3.98 3.73 3.02 -0.98
June 22, 2015 -9.95 10.05 5.35 8.05 -0.95
June 25, 2015 -20.5 11.5 22.5 1.5 -11.5
July 19, 2015 -31.0 4.5 2.5 5.5 -0.5
August 12, 2015 -8.72 19.28 25.43 16.3 20.28
Sep 04, 2015 -6.47 18.53 69.53 34.53
Sep 18, 2015 5.55 15.55 11.78 6.55 25.55
Oct 22, 2015 0.56 6.57 -6.62 2.57 -6.43
Nov 4, 2015 5.43 21.43 15.10 19.43 10.43
Absolute Mean 13.50 11.88 13.98 13.51 11.75
Case study: 17 CMEs
Lessons Learned
Research models can not be put into operational use
directly without evaluation, verification, validation and
some necessary modification
– Research models and operational model focus on
different targets (science question vs forecast accuracy)
– There may be several codes exist for a specific problem
(i.e., functions relating flux tube expansion factor to the
solar wind speed). Verification and Validation are needed
to choose the best (or the proper) one.
– Models should be optimized when driven by real time (or
quick-look) data rather than science data.
– Some models failed to meet the requirements of the
downstream models, which may significantly influence
the success of the whole project (such as the CME auto-
detection tool). Automation also brings some problems.
– Modifications are needed to connect different models (the
HAF resolution is changed then optimized codes are
needed to meet the requirements by SEP simulation).
Lessons Learned
Sustainable interaction between research community
and operation community is needed.
– Defining & developing what’s needed is non-trivial.
– Modelers may be unclear about what forecasters want.
Forecasters know exactly what are the most urgent
requirements in operational services.
– Forecasters may don’t know what models can (or could)
do, or the circumstances under which the models are
applicable.
– Forecasters do comprehensive verification and validation
works, focusing on the operational usage. The result
needed to be fed back to the modelers to optimize the
model.
– Iteration is required to derive good forecast products.
A “transition team” approach is workable (forecasters,
computation experts, scientists, managers).
Thanks for you attention.