lessons learned in transitioning solar-interplanetary...
TRANSCRIPT
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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
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Outlines
Motivations
Progress
Lessons Learned
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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
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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
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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
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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
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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
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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.
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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.
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Part 2 : CME detection and fitting – manned detection
Find CME Draw line
Getting pointsDeriving CME trace
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Part 2 : CME detection and fitting – Ice-cream cone model
CME front trace Ice-cream Cone model assumption
Fitting
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SW density and Speed
SW pressure and magnetic field
Part 3 : CME Propagation Model - HAF
Source Surface field
Source Surface SW speed
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Part 4 : SEP simulation (Diffusive Shock accelerative Code)
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Progress: System Integration
CME Searching Trace CME front
Output from CME fittingInput for propagationDownload results
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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
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Case study : March 15 ,2015
IMF at Earth
DVE at Earth
Density and magnetic field
Pressure and magnetic field
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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
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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
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Case study : June 18 and 21 ,2015
IMF at Earth
DVE at Earth
Density and magnetic field
Pressure and magnetic field
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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
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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
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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
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Case study : August 12 and 14 ,2015
IMF at Earth
DVE at Earth
Density and magnetic field
Pressure and magnetic field
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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
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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
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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
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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).
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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).
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Thanks for you attention.