Lessons Learned in Transitioning Solar-Interplanetary Research models into Operational Services
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1 Lessons Learned in Transitioning Solar-Interplanetary Research models into Operational Services Siqing Liu 1, Bingxian Luo 1, Jiancun Gong 1, Wengeng Huang, 1 Jingjing Wang 1, Yuming Wang 2, Chuanbing Wang 2 1 National Space Science Center, CAS 2 University of Science and Technology of China
2 Outlines Motivations Progress Lessons Learned
3 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
4 Motivations Solar Interplanetary Forecasting System Solar Observations Empirical Models Kinematic Models Solar Magnet -ogram CME s Corona -graph PFSS + WSA Coronal B Field Solar Wind speed CME detection and fitting Model CME detection, CME size, initial speed and direction HAF + SEPM Solar Wind speed, density, B Field, and energetic particles Earth Sun Near the sun Interplanetary Earth
5 Progress Transition Team 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.
6 Progress Chart of R2O process Forecast needs CIRs arrival CME arrival CME-driven shock SEP Models/tools we have Background SW models (PFSS+HAF) CME detection and fitting models (SEEDS+Cones) SW propagation models (HAF) Particle acceleration models (PATH) Input data we can get Solar magnetic field map Coronagraph images System integration Background solar wind model CME-detection/fitting model CME propagation model SEP simulation model Model improvement Focusing on halo-cmes (new CME detection models) Human-computer interaction (CME fitting accuracy) Model verification CME detection percentage CME parameters accuracy CME propagation evaluation SEP spectrum
7 Progress Solar Interplanetary Forecasting System Background solar wind (CIR) CME detection and fitting CME propagation SEP Simulation PFSS model SCC/HCCS/CSSS WSA model HAF model Auto-detection (SEEDS) Manned-detection Cone model Ice-cream Cone HAF model Diffusive Shock acceleration model
8 Part 1 : background solar wind - PFSS+WSA+HAF model Photospheric field Source Surface field Problem: Source Surface There are four different functions Derivedto relate the SW speed coronal holes solar wind speed to the flux tube expansion factor.
9 Part 2 : CME detection and fitting auto detection Corona graphic observations J-map and Hough transform Problem: CME collection The SEEDS algorithm yields CME poor front performance trace for Halo-CME and identification detections.
10 Part 2 : CME detection and fitting manned detection Find CME Draw line Deriving CME trace Getting points
11 Part 2 : CME detection and fitting Ice-cream cone model CME front trace Ice-cream Cone model assumption Fitting
12 Part 3 : CME Propagation Model - HAF Source Surface field SW density and Speed Source Surface SW speed SW pressure and magnetic field
13 Part 4 : SEP simulation (Diffusive Shock accelerative Code)
14 Progress: System Integration CME Searching Trace CME front Download results Output from CME fitting Input for propagation
15 Case study : March 15,2015 CME observation C9.1 flare erupted from AR2297 (S24W38) on 00:45UTC, March 15 Cone model fitting Propagation direction : N01E07 Angular width : 126 degree Propagation velocity: 1130 km/s
16 Case study : March 15,2015 IMF at Earth Density and magnetic field DVE at Earth Pressure and magnetic field
17 Case study : March 15,2015 Predicted Shock Arrival Time Difference (hrs) Confidence (%) Method 03-16T23:00Z SEPC model 03-17T18:00Z WSA-ENLIL + Cone (NOAA/SWPC) 03-17T18:08Z (-15.0h, +26.3h) COMESEP 03-16T18:00Z (-12.0h, +12.0h) Other (SIDC) 03-17T12:00Z (-12.0h, +6.0h) WSA-ENLIL + Cone (Met Office) 03-17T11:39Z (-7.0h, +7.0h) WSA-ENLIL + Cone (GSFC SWRC) 03-17T11:48Z (-5.3h, +7.7h) Ensemble WSA-ENLIL + Cone (GSFC SWRC) 03-17T10:55Z Average of all Methods Data from CCMC
18 Case study : June 18 and 21,2015 CME observation M3 flare erupted from S24W38 on 13:30UTC, June 18 CME observation M2 flare erupted from AR2371 (N13W00) on 01:42UTC, June 21 Cone model fitting Propagation direction : S21W32,S09W04 Angular width : 194 degree,148 degree Propagation velocity: 630 km/s,850 km/s
19 Case study : June 18 and 21,2015 IMF at Earth Density and magnetic field DVE at Earth Pressure and magnetic field
20 Case study : June 18,2015 Predicted Shock Arrival Time Difference (hrs) Confidence (%) Method 06-20T20:00Z SEPC model 06-21T02:20Z (-4.53h, +7.12h) Ensemble WSA-ENLIL + Cone (GSFC SWRC) 06-21T21:008Z WSA-ENLIL + Cone (NOAA/SWPC) 06-21T09:26Z (-7.0h, +7.0h) WSA-ENLIL + Cone (GSFC SWRC) 06-21T08:00Z (-12.0h, +12.0h) Other (SIDC) 06-21T16:00Z (-12.0h, +12.0h) WSA-ENLIL + Cone (Met Office) 06-21T11:21Z Average of all Methods Data from CCMC
21 Case study : June 21,2015 Predicted Shock Arrival Time Difference (hrs) Confidence (%) Method 06-22T16:00Z SEPC model 06-22T17:00Z (-12.0h, +12.0h) Other (SIDC) 06-22T21:00Z WSA-ENLIL + Cone (Met Office) 06-22T21:43Z (-7.0h, +7.0h) WSA-ENLIL + Cone (GSFC SWRC) 06-22T19:03Z (-5.15h, +3.33h) Ensemble WSA-ENLIL + Cone (GSFC SWRC) 06-22T23:00Z (+7.0h) DBM 06-22T22:50Z (-5.0h, +8.0h) ElEvo 06-22T14:00Z WSA-ENLIL + Cone (NOAA/SWPC) 06-22T19:48Z Average of all Methods Data from CCMC
22 Case study : August 12 and 14,2015 CME observation Associated with prominence erupted on 13:42Z, August 12 on 13:30UTC, June 18 CME observation Associated with filament erupted below AR2399 (S07W47) on 06:30Z, August 14 Cone model fitting Propagation direction : N28E34,N34W00 Angular width : 170 degree,34 degree Propagation velocity: 570 km/s,490 km/s
23 Case study : August 12 and 14,2015 IMF at Earth Density and magnetic field DVE at Earth Pressure and magnetic field
24 Case study : August 12,2015 Predicted Shock Arrival Time Difference (hrs) Confidence (%) Method 08-14T23:00Z SEPC model 08-16T04:00Z (-12.0h, +12.0h) Other (SIDC) 08-16T09:09Z (-7.0h, +7.0h) Ensemble WSA-ENLIL + Cone (GSFC SWRC) 08-16T03:00Z WSA-ENLIL + Cone (NOAA/SWPC) 08-16T06:02Z (-7.0h, +6.66h) WSA-ENLIL + Cone (GSFC SWRC) 08-16T00:01Z (-6.0h, +6.0h) WSA-ENLIL + Cone (Met Office) 08-16T04:26Z Average of all Methods Data from CCMC
25 Case study : August 14,2015 Predicted Shock Arrival Time Difference (hrs) Confidence (%) Method 08-19T00:00Z (SWIDM: Will not arrive! ) ---- SEPC model 08-18T12:00Z (-7.0h, +7.0h) WSA-ENLIL + Cone (GSFC SWRC) 08-18T05:00Z WSA-ENLIL + Cone (NOAA/SWPC) 08-19T12:00Z (-12.0h, +12.0h) Other (SIDC) 08-18T18:00Z (-12.0h, +8.0h) WSA-ENLIL + Cone (Met Office) 08-18T17:45Z Average of all Methods Data from CCMC
26 Case study: 17 CMEs Difference (hrs) SEPC SWPC SWRC MET SIDC March 15, April 4, April 6, May 6, May 2, June 9, June 18, June 19, June 21, June 22, June 25, July 19, August 12, Sep 04, Sep 18, Oct 22, Nov 4, Absolute Mean
27 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 autodetection 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).
28 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).
29 Thanks for you attention.
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