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Solar Physics

, Volume 290, Issue 6, pp 1775–1814 | Cite as

Ensemble Modeling of CMEs Using the WSA–ENLIL+Cone Model

  • M. L. Mays
  • A. Taktakishvili
  • A. Pulkkinen
  • P. J. MacNeice
  • L. Rastätter
  • D. Odstrcil
  • L. K. Jian
  • I. G. Richardson
  • J. A. LaSota
  • Y. Zheng
  • M. M. Kuznetsova
Article

Abstract

Ensemble modeling of coronal mass ejections (CMEs) provides a probabilistic forecast of CME arrival time that includes an estimation of arrival-time uncertainty from the spread and distribution of predictions and forecast confidence in the likelihood of CME arrival. The real-time ensemble modeling of CME propagation uses the Wang–Sheeley–Arge (WSA)–ENLIL+Cone model installed at the Community Coordinated Modeling Center (CCMC) and executed in real-time at the CCMC/Space Weather Research Center. The current implementation of this ensemble-modeling method evaluates the sensitivity of WSA–ENLIL+Cone model simulations of CME propagation to initial CME parameters. We discuss the results of real-time ensemble simulations for a total of 35 CME events that occurred between January 2013 – July 2014. For the 17 events where the CME was predicted to arrive at Earth, the mean absolute arrival-time prediction error was 12.3 hours, which is comparable to the errors reported in other studies. For predictions of CME arrival at Earth, the correct-rejection rate is 62 %, the false-alarm rate is 38 %, the correct-alarm ratio is 77 %, and the false-alarm ratio is 23 %. The arrival time was within the range of the ensemble arrival predictions for 8 out of 17 events. The Brier Score for CME arrival-predictions is 0.15 (where a score of 0 on a range of 0 to 1 is a perfect forecast), which indicates that on average, the predicted probability, or likelihood, of CME arrival is fairly accurate. The reliability of ensemble CME-arrival predictions is heavily dependent on the initial distribution of CME input parameters (e.g. speed, direction, and width), particularly the median and spread. Preliminary analysis of the probabilistic forecasts suggests undervariability, indicating that these ensembles do not sample a wide-enough spread in CME input parameters. Prediction errors can also arise from ambient-model parameters, the accuracy of the solar-wind background derived from coronal maps, or other model limitations. Finally, predictions of the K P geomagnetic index differ from observed values by less than one for 11 out of 17 of the ensembles and K P prediction errors computed from the mean predicted K P show a mean absolute error of 1.3.

Keywords

Coronal mass ejections, modeling Coronal mass ejections, interplanetary Coronal mass ejections, forecasting 

Notes

Acknowledgments

The work was carried out as a part of NASA’s Game Changing Development Program Advanced Radiation Protection Integrated Solar Energetic Proton (ISEP) project. L.K. Jian acknowledges the support of NSF grants AGS 1242798 and 1321493. M.L. Mays thanks T. Nieves-Chinchilla and B.J. Thompson for useful discussions. We gratefully acknowledge the participants of the CME Arrival Time Scoreboard ( kauai.ccmc.gsfc.nasa.gov/CMEscoreboard ). The ACE and Wind solar-wind plasma and magnetic-field data were obtained at NASA’s CDAWeb ( cdaweb.gsfc.nasa.gov ). OMNI data were obtained from NASA’s COHOWeb ( omniweb.gsfc.nasa.gov/coho ). The Dst geomagnetic index was obtained from the World Data Center for Geomagnetism in Kyoto, Japan. Estimated real-time planetary K P indices are from NOAA and the NGDC, and final definitive K P indices are from the Helmholtz Center Potsdam GFZ German Research Centre for Geosciences. The SOHO/LASCO CME catalog is generated and maintained at the CDAW Data Center by NASA and the Catholic University of America in cooperation with the Naval Research Laboratory. SOHO is a mission of international cooperation between the European Space Agency and NASA. The STEREO/SECCHI data are produced by an international consortium of the NRL, LMSAL and NASA GSFC (USA), RAL and University of Birmingham (UK), MPS (Germany), CSL (Belgium), IOTA and IAS (France). Some figure colors are based on ColorBrewer.org .

Disclosure of Potential Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

  1. Anderson, J.L.: 1996, A method for producing and evaluating probabilistic forecasts from ensemble model integrations. J. Climate 9, 1518. ADSCrossRefGoogle Scholar
  2. Arge, C.N., Pizzo, V.J.: 2000, Improvement in the prediction of solar wind conditions using near-real time solar magnetic field updates. J. Geophys. Res. 105, 10465.  DOI. ADS. ADSCrossRefGoogle Scholar
  3. Arge, C.N., Luhmann, J.G., Odstrčil, D., Schrijver, C.J., Li, Y.: 2004, Stream structure and coronal sources of the solar wind during the May 12th, 1997 CME. J. Atmos. Solar-Terr. Phys. 66, 1295.  DOI. ADS. ADSCrossRefGoogle Scholar
  4. Arge, C.N., Henney, C.J., Koller, J., Compeau, C.R., Young, S., MacKenzie, D., Fay, A., Harvey, J.W.: 2010, Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model. In: AIP Conference Proceedings, Twelfth International Solar Wind Conference 1216, 343.  DOI. Google Scholar
  5. Bartels, J., Heck, N.H., Johnston, H.F.: 1939, The three-hour-range index measuring geomagnetic activity. J. Geophys. Res. 44, 411.  DOI. ADS. ADSCrossRefGoogle Scholar
  6. Bock, A., Mays, M.L., Rastaetter, L., Ynnerman, A., Ropinski, T.: 2014, VCMass: A framework for verification of coronal mass ejection ensemble simulations. In: IEEE Scientific Visualization Conference Abstracts. Google Scholar
  7. Brier, G.W.: 1950, Verification of forecasts expressed in terms of probability. Mon. Weather Rev. 78, 1. ADS. ADSCrossRefGoogle Scholar
  8. Brueckner, G.E., Howard, R.A., Koomen, M.J., Korendyke, C.M., Michels, D.J., Moses, J.D., et al.: 1995, The Large Angle Spectroscopic Coronagraph (LASCO) Solar Phys. 162, 357.  DOI. ADS. ADSCrossRefGoogle Scholar
  9. Cohen, C.M.S., Mason, G.M., Mewaldt, R.A., Wiedenbeck, M.E.: 2014, The longitudinal dependence of heavy-ion composition in the 2013 April 11 solar energetic particle event. Astrophys. J. 793, 35.  DOI. ADS. ADSCrossRefGoogle Scholar
  10. Colaninno, R.C., Vourlidas, A., Wu, C.C.: 2013, Quantitative comparison of methods for predicting the arrival of coronal mass ejections at Earth based on multiview imaging. J. Geophys. Res. 118, 6866.  DOI. ADS. CrossRefGoogle Scholar
  11. Davies, J.A., Perry, C.H., Trines, R.M.G.M., Harrison, R.A., Lugaz, N., Möstl, C., Liu, Y.D., Steed, K.: 2013, Establishing a stereoscopic technique for determining the kinematic properties of solar wind transients based on a generalized self-similarly expanding circular geometry. Astrophys. J. 777, 167.  DOI. ADS. ADSCrossRefGoogle Scholar
  12. Domingo, V., Fleck, B., Poland, A.I.: 1995, The SOHO mission: An overview. Solar Phys. 162, 1. ADSCrossRefGoogle Scholar
  13. Dryer, M.: 1974, Interplanetary shock waves generated by solar flares. Space Sci. Rev. 51, 403. ADSGoogle Scholar
  14. Dryer, M., Fry, C.D., Sun, W., Deehr, C., Smith, Z., Akasofu, S.-I., Andrews, M.D.: 2001, Prediction in real time of the 2000 July 14 heliospheric shock wave and its companions during the ‘Bastille’ epoch. Solar Phys. 204, 265.  DOI. ADS. ADSCrossRefGoogle Scholar
  15. Emmons, D., Acebal, A., Pulkkinen, A., Taktakishvili, A., MacNeice, P., Odstrčil, D.: 2013, Ensemble forecasting of coronal mass ejections using the WSA–ENLIL with CONED Model. Space Weather 11, 95.  DOI. ADS. ADSCrossRefGoogle Scholar
  16. Fry, C.D., Dryer, M., Smith, Z., Sun, W., Deehr, C.S., Akasofu, S.-I.: 2003, Forecasting solar wind structures and shock arrival times using an ensemble of models. J. Geophys. Res. 108, 1070.  DOI. ADS. CrossRefGoogle Scholar
  17. Gopalswamy, N., Yashiro, S., Michalek, G., Stenborg, G., Vourlidas, A., Freeland, S., Howard, R.: 2009, The SOHO/LASCO CME catalog. Earth Moon Planets 104, 295.  DOI. ADS. ADSCrossRefGoogle Scholar
  18. Hamill, T.M.: 2001, Interpretation of rank histograms for verifying ensemble forecasts. Mon. Weather Rev. 129, 550. ADS. ADSCrossRefGoogle Scholar
  19. Hamill, T.M., Colucci, S.J.: 1997, Verification of Eta RSM short-range ensemble forecasts. Mon. Weather Rev. 125, 1312. ADS. ADSCrossRefGoogle Scholar
  20. Harvey, L.O., Hammond, K.R., Lusk, C.M., Mross, E.F.: 1992, The application of signal detection theory to weather forecasting behavior. Mon. Weather Rev. 120, 863. ADS. ADSCrossRefGoogle Scholar
  21. Harvey, J.W., Hill, F., Hubbard, R.P., Kennedy, J.R., Leibacher, J.W., Pintar, J.A., Gilman, P.A., Noyes, R.W., Title, A.M., Toomre, J., Ulrich, R.K., Bhatnagar, A., Kennewell, J.A., Marquette, W., Patron, J., Saa, O., Yasukawa, E.: 1996, The Global Oscillation Network Group (GONG) project. Science 272, 1284.  DOI. ADS. ADSCrossRefGoogle Scholar
  22. Henney, C.J., Toussaint, W.A., White, S.M., Arge, C.N.: 2012, Forecasting F10.7 with solar magnetic flux transport modeling. Space Weather 10, 2011.  DOI. ADS. ADSCrossRefGoogle Scholar
  23. Hidalgo, M.A., Cid, C., Medina, J., Viñas, A.F.: 2000, A new model for the topology of magnetic clouds in the solar wind. Solar Phys. 194, 165.  DOI. ADS. ADSCrossRefGoogle Scholar
  24. Howard, T.A., DeForest, C.E.: 2012, The Thomson surface. I. Reality and myth. Astrophys. J. 752, 130.  DOI. ADS. ADSCrossRefGoogle Scholar
  25. Howard, R.A., Moses, J.D., Vourlidas, A., Newmark, J.S., Socker, D.G., Plunkett, S.P., et al.: 2008, Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI). Space Sci. Rev. 136, 67.  DOI. ADS. ADSCrossRefGoogle Scholar
  26. Jackson, B.V., Hick, P.P., Buffington, A., Bisi, M.M., Clover, J.M., Tokumaru, M., Kojima, M., Fujiki, K.: 2011, Three-dimensional reconstruction of heliospheric structure using iterative tomography: A review. J. Atmos. Solar-Terr. Phys. 73, 1214.  DOI. ADS. ADSCrossRefGoogle Scholar
  27. Jian, L.K., Russell, C.T., Luhmann, J.G., MacNeice, P.J., Odstrčil, D., Riley, P., Linker, J.A., Skoug, R.M., Steinberg, J.T.: 2011, Comparison of observations at ACE and Ulysses with Enlil model results: Stream interaction regions during Carrington rotations 2016 – 2018. Solar Phys. 273, 179.  DOI. ADS. ADSCrossRefGoogle Scholar
  28. Jian, L.K., MacNeice, P.J., Taktakishvili, A., Odstrcil, D., Jackson, B., Yu, H.-S., Riley, P., Sokolov, I.V., Evans, R.M.: 2015, accepted, Validation for solar wind prediction at Earth: Comparison of coronal and heliospheric models installed at the CCMC. Space Weather. Google Scholar
  29. Jolliffe, I.T., Stephenson, D.B. (eds.): 2011, Forecast Verification: A Practioner’s Guide in Atmospheric Science, 2nd edn. Wiley, New Jersey. Google Scholar
  30. Kaiser, M.L., Kucera, T.A., Davila, J.M., St. Cyr, O.C., Guhathakurta, M., Christian, E.: 2008, The STEREO mission: An introduction. Space Sci. Rev. 136, 5.  DOI. ADS. ADSCrossRefGoogle Scholar
  31. Lario, D., Raouafi, N.E., Kwon, R.-Y., Zhang, J., Gómez-Herrero, R., Dresing, N., Riley, P.: 2014, The solar energetic particle event on 2013 April 11: An investigation of its solar origin and longitudinal spread. Astrophys. J. 797, 8.  DOI. ADS. ADSCrossRefGoogle Scholar
  32. LaSota, J.A.: 2013, STEREO analysis. Undergraduate Honors Thesis, University of Alaska Fairbanks. Google Scholar
  33. Lee, C.O., Arge, C.N., Odstrčil, D., Millward, G., Pizzo, V., Quinn, J.M., Henney, C.J.: 2013, Ensemble modeling of CME propagation. Solar Phys. 285, 349.  DOI. ADS. ADSCrossRefGoogle Scholar
  34. Liu, Y., Davies, J.A., Luhmann, J.G., Vourlidas, A., Bale, S.D., Lin, R.P.: 2010, Geometric triangulation of imaging observations to track coronal mass ejections continuously out to 1 AU. Astrophys. J. Lett. 710, L82.  DOI. ADS. ADSCrossRefGoogle Scholar
  35. Lugaz, N., Hernandez-Charpak, J.N., Roussev, I.I., Davis, C.J., Vourlidas, A., Davies, J.A.: 2010, Determining the azimuthal properties of coronal mass ejections from multi-spacecraft remote-sensing observations with STEREO SECCHI. Astrophys. J. 715, 493.  DOI. ADS. ADSCrossRefGoogle Scholar
  36. MacNeice, P.: 2009, Validation of community models: 2. Development of a baseline using the Wang–Sheeley–Arge model. Space Weather 7, 12002.  DOI. ADS. ADSGoogle Scholar
  37. Manoharan, P.K.: 2006, Evolution of coronal mass ejections in the inner heliosphere: A study using white-light and scintillation images. Solar Phys. 235, 345.  DOI. ADS. ADSCrossRefGoogle Scholar
  38. Mays, M.L., Taktakishvili, A., Romano, M., MacNeice, P.J., Zheng, Y., Pulkkinen, A.A., Kuznetsova, M.M., Odstrčil, D.: 2015, Validation of real-time modeling of coronal mass ejections using the WSA–ENLIL+Cone Heliospheric Model. Space Weather. (In preparation.) Google Scholar
  39. McKenna-Lawlor, S.M.P., Dryer, M., Kartalev, M.D., Smith, Z., Fry, C.D., Sun, W., Deehr, C.S., Kecskemety, K., Kudela, K.: 2006, Near real-time predictions of the arrival at Earth of flare-related shocks during Solar Cycle 23. J. Geophys. Res. 111, 11103.  DOI. ADS. CrossRefGoogle Scholar
  40. Menvielle, M., Berthelier, A.: 1991, The K-derived planetary indices – Description and availability. Rev. Geophys. 29, 415.  DOI. ADS. ADSCrossRefGoogle Scholar
  41. Millward, G., Biesecker, D., Pizzo, V., Koning, C.A.: 2013, An operational software tool for the analysis of coronagraph images: Determining CME parameters for input into the WSA–Enlil heliospheric model. Space Weather 11, 57.  DOI. ADS. ADSCrossRefGoogle Scholar
  42. Müller, D., Dimitoglou, G., Caplins, B., Ireland, J., Wamsler, B., Hughitt, K., Agheksanterian, A. Amadigwe, D.: 2009, JHelioviewer – Visualizing large sets of solar data using JPEG 2000. Comput. Sci. Eng. 11, 38. CrossRefGoogle Scholar
  43. Murphy, A.H.: 1973, A new vector partition of the probability score. J. Appl. Meteorol. 12, 595. ADSCrossRefGoogle Scholar
  44. Newell, P.T., Sotirelis, T., Liou, K., Meng, C.-I., Rich, F.J.: 2007, A nearly universal solar wind-magnetosphere coupling function inferred from 10 magnetospheric state variables. J. Geophys. Res. 112, 1206.  DOI. ADS. CrossRefGoogle Scholar
  45. Odstrčil, D.: 2003, Modeling 3-D solar wind structure. Adv. Space Res. 32, 497.  DOI. ADS. ADSCrossRefGoogle Scholar
  46. Odstrčil, D., Pizzo, V.J.: 1999a, Three-dimensional propagation of CMEs in a structured solar wind flow: 1. CME launched within the streamer belt. J. Geophys. Res. 104, 483.  DOI. ADS. ADSCrossRefGoogle Scholar
  47. Odstrčil, D., Pizzo, V.J.: 1999b, Three-dimensional propagation of coronal mass ejections in a structured solar wind flow 2. CME launched adjacent to the streamer belt. J. Geophys. Res. 104, 493.  DOI. ADS. ADSCrossRefGoogle Scholar
  48. Odstrčil, D., Riley, P., Zhao, X.P.: 2004, Numerical simulation of the 12 May 1997 interplanetary CME event. J. Geophys. Res. 109, 2116.  DOI. ADS. CrossRefGoogle Scholar
  49. Odstrčil, D., Smith, Z., Dryer, M.: 1996, Distortion of the heliospheric plasma sheet by interplanetary shocks. Geophys. Res. Lett. 23, 2521.  DOI. ADS. ADSCrossRefGoogle Scholar
  50. Pizzo, V.J., Biesecker, D.A.: 2004, Geometric localization of STEREO CMEs. Geophys. Res. Lett. 31, 21802.  DOI. ADS. ADSCrossRefGoogle Scholar
  51. Pulkkinen, A., Oates, T., Taktakishvili, A.: 2010, Automatic determination of the conic coronal mass ejection model parameters. Solar Phys. 261, 115.  DOI. ADS. ADSCrossRefGoogle Scholar
  52. Pulkkinen, A.A., Taktakishvili, A., Odstrčil, D., MacNeice, P.J.: 2011, Ensemble forecasting of coronal mass ejection propagation in the interplanetary medium. NOAA Space Weather Workshop Abstracts. Google Scholar
  53. Richardson, I.G., Cane, H.V.: 2010, Near-Earth interplanetary coronal mass ejections during Solar Cycle 23 (1996 – 2009): Catalog and summary of properties. Solar Phys. 264, 189.  DOI. ADS. ADSCrossRefGoogle Scholar
  54. Riley, P., Linker, J.A., Mikić, Z.: 2001, An empirically-driven global MHD model of the solar corona and inner heliosphere. J. Geophys. Res. 106, 15889.  DOI. ADS. ADSCrossRefGoogle Scholar
  55. Romano, M., Mays, M.L., Taktakishvili, A., MacNeice, P.J., Zheng, Y., Pulkkinen, A.A., Kuznetsova, M.M., Odstrčil, D.: 2013, Validation of real-time modeling of coronal mass ejections using the WSA–ENLIL+Cone Heliospheric Model. AGU Fall Meeting Abstracts, A2156. ADS.
  56. Rostoker, G.: 1972, Geomagnetic indices. Rev. Geophys. Space Phys. 10, 935.  DOI. ADS. ADSCrossRefGoogle Scholar
  57. Sivillo, J.K., Ahlquist, J.E., Toth, Z.: 1997, An ensemble forecasting primer. Weather Forecast. 12, 809. ADS. ADSCrossRefGoogle Scholar
  58. Smith, Z., Dryer, M.: 1990, Mhd study of temporal and spatial evolution of simulated interplanetary shocks in the ecliptic plane within 1 AU. Solar Phys. 129(2), 387.  DOI. ADSCrossRefGoogle Scholar
  59. Smith, Z.K., Dryer, M., McKenna-Lawlor, S.M.P., Fry, C.D., Deehr, C.S., Sun, W.: 2009, Operational validation of HAFv2’s predictions of interplanetary shock arrivals at Earth: Declining phase of Solar Cycle 23. J. Geophys. Res. 114, 5106.  DOI. ADS. CrossRefGoogle Scholar
  60. Sugiura, M.: 1964, Hourly values of equatorial Dst for the IGY. Ann. Int. Geophys. Year 35(9), 945. Google Scholar
  61. Taktakishvili, A., MacNeice, P., Odstrčil, D.: 2010, Model uncertainties in predictions of arrival of coronal mass ejections at Earth orbit. Space Weather 8, 6007.  DOI. ADS. ADSCrossRefGoogle Scholar
  62. Talagrand, O., Vautard, R., Strauss, B.: 1997, Evaluation of probabilistic prediction systems. In: Proceedings of the ECMWF Workshop on Predictability, ECMWF, Shinfield Park Reading, 157. Google Scholar
  63. Thernisien, A.F.R., Howard, R.A., Vourlidas, A.: 2006, Modeling of flux rope coronal mass ejections. Astrophys. J. 652, 763.  DOI. ADS. ADSCrossRefGoogle Scholar
  64. Vršnak, B., Temmer, M., Žic, T., Taktakishvili, A., Dumbović, M., Möstl, C., Veronig, A.M., Mays, M.L., Odstrčil, D.: 2014, Heliospheric propagation of coronal mass ejections: Comparison of numerical WSA–ENLIL+Cone Model and Analytical Drag-Based Model. Astrophys. J. Suppl. 213, 21.  DOI. ADS. ADSCrossRefGoogle Scholar
  65. Weigel, R.S., Detman, T., Rigler, E.J., Baker, D.N.: 2006, Decision theory and the analysis of rare event space weather forecasts. Space Weather 4, 5002.  DOI. ADS. ADSCrossRefGoogle Scholar
  66. Wilks, D.S.: 1995, Statistical Methods in Atmospheric Sciences: An Introduction, Academic Press, Massachusetts. Google Scholar
  67. Xie, H., Ofman, L., Lawrence, G.: 2004, Cone model for halo CMEs: Application to space weather forecasting. J. Geophys. Res. 109, 3109.  DOI. ADS. CrossRefGoogle Scholar
  68. Yashiro, S., Gopalswamy, N., Michalek, G., St. Cyr, O.C., Plunkett, S.P., Rich, N.B., Howard, R.A.: 2004, A catalog of white light coronal mass ejections observed by the SOHO spacecraft. J. Geophys. Res. 109, 7105.  DOI. ADS. CrossRefGoogle Scholar
  69. Zhao, X., Dryer, M.: 2014, Current status of CME/shock arrival time prediction. Space Weather 12, 448.  DOI. ADS. ADSCrossRefGoogle Scholar
  70. Zhao, X.P., Plunkett, S.P., Liu, W.: 2002, Determination of geometrical and kinematical properties of halo coronal mass ejections using the cone model. J. Geophys. Res. 107, 1223.  DOI. ADS. CrossRefGoogle Scholar
  71. Zheng, Y., Macneice, P., Odstrčil, D., Mays, M.L., Rastaetter, L., Pulkkinen, A., Taktakishvili, A., Hesse, M., Masha Kuznetsova, M., Lee, H., Chulaki, A.: 2013, Forecasting propagation and evolution of CMEs in an operational setting: What has been learned. Space Weather 11, 557.  DOI. ADS. ADSCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • M. L. Mays
    • 1
    • 2
  • A. Taktakishvili
    • 1
    • 2
  • A. Pulkkinen
    • 2
  • P. J. MacNeice
    • 2
  • L. Rastätter
    • 2
  • D. Odstrcil
    • 2
    • 3
  • L. K. Jian
    • 2
    • 4
  • I. G. Richardson
    • 4
    • 5
  • J. A. LaSota
    • 6
  • Y. Zheng
    • 2
  • M. M. Kuznetsova
    • 2
  1. 1.Catholic University of AmericaWashingtonUSA
  2. 2.Heliophysics Science DivisionNASA Goddard Space Flight CenterGreenbeltUSA
  3. 3.George Mason UniversityFairfaxUSA
  4. 4.Department of AstronomyUniversity of MarylandCollege ParkUSA
  5. 5.CRESST/Department of AstronomyUniversity of MarylandCollege ParkUSA
  6. 6.University of Illinois at Urbana-ChampaignChampaignUSA

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