Improving solar wind forecasting using data assimilation

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Lang, M. ORCID: https://orcid.org/0000-0002-1904-3700, Witherington, J., Turner, H. ORCID: https://orcid.org/0000-0002-4012-8004, Owens, M. J. ORCID: https://orcid.org/0000-0003-2061-2453 and Riley, P. ORCID: https://orcid.org/0000-0002-1859-456X (2021) Improving solar wind forecasting using data assimilation. Space Weather, 19 (7). ISSN 1542-7390

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To link to this item DOI: 10.1029/2020SW002698

Abstract/Summary

Data Assimilation (DA) has enabled huge improvements in the skill of terrestrial operational weather forecasting. In this study, we use a variational DA scheme with a computationally efficient solar wind model and in situ observations from STEREO-A, STEREO-B and ACE. This scheme enables solar-wind observations far from the Sun, such as at 1 AU, to update and improve the inner boundary conditions of the solar wind model (at $30$ solar radii). In this way, observational information can be used to improve estimates of the near-Earth solar wind, even when the observations are not directly downstream of the Earth. This allows improved initial conditions of the solar wind to be passed into forecasting models. To this effect, we employ the HUXt solar wind model to produce 27-day forecasts of the solar wind during the operational lifetime of STEREO-B (01 November 2007 - 30 September 2014). In near-Earth space, we compare the accuracy of these DA forecasts with both non-DA forecasts and simple corotation of STEREO-B observations. We find that $27$-day root mean-square error (RMSE) for STEREO-B corotation and DA forecasts are comparable and both are significantly lower than non-DA forecasts. However, the DA forecast is shown to improve solar wind forecasts when STEREO-B's latitude is offset from Earth, which is an issue for corotation forecasts. And the DA scheme enables the representation of the solar wind in the whole model domain between the Sun and the Earth to be improved, which will enable improved forecasting of CME arrival time and speed.

Item Type:	Article
Refereed:	Yes
Divisions:	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:	98785
Uncontrolled Keywords:	Data Assimilation, Space Weather, Solar Wind
Publisher:	American Geophysical Union

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Amezcua, J., M. Goodliff, and P. J. Van Leeuwen (2017), A weak-constraint 4DEnsembleVar. Part I: formulation and simple model experiments,Tellus A: Dynamic Meteorology and Oceanography,69(1), 1271,564, doi:10.1080/16000870.2016.1271564. Arge, C. N., D. Odstrcil, V. J. Pizzo, and L. R. Mayer (2003), Improved Method for Specifying Solar Wind Speed Near the Sun, in AIP Conference Proceedings, vol.679, pp. 190–193, American Institute of Physics Inc., doi:10.1063/1.1618574. Arge, C. N., C. J. Henney, J. Koller, C. R. Compeau, S. Young, D. MacKenzie,A. Fay, J. W. Harvey, M. Maksimovic, K. Issautier, N. Meyer-Vernet, M. Moncuquet, and F. Pantellini (2010), Air Force Data Assimilative Photospheric Flux Transport (ADAPT) Model, in AIP Conference Proceedings, vol. 1216, pp. 343–346, American Institute of Physics, doi:10.1063/1.3395870. Bannister, R. (2007), Elementary 4D-Var, DARC Technical Report No.2. Barnard, L., M. J. Owens, C. J. Scott, and C. A. Koning (2020), Ensemble CME Modeling Constrained by Heliospheric Imager Observations, AGU Advances, 1(3), doi:10.1029/2020av000214. Bayes, T., and R. Price (1763), An Essay towards solving a Problem in the Doctrine of Chances. By the late Rev. Mr. Bayes, FRS communicated by Mr. Price,in a letter to John Canton, AMFRS, Philosophical Transactions (1683-1775), pp.370–418. Bocquet, M., and P. Sakov (2014), An iterative ensemble Kalman smoother, Quarterly Journal of the Royal Meteorological Society,140(682), 1521–1535, doi:10.1002/qj.2236. Breen, A. R., R. A. Fallows, M. M. Bisi, P. Thomasson, C. A. Jordan, G. Wannberg, and R. A. Jones (2006), Extremely long baseline interplanetary scintillation measurements of solar wind velocity, Journal of Geophysical Research,111(A8),A08,104, doi:10.1029/2005JA011485. Browne, P. A., and S. Wilson (2015), A simple method for integrating a complex model into an ensemble data assimilation system using MPI, Environmental Mod-elling & Software,68, 122–128. Bust, G. S., and C. N. Mitchell (2008), History, current state, and future directions of ionospheric imaging,Reviews of Geophysics,46(1), RG1003, doi:–26– Confidential manuscript submitted to Space Weather 10.1029/2006RG000212. Durazo, J. A., E. J. Kostelich, and A. Mahalov (2017), Local ensemble transform Kalman filter for ionospheric data assimilation: Observation influence analysis during a geomagnetic storm event, Journal of Geophysical Research: Space Physics, 122(9), 9652–9669, doi:10.1002/2017JA024274. Errico, R. M. (1997), What is an adjoint model?, Bulletin of the American Meteorological Society,78(11), 2577–2591. Evensen, G., D. P. Dee, and J. Schr oter (1998), Parameter estimation in dynamical models, in Ocean Modeling and Parameterization, pp. 373–398, Springer. Eyles, C. J., R. A. Harrison, C. J. Davis, N. R. Waltham, B. M. Shaughnessy, H. C. A. Mapson-Menard, D. Bewsher, S. R. Crothers, J. A. Davies, G. M. Sim-nett, R. A. Howard, J. D. Moses, J. S. Newmark, D. G. Socker, J.-P. Halain, J.-M.Defise, E. Mazy, and P. Rochus (2009), The Heliospheric Imagers Onboard the STEREO Mission, Solar Physics,254(2), 387–445, doi:10.1007/s11207-008-9299-0. Goodliff, M., J. Amezcua, and P. J. Van Leeuwen (2017), A weak-constraint 4DEnsembleVar. Part II: experiments with larger models, Tellus A: Dynamic Meteorology and Oceanography,69(1), 1271,565, doi:10.1080/16000870.2016.1271565. Hapgood, M. A. (2011), Towards a scientific understanding of the risk from extreme space weather, Advances in Space Research,47(12), 2059–2072, doi:10.1016/j.asr.2010.02.007. Howes, K. E., A. M. Fowler, and A. S. Lawless (2017), Accounting for model error in strong-constraint 4D-Var data assimilation, Quarterly Journal of the Royal Meteorological Society,143(704), 1227–1240, doi:10.1002/qj.2996. Kaiser, M. L., T. A. Kucera, J. M. Davila, O. C. S. Cyr, M. Guhathakurta, and E. Christian (2008), The STEREO mission: An introduction, in The STEREO Mission, pp. 5–16, Springer. Kalnay, E. (2003), Atmospheric modeling, data assimilation and predictability, Cambridge university press. Kohutova, P., F.-X. Bocquet, E. M. Henley, and M. J. Owens (2016), Improving solar wind persistence forecasts: Removing transient space weather events, and using observations away from the Sun-Earth line, Space Weather,14(10), 802–818,doi:10.1002/2016SW001447.–27– Confidential manuscript submitted to Space Weather Lang, M., and M. J. Owens (2019), A Variational Approach to Data Assimilation in the Solar Wind, Space Weather, 17(1), 59–83, doi:10.1029/2018SW001857. Lang, M., P. J. van Leeuwen, and P. A. Browne (2016), A systematic method of parameterisation estimation using data assimilation,Tellus A,68(0). Lang, M., P. Browne, P. J. van Leeuwen, and M. Owens (2017), Data Assimilation in the Solar Wind: Challenges and First Results, Space Weather,15(11), 1490–1510, doi:10.1002/2017SW001681. Linker, J. A., Z. Miki ́c, D. A. Biesecker, R. J. Forsyth, S. E. Gibson, A. J. Lazarus, A. Lecinski, P. Riley, A. Szabo, and B. J. Thompson (1999), Magnetohydrodynamic modeling of the solar corona during Whole Sun Month, Journal of Geophysical Research: Space Physics, 104(A5), 9809–9830, doi:10.1029/1998JA900159. Mackay, D., and A. Yeates (2012), The Sun’s Global Photospheric and Coronal Magnetic Fields: Observations and Models, Living Reviews in Solar Physics, 9(1), 6,doi:10.12942/lrsp-2012-6. McGregor, S. L., W. J. Hughes, C. N. Arge, M. J. Owens, and D. Odstrcil(2011), The distribution of solar wind speeds during solar minimum: Calibration for numerical solar wind modeling constraints on the source of the slowsolar wind, Journal of Geophysical Research: Space Physics, 116(A3), doi:10.1029/2010JA015881. Merkin, V. G., J. G. Lyon, D. Lario, C. N. Arge, and C. J. Henney (2016), Time-dependent magnetohydrodynamic simulations of the inner heliosphere, Journal of Geophysical Research: Space Physics,121(4), 2866–2890, doi:10.1002/2015JA022200. Odstrcil, D. (2003), Modeling 3-D solar wind structure, Advances in Space Research, 32(4), 497–506. Owens, M., M. Lang, L. Barnard, P. Riley, M. Ben-Nun, C. J. Scott, M. Lockwood, M. A. Reiss, C. N. Arge, and S. Gonzi (2020a), A Computationally Efficient, Time-Dependent Model of the Solar Wind for Use as a Surrogate to Three-Dimensional Numerical Magnetohydrodynamic Simulations, Solar Physics,295(3),43, doi:10.1007/s11207-020-01605-3. Owens, M. J. (2018), Time-Window Approaches to Space-Weather Forecast Metrics: A Solar Wind Case Study, Space Weather,16(11), 1847–1861, doi:10.1029/2018SW002059.–28– Confidential manuscript submitted to Space Weather Owens, M. J. (2020), Solar-Wind Structure, in Oxford Research Encyclopedia of Physics, Oxford University Press, doi:10.1093/acrefore/9780190871994.013.19. Owens, M. J., and R. J. Forsyth (2013), The heliospheric magnetic field, Living Reviews in Solar Physics,10(5), 5, doi:10.12942/lrsp-2013-5. Owens, M. J., and P. Riley (2017), Probabilistic Solar Wind Forecasting Using Large Ensembles of Near-Sun Conditions With a Simple One-Dimensional “Upwind” Scheme, Space Weather, 15(11), 1461–1474, doi:10.1002/2017SW001679. Owens, M. J., M. Lang, P. Riley, M. Lockwood, and A. S. Lawless (2020b), Quantifying the latitudinal representivity of in situ solar wind observations, Journal of Space Weather and Space Climate,10, 8, doi:10.1051/swsc/2020009. Poedts, S., and J. Pomoell (2017), EUHFORIA: a solar wind and CME evolution model, 19th EGU General Assembly, EGU2017, proceedings from the conference held 23-28 April, 2017 in Vienna, Austria., p.7396,19, 7396. Riley, P., and R. Lionello (2011), Mapping Solar Wind Streams from the Sun to 1 AU: A Comparison of Techniques, Solar Physics,270(2), 575–592, doi:10.1007/s11207-011-9766-x. Riley, P., J. A. Linker, R. Lionello, and Z. Mikic (2012), Corotating interaction regions during the recent solar minimum: The power and limitations of global MHD modeling, Journal of Atmospheric and Solar-Terrestrial Physics, 83, 1–10, doi:10.1016/J.JASTP.2011.12.013. Riley, P., J. A. Linker, and C. N. Arge (2015), On the role played by magnetic expansion factor in the prediction of solar wind speed, Space Weather,13(3),154–169, doi:10.1002/2014SW001144. Stone, E. C., A. M. Frandsen, R. A. Mewaldt, E. R. Christian, D. Margolies,J. F. Ormes, and F. Snow (1998), The advanced composition explorer, in The Advanced Composition Explorer Mission, vol. 86, pp. 1–22, Springer, doi:10.1023/A:1005082526237. Thomas, S. R., A. Fazakerley, R. T. Wicks, and L. Green (2018), Evaluating the Skill of Forecasts of the Near-Earth Solar Wind Using a Space Weather Monitor at L5,Space Weather,16(7), 814–828, doi:10.1029/2018SW001821

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Improving solar wind forecasting using data assimilation

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