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 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.

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Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/98785
Identification Number/DOI	10.1029/2020SW002698
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Uncontrolled Keywords	Data Assimilation, Space Weather, Solar Wind
Publisher	American Geophysical Union
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Date Deposited:	24 Jun 2021 09:34	Date item deposited into CentAUR
Last Modified:	23 Jun 2024 03:04	Date item last modified

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