Improving Solar Wind Forecasting Using Data Assimilation. Issue 7 (8th July 2021)

• Record Type: Journal Article
• Authors: Lang, Matthew; Witherington, Jake; Turner, Harriet; Owens, Mathew J.; Riley, Pete
• Is Part Of: Space weather. Volume 19:Issue 7(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-08
• Subjects: solar wind -- data assimilation -- space weather -- forecasting -- corotation; Space environment -- Periodicals; 551.509992
• DOI: 10.1029/2020SW002698
• Languages: English
• ISSNs: 1542-7390

Abstract

Abstract: 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 (November 01, 2007–September 30, 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 the 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. Plain Language Summary: Forecasting the adverse effects of space weather at Earth requires accurate modeling of the solar wind, the continuous outflow of matter from the Sun's atmosphere into the solar system. At present, computer simulations of the solar wind are generated from telescopic observations of the Sun's surface. The simulations then propagate structures to the Earth with no further input from observations. However, spacecraft also take direct measurements of the solar wind near Earth's orbit. Using a technique called "data assimilation" (DA) we are able to combine these direct measurements with solar wind models to provide improved forecasts of the solar wind. To quantify the ability of DA to improve forecasts, we generate 27‐day forecasts for the solar wind using DA over the lifetime of the STEREO‐B spacecraft (2007–2014) and find that, on average over the 7‐year period, DA can reduce forecast errors by around a third. Furthermore, we see that DA is able to reduce biases from available spacecraft observations being misaligned with Earth. Key Points: Data assimilation (DA) is shown to improve solar wind speed forecasts over the interval 2007–2014 Initializing forecasts with DA leads to a 31.4% reduction in solar wind speed root mean square error (RMSE) compared to forecasts with no DA DA can remove significant positive correlations between forecast RMSE and latitude of STEREO‐B