EGU21-2771
https://doi.org/10.5194/egusphere-egu21-2771
EGU General Assembly 2021
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Improving solar wind forecasting using Data Assimilation

Matthew Lang1, Jake Witherington1, Harriet Turner1, Mathew Owens1, and Pete Riley2
Matthew Lang et al.
  • 1University of Reading, Meteorology, Reading, United Kingdom of Great Britain – England, Scotland, Wales (matthew.lang@reading.ac.uk)
  • 2Predictive Science Inc., 9990 Mesa Rim Rd., Suite 170, San Diego, CA, 92121, USA

In terrestrial weather prediction, Data Assimilation (DA) has enabled huge improvements in operational forecasting capabilities. It does this by producing more accurate initial conditions and/or model parameters for forecasting; reducing the impacts of the “butterfly effect”. However, data assimilation is still in its infancy in space weather applications and it is not quantitatively understood how DA can improve space weather forecasts.

To this effect, we have used a solar wind DA scheme to assimilate observations from STEREO A, STEREO B and ACE over the operational lifetime of STEREO-B (2007-2014). This scheme allows observational information at 1AU to update and improve the inner boundary of the solar wind model (at 30 solar radii). These improved inner boundary conditions are then input into the efficient solar wind model, HUXt, to produce forecasts of the solar wind over the next solar rotation.

In this talk, I will be showing that data assimilation is capable of improving solar wind predictions not only in near-Earth space, but in the whole model domain, and compare these forecasts to corotation of observations from STEREO-B at Earth. I will also show that the DA forecasts are capable of reducing systematic errors that occur to latitudinal offset in STEREO-B’s corotation forecast.

How to cite: Lang, M., Witherington, J., Turner, H., Owens, M., and Riley, P.: Improving solar wind forecasting using Data Assimilation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2771, https://doi.org/10.5194/egusphere-egu21-2771, 2021.

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