A document by Harriet Turner. Click on the document to view its contents.

Knowledge of the ambient solar wind is important for accurate space-weather forecasting. A simple-but-effective method of forecasting near-Earth solar-wind conditions is “corotation”, wherein solar-wind structure is assumed to be fixed in the reference frame rotating with the Sun. Under this approximation, observations at a source spacecraft can be rotated to a target location, such as Earth. Forecast accuracy depends upon the rate of solar-wind evolution, longitudinal and latitudinal separation between the source and target, and latitudinal structure in the solar wind itself. The time-evolution rate and latitudinal structure of the solar wind are both strongly influenced by the solar cycle, though in opposing ways. A latitudinal offset is typically present, introducing an error to corotation forecasts. In this study, we use observations from the STEREO and near-Earth spacecraft to quantify the latitudinal error. Aliasing between the solar cycle and STEREO orbits means that individual contributions to the forecast error are difficult to isolate. However, by considering an 18-month interval near the end of solar minimum, we find that the latitudinal-offset contribution to corotation-forecast error cannot be directly detected for offsets <6º, but is increasingly important as offsets increase. This result can be used to improve solar-wind data assimilation, allowing representivity errors in solar-wind observations to be correctly specified. Furthermore, as the maximum latitudinal offset between L5 and Earth is ≈5º, corotation forecasts from a future L5 spacecraft should not be greatly affected by latitudinal offset.

Accurate space weather forecasting requires advanced knowledge of the solar wind conditions in near-Earth space. Data assimilation (DA) combines model output and observations to find an optimum estimation of reality and has led to large advances in terrestrial weather forecasting. It is now being applied to space weather forecasting. Here, we use solar wind DA with in-situ observations to reconstruct solar wind speed in the ecliptic plane between 30 solar radii and Earth’s orbital radius. This is used to provide solar wind speed hindcasts. Here, we assimilate observations from the Solar Terrestrial Relations Observatory (STEREO) and the near-Earth dataset, OMNI. Analysis of two periods of time, one in solar minimum and one in solar maximum, reveals that assimilating observations from multiple spacecraft provides a more accurate forecast than using any one spacecraft individually. The age of the observations also has a significant impact on forecast error, whereby the mean absolute error (MAE) sharply increases by up to 23% when the forecast lead time first exceeds the corotation time associated with the longitudinal separation between the observing spacecraft and the forecast location. It was also found that removing coronal mass ejections from the DA input and verification time series reduces the forecast MAE by up to 10% as it removes false streams from the forecast time series. This work highlights the importance of an L5 space weather monitoring mission for near-Earth solar wind forecasting and suggests that an additional mission to L4 would further improve future solar wind DA forecasting capabilities.