| Autor: |
Turner, Harriet, Owens, Mathew, Lang, Matthew, Gonzi, Siegfried, Riley, Pete |
| Předmět: |
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| Zdroj: |
Space Weather: The International Journal of Research & Applications; Aug2022, Vol. 20 Issue 8, p1-15, 15p |
| Abstrakt: |
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 orbit. This is used to provide solar wind speed hindcasts. Here, we assimilate observations from the Solar Terrestrial Relations Observatory and the near‐Earth data set, 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. Plain Language Summary: The effects of space weather can be damaging to technologies on Earth, potentially causing power outages and posing a hazard to humans in space. Accurate space weather forecasting requires advanced knowledge of the solar wind; a continual outflow of material from the Sun. Data assimilation (DA) is one method used in terrestrial weather forecasting, whereby model results are combined with observations to create an optimum estimation of reality. Here, we use a solar wind DA scheme to create 3 years of forecasts. It is found that assimilating observations from multiple spacecraft produces better forecasts than assimilating observations from a single spacecraft. It was also found that removing large eruptions, known as coronal mass ejections, from the DA input improves forecasts by reducing false alarms. Key Points: Assimilating in situ data from multiple spacecraft provides higher forecast skill than from any one spacecraft individuallyThe age of observations, in terms of time when the required Carrington longitude was last observed, has a large effect on forecast skillRemoving interplanetary manifestations of CMEs from the assimilated time series provides a small increase in forecast skill [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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