Quantifying the Effect of ICME Removal and Observation Age for in Situ Solar Wind Data Assimilation
Abstract 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...
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| Format: | Article |
| Language: | English |
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Wiley
2022-08-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2022SW003109 |
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| _version_ | 1841536464168419328 |
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| author | Harriet Turner Mathew Owens Matthew Lang Siegfried Gonzi Pete Riley |
| author_facet | Harriet Turner Mathew Owens Matthew Lang Siegfried Gonzi Pete Riley |
| author_sort | Harriet Turner |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-1ebf346bc33c4cfe905d2760e77eb20a |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2022-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-1ebf346bc33c4cfe905d2760e77eb20a2025-01-14T16:27:07ZengWileySpace Weather1542-73902022-08-01208n/an/a10.1029/2022SW003109Quantifying the Effect of ICME Removal and Observation Age for in Situ Solar Wind Data AssimilationHarriet Turner0Mathew Owens1Matthew Lang2Siegfried Gonzi3Pete Riley4Department of Meteorology University of Reading Reading UKDepartment of Meteorology University of Reading Reading UKDepartment of Meteorology University of Reading Reading UKMet Office Exeter UKPredictive Science Inc San Diego CA USAAbstract 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.https://doi.org/10.1029/2022SW003109solar windspace weatherforecastingdata assimilation |
| spellingShingle | Harriet Turner Mathew Owens Matthew Lang Siegfried Gonzi Pete Riley Quantifying the Effect of ICME Removal and Observation Age for in Situ Solar Wind Data Assimilation Space Weather solar wind space weather forecasting data assimilation |
| title | Quantifying the Effect of ICME Removal and Observation Age for in Situ Solar Wind Data Assimilation |
| title_full | Quantifying the Effect of ICME Removal and Observation Age for in Situ Solar Wind Data Assimilation |
| title_fullStr | Quantifying the Effect of ICME Removal and Observation Age for in Situ Solar Wind Data Assimilation |
| title_full_unstemmed | Quantifying the Effect of ICME Removal and Observation Age for in Situ Solar Wind Data Assimilation |
| title_short | Quantifying the Effect of ICME Removal and Observation Age for in Situ Solar Wind Data Assimilation |
| title_sort | quantifying the effect of icme removal and observation age for in situ solar wind data assimilation |
| topic | solar wind space weather forecasting data assimilation |
| url | https://doi.org/10.1029/2022SW003109 |
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