Research on the Effect of Data Assimilation for Three‐Dimensional MHD Simulation of Solar Wind
Abstract As an important part of space weather forecasting, the prediction of solar wind parameters in the near‐Earth space is particularly significant. The introduction of data assimilation (DA) method can improve the reliability of numerical prediction. In this study, we use a three‐dimensional (3...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-07-01
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| Series: | Space Weather |
| Online Access: | https://doi.org/10.1029/2023SW003429 |
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| Summary: | Abstract As an important part of space weather forecasting, the prediction of solar wind parameters in the near‐Earth space is particularly significant. The introduction of data assimilation (DA) method can improve the reliability of numerical prediction. In this study, we use a three‐dimensional (3D) magnetohydrodynamics (MHD) numerical model with Kalman filter to infer the impact of the DA on solar wind modeling. We use the 3D MHD numerical model with near‐Earth in situ observations from the OMNI database to reconstruct solar wind parameters between 21.5 solar radii and 1 AU. The period from 2018 to 2021 is simulated, when the solar activity in the decay of the 24th solar cycle to the rising of 25th solar cycle. The numerical model generates two separate results, one without DA and one with DA directly performed on the model‐only results. Statistical analysis of observed, modeled and assimilated solar wind parameters at 1 AU reveals that assimilating simulations provide a more accurate forecast than the model‐only results with a sharp reduction in the root mean square error and an increase of correlation coefficient. |
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| ISSN: | 1542-7390 |