Improving solar wind forecasting using Data Assimilation

Lang, Matthew; Witherington, Jake; Turner, Harriet S.; Owens, M. J.; Riley, Pete

doi:10.5194/egusphere-egu21-2771

Cited by 6 publications

(11 citation statements)

References 31 publications

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“…This expands on the work in Lang et al. (2021), where BRaVDA was run every 27 days. The prior state from HelioMAS, however, is only available as Carrington rotation solutions (i.e., every 27 days).…”

Section: Methodsmentioning

confidence: 63%

“…Lang et al. (2021) showed that whilst the 27‐day forecast root mean square error was comparable to that of corotation forecasts, it showed improvement over non‐DA forecasts. To further investigate the performance of the BRaVDA scheme and perform a more rigorous analysis, we have increased the hindcast cadence from 27‐day to 1‐day, as this is how forecasts would be generated if a DA scheme were deployed operationally.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying the Effect of ICME Removal and Observation Age for in Situ Solar Wind Data Assimilation

et al. 2022

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The changing plasma conditions in the near-Earth space environment is a major component of space weather (Lilensten & Belehaki, 2009). It poses a threat to modern life through damaging technology, causing power failures and posing a risk to the health of humans in space (Cannon, 2013). For accurate space weather forecasting, advanced knowledge of the solar wind conditions is required. The solar wind is a continual stream of charged particles that flows from the high temperature corona (Parker, 1958). The most severe space weather events occur as a result of coronal mass ejections (CMEs), large eruptions of coronal plasma and magnetic field (Webb & Howard, 2012). CMEs have to propagate through the ambient solar wind, so it acts to modulate the severity of the CME and its impacts on Earth (Cargill, 2004;Case et al., 2008). Stream interaction regions (SIRs) are an inherent feature of the ambient solar wind and are caused by fast streams catching up with slower streams and creating regions of higher plasma density and stronger magnetic field (Gosling & Pizzo, 1999;Richardson & Cane, 2012). SIRs which persist for more than one solar rotation, are also referred to as corotating interaction regions and provide a source of recurring space weather.Solar wind forecasting can be achieved through simple empirical methods, such as corotation (Kohutova

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Section: Methodsmentioning

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Quantifying the Effect of ICME Removal and Observation Age for in Situ Solar Wind Data Assimilation

et al. 2022

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“…Owens et al (2019) and M. J. Owens et al (2020) work was motivated by improving solar-wind data assimilation (DA) capabilities (Lang et al, 2017(Lang et al, , 2021.…”

Section: Accepted Articlementioning

confidence: 99%

“…DA is a step forward in the use of observations for solar wind forecasting as it allows for the observations to be mapped to all longitudes and radial distances, whereas corotation only gives a forecast for a single point. Current DA schemes developed for solar-wind forecasting make use of solar wind speed observations (Lang et al, 2017;Lang et al, 2021). Although both corotation and DA can be used for forecasting parameters such as plasma density and magnetic polarity, at present, the DA methods presented in Lang et al (2017) and Lang and Owens (2019) only use solar wind speed.…”

Section: Accepted Articlementioning

confidence: 99%

The Influence of Spacecraft Latitudinal Offset on the Accuracy of Corotation Forecasts

et al. 2021

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Knowledge of the ambient solar wind is important for accurate space-weather forecasting. A simple-but-effective method of forecasting near-Earth solar-wind speed 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 separation (offset) between source and target spacecraft 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 18month interval near the end of solar minimum, we find that the latitudinal-offset contribution to corotation-forecast error cannot be directly detected for offsets < 6 • , but is increasingly important as offsets increase. This result can be used to improve solarwind data assimilation, allowing representivity errors in solar-wind observations to be correctly specified. Furthermore, as the maximum latitudinal offset between L5 and Earth is ≈ 5 • , corotation forecasts from a future L5 spacecraft should not be greatly affected by latitudinal offset.

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“…It is shown that the time smoothing form of corotation produces an improvement over simple corotation, greatly reducing the artifical discontinuities, but that there are still significant errors in reconstructing a time-evolving solar wind structure. Ideally, the strengths of the magnetogramconstrained solar wind models would be combined with the information from in-situ observations using data assimilation (Lang & Owens 2019;Lang et al 2021). However, this is only currently possible with reduced-physics models, and is computationally intensive for the long runs required for outer-heliosphere simulations.…”

Section: Introductionmentioning

confidence: 99%

Using in situ solar-wind observations to generate inner-boundary conditions to outer-heliosphere simulations – I. Dynamic time warping applied to synthetic observations

Owens

Nichols

2021

Monthly Notices of the Royal Astronomical Society

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The structure and dynamics of the magnetospheres of the outer planets, particularly Saturn and Jupiter, have been explored both through remote and in-situ observations. Interpreting these observations often necessitates simultaneous knowledge of the solar-wind conditions impinging on the magnetosphere. Without an available upstream monitor, solar-wind context is typically provided using models initiated with either the output of magnetogram-constrained coronal models or, more commonly, in-situ observations from 1 AU. While 1-AU observations provide a direct measure of solar wind conditions, they are single-point observations and thus require interpolation to provide inputs to outer-heliosphere solar-wind models. In this study we test the different interpolation methods using synthetic 1-AU observations of time-evolving solar-wind structure. The simplest method is “corotation”, which assumes solar-wind structure is steady state and rotates with the Sun. This method of reconstruction produces discontinuities in the solar-wind inputs as new observations become available. This can be reduced by corotating both back and forward in time, but this still introduces large errors in the magnitude and timing of solar wind streams. We show how the dynamic time warping (DTW) algorithm can provide around an order-of-magnitude improvement in solar-wind inputs to outer-heliosphere model from in-situ observations near 1 AU. This is intended to build the foundation for further work demonstrating and validating methods to improve inner-boundary conditions to outer-heliosphere solar-wind models, including dealing with solar wind transients and quantifying the improvements at Saturn and Jupiter.

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Improving solar wind forecasting using Data Assimilation

Cited by 6 publications

References 31 publications

Quantifying the Effect of ICME Removal and Observation Age for in Situ Solar Wind Data Assimilation

Quantifying the Effect of ICME Removal and Observation Age for in Situ Solar Wind Data Assimilation

The Influence of Spacecraft Latitudinal Offset on the Accuracy of Corotation Forecasts

Using in situ solar-wind observations to generate inner-boundary conditions to outer-heliosphere simulations – I. Dynamic time warping applied to synthetic observations

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