Solar-Wind Structure

Owens, M. J.

doi:10.1093/acrefore/9780190871994.013.19

Cited by 17 publications

(20 citation statements)

References 91 publications

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“…These model results were verified by the high-latitude Ulysses spacecraft observations [54]. See [61] for more detail. The change in global solar wind momentum and pressure balance with the interstellar medium means that the solar cycle extends even to the size of the heliosphere itself [69].…”

Section: Electromagnetic Spectrumsupporting

confidence: 65%

Solar Cycle

Driel‐Gesztelyi¹,

Owens²

2020

Oxford Research Encyclopedia of Physics

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The Sun’s magnetic field drives the solar wind and produces space weather. It also acts as the prototype for an understanding of other stars and their planetary environments. Plasma motions in the solar interior provide the dynamo action that generates the solar magnetic field. At the solar surface, this is evident as an approximately 11-year cycle in the number and position of visible sunspots. This solar cycle is manifest in virtually all observable solar parameters, from the occurrence of the smallest detected magnetic features on the Sun to the size of the bubble in interstellar space that is carved out by the solar wind. Moderate to severe space-weather effects show a strong solar cycle variation. However, it is a matter of debate whether extreme space-weather follows from the 11-year cycle. Each 11-year solar cycle is actually only half of a solar magnetic “Hale” cycle, with the configuration of the Sun’s large-scale magnetic field taking approximately 22 years to repeat. At the start of a new solar cycle, sunspots emerge at mid-latitude regions with an orientation that opposes the dominant large-scale field, leading to an erosion of the polar fields. As the cycle progresses, sunspots emerge at lower latitudes. Around solar maximum, the polar field polarity reverses, but the sunspot orientation remains the same, leading to a build-up of polar field strength that peaks at the start of the next cycle. Similar magnetic cyclicity has recently been inferred at other stars.

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Section: Electromagnetic Spectrumsupporting

confidence: 65%

Solar Cycle

Driel‐Gesztelyi¹,

Owens²

2020

Oxford Research Encyclopedia of Physics

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“…This is likely due to the Sun being in the solar minimum phase, meaning that greater representivity errors due to latitudinal offsets are observed (see Owens, Lang, Riley, et al, 2020 for more details). Also the solar wind speed is generally higher and more (spatially) variable at this time (Owens, 2020). We can see that the data assimilation is able to produce more accurate solar wind speed forecasts at all observation locations for the vast majority of this time period.…”

Section: Forecasting Results Over the Whole 7−year Periodmentioning

confidence: 73%

Improving solar wind forecasting using Data Assimilation

Lang¹,

Witherington²,

Turner³

et al. 2021

Preprint

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<div> <p>In terrestrial weather prediction, Data Assimilation (DA) has enabled huge improvements in operational forecasting capabilities. It does this by producing more accurate initial conditions and/or model parameters for forecasting; reducing the impacts of the &#8220;butterfly effect&#8221;. However, data assimilation is still in its infancy in space weather applications and it is not quantitatively understood how DA can improve space weather forecasts.</p> <p>To this effect, we have used a solar wind DA scheme to assimilate observations from STEREO A, STEREO B and ACE over the operational lifetime of STEREO-B (2007-2014). This scheme allows observational information at 1AU to update and improve the inner boundary of the solar wind model (at 30 solar radii). These improved inner boundary conditions are then input into the efficient solar wind model, HUXt, to produce forecasts of the solar wind over the next solar rotation.</p> <p>In this talk, I will be showing that data assimilation is capable of improving solar wind predictions not only in near-Earth space, but in the whole model domain, and compare these forecasts to corotation of observations from STEREO-B at Earth. I will also show that the DA forecasts are capable of reducing systematic errors that occur to latitudinal offset in STEREO-B&#8217;s corotation forecast.</p> </div>

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“…This is likely due to the Sun being in the solar minimum phase, meaning that greater representivity errors due to latitudinal offsets are observed (see Owens, Lang, Riley, et al., 2020 for more details). Also the solar wind speed is generally higher and more (spatially) variable at this time (Owens, 2020). We can see that the data assimilation is able to produce more accurate solar wind speed forecasts at all observation locations for the vast majority of this time period.…”

Section: Resultsmentioning

confidence: 99%

Improving Solar Wind Forecasting Using Data Assimilation

et al. 2021

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Data Assimilation (DA) has enabled huge improvements in the skill of terrestrial operational weather forecasting. In this study, we use a variational DA scheme with a computationally efficient solar wind model and in situ observations from STEREO-A, STEREO-B and ACE. This scheme enables solar-wind observations far from the Sun, such as at 1 AU, to update and improve the inner boundary conditions of the solar wind model (at 30 solar radii). In this way, observational information can be used to improve estimates of the near-Earth solar wind, even when the observations are not directly downstream of the Earth. This allows improved initial conditions of the solar wind to be passed into forecasting models. To this effect, we employ the HUXt solar wind model to produce 27day forecasts of the solar wind during the operational lifetime of STEREO-B (01 November 2007 -30 September 2014). In near-Earth space, we compare the accuracy of these DA forecasts with both non-DA forecasts and simple corotation of STEREO-B observations. We find that 27-day root mean-square error (RMSE) for STEREO-B corotation and DA forecasts are comparable and both are significantly lower than non-DA forecasts. However, the DA forecast is shown to improve solar wind forecasts when STEREO-B's latitude is offset from Earth, which is an issue for corotation forecasts. And the DA scheme enables the representation of the solar wind in the whole model domain between the Sun and the Earth to be improved, which will enable improved forecasting of CME arrival time and speed.

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Solar-Wind Structure

Cited by 17 publications

References 91 publications

Solar Cycle

Solar Cycle

Improving solar wind forecasting using Data Assimilation

Improving Solar Wind Forecasting Using Data Assimilation

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