On the Predictive Ability of Geomagnetic Disturbances from Solar Wind Measurements at Separated Solar Longitude

Abstract

In-situ solar wind measurement at a solar longitude separated from the earth in interplanetary space is expected to provide a great progress in practical space weather forecast, which has been confirmed by some recent studies. We introduce geoeffective solar wind conditions in correlation analysis between STEREO and ACE measurements. We sort solar wind data of ACE by using geomagnetic condition, and evaluate actual ability for predicting geoeffective solar wind arrival at ACE from STEREO-A and B solar wind measurement, by assuming simple corotating structures in interplanetary space. The results show that geomagnetic disturbances are more difficult to be predicted than quiet intervals, suggesting that the simple correlation method of solar wind measurement at separated solar longitude is not enough for accurately predicting geomagnetic disturbances, even though the correlation seems generally high. Although in-situ solar wind monitoring at a vantage point trailing behind the earth would definitely improve the prediction capability of solar wind structure arriving at the terrestrial plasma environment, we emphasize that the predictive ability of geoeffective disturbances would still remain low. We suggest that more sophisticated prediction schemes should be developed.

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Miyake, W. and Nagatsuma, T. (2012) On the Predictive Ability of Geomagnetic Disturbances from Solar Wind Measurements at Separated Solar Longitude. International Journal of Astronomy and Astrophysics, 2, 63-73. doi: 10.4236/ijaa.2012.22009.

1. Introduction

The disturbed solar wind impact on terrestrial magnetosphere leading to geomagnetic storms does not only originate from transient phenomena such as coronal mass ejections (CMEs), but also from high-speed solar wind streams emanating from coronal holes. Corotating highspeed streams are known as the source of recurrent geomagnetic storms which tend to be middle-class disturbances, whereas severe geomagnetic storms are generally caused by CMEs [1]. However, impact of the recurrent disturbances is not less important than those of CMEs origin. The flux of relativistic electrons at geostationary orbit, which sometimes damage GEO satellite system, is better correlated with geomagnetic storms due to highspeed streams [2].

Arrival of corotating high-speed streams may be predictable because they are recurrent. The prediction has leading time of almost 27 days (i.e., the rotation period of the sun), which seems magnificent. However, its accuracy is not so impressive. Temporal variation of corotating solar wind structures during 27 days is not negligible. A way of improving accuracy for predicting geomagnetic disturbances due to corotating high-speed streams is to deploy a solar wind monitor at separated solar longitude from the earth. A vantage location for the purpose, for example, is the L5 Lagrange point of the sun-earth system [3-5]. The L5 point, the sun and the earth compose a huge regular triangle in the ecliptic plane. The L5 point is about at solar longitude of 60 degree eastward from the earth, and thus “upstream” with respect to the solar rotation. By placing a solar wind monitor at the L5 point, corotating solar wind streams can be observed in situ about 4.5 days prior to their arrival at the earth.

The solar wind measurement at the L5 point also provides information on background plasma and field structure between the sun and the earth, which is essential for modeling interplanetary transients heading towards the earth. The transients like CMEs are accelerated or decelerated through interaction with the background solar wind (e.g., [6]). The characteristics of the sheath region formed by compressed background solar wind in front of the fast transients are important for development of a geomagnetic storm [7]. In situ measurement of the solar wind at separated solar longitude is expected to provide a great progress in practical space weather forecast.

Some recent studies have correlated solar wind measurement at separated solar longitude and discussed possible applications for space weather forecast [8,9], including predictive ability of solar wind monitoring at the L5 point [10,11]. Miyake et al. [10] made a correlation study using data from Nozomi heading toward Mars, and ACE stationed at the L1 point, when they were separated by greater than 50 degrees heliographic longitude. Simunac et al. [11] correlated STEREO-A and B solar wind observations [12,13] when their longitude separation reached about 60 degrees. They reported, in general, a good correlation and remarked its usefulness in forecasting geomagnetic disturbances due to corotating structures in the solar wind.

However, the previous studies never took into account geoeffective solar wind conditions in the correlation analysis and discussion on its application to predicting geomagnetic activities. It may not be so useful to obtain a high correlation only for geomagnetically quiet intervals. What is more needed to predict is the occurrence and magnitude of geomagnetic disturbances. It is well known that geomagnetic disturbances cause various space weather hazards, ranging from satellite system to ground facilities, such as induction current, drastic variation of radiation belt particle flux, heating and expansion of polar upper atmosphere, and development of ionospheric storms. All these phenomena have been a subject under intensive space weather research and are worth being predicted accurately in practical space weather forecast.

In this paper, we introduce geoeffective solar wind conditions in correlation analysis between STEREO and ACE solar wind measurements [14,15]. In Section 2, we describe how to use solar wind data from ACE, STEREO-A and B and how to sort the solar wind data by geomagnetic condition: Kp index (see, for example, [16]). We then try a prediction of the ACE solar wind data from STEREO-A and B solar wind measurements in Section 3. We then evaluate actual ability for predicting geomagnetic disturbances from solar wind measurement at a separate solar longitude in Section 4.

2. Data and Method

Our analysis period is from late half of 2007 through early half of 2009. Figure 1 shows histograms of 1-hour averaged solar wind velocity V (four upper panels) and magnitude of interplanetary magnetic field |B| (four lower panels) provided from the three spacecraft. We use these 1-hour averaged solar wind parameters throughout the paper. The entire analysis period is divided into four half-year intervals as indicated with DOY (Day Of Year) in the title of each panel. We use the four half-year intervals in the following analysis.

The velocity has decreased in later period. The very slow solar wind in the prolonged solar minimum was already reported [17,18]. There is no significant differrence in solar wind velocity among the spacecraft. |B| is, however, slightly larger at STEREO-A than at STEREO-B, probably due to the difference in radial distance from the sun. The average magnitude is shown in the parentheses. There is no large systematic difference among the three spacecraft data and we use them without any correction.

For the purpose of correlating the data of two measurements, we need to consider the time lag due to the difference in radial distance from the sun and solar longitude. Assuming that the solar wind velocity is constant for the radial expansion, the time lag is calculated by the equation T = (Rs – Ra)/Vsw + (φs – φa)/ω, where Rs and Ra are the heliocentric distance of STEREO and ACE, Vsw is the measured solar wind velocity, φs and φa are the solar longitude of STEREO and ACE, and ω is the angular velocity of the solar rotation, respectively. We neglect the effect of difference in solar latitude between the two spacecraft in this analysis, though large latitudinal gradient of the solar wind velocity have been sometimes reported at the boundaries between the highand low-speed streams (e.g., [19,20]). The assumption employed here has been widely introduced, and we try a simple prediction scheme of corotating structures for the first step.

Figure 2 summarizes difference in solar wind velocity between STEREO and ACE measurements after the time lag correction. The average of the difference magnitude is plotted as a function of solar longitude separation between the two spacecraft. The left and right panels show the difference between STEREO-B and ACE measurements and between STEREO-A and ACE measurements, respectively. The difference is, in general, increased with the longitude separation, suggesting that larger lag time allows more influence of possible temporal variations in the solar wind.

STEREO-A and B are located in the “downstream” and “upstream” of the earth with respect to the solar rotation, respectively. In this regard, only STEREO-B can be used to measure the solar wind parameters ahead of the earth. We, however, use STEREO-A solar wind measurement by means of inverse time lag. Comparison of the predictions from the STEREO-A and B measurements suggests radial and latitudinal effect, temporal evolution such as transient phenomena, which we neglect in the analysis.

We use Kp index for a geomagnetic parameter, the solar wind velocity V, magnitude of interplanetary magnetic field |B|, and upper limit of possible magnitude of solar wind electric field V∙|B| for solar wind parameters as correlated with Kp. Since geomagnetic activities are mainly driven by magnetic reconnection between antiparallel magnetic fields, north-south component of interplanetary magnetic field is the most important parameter

Figure 1. Histograms of 1-hour averaged solar wind velocity (four upper panels) and magnitude of interplanetary magnetic field (four lower panels) used in this study provided from ACE, STEREO-A (SA) and B (SB) spacecraft. The average magnitude of interplanetary magnetic field is shown in the parentheses.

Figure 2. Difference in solar wind velocity between STEREO and ACE measurements as a function of solar longitude separation. Averages are taken over each longitude bin of 2.5 degree. The left side shows difference between STEREO-B and ACE measurements, whereas the right shows that between STEREO-A and ACE measurements. The difference is, in general, increased with the longitude separation.

to result in geomagnetic disturbances. Similarly, dawnto-dusk component of solar wind electric field V∙Bz is intruded into the terrestrial magnetosphere and drives magnetospheric convection. However, prediction of the north-south component Bz becomes impossible after the longitude separation is not negligible (e.g., [8]). Therefore, we use neither Bz nor V∙Bz in this study.

The analysis period is just in the solar minimum, so that most of disturbances were due to Corotating Interaction Region (CIR) [21]. The fast solar wind stream overtakes slower streams ahead and compressed region is developed at CIRs. Richardson et al. [22] reported 70% of geomagnetic disturbances were caused by CIRs during the solar minimum. Negative large Bz conditions appear as a fluctuating field line in the compressed |B| region of CIRs. |B| gives an envelope of fluctuating Bz. Therefore, we use |B| instead of Bz and, also use V∙|B| instead of V∙Bz, in this study.

Figure 3 shows examples of relation of Kp with the three solar wind parameters measured by ACE, in which we corrected the propagation delay of the solar wind from the ACE to the earth. The red line connects averaged parameter values at each Kp index. We see fair correlations between Kp and the three solar wind parameters measured by the ACE, indicating that the three solar wind parameters are worth being predicted.

The purpose of this study is to introduce geoeffectiveness into the correlation analysis between ACE and STEREO solar wind measurements. Large Kp conditions are mostly observed during large V, large |B|, and/or large V∙|B| conditions as shown in Figure 3. Correlation coefficient is not suitable for evaluating prediction ability in this study, since, for example, we compare the “correlation” of small V with that of large V. Correlation coefficient is significant for variables with enough variation range. Therefore, we do not treat correlation coefficient but difference of the parameter, as in Figure 2, in this study.

3. Results

Figure 4 shows histograms of velocity difference between STEREO-B and ACE measurements (left row) and between STEREO-A and ACE measurements (right) sorted by Kp index. Each histogram for a Kp range is normalized so that integration over velocity difference is 100%. Color of the lines represents a Kp range as indicated in the figure. The distribution of velocity difference generally spreads more widely in the later period, reflecting increasing difference in solar longitude as shown in Figure 2. The distribution also tends to be narrower centered around zero for smaller Kp. During quiet intervals (e.g., Kp ≤ 1), the distribution is almost symmetric about zero, whereas it is asymmetric for active conditions (Kp ≥ 4). The occasion of negative difference is much more frequent for large Kp, which means that more geomagnetic disturbances occur when we underestimate solar wind velocity at ACE. This result is quite understandable

Conflicts of Interest

The authors declare no conflicts of interest.

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