# AKR Observations From All Local Times Indicate Substorm Activity

J E Waters, L. Lamy, J. Coxon, C M Jackman, C J Lao, C. Forsyth, A R Fogg

► To cite this version:

J E Waters, L. Lamy, J. Coxon, C M Jackman, C J Lao, et al. AKR Observations From All Local Times Indicate Substorm Activity. Journal of Geophysical Research Space Physics, 2025, 130 (9), 10.1029/2025ja033955. [https://doi.org/10.5194/jgrs-sr-2025-033955](https://doi.org/10.5194/jgrs-sr-2025-033955)

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# JGR Space Physics

## RESEARCH ARTICLE

10.1029/2025JA033955

**Key Points:**

* Remote Auroral Kilometric Radiation (AKR) observations have a positive correlation with substorm activity from all local times

* AKR observations from the dusk flank show the greatest correlation, due to the advancing westward electrojet during multiple onset substorms

* AKR below 70 kHz improves correlation for near-midnight observations, indicating a potential threshold for substorm-driven emission

**Correspondence to:**

J. E. Waters,
james.waters@lam.fr

**Citation:**

Waters, J. E., Lamy, L., Coxon, J., Jackman, C. M., Lao, C. J., Forsyth, C., & Fogg, A. R. (2025). AKR observations from all local times indicate substorm activity. *Journal of Geophysical Research: Space Physics*, 130, e2025JA033955.
https://doi.org/10.1029/2025JA033955

Received 12 MAR 2025

Accepted 18 AUG 2025

## AKR Observations From All Local Times Indicate Substorm Activity

J. E. Waters¹ ID, L. Lamy¹,² ID, J. Coxon³ ID, C. M. Jackman⁴ ID, C. J. Lao⁵ ID, C. Forsyth⁵ ID, and A. R. Fogg⁴ ID

¹Aix Marseille Univ, CNRS, CNES, LAM, Marseille, France, ²Observatoire de Paris, LESIA, PSL Research University, CNRS, Sorbonne Université, University of Paris, Meudon, France, ³Solar and Space Physics Research Group, Department of Mathematics, Northumbria University, Newcastle, UK, ⁴Astronomy & Astrophysics Section, School of Cosmic Physics, DIAS Dunsink Observatory, Dublin Institute for Advanced Studies, Dublin, Ireland, ⁵Mullard Space Science Laboratory, University College London, Dorking, UK

**Abstract** Auroral Kilometric Radiation (AKR) is known to present emission at lower frequencies near substorm onset, corresponding to activation of higher altitude sources along auroral magnetic field lines and acting as a proxy of the vertical distribution of acceleration processes during these events. While remote observations are more difficult to relate to ionospheric activity than those made in situ due to the illumination of the instrument by multiple AKR sources, there is a question of how the association of a particular observation with substorm activity can change with observer local time (LT) and more generally whether AKR observations can track geomagnetic activity in the same way as other proxies. This study addresses these questions by computing binary contingency tables and classification statistics including the Matthews Correlation Coefficient (MCC), using automatically selected AKR observations from Wind/WAVES and substorm event lists. AKR observations made on the dusk and dawn flanks present the greatest balance between true positive and negative associations with substorm activity, where active acceleration regions due to the enhanced substorm current wedge are distinct from typical nightside activity. Observations from the dayside and L1 can also be a reliable indicator, with AKR rarely observed from this region outside of substorm activity. Our results show that observations of AKR below 70 kHz (corresponding to active source regions above ~12,000 km at an L-shell of 7) can act as a good discriminator of substorm activity, particularly for observations from LTs near midnight.

**Plain Language Summary** Auroral Kilometric Radiation (AKR) is terrestrial radio emission that is produced along magnetic field lines above the auroral oval. It coincides with acceleration of electrons into the upper atmosphere and is colocated with bright, discrete optical and UV aurora. Substorms, a global process of energy transfer in the terrestrial magnetosphere, are related to the production of low frequency AKR. Due to the beaming pattern of AKR sources the spacecraft position must be considered when relating remote observations to magnetospheric processes. We address the question of how remote AKR observations from different positions can be associated with substorms. Through binary classification analyses of AKR observations and substorm event lists from 1995 to 2005, we show that observations from all local times can indicate substorm activity. Observations made on the flanks of the magnetosphere show positive correlation, with AKR occurring frequently with events due to morphological changes in the ionosphere during substorms. Observations near noon have a high probability of coinciding with extended auroral features during substorms and can also be a reliable indicator. While midnight observations have weaker correlation due to the spatial distribution of typical auroral activity, but low frequencies improve the correlation.

## 1. Introduction

Substorms are a global process that result in the transfer of plasma throughout the terrestrial magnetosphere, with extreme unloading of energy from the magnetotail to the nightside ionosphere during the substorm expansion phase. The phenomenology of substorms is well-studied, with their timeline first defined using the morphology of optical auroral emission observed with ground-based all-sky cameras (Akasofu, 1964). Their occurrence is heavily dependent on the solar wind and interplanetary magnetic field (IMF) conditions; these drive magnetic reconnection at the dayside magnetopause which, due to an imbalance with the reconnection rate in the magnetotail, builds magnetic energy in the lobes (Rostoker & Fälthammar, 1967). The onset of the substorm

© 2025. The Author(s).
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
expansion phase diverts a portion of the current in the magnetotail to the nightside auroral ionosphere to form the substorm current wedge and produce significant magnetic disturbances that are measured by geomagnetic indices such as the AE/AU/AL indices (Davis & Sugiura, 1966) or mid-latitude positive bay (MPB) indices (Caan et al., 1978; McPherron et al., 1973). Such signatures exist alongside other proxies that occur near substorm onset, such as the dipolarisation of magnetic field lines that travel Earthward with the substorm onset plasma (Angelopoulos et al., 1992; Hones et al., 1972) and enhancements in ULF wave activity (McPherron et al., 1968; Sakurai & Saito, 1976). Auroral kilometric radiation (AKR) is an auroral radio emission whose frequency can be used as a proxy for the vertical spatial development of the auroral acceleration region (Morioka et al., 2007; Wu & Lee, 1979). Extension of the emission to lower frequencies appears to be particular to substorm onset (Kaiser & Alexander, 1977a, 1977b; Morioka et al., 2014), enabling AKR to provide a unique perspective on the magnetosphere-ionosphere coupling region in the context of such events (Fairfield et al., 1999). While in situ observations of AKR have been made close to the source region that allow determination of the spatial origin and local microphysics (Ergun et al., 2000), those from a greater distance are not always able to determine the origin precisely due to limitations of spacecraft instrumentation (e.g., Waters et al., 2021, and references therein). The ability to pair remote observations with particular dynamics in the ionosphere, such as other signatures of substorm dynamics and their known spatial origins, could therefore provide another tool with which to identify periods of interest for study. This paper aims to explore this link on a statistical basis and determine how the observer position and frequency of AKR affects the correlation with substorm phenomenology.

Substorms have three main phases that describe the evolution of the global magnetosphere throughout the event development (McPherron et al., 1973). First, the growth phase follows an increase in the dayside reconnection rate under the influence of a strongly southward IMF component. This increases the open magnetic flux in the magnetosphere and causes an expansion of the polar cap to lower latitudes (McPherron, 1970). The auroral morphology involves a discrete “growth phase arc” that similarly moves to lower latitudes. As the magnetic flux increases in the lobes, the plasma sheet is compressed (Aubry & McPherron, 1971). This compression leads to magnetotail reconnection and Earthward particle injections near the onset of the expansion phase, the most explosive phase of the substorm timeline (Voronkov et al., 2003). Accompanied by intense auroral brightenings the expansion phase, or substorm onset, sees the dawn-dusk cross-tail current diverted to generate a separate current system in the ionosphere; the substorm current wedge (Kaufmann, 1987; Nishimura et al., 2010; Ohtani et al., 1992). While there are smaller scale complexities present within the substorm current wedge (Forsyth et al., 2014; Kalmoni et al., 2015; Murphy et al., 2013; Nishimura et al., 2020), the net effect is to intensify the ionospheric westward electrojet (Kamide, Akasofu, & Rostoker, 1976; Kamide, Yasuhara, & Akasofu, 1976), that generally extends across the nightside and is driven by enhanced region 1 and region 2 Birkeland or field-aligned currents (Coxon et al., 2014, 2017; Iijima & Potemra, 1978). The westward traveling surge is a dominant feature of the optical auroral substorm, with large, bright regions of aurora traveling toward premidnight and dusk local times (LTs) at the expansion phase while accompanied by upward field-aligned currents that pair with postmidnight downward field-aligned currents to close the substorm current wedge (Gjerloev & Hoffman, 2002; Hoffman et al., 1994). Kepko et al. (2015) give a comprehensive review of the current morphology, with the upward field-aligned currents generally localized to premidnight MLTs. This corresponds to the average statistical location of auroral brightenings during auroral substorm onset at premidnight, between 22 and 23 hr magnetic local time (MLT) (Craven & Frank, 1991; Gjerloev et al., 2007; Liou et al., 2001). Driving conditions can be such that other expansion phase onsets occur. These increase the intensity of the substorm current wedge and westward electrojet in a step-like manner, and can produce continuous westward motion of auroral activity (Cheng et al., 2002; Kepko et al., 2015; Pytte et al., 1976; Wei et al., 2022). Other auroral features, such as poleward boundary intensifications and auroral streamers, can occur following the auroral substorm onset (Lyons et al., 2013; Yadav et al., 2024). The recovery phase sees the return of the magnetosphere to its quiescent state, which lasts approximately 1 hr (Forsyth et al., 2015) and can also exhibit energetic electron precipitation and auroral features that occur at postmidnight or on the dawnside (Oyama et al., 2017).

AKR is a non-thermal radio emission generated by the electron cyclotron maser instability that occurs at Earth within a plasma cavity found along magnetic field lines at auroral latitudes (Gurnett, 1974; Wu & Lee, 1979). Emission occurs at frequencies approximately between 50 and 800 kHz (de Feraudy et al., 2001; Gurnett, 1974; Olsson et al., 2004) with source regions found close to the local relativistic electron gyrofrequency, requiring an electron distribution function that follows acceleration due to double-layer potential drops or Alfvénic processes (Ergun et al., 1998, 2000; Olsson et al., 2004). AKR sources are most often found colocated with discrete UV
aurora (Huff et al., 1988; Menietti et al., 2011) as a result of the associated electron acceleration, and so is generally distributed at nightside magnetic local times (MLTs) (Alexander & Kaiser, 1976) (also the region from which the most complete, remote observations of AKR can be obtained). It can be observed from all MLTs including the dayside however, with sources being distributed more widely under more disturbed conditions (Alexander & Kaiser, 1977; Hanasz et al., 2000; Zhao et al., 2019). The AKR frequency provides a proxy for the spatial distribution of active acceleration regions along a given magnetic field line, with lower frequency sources generated at higher altitudes. Studies have shown that extension of the acceleration region to higher altitudes is associated with substorm onset, and corresponds to the so-called low frequency extensions of AKR (Desch et al., 1996; Morioka et al., 2007; Olsson et al., 2004). AKR emission is generated at near-perpendicular angles to the magnetic field and, after propagating along the cavity, is emitted in an approximately conical beam that is longitudinally wide and more constrained in the latitudinal direction with respect to the source (Kaiser & Alexander, 1977a, 1977b; Kasaba et al., 1997; Mutel et al., 2008). The greatest consequence for remote observations is that, while satellite instrumentation is not able to observe all active sources from a given position, it is possible for multiple sources to simultaneously illuminate a spacecraft when at radial distances greater than ~10 $R_E$ (Earth radii, $1 R_E = 6,370$ km) (Lamy et al., 2008; Morioka et al., 2011). In such cases, the observation position and geomagnetic conditions are necessary to relate the observed AKR (as well as its spectral range and therefore approximate vertical extent of the associated acceleration region) to magnetospheric or ionospheric activity. While ray tracing has previously been used to associate AKR observations with a given source (Huff et al., 1988; Mutel et al., 2004; Panchenko, 2003; Xiao et al., 2016), it is non-trivial and computationally expensive to find all possible solutions of source locations for all observations when a wide range of observing positions are covered.

The Wind/WAVES instrument (Bougeret et al., 1995) is well equipped to observe AKR emissions and has been utilized recently for its automatic selection and long term study (Fogg et al., 2022; Waters et al., 2021). Since its launch in 1995, measurements by Wind/WAVES have been made from a wide range of magnetic latitudes, longitudes and radial distances before the spacecraft entered orbit around the Lagrangian point L1 in 2004, where it remains. The response of the AKR power to substorm onset was examined at high and low frequencies using 10 years of observations from 1995 to 2004. Superposed epoch analyses were performed using substorm event lists, based on enhancements of the substorm current wedge (Chu et al., 2015; Forsyth et al., 2015) and electron injections from the magnetotail (Borovsky & Yakymenko, 2017). These highlighted a statistical response to substorm onset given an intensification in the substorm current wedge, with potential precursor activity at frequencies above and below 100 kHz and a pre-midnight bias of frequencies less than 100 kHz (Waters et al., 2022). Wind observations of radio emissions for many of the substorm events showed inconsistent or little AKR activity. It is unclear whether or not this is due to distinctions in the substorm activity, distinctions in the AKR activity or due to position-based viewing constraints on the spacecraft. In certain instances, it has been possible to observe substorm-associated AKR from L1, when the westward electrojet and auroral activity extended to the afternoon MLT sector (Waters et al., 2023), lending further complexity to the question of under which conditions, or from what position, AKR can be expected to be observed with substorm activity. In this study, we aim to examine the general relationship between AKR occurrence and substorm activity using the statistical breadth of the available AKR observations with Wind/WAVES. Analyses of how related an observable is to a particular event is often carried out using a binary contingency table and the statistical classification metrics that can be derived from this. By using event lists of substorms with AKR observations, we use 10 years of Wind data from 1995 to 2005 to explore how the viewing position at all LTs affects the correlation of AKR occurrence with substorm activity.

Section 2 describes the substorm event lists, Wind/WAVES AKR observations and the binary classification analyses used in this study. Section 3 describes the results of three binary classification analyses, giving an overview of the statistics with broad six hour local time (LT) sectors (Section 3.1), then with smaller, subsidiary LT sectors (Section 3.2) and finally including the frequency of AKR observations (Section 3.3). Section 4 then explores the implications of these analyses on the relationship between remote AKR observations and their use in acting as an indicator of substorm activity.

## 2. Data and Methodology

### 2.1. Radio Observations

Wind/WAVES is equipped with the RAD1 radio receiver, measuring 32 independent frequency channels between 20 and 1,040 kHz at variable rates over a ~3 min sweep (Bougeret et al., 1995). Following the procedure
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Position of the Wind spacecraft for the time period comprising the statistical study here, from 1995 to 2005 (exclusive) showing (a) the local time (LT) of Wind in geocentric solar ecliptic (GSE) coordinates, (b) the magnetic latitude of Wind in geocentric solar magnetic (GSM) coordinates and (c) the radial distance of Wind from Earth, in Earth radii ($R_E$). Panels (d) and (e) show the XY and XZ GSE coordinates, respectively, with darker colors indicating times closer to 1994.</figcaption></figure>

outlined in Waters et al. (2021) and Fogg et al. (2022), the emission attributable to AKR sources is isolated from other radio sources and compiled into a list of observations where AKR is measured continuously (denoted here as AKR bursts; the data can be found online at Fogg et al. (2023)). The start and end times of each period of emission or AKR burst are listed, as well as information on the upper and lower frequency bounds of the burst. We use data between Jan 1995 and Dec 2004; this encompasses the portion of Wind's trajectory in which a wide range of viewing positions were covered. This period is almost a complete solar cycle and so the effect of changing solar wind conditions on substorm occurrence is unlikely to have a significant effect on average.

To provide context on the viewing position throughout this period and the potential effect of seasonal or solar cycle conditions, Figure 1 shows the hourly position of Wind for the entire period considered in this article. Figure 1a shows the local time (LT) of the spacecraft in geocentric solar ecliptic (GSE) coordinates, Figure 1b shows its magnetic latitude in geocentric solar magnetic (GSM) coordinates, and Figure 1c shows its radial distance in Earth radii ($R_E$ = 6371 km). Figures 1d and 1e show the XY and XZ GSE coordinates of Wind, respectively, with darker colors indicating earlier times in the mission. Wind has been at the Lagrangian point L1 since mid-2004, and predominantly sampled dayside LTs for the majority of the mission. Figure 1a shows that it is typical for Wind to have covered nighttime LTs during orbital perigee, but occasionally (such as northern hemispheric winter of 2003) spent extended periods on the nightside or (such as northern hemispheric winter of 2001) at dusk or dawn LTs.

The GSM latitude distribution of Wind sees the spacecraft mostly near the magnetic equator. Observations from low magnetic latitudes can be subject to the equatorial shadow zone, where AKR sources cannot be found at low enough latitudes in either hemisphere to illuminate the spacecraft. This occurs within a given radial distance, which estimates have given as ~7 $R_E$ (Morioka et al., 2011) and is rarely satisfied by Wind (~1% of observations at $\le 7 R_E$). Outside of the equatorial shadow zone it is possible for Wind to be illuminated by AKR emission cones with sources in either hemisphere. In general, the observed AKR power increases as the spacecraft reaches higher magnetic latitudes (Lamy et al., 2010; Waters et al., 2021, 2022), where the emission cones of the corresponding hemisphere are more visible, although it is possible for spacecraft at closer distances to observe AKR at lower and middle latitudes (Xiao et al., 2022). A cutoff of higher frequencies when observing from high latitudes may also occur, as has been observed in the analogous emission at Saturn, the Saturn Kilometric Radiation (Lamy et al., 2008). Again, although not explored explicitly, we note that further implicit biases could arise from periods where Wind is at higher magnetic latitudes than usual, such as during the northern hemispheric winter of 1999. Such biases are non-trivial to resolve due to the complexities of discontinuous observations at different latitudes and is therefore out of the scope of the current study. We note that although substorm activity has been seen to
reduce interhemispheric asymmetry (Elhawary et al., 2024), the contrary has been observed for substorm-related AKR (Lamy et al., 2010; Morioka et al., 2011).

## 2.2. Substorm Event Lists

To produce a contingency table and subsequently evaluate the binary classification of a given event (see Section 2.3), both a predictive observable and a “ground truth” observable are required. The latter are provided from well-known, published lists of substorm events and are described in the following. The substorm event list by Newell and Gjerloev (2011, hereafter referred to as N11) utilizes the SuperMAG ground magnetometer network (Gjerloev, 2012), which comprises more than 300 magnetometer stations predominantly found in the Northern hemisphere and covering middle and high latitudes. The network produces indices (SME/SMU/SML), based on the largest and smallest diversions to the northward H component as measured by the magnetometers (analogous to the historic AE/AU/AL indices (Davis & Sugiura, 1966)), that characterize the strength and location of the auroral electrojets. The SML index is of particular importance to substorm onset dynamics, as it characterizes the westward electrojet (via the effect of negative diversions to the northward H component) that intensifies at substorm expansion phase onset and forms the ionospheric portion of the substorm current wedge. The substorm list is based on criteria that demand successive decreases in the SML index over a 30 min window (Newell & Gjerloev, 2011).

The list published by Chu et al. (2015, hereafter referred to as C15) also uses the SuperMAG network to produce an index of auroral activity specific to the substorm timeline. In this case, the MPB index is derived from the northward H component and the eastward D component of the geomagnetic field and characterizes the field-aligned currents of the substorm current wedge. Ohtani and Gjerloev (2020) published a substorm event list (hereafter referred to as O20) that also utilizes the SuperMAG network, particularly the SML index, with a more selective approach than that used for the N11 list. This list determines substorm onsets that in particular are isolated, rather than those that could be part of substorms with multiple intensifications (where further expansion phase onsets occur after a previous recovery phase has occurred). SuperMAG provides the magnetic coordinates of the magnetometer station that contributes the most significant perturbation. The O20 list further selects onsets based on the MLT of the contributing station with a six hour wide MLT range, centered on the midnight meridian, that is used to ensure that the recorded signature is in an appropriate location with respect to global substorm morphology. We note that N11 and O20 do not use the SMU index and therefore do not explicitly account for periods where both eastward and westward electrojets are enhanced, such as during steady magnetospheric convection events. Events derived using the N11, C15, and O20 algorithms were used for the complete time span of Wind observations used here.

One of the first published lists of substorm onset was published by Frey et al. (2004, hereafter referred to as F04) and differs significantly from the aforementioned lists. In this case, the IMAGE/Far Ultraviolet Imager (FUV) instrument was used to visually examine auroral substorm morphology from remote, global observations of the auroral oval in the northern hemisphere. Times of substorm onset are determined via brightenings of the auroral bulge and poleward auroral expansion characteristic of the beginning of the expansion phase as defined by Akasofu (1964). This list covers a much shorter time range than the other lists, including observations over 2.5 years between May 2000 and December 2002. Given the various scales of phenomena and instrumental techniques used to provide proxies for substorm activity, events from multiple lists have been combined to allow for temporal inconsistencies between signatures and produce event lists that show good agreement with the original, individual lists (Haiducek et al., 2020). While not used here, we note this to highlight that event lists capture various aspects of substorm activity. Recently, event occurrence between lists has been examined with a contingency table analysis, showing that selection algorithms based on gradient changes in indices such as SML show the best agreement within typical durations of expansion phases, and that the determination of substorm onset based on global observations of the aurora in F04 may be biased to smaller events (Lao et al., 2024).

## 2.3. Binary Classification and Data Selection

Binary classification is a tool used to determine the effectiveness of a given metric or observable to forecast or predict a particular event, given knowledge of that event's occurrence. Historically implemented in weather forecasting, the technique has more recently been applied to forecasting space weather and solar activity, as well as for model verification (Forsyth et al., 2020; Georgoulis et al., 2021; Haiducek et al., 2020; Lao et al., 2024;
<caption>**Table 1**</caption>

Table Denoting the Primary Classification Statistics Mostly in Section 3.1, Giving the Name of the Metric, Its Definition, and Explanation of the Variables

<table><thead><tr><th>Classification metric</th><th>Definition</th><th>Notes</th></tr></thead><tbody><tr><td>True Positive Rate (TPR)/Recall</td><td><sup>TP</sup>/<sub>P</sub></td><td>TP = total number of true positives;<br>P = total number of observed events</td></tr><tr><td>False Positive Rate (FPR)/False Alarm Probability</td><td><sup>FP</sup>/<sub>N</sub></td><td>FP = total number of false positives;<br>N = total number of non-events</td></tr><tr><td>True Negative Rate (TNR)/Specificity</td><td><sup>TN</sup>/<sub>N</sub> = 1 - FPR</td><td>TN = total number of true negatives</td></tr><tr><td>False Negative Rate (FNR)/Miss Rate</td><td><sup>FN</sup>/<sub>P</sub> = 1 - TPR</td><td>FN = total number of false negatives</td></tr><tr><td>Prevalence</td><td><sup>P</sup>/<sub>P+N</sub></td><td></td></tr></tbody></table>

Lopez et al., 2007; Machol et al., 2012; Mooney et al., 2021). By splitting a given time period into subsets of equal length, the presence of the “ground truth” event (here the substorms from a given list) and the observable aimed to be used as a potential predictor (here the AKR) are counted for each subset. From this a 2 × 2 contingency table can be formed, in which the co-occurrence of the event and its predictor is evaluated. The contingency tables in this study therefore yield the number of true positives, where AKR occurs in a window with at least one substorm; the number of false positives, where AKR occurs in a window without a substorm; the number of true negatives, where neither AKR nor a substorm occur; and the number of false negatives, where AKR does not occur but at least one substorm is present. Further classification statistics can then be derived which depend on the total number of the four possibilities in the considered timeframe. Table 1 defines the classification statistics and the variables used in Equations 1–4.

From these sums, the proportion of correctly or incorrectly predicted events can be computed for both occurrence and non-occurrence. For example, the true positive rate gives the proportion of correctly predicted events, while the false positive rate gives the proportion of incorrectly predicted events. The definitions are such that the proportions are paired; the true positive rate and false negative rate sum to unity for example, and as such higher values of the true positive rate are more favorable than higher values of the false negative rate. The same is true for the false positive rate and the true negative rate. These values can be aggregated to give an overall performance metric; here the remote AKR observations are evaluated using the Matthews Correlation Coefficient (MCC) (Matthews, 1975). Similarly to the Pearson Correlation Coefficient used to evaluate the linear correlation between two variables, the MCC is a measure of the association between the observed events and the predictive metric. Its definition is given in Equation 1. The observations have a negative association with the events if the MCC < 0, with complete misclassification represented by the minimum value of MCC = -1. An association no better than random chance is shown with MCC = 0, and a positive association with the event if the MCC > 0, with a perfect classification given by the maximum value of MCC = 1.

$$MCC = \frac{TP.TN - FP.FN}{\sqrt{(TP + FP).(TP + FN).(TN + FP).(TN + FN)}} \quad (1)$$

The error in the MCC can be estimated by treating the elements of a given contingency table with Poissonian counting statistics and taking the error of TP, $\epsilon_{TP} = \sqrt{TP}$, for example. This leads to the following expression for the absolute error on the MCC ($\epsilon_{MCC}$):

$$\epsilon_{MCC} = |MCC|\sqrt{A + B} \quad (2)$$

$$A = \frac{TP.TN(TP + TN) + FP.FN(FP + FN)}{(TP.TN - FP.FN)^2} \quad (3)$$

$$B = \frac{1}{4} \left\{ \frac{1}{TP + FP} + \frac{1}{TP + FN} + \frac{1}{TN + FP} + \frac{1}{TN + FN} \right\} \quad (4)$$
To organize the substorm event lists, which give individual times of onset within a given resolution, and the AKR observations, which are bursts of variable length, the common time period between the two is divided into 2 hr windows. For all lists other than F04, this covers the entire range of the AKR observations between January 1995 and December 2005. A Boolean assignment is made for each window; for substorms if at least one onset is present, and for an AKR observation if there is a burst present. If a single AKR burst crosses multiple windows, then each window containing the burst is counted.

AKR is known to exhibit increased activity at low frequencies at substorm onset (e.g., Morioka et al., 2007, and references therein). There may also be precursor AKR activity prior to onset and related variability throughout the substorm timeline, as well as known inconsistencies in remote observations between events (Waters et al., 2022). A smaller window size could be considered; one closer to the average substorm expansion phase duration of ~20 min (Forsyth et al., 2015), for example. Counting AKR occurrence using only the start time of the burst would then allow the relationship between the burst activation and the substorm onset to be explored. This was seen to severely affect the prevalence of events, however, since many of the more numerous windows are without substorms; a window size of 30 min gives a prevalence of ~0.1 for all data. These reasons prompted the choice of a 2 hr window. This choice allows for the study of AKR activity that may vary temporally relative to substorm onset depending on the observer LT, as well as the effect of longer substorms with multiple onsets or separate events that occur more closely. Although it forces a broad perspective on the link between remote AKR observations and substorm activity, the breadth of the statistics is maximized. On the other hand it is not too large to introduce a positive bias to the association as it is less than the expected substorm waiting time. This is based on the minimum quasiperiodic waiting time found via statistical analysis of solar wind-magnetosphere driving parameters and geomagnetic activity (Borovsky et al., 1993; Borovsky & Yakymenko, 2017; Freeman & Morley, 2004).

The F04 substorm list is given a particular treatment to account for periods where IMAGE may not be able to determine whether a substorm has occurred. The quality flag for the IMAGE Wideband Imaging Camera (WIC, the specific instrument of IMAGE/FUV used to determine F04 onsets) indicates that all observations in the period have the correct pointing (toward Earth) for the northern hemisphere. Some windows do not have complete observations however; a lower limit of 70% of observations for each 2 hr window is imposed for F04. To explore the effect of the LT of the remote AKR observation on the association with substorm, windows are subset based on the corresponding 2 hr average LT of Wind in geocentric solar ecliptic (GSE) coordinates.

# 3. Results

## 3.1. Broad Trends in Local Time

Here, the N11 substorm list is used exclusively to explore broad trends in the association of AKR observations with events. Figure 2 shows the classification statistics described in Table 1 for analyses performed using AKR emission at all frequencies and the N11 substorm list. Again, the 2 hr long windows are assigned an average LT, from the position of Wind, and then separate contingency table analyses are performed for four 6 hr wide sectors centered on the midnight, dawn, noon and dusk meridians. Figure 2a shows summary statistics that characterize the performance explicitly (as described in Section 2.3 and are defined in Table 1). They correspond to the metrics derived from the contingency tables, namely the true positive rate, the false positive rate, the false negative rate and the true negative rate. Although the true positive rate and false negative rate sum to unity for example, both pairs are included in Figure 2a for immediate visual comparison of the results of each LT sector. Figure 2b shows the MCC for each of the LT subsets and all of the data.

Figures 2c–2e highlight the effect of the LT sampling of Wind on the event prevalence. Each panel shows the number of windows used in the contingency table analysis (corresponding to *N* + *P* as denoted in Table 1), the number of windows with substorms (*P*) and their ratio (giving the event prevalence). Figure 2c shows bins corresponding to the 6 hr LT sectors used to derive the classification statistics of Figures 2a and 2b. Figures 2d and 2e show exemplary 3 and 0.25 hr LT bins respectively; the choice of bin size can change the prevalence significantly. These varying widths of the LT sectors correspond to the subsets used for contingency table schemes used in later analyses. These panels highlight how the event prevalence is dependent on both the LT of Wind, again due to sampling, and the general occurrence of substorms during the times when Wind samples these LTs.
<figure><img src="image_3.png" /><figcaption>**Figure 2.** (a) Binary classification statistics and (b) Matthews Correlation Coefficient (MCC) for AKR with respect to the N11 substorm list. Analysis is performed with 120 min periods and considers AKR observations subset into six hour wide local time (LT) sectors. Results from the analysis using all data is shown in dark blue. Other analyses consider AKR observations subset into six hour wide local time (LT) sectors and are represented as shown in the legend; results from subset local time sectors are shown in orange, light gray, dark gray, and light blue and are centered on midnight, dusk, noon and dawn meridians respectively. The definitions of the statistics in panel (a) are given in Table 1, while the MCC is defined in Equation 1. Panels (c) to (e) represent the number of windows, number of substorm windows and the corresponding event prevalence for LT bins of various sizes. The LT bins shown in panel (c) corresponds to the 6 hr wide LT sectors used to derive the results in panels (a) and (b). Panels (d) and (e) highlight the effect of using smaller LT sectors on the event prevalence, and the bin widths correspond to those used in the latter parts of this study. Note that panel (e) is shown with a log scale.</figcaption></figure>

Generally, examining data from all LTs (shown by the dark blue bars of Figure 2) provokes interesting questions between the association of AKR occurrence and substorms. The low true positive rate (0.34) and MCC (0.17) suggests that it is unlikely for AKR to occur with a substorm and that variability of AKR occurrence does not account well for the variability of substorm occurrence. However, given the aforementioned anisotropic beaming of AKR and the variety of positions sampled by Wind, as well as a sampling bias against nightside LTs (see Figures 1a, 1d, and 2c–2e), the lack of association is unsurprising. The response of AKR is known to be much weaker during the substorm timeline even when observing from postmidnight LTs on the nightside (Waters et al., 2022). The random distribution of contingency table windows with respect to the actual onset time, the inclusion of dayside Wind observations where viewing is limited and the complexity and variety of substorms likely contribute to this. The weak performance when aggregating all AKR observations highlights the necessity of the LT subsets to further interpret the association, while the classification metrics provide context to the potential utility of observations from each sector.

The analysis subset corresponding to the midnight-centered LT sector (21-03 hr LT), represented by the orange bar, has the highest true positive rate (0.66) and false positive rate (0.68). These values indicate that the proportion of both correctly predicted (where AKR is observed during the same period as substorm(s)) and the incorrectly predicted (where AKR is observed during a period without a substorm) cases is highest when Wind is in the midnight LT sector. Consequently, the false negative rate (0.34) and true negative rate (0.32) are lowest for the midnight LT sector. While similar across the LT sectors the prevalence is higher for midnight, as seen in Figure 2c, indicating that substorms in this list were more prevalent during the times that Wind spent in this LT sector. This could be due to the solar cycle activity, since Wind spent a significant amount of time in this LT sector during northern hemisphere winter of 2003/2004, in the declining phase of solar cycle 23 where geomagnetic activity is thought to increase in general (Chu et al., 2015). This is supported by Figure 2e in particular, where 0.25 hr wide LT bins near midnight clearly show a higher prevalence. The increased geomagnetic activity will also increase the auroral power in general, where auroral cavities necessary for AKR generation will also be formed above discrete aurora outside of substorm occurrences (Ergun et al., 2000; Luan et al., 2013). The midnight-centered sector is the only LT subset with a negative value of the MCC (−0.02), indicating that the
classification performance is very slightly worse than random chance. It is likely that this poor association follows
from auroral activity during disturbed periods outside of substorms, for which discrete aurora is generally
spatially distributed on the nightside. This is important to highlight; while remote AKR observations near
midnight can be considered the most comprehensive in terms of viewing of auroral activity, these results suggest a
more complex picture when examining emission specific to substorms.

The analysis subset corresponding to the dusk-centered LT sector (15–21 hr LT), represented by the light gray
bars in Figure 2, has a similar recall value (0.62) but a false positive rate (0.34) approximately half that of the
midnight-centered sector. Therefore, the false negative rate (0.38) is also similar while the true negative rate
(0.66) is approximately doubled. This indicates that, for the dusk-centered sector, the majority of the periods
during which substorm(s) have occurred also contain AKR observations with Wind. In this LT sector, it is also
more common for AKR to be observed with a substorm event than in the midnight LT sector (or it is more unlikely
for AKR to be observed without a substorm). This suggests that the AKR observed from this position is more
closely related to substorm activity, and likely originates from sources that are activated along field lines with
field-aligned currents that drive the westward electrojet (Hoffman et al., 1994) where such intense currents are
atypical outside of substorms. This shows that the “evening bulge” of AKR observed by Kasaba et al. (1997) is
indeed specific to duskward extension of AKR sources, particularly during substorms and not just disturbed
periods. The prevalence of events is the lowest (0.27) in the dusk LT sector; within ~36% of the value for the
midnight LT sector and ~13% of that for all observations (0.31). The decreased false positive rate over the
midnight sector is reflected in the increased MCC (0.26). This is the highest of all LT sectors, which is a sig-
nificant result to be explored throughout the study.

The analysis subset of the noon-centered LT sector (09–15 hr LT) has the lowest true positive rate (0.2) and also
the lowest false positive rate (0.07) of all the considered LT sector subsets. Conversely, this LT sector has the
highest false negative rate (0.8) and true negative rate (0.93). This is a diversion from the results of other LT
sectors. The low true positive rate indicates that it is unlikely for AKR activity to be observed from noon LTs
when a substorm is occurring. However, given the very high true negative rate (and conversely, a very low false
positive rate), it is highly likely that an observation of AKR from noon LTs will be related to substorm activity.
This is another novel result that has direct consequences for interpretations of AKR observations made on the
dayside. Although the generally low occurrence of events will favor the performance of the noon LT sector, with a
prevalence of 0.31 and where AKR is not often observed, the prevalence-independent MCC remains high (0.19)
relative to the other LT sectors. The slightly positive correlation and very high true negative rate indicate that
observations of AKR close to noon have specific characteristics to substorm dynamics and as such can be fairly
associated with such events. The dawn-centered LT sector (03–09 hr LT) has a true positive rate of 0.44 and a
false positive rate of 0.29. Similarly to the noon-centered LT sector, observations from dawn are less likely (than
those from the dusk- or midnight-centered LT sectors) to capture AKR during every substorm. When observed
however, there is a greater likelihood that it can be associated with substorm activity as defined by the N11 list.
This can be seen from the increase of the true negative rate relative to the false negative rate at dawn, which is
much higher than the relative increase of the two metrics at midnight, for example,

**3.2. Contributions From Supplementary LTs**

In previous statistical studies of the AKR response to substorms, a premidnight bias was apparent (Waters
et al., 2022), which is unsurprising given the average location of auroral substorm onset (Gjerloev et al., 2007;
Liou et al., 2001). Similar LT biases have also been seen in previous observations of AKR from extreme viewing
positions on the dayside related to both substorms (Hashimoto et al., 1998; Waters et al., 2023) and non-substorm
auroral features (Kaiser & Alexander, 1977a, 1977b; Pedersen et al., 1992). In this Section, we aim to explore the
link between more constrained regions of observation, in LT, and equivalent regions of the ionosphere that are
active during substorms from the N11 list (Newell & Gjerloev, 2011). To reiterate, this list tracks successive
negative diversions in the SML index as a proxy for the intensification of the westward electrojet at substorm
onset, as well as the field-aligned currents that drive the ionospheric part of the substorm current wedge and
ultimately create bright and spatially extended auroral displays. Here, contingency table windows are further
subset with more constrained regions of LT than the broad perspective in Section 3.1. This allows a more exact
identification of limiting regions where AKR observations have a higher or lower association with the intensi-
fication of the westward electrojet, and can provide an insight into how to differentiate future observations. The
contingency table analyses are extended across LT sectors with widths between 15 min LT and 24 hr LT. Separate
<figure><img src="image_4.png" /><figcaption>**Figure 3.** Panels correspond to (a) the Matthews Correlation Coefficient (MCC), (b) the absolute error in MCC and (c) the relative error in MCC computed for subsets of AKR observations based on the local time (LT) of Wind. Panel (d) shows a labeled grid for reference in the text. Results here are derived from the N11 substorm list, using 2 hr windows.</figcaption></figure>

analyses are performed within a rolling LT window that is applied at 15 min LT intervals and increases in size in
steps of 15 min LT.

Figure 3 illustrates the resulting MCCs of analyses that cover the aforementioned LT sectors. LT sectors that cross the noon meridian are not shown. Figures 3a–3c represent the MCC, absolute error and relative error of each LT sector respectively. The LT sector corresponding to each value shown in the grid is defined by reading the boundary values (in hours LT) from the left and then the top axes. Since the matrix of values is triangular, only one half of the grid is shown for clarity. Figure 3d shows a schematic of the grid with labels corresponding to regions of interest to facilitate the discussion. As per the above, grid squares A–D cover the smallest LT sectors across the range, with cells closest to the central diagonal (from top-left to bottom-right) corresponding to 15 min wide LT sectors (as represented in Figure 2e). Those toward the top-right of the grid are largest, with cells corresponding to sectors ≥12 hr LT found in squares H, I and J. The cells at the center of squares E, F and G correspond to the dusk-, midnight- and dawn-centered sectors explored in Figure 2.

Figure 3a has a minimum and maximum MCC of -0.23 and 0.42 respectively; negative values are shown in pink and positive values in green. Squares B, C and F represent LT sectors near the midnight sector, where the poor performance (MCC ~ 0) is seen for the majority of those considered on the nightside. The region representing the evening sector (square B) illustrates the LTs that contribute negatively to the duskside correlation in Figure 2b. For example, LT sectors defined between 18 and 20 hr LT give MCCs ≥ 0.1. For the majority of sectors in square B, that begin later than 18 hr LT and end prior to 00 hr LT, MCC values are close to zero or negative. This is also the case for the majority of LT sectors in square F, that start between ~19 and 00 hr LT and end prior to 06 hr LT. LT regions in square F, starting and ending between 18 and 06 hr LT respectively, have MCCs close to zero. Correlations only become positive when considering LT sectors that begin earlier than the dusk meridian (square H). This highlights the ubiquity of the negative correlation seen for the midnight sector in Figure 2b across the nightside; auroral
activity for disturbed periods outside of substorms occurs at these LTs in general and produces AKR that weaken the association.

On the dusk flank, in squares A and E, nearly all of the observations show positive correlation. All LTs in the afternoon sector (square A, between 12:00 and 18:00 hr LT) produce a positive correlation. LT sectors considered between ~13 hr and ~19 hr have the highest MCC, with values between ~0.1 and 0.4 when considering results with absolute errors <0.1 (between 0.25 and 0.28 when considering relative errors <60%). Observations from LT sectors between 15 and 18 hr LT give MCCs between ~0.3 and 0.4. There are exceptions when considering LT sectors <1 hr wide and starting between ~15–17 hr LT where the MCC ~ 0.1. As previously mentioned, the correlation is only zero or negative when considering analyses with the initial LT boundary later than 18 hr LT. Any LT sector that has a final boundary at 20 hr LT or earlier returns a positive MCC. This again links the LT of the remote AKR observation to a similarly located (in MLT) ionospheric phenomena and highlights the specificity of AKR observations made at dusk to substorm activity. Given the derivation of N11 events from a criteria involving diversions to SML, the group of LT sectors with positive correlation between ~15–20 hr LT indicates those where AKR sources generated above the westward end of the westward electrojet are primarily visible.

The second highest-performing LT region that corresponds to the dawn bar of Figure 2b (represented by squares C, D and G in Figure 3) is much more constrained than the duskside region. In general, early morning LT sectors in square C, with LT boundaries between 00 and 06 hr LT have a poor correlation. For LT sectors between ~06-06 hr LT however, in the top left of square D, the correlation is positive with a maximum of ~0.3. This is likely related to the intensification of Region 2 currents that contribute to the westward electrojet in the morning sector, and dominant upward currents in pairs of filamentary field-aligned currents at these LTs in general (Kamide, Akasofu, & Rostoker, 1976; Kamide, Yasuhara, & Akasofu, 1976). Extending the final boundary to include LTs later than 09 hr LT tends to decrease the MCC. It is clear that, while considering LT sectors of equivalent size on the dusk and dawn sides, the dusk side has a better correlation in general. This represents well the predominant global current morphology at substorm onset, with premidnight bias of the brightening of auroral and duskward longitudinal expansion of the westward electrojet (Gjerloev et al., 2007).

The absolute errors shown in Figure 3b illustrate the effects of limiting the width of the LT sector in the analysis and the LT sampling bias of Wind. In general, smaller LT sectors have a lower number of windows and so have a greater error. Duskside observations show a greater error for LT sectors of an equivalent size on the dawnside due to relative undersampling of this space by Wind and therefore lower numbers of observations. While considering LT sectors <3 hr wide can produce large absolute errors, these analyses can be useful for evaluating whether more constrained regions can lead to a positive or negative contribution to the MCC of a broader set of observations, as seen with the dusk or dawn skill scores presented above. The relative errors shown in Figure 3c become very large around midnight, where the skill scores are closest to zero and the aforementioned LT sampling bias is greatest. Given the swift decrease in relative error for LT sectors larger than 3 hr and the broader analyses shown in Figures 2 and 3 hour wide LT sectors are considered in the following sections. This compromises between an analysis too broad in scope to examine the spatial relationship of remote observations with the substorm events and too narrow to contain reasonable errors. Although some of the 3 hr sectors toward the nightside still have relative errors much larger than 100%, it has already been shown that these AKR observations are similar to random chance (MCC = 0).

### 3.3. Comparing Substorm Lists and Dependence on AKR Frequencies

Given the link between low frequency AKR emission, known as low frequency extensions, and substorm onset (Morioka et al., 2007), we now use various upper frequency limits to count AKR occurrence. This allows the specific association of a given AKR frequency to the substorm events to be examined with LT in a similar way to the above, with a particular focus on lower frequencies. Such emission represents the activation of AKR sources at higher altitudes in the auroral acceleration region. In this way, the relationship between electron precipitation at such altitudes, the associated production of cavities, and substorm activity in general is assessed. This may allow the statistical determination of AKR frequencies specific to substorms. Liou et al. (2000) explored average AKR frequencies with respect to substorm onset itself, finding an average lower and upper bound of AKR emission after onset at 60 and 600 kHz respectively. Imhof et al. (2004) found that enhancements of AKR emission below 100 kHz show better correlation with electron precipitation than that at higher frequencies. The peak frequency of
AKR is found between 100 and 500 kHz and may be closer to 250 kHz depending on seasonal variability (Green et al., 2004; Kaiser & Alexander, 1977a, 1977b).

First, we examine AKR occurrence using two distinct upper frequency limits, for which only AKR emission below the limit is counted. To ensure proper differentiation between the components of AKR, including more continuous emission at higher frequencies (Morioka et al., 2007) and shorter duration, substorm-related low frequency extensions, the chosen frequency limits are relatively extreme. The first concerns occurrence of AKR below 50 kHz and is referred to as low frequency AKR in the following. The second frequency limit aims to explore the “main band” AKR and is referred to as such in the following, examining occurrence of emission at or below the approximate peak frequency. The main band AKR is represented by occurrence below 200 kHz. Taking into account the limitations of considering too limited or broad a LT sector from the previous Section 3.2, windows are subset in 3 hr wide LT sectors at 15 min LT intervals for the N11 as well as C15, O20, and F04 substorm lists (see Section 2.2). Not shown here is the result of using the whole AKR spectrum below 1,040 kHz (the upper limit of the RAD1 receiver of Wind/WAVES, as in the previous sections), which is very similar to the resulting MCC below 200 kHz for each event list (indicating that these frequencies indeed dominate the spectrum).

Figure 4 shows the resulting MCCs, with each value centered on the midpoint of the associated LT sector and errorbars representing the absolute error on the MCC as shown in Figure 3b. Each panel of Figure 4 corresponds to performing the analysis with a different substorm list, shown in each column.

For the N11 substorm list, main band AKR (shown in orange) highlights the features of Figure 3a; dusk and dawn regions exhibit the largest skill scores for LT sectors centered between ~13–19 hr LT (MCC~0.3) and 6–9 hr LT (MCC~0.2) respectively. All main band observations have weakly positive correlation across the dayside between these regions (MCC ≥ 0.14). As observed in Figure 3a, there is a sharp decrease in the skill score of the duskside region closest to midnight, around 20 hr LT. Similarly to Figure 2b, LT sectors evaluated near the noon meridian are ~0.2. Figure 4a presents an increasing skill score from ~1.5 from LT sectors on the morning side (e.g., from sectors centered noonward of 10.5 hr LT) to ~0.22 on the afternoon side (LT sector centered on 13.5 hr LT). The dawn region of positive skill has a clearer peak centered between 7 and 8 hr LT, with no sharp decrease toward the midnight meridian. Figure 4a shows more clearly the change in MCC across the midnight meridian; while the majority of 3 hr sectors have negative skill, there are regions which have an increased skill between ~22 and 23 hr LT.

For LT sectors centered between ~20 hr LT and ~3 hr LT there is a clear discrepancy in the MCCs for low frequency AKR (shown in blue) when compared to main band AKR, with positive MCCs observed to a peak of ~0.23 in this region for observations between ~21-00 hr LT. This highlights again the statistical relationship between low frequency AKR activity and substorm onset. Assuming that the zero or negative correlation of higher frequency AKR at midnight from Figures 2 and 3 is due to activation of AKR during discrete auroral activity on the nightside in non-substorm windows, this increased correlation over main band AKR could be interpreted as indicating a frequency below which AKR observations are more specific to substorms, although this is not confirmed in the scope of this study. A broad plateau of similarly valued MCCs is seen on the duskside from ~16–19 hr LT, as with higher frequencies, although no such equivalent peak is seen on the dawnside where the correlations between 6 and 9 hr LT are no greater than those near the noon meridian.

Figures 4b–4d represent the same analyses performed over the other substorm lists mentioned in Section 2.2. Figure 4b, with the analysis performed for the C15 list, shows similar MCC profiles to those of the analysis with N11 in Figure 4a; main band AKR shows a broad region of maximum correlation on the duskside (again between ~13–19 hr LT) and a second region on the dawnside. The main band AKR shows close to zero correlation across the midnight meridian, although post-midnight LT sectors centered between ~0–4 hr LT show positive correlations <0.1 contrary to the results from the N11 list. Low frequency AKR, again similar to the N11 list, shows higher correlations across LT sectors centered between ~20 and 3 hr LT. The peak correlation across the midnight sectors is centered nearer postmidnight (~1 hr LT) for C15, instead of premidnight for N11.

Figure 4c shows results from analyses using the O20 substorm list. Similarly to the N11 and C15 lists, the region of maximum correlation is found on the duskside for main band AKR. The duskside region of increased correlation is more constrained than for N11 and C15, extending only to LT sectors centered between ~16 and 20 hr LT. The maximum correlation (0.18) for the duskside is found closer to midnight (LT sector centered ~19 hr) than
<figure><img src="image_5.png" /><figcaption>**Figure 4.** MCCs resulting from analyses that include 3 hr wide LT sectors centered at LTs separated by 15 min LT for occurrence of low frequency and main band AKR, accounting for <50 kHz and <200 kHz respectively. Each row indicates the use of a different substorm event list, as described in Section 2.2. Errorbars show the absolute error, given by Equation 2.</figcaption></figure>
for the N11 and C15 lists (LT sector centered ~16 hr). For midnight LT sectors, the main band AKR produces slightly higher correlation (~0.07) than for N11 and C15. This is true for both lists at premidnight LTs, but the discrepancy is most notable between O20 and N11. Contrary to this, the O20 list produces close to zero (or weakly negative) correlation across the majority of the dayside; for both main band and low frequency AKR. On the dawnside, no such region of increased correlation is seen as for N11 and C15. At these LTs, the MCC of main band AKR mirrors that of the midnight sectors. There is a relatively consistent MCC across most of the nightside, with values ≤0.1 for LT sectors centered between ~20 and 9 hr.

In general, the correlation with the O20 event list is lower. This could be related to the decrease in prevalence based on the more selective procedure employed by O20, although the MCC is unlikely to be so strongly dependent on the prevalence in this case (Guesné et al., 2024; Luque et al., 2019). The prevalence of events when considering all data is 0.22 for O20 and 0.31 for N11. For main band AKR, considering data from all LTs, the true positive rate and true negative rate values for O20 are 0.26 and 0.78, respectively. The true positive rate and true negative rate for N11 are 0.33 and 0.82 respectively, indicating a worse performance of O20 overall and corroborating the general decrease in MCC in Figure 4c. The true negative rate decrease for O20 is easily explained by the more constrained event selection, whereby events exhibiting AKR activation may no longer be classified as substorms in this list. The decrease in true positive rate is less simple to reason based on data from all LTs. To further elucidate the correlation discrepancy between O20 and N11, we examine the true positive rate and true negative rate values for a 6 hr wide LT sector at midnight (21-03 hr LT), similar to that explored for N11 in Figure 2. For the midnight sector, between 21 and 03 hr, O20 has a true positive rate of 0.72 and a true negative rate of 0.78. These values are improved over the corresponding true positive rate of 0.66 and true negative rate of 0.34 for N11, corroborating the increase in MCC across midnight for O20 compared to N11 (Figures 4c and 4a respectively) and indicating a better correlation of near-midnight AKR observations for the O20 list.

The MCC of low frequency AKR for O20 does not increase significantly over a wide range of nightside LTs, contrary to N11 and C15. However, AKR at these frequencies does show peaks at both premidnight (~21–22 hr LT), shared also by main band AKR, and postmidnight (~1–2 hr LT). The former is found at a similar LT to the peak of low frequency AKR for N11, while the latter is at a similar LT to the peak of low frequency AKR for C15. The postmidnight peak is the only region of LT where low frequency AKR has a higher MCC than the main band, although marginal. Low frequency AKR has an MCC that remains close to zero on the duskside, where other lists produce an increase similar to the main band. The main band and low frequency correlations converge toward 20 hr LT, however.

Figure 4d shows results from the F04 substorm list. This list differs from others in that it is based on global UV images of the auroral oval, and that onsets are identified by eye based on presence of auroral morphology typical to substorms. There are some similarities when compared to the other lists, particularly in the duskside and dawnside regions for main band AKR. Greater fluctuations in the MCC are observed across the dayside for all frequency limits, and for the majority of LT sectors there is no clear distinction between profiles due to larger absolute errors. For the sectors evaluated on the nightside near midnight, for those centered between ~20 and 3 hr LT, the errors are too large to confidently determine particular features. Generally however, main band AKR exhibits higher correlations at all LTs than low frequency AKR. This is contrary to the features of the previous lists in Figures 4a–4c.

The increased MCC at midnight LTs for N11 and C15 (Figures 4a and 4b) for the decreasing upper frequency threshold used to consider AKR burst occurrence was suggested to represent an increased specificity of the lower frequency emission to substorm activity. Figure 5 aims to further constrain the altitude at which acceleration may indicate substorm activity for a given observation LT. Upper frequency limits between 30 and 120 kHz in steps of 10 kHz are used to evaluate AKR occurrence. Profiles for frequency limits between 30 and 70 kHz are shown in the top row of panels, and those between 80 and 120 kHz are shown in the bottom row. Profiles are color-coded for a given list such that lower frequency limits appear darker, across both panels. As with Figure 4, the upper frequency limits are given in the legend, as well as the approximate altitudes of corresponding AKR sources. The altitude is calculated under the assumption that the remote observations made at a given frequency correspond directly to the AKR source, and that a source is emitted close to the local electron gyrofrequency. This is related to the local magnetic field strength via $f_{ge} \sim 28B$, and thus the altitude is approximated using the International Geomagnetic Reference Field (IGRF) with geographic coordinates typical of auroral substorm latitudes at an approximate L-shell of 7. By manually compiling a list of altitudes and their corresponding magnetic field
<figure><img src="image_6.png" /><figcaption>**Figure 5.** Resulting MCCs for analyses covering frequency bounds between 30 and 120 kHz. Frequencies of each analysis are shown in the legend, with the approximate altitude of an AKR source on a magnetic field line at an L-shell $L \sim 7$ also given (see text).</figcaption></figure>

strength *B* at a given location, altitudes corresponding to frequencies close to the limits in Figure 5 were estimated by interpolation.

Generally, the profiles are similar to those of Figure 4. Most notably, for the lowest frequencies examined with the N11 and C15 lists (Figures 5a and 5c), the MCCs exhibit higher values across midnight sectors and lower values in the dusk and dawn regions. The inverse is true as the frequencies increase; when considering an upper frequency limit of at least 50 kHz the correlations are higher in the dusk and dawn regions than for those at midnight. This is similar to the trends seen in Figures 4a and 4b, and with similar distribution in LT; although the lowest frequencies see reduced correlations across the dayside for the N11 and C15 lists. At frequency limits $\ge$60 kHz, these lists exhibit a convergence to a common MCC profile at all LTs (predominantly seen in Figures 5b and 5d). Interestingly, an opposite effect is seen at midnight, between 20 and 03 hr LT, to that near dusk, dawn and noon. For the former the MCC profile decreases in magnitude as the frequency limit increases, presumably due to a decreasing true negative rate. For the latter LTs the MCC profile sees an increase, although more subtle. The maximal MCC at 30 kHz near midnight indicates that these higher altitude sources best represent substorm related AKR activity at these LTs. Assuming that only events with the most intense substorm current wedge produce AKR sources at such altitudes, it follows that this frequency limit balances the decreasing true positive rate with an increasing true negative rate as auroral activity that produces lower altitude radio sources is not accounted for.

Again, a similar trend is observed for the O20 list as that in Figure 4; MCCs are close to 0 for LT sectors centered between ~9 and 15 hr, although for sectors centered between ~15 and 9 hr the correlation increases with increasing frequency limit. Figure 5e shows the greatest discrepancy with increasing frequency near dusk and midnight LTs, particularly for frequencies below 60 kHz. Above this frequency, Figures 5e and 5f show a similar convergence in MCC profile for O20 as for N11 and C15. Although more difficult to observe for the F04 list in Figure 5g due to the large errors and fluctuations, particularly for sectors nearest midnight, there is a general tendency for the correlation to increase with increasing frequency limit. Away from midnight, a similar convergence is seen with increasing frequency above 60 kHz.

# 4. Discussion

## 4.1. Preferential AKR Viewing for Substorm Dynamics

Figure 2 illustrated the main classification statistics determined for AKR observations when subset by broad, six hour wide LT sectors centered on meridians at noon, dusk, midnight and dawn LTs. AKR observations made on the nightside, particularly near midnight, have been assumed to best represent substorm-related variability given the colocation of AKR sources with the discrete aurora the plasma flows and energetic precipitation observed on the nightside (Morioka et al., 2013; Waters et al., 2022). The maximal true positive rate values of this LT sector confirm this, with the visibility being such that substorm events as appearing in the N11 list are most often observed from the midnight-centered sector. However, the false positive rate is also maximized in this LT sector.
This is likely due to the activation of AKR sources that are related to general electron precipitation, not specific to substorm activity. While the prevalence of substorms from N11 is highest in the midnight-centered sector (~0.4) compared to the other LT subsets, the high false positive rate results in a negative MCC overall for AKR observations in this LT sector. This novel result may be important to consider when evaluating nightside remote observations of AKR in a substorm context, whether through case studies or statistically, as the spectra may not be entirely specific to substorm activity.

AKR observations made in the broad duskside LT sector also see a high true positive rate, illustrating the bias toward pre-midnight for substorm dynamics. Namely, active aurora move to the duskside with the westward traveling surge during substorms. The intensified Region 1 currents in the expanded active regions of the auroral oval produce AKR sources which can then be detected. This highlights the effectiveness of AKR to track such substorm phenomena, with a similar true positive rate to observations made in the midnight LT sector. At dusk, the false positive rate is much lower than those at midnight, illustrating that activation of AKR sources on the duskside have a closer association with substorm activity. This is likely due to the exclusion of general auroral variability and the associated AKR activation. Examining the contributions of more constrained LT sectors showed that the positive MCC from duskside observations is sustained in afternoon LTs (for sectors considered from 12 to 15 hr LT) but limited for LTs later than ~20 hr. The average MLT of substorm onset is often found to be between ~22–23 hr MLT (Frey et al., 2004; Gjerloev et al., 2007; Walach et al., 2017). Based on the brightenings of the auroral bulge, Gjerloev et al. (2007) found its western end at an average of ~20 hr MLT. Given the approximately conical shape of the AKR source beaming, the MCC cut-off at 20 hr could be related to the visibility of AKR sources colocated with such an auroral feature. At LTs noonward of 20 hr, the remote observations of Wind may present a positive skill since they capture the noonward portion of the emission cone of AKR sources toward the end of the substorm timeline, and negative skill for LTs toward midnight since AKR emission cones can be observed for general auroral features.

Generally, this broad duskside region of maximal MCC is likely explained by the westward propagating ionospheric currents typical to substorms, rendering these LTs preferential for observing substorm-specific AKR to those near midnight. Again, this should be considered when interpreting AKR observations of substorm dynamics. We note that, given that substorms vary greatly in intensity (in terms of their geomagnetic disturbance) and spatial distribution, the breadth of positive correlation on the duskside highlight the wide range of events represented by the N11 list, particularly those with multiple intensifications and greater longitudinal distribution of ionospheric flows.

Similarly to the dusk sector, the significant decrease of the false alarm probability in the dawn sector over that at midnight indicates activation of AKR sources near this LT that are specific to substorm activity. Again similarly to dusk observations, the miss rate of AKR observations for the dawn sector is lower than that for noon, suggesting that substorms more frequently exhibit activation of AKR sources that are better observed from near dawn LTs. AKR sources are found on the dayside during more active geomagnetic conditions, and possibly from the cusp or low latitude boundary layer which could be active around substorm onset (Hanasz et al., 2003; Zhao et al., 2019). The positive correlation could also be related to electron precipitation and associated aurora that can increase near dawn or morning sectors in the substorm recovery phase (Oyama et al., 2017; Royrvik & Davis, 1977; Troyer et al., 2022). This could also be a signature of enhanced Region 2 currents at substorm onset, occurring at MLTs eastward of the onset (Coxon et al., 2017). A related cause could be the dawnward advancement of closing magnetic flux and so the eastward electrojet (Tanaka et al., 2015), albeit at a slower rate than seen in the duskward direction for the westward electrojet (Mooney et al., 2020). Another explanation is that the N11 algorithm could include disturbed magnetospheric periods that are not related specifically to substorm onset, such as during steady magnetospheric convection where both westward and eastward electrojets are enhanced (Walach et al., 2017). The SOPHIE (Substorm Onsets from Phases and Indices of the Electrojets) algorithm (Forsyth et al., 2015) produces an event list that flags such periods, but performing the analysis with the SOPHIE list after their removal gave results that were closely similar to N11. The SOPHIE list produces start and end times of substorm phases instead of just the onset; future studies could explore this further by counting substorm occurrence with the entire expansion and/or recovery phases, similarly to that done here for AKR bursts.

The aforementioned duskside cutoff at 20 hr LT may have an analogous feature on the dawnside; the dawnward or eastward portion of the auroral oval extends to an average of 03 hr MLT for substorms studied by Gjerloev et al. (2007). This may result in a statistical cutoff in LT from which non-substorm related auroral dynamics are
observed closer to midnight. Conversely, AKR observed dawnward of this cutoff likely has a closer association with substorm activity. Other auroral signatures related to steady magnetospheric convection or sawtooth events can occur in postmidnight MLTs (Walach et al., 2017) and so the region of positive correlation between 03 and 06 hr LT may be closer to zero due to the combination of these phenomena. Intensifications of the westward electrojet on the dawnside are also observed for both geomagnetic storms and substorms (Ohtani et al., 2023; Ohtani & Gjerloev, 2020). Although the N11 algorithm is built to capture variability in the substorm current wedge and intensification of the westward electrojet, the 2 hr window is large enough to capture such phenomena, whether related to substorms or not. For the region of higher positive MCC between 06 and 09 hr LT, this may represent the observations of AKR sources specific to the eastward motion of the auroral oval around substorm onset.

While it is possible to observe substorm-related AKR from noon LTs this is expected to occur mostly for very disturbed geomagnetic conditions when substorms see repeat intensifications and furthest westward motion of the electrojets (Hashimoto et al., 1998; Waters et al., 2023). As such, the true positive rate is very low for AKR observations made between 09 and 15 hr LT. Similarly, the false positive rate is the lowest of the broad LT sectors. AKR is rarely observed in any case from noon, since non-substorm related acceleration and auroral features may activate AKR sources less frequently near dayside MLTs. Given the aforementioned link between highly disturbed conditions and westward propagation during substorms, the noon sector is where observations are least likely to exhibit AKR activity outside of a substorm. If significant AKR is observed near noon, it is highly likely to be related to large substorm activity. Although many events are missed, this increased specificity results in the second highest MCC of the four sectors and demonstrates an interesting association of AKR observations near noon with substorms. This could be exploited as a search criteria to return substorms where significant propagation of the westward electrojet has occurred; particularly given the persistent viewing position of Wind at L1 since mid-2004.

## 4.2. Correlation of Lower Frequency AKR

The N11 and C15 lists produce very similar correlations for both main band and low frequency AKR. This is likely due to the connection between the SML and MPB indices in representing the global nature of the substorm current wedge, with the former representing the ionospheric electrojet and the latter the field-aligned currents that drive the system. Both lists produce improved correlations of low frequency AKR, here given by emission below 50 kHz (above ~14,000 km), for LT sectors near midnight, between ~20-03 hr LT. While speculative in the scope of this work, this implies an upper frequency limit, and as such a lower altitude limit, past which AKR sources have a particular relation to the acceleration during substorm activity. In this case, such AKR sources could be distinguished, on average, from those at higher frequencies that are more active during disturbed periods in general. While this is a first step to determine the presence of such a limit and its value, further work is required to discriminate between AKR from other disturbed periods and that specific to substorms. The limit may vary under certain conditions, which could be checked by examining certain classes of events within a given list more explicitly. However, this must be disentangled from the effect on the correlation due to changing visibility of AKR sources at different frequencies to be properly verified, especially for the remote observations used here. We do not suggest that there is an upper frequency limit above which AKR sources cannot be associated with substorms, however; the maximal spectral extent of the AKR burst may be of closer relation and could be an avenue for future study. The duskside and dawnside LTs for N11 and C15 see significant increases in MCC from 30 to 60 kHz, most clearly on the duskside. This is likely due to poorer visibility of lower frequency sources and the emission cones of such high altitude sources, although a more rigorous statistical investigation of beaming from such sources would be required to validate this. This could be explored with ray tracing models from ensembles of sources, which we leave for future studies. Although the effect on the correlation of varying visibility of lower or higher frequency AKR sources when viewed from different positions cannot be completely discounted, it is unlikely to have a significant effect given the similarities between event lists. Given the clear utility in using AKR observations to infer details of magnetospheric activity during substorms, it highlights the need for a global statistical model of AKR source visibility for a remote instrument without specific direction finding or goniopolarimetric capabilities.

While low frequency AKR produces an improved correlation near midnight relative to higher frequency emission, the correlation decreases toward the dayside for both N11 and C15, relative to the main band AKR. This suggests that AKR sources are not as commonly activated or observed (limited by AKR viewing) at these
frequencies for LT sectors further from midnight, but can still be present in some cases. With further study, this
could be used to determine a constraint on the spatial distribution, both vertical and longitudinal, of duskside and
dawnside AKR sources that are more closely related to the longitudinal expansion of the westward electrojet
during substorms.

There is an asymmetry in the N11 and C15 low frequency AKR peak at midnight, where the former has a slight
premidnight bias (with a peak for LTs between ~21-00 hr LT) and the latter a slight postmidnight bias (between
~00-02 hr LT). This difference is not so significant given the magnitude of the errors at these LTs and the similar
correlation to the duskside regions of the respective lists. These peaks are generally related to the average pre-
midnight MLT of auroral substorm onset, with the offsets between this and the LT of the remote observation
reasonable for the broad emission cones exhibited by AKR sources. However, this discrepancy in the peak
correlations between the N11 and C15 lists may again highlight the spatial link between the LT of remote AKR
observations and the MLT of upward field-aligned currents related to specific ionospheric phenomena. The
westward electrojet can extend further toward the evening and afternoon sectors during substorms and is itself
related to the auroral westward traveling surge that hosts upward field-aligned currents in its most western edge, at
premidnight or earlier MLTs. The upward field-aligned currents that drive the global substorm current wedge in
general are found nearer postmidnight. Another interpretation relates to the spatial sampling of ground magne-
tometers when using the SML or MPB indices. Given that the latter calls on a greater range of stations due to the
inclusion of those at mid-latitudes, the LT dependency of the correlation of low frequency AKR for C15 could
better represent the average correlation with substorms. If the MPB index represents the substorm current wedge
and its typical location near midnight MLTs more accurately than the SML index, for which the westward
electrojet may vary in MLT between events and could be susceptible to timing or spatial inaccuracies due to
poorer coverage of stations for some longitudes, then it follows that the postmidnight correlation may similarly
represent the association of AKR observations with the substorm current wedge. Similarly, the MPB index is
thought to be less susceptible to the effects of changing magnetic latitude of substorm onset and could also be
more accurate for this reason. In general, the similarities in the N11 and C15 correlations for all LTs highlight the
global nature of the substorm current wedge, as well as the clear utility of lower frequency AKR observations in
differentiating between emissions that is more specific to substorm activity at midnight LTs.

4.2.1. Discrepancies of the O20 Substorm List

Clear discrepancies are seen between the O20 results in Figure 4c and those of N11 and C15; namely a decreased
correlation for main band AKR at most LTs, and no significant increase in the MCC of low frequency AKR at
midnight LTs. However, there is an increased correlation of main band AKR nearer midnight supported by an
improved true positive rate and decreased false positive rate. Both the decrease in MCC for most LTs and the local
increase near midnight can be explained by the tighter constraints on event selection for O20. The former, as
mentioned previously, likely sees an overall decrease due to the fewer event windows. Near midnight, while some
events of other lists may no longer be considered by O20, there is a greater correspondence between the LT of the
window used in the contingency table and the MLT of substorm onset. Observations from other LT sectors are
unlikely to exhibit AKR activity related to the initial onsets. On the other hand, N11 events do not necessarily see
contributions from MLTs near midnight; the associated window could be related to a repeat intensification from a
wider range of MLT values.

As well as rejecting events that have contributions from magnetometer stations far from the midnight meridian,
events with significant SML variations close to the onset time are rejected to ensure that substorm onsets are
isolated. The decrease to near-zero MCC for the majority of dayside LTs are thought to represent this; events with
abnormal onsets further from the midnight meridian are discounted, so the AKR sources present near onset itself
are not associated with a given event. This would also be true for AKR sources that follow the longitudinal
extension of the westward electrojet as the substorm sees repeat intensifications. Similarly to the results for the
N11 and C15 lists however, the duskside region of positive skill persists and suggests that this could also be due to
specific visibility of emission cones that are colocated with the westward traveling surge from duskside LTs. In
this case, the Region 1 currents and colocated AKR sources associated with the auroral bulge are likely found
more eastward than the typical events of N11 or C15, and thus produces a smaller LT region of higher correlation.
This is shown explicitly by the O20 duskside peak correlation for main band AKR appearing closer to midnight
than for N11 and C15, and again highlights the ability of remote AKR observations to track spatial constraints in
the ionosphere. However, Yadav et al. (2024) explored the question of whether or not multiple onsets are required
for a large spatial development of auroral features during a substorm. For a handful of events they found that, while single onset substorms can produce a broad region of auroral development that resembles that of multiple onset substorms, it is not as common. The statistical effect of this remains to be explored, and could be facilitated by Wind/WAVES AKR observations. Namely, cross-comparing AKR observations from noon or post-noon with substorm lists could highlight events where the largest azimuthal development has occurred, and provide a basis for study with other magnetospheric and solar wind driving parameters.

The convergence of the MCCs for low frequency and main band AKR observations at midnight LTs is particular to O20 and could imply that AKR below 50 kHz is most relevant for substorms with repeat intensifications. If further energy unloading in the magnetosphere during the substorm expansion phase leads to continued extension of the auroral acceleration region and AKR sources to higher altitudes, then it would follow that such low frequency emission may not be a feature of the isolated substorms selected by O20. Near dusk, low frequency AKR exhibits a lower correlation relative to the main band for the O20 list. For N11 and C15 events, the low frequency AKR correlation is much closer to the correlation of the main band at dusk. This could also be reasoned by the above; if lower frequency, higher altitude AKR sources are ignited during substorms with repeat intensifications, then they would not be readily observed from neither dusk nor midnight when using O20 events. Higher frequency, lower altitude emission still appears to be more specific, on average, for the isolated substorms returned by O20 when observed from the duskside. Conversely, if the O20 list represents substorm activity more accurately than N11 or C15, the convergence of main band and low frequency AKR at midnight LTs may suggest a more variable discriminatory frequency limit. In any case, more work is needed to differentiate the frequency limits specific to substorm onset on a statistical basis.

The lack of an increase in MCC values at the dawnside for all frequency thresholds suggests also that some driving of AKR sources at these LTs is less present for the O20 events. As we are examining the pattern of AKR correlation with substorms statistically and predominantly with ground magnetometer-derived event lists, they are most related to the large-scale system of the substorm current wedge. Ohtani and Gjerloev (2020) show that the substorm current wedge is best approximated by a net field-aligned current pattern that extends across the nightside. If the increased dawnside MCCs of the N11 and C15 results are a result of the enhancement of the Region 2 currents involved in this net current pattern, it could be that the more confidently selected, isolated O20 events do not represent the complete or maximal spatial enhancement of the upward field-aligned currents of the Region 2 currents on the dawnside. Given that the O20 events represent a subset of all substorms and enhancements of the substorm current wedge, the effect of comparing AKR and substorms with more limited spatial enhancement using randomly distributed windows (with respect to substorm onset times) could smooth the correlation profile at these LTs.

Figures 5e and 5f show the changes in MCC profiles for lower frequency AKR observations for the O20 list. The increase of the MCC to a roughly fixed profile, similar to that seen in Figure 4 implies that the MLT constraint on the SML disturbance used for event selection creates a strong dependency for the LT of AKR observations, and that including AKR emission at all frequencies produces the best correlation for isolated substorms on the nightside when viewed between ~15-9 hr LT. The MCCs have much lower variation between frequency limits with generally increasing values with frequency. This could imply that the isolated substorms have less of a tendency to generate AKR sources at the higher altitudes. Further, this would imply that such sources are generated only after repeat injections of plasma and so further intensifications of the substorm onset. Although the overlap in profiles and their errors again make it difficult to draw a confident conclusion, there is a maximal MCC value for 70 kHz around 1–2 hr LT. It follows that the LT region exhibiting the most substorm-specific AKR observations is more constrained relative to the other lists, given the spatial selection of O20, but the results show that these frequencies show marginally greater correlation with substorm activity over the higher frequencies. For the duskside, a clear increase in MCC is seen with increasing frequency, similar to the N11 and C15 lists, but this is also more marginal. However, again as with the N11 and C15 lists, the increases suggest that remote observations made on (particularly the dusk) the flanks should consider the available spectrum to best identify substorm activity.

### 4.2.2. Discrepancies of the F04 Substorm List

In the F04 list, there is no clear trend in LT for MCC. This is likely due to the more restricted time range (approximately quarter of the windows considered compared to the other lists) and the relative inconsistency
present in the event selection (given that the auroral onsets are visually identified so only the brightest or other onsets with clearer particular features may be chosen). Criteria for F04 include spatiotemporal constraints to ensure longitudinal propagation of UV aurora over 20 min, whether westward or eastward, as well as selecting isolated onsets with a minimum threshold of 30 min between events. Although the latter criteria is similar to the isolated onsets of O20, with the same temporal threshold of 30 min used, the predominant duskward and dawnward peaks are likely present due to the former criteria of longitudinal expansion. This distinction between F04 and O20 is implied by the increased correlation of low frequency AKR around the duskside peak; not seen for O20 and more similar to N11 and C15. Inferences cannot be made with much certainty at other LTs due to the aforementioned noise in the data. We conclude that these features highlight issues with using a non-algorithmic selection of substorm onsets, although recognize that such lists can be useful when employed alongside other methods of derivation.

## 5. Summary

We have explored the correlation between 10 years of remote AKR observations from Wind/WAVES with substorms using contingency tables and a binary classification framework. In particular, observations are subset by the LT of Wind to statistically assess the utility of not only observations near midnight, where AKR observations are thought to be most comprehensive, but also those near dusk, dawn and noon. When combining all observations in a single analysis, we find that AKR has a weakly positive correlation in general. However, examining classification statistics from broad subsets of LT sectors reveals a range of characteristics that render all observations as useful in determining the presence of substorm activity, which could be particularly effective alongside other magnetospheric proxies. Surprisingly, observations made near midnight, in the range of 20-03 hr LT, have a negative correlation with substorms when using the entire spectrum of Wind/WAVES. This is due to a high false alarm probability; AKR is produced above the auroral zone with electron precipitation and bright, discrete aurora which commonly occurs on the nightside and is not limited to substorm activity. This novel result implies that, while remote observations from near midnight are likely to capture the widest range of active AKR sources for auroral dynamics in general, occurrence of AKR may not be sufficient to solely indicate substorm activity. LFEs are known to be specific to substorm onset, which is corroborated by the improved, positive correlation of low frequency AKR in this LT range. Observations made near dusk and noon have a higher correlation than produced when combining all data, and those near dawn also produce a positive correlation. These results show that, although the viewing of AKR sources may be limited in some cases, remote AKR observations from all LTs have a positive correlation with substorm activity and can indicate the presence of substorm phenomena.

While low frequency AKR occurrence must be considered when assessing substorm activity for observations near midnight, those from dusk, dawn and noon all show positive correlation for occurrence of AKR across the whole spectrum. The dusk sector has the highest correlation, balancing high event recall and low false alarm probability. This is related to the expansion into evening, dusk (and sometimes afternoon) MLTs of the westward electrojet and upward field-aligned currents associated with the westward traveling surge. This produces a broad region of maximal correlation beginning in the afternoon sector (around 13–15 hr LT) and extending to 20 hr LT. AKR observations near dawn perform well for similar reasons; the manifestation of discrete aurora in the postmidnight and dawn ionosphere following substorm expansion phase produces positive correlation for AKR observed between ~6–9 hr LT. Observations from noon are too limited to capture AKR associated with every substorm, exhibiting a large miss rate, but are highly likely to indicate substorm activity when AKR is present. Again, this is due to the broad azimuthal extension of the westward electrojet during some substorms. These characteristics of remote AKR observations from LTs away from midnight can be used to identify periods for further study; those from dusk in particular could be used to help identify onsets themselves, while noon observations could be used to search for substorms with significant azimuthal expansion of ionospheric currents.

As with the midnight sector, the dependency of the correlation with LT changes when considering occurrence of AKR below certain frequencies. We examined the correlation of the lowest frequencies to investigate a potential limit that indicates specific substorm-related AKR. These results suggest that AKR below 70 kHz may signify the onset of a substorm within a 2 hr window when observed remotely from midnight LTs. By comparing the frequency and LT dependence of the correlation between AKR and substorms using different event lists, we see that the correlation is improved at midnight for the N11 and C15 lists but not for the O20 list. Given the objective of O20 to confidently identify isolated, single onset substorms, it may suggest that emission below these frequencies
is only produced for substorms with multiple intensifications and is constrained to a smaller longitudinal range than for higher frequencies. Such low frequency emission or LFEs act as a proxy for greater extension of the auroral acceleration region to higher altitudes, with emission below 70 kHz corresponding to source regions above ~12,000–14000 km. Further work is required to identify whether this is an absolute limit or one that changes between events, or whether these are related to certain classes of substorms within the broad classes of events captured by N11. However, the changes in the correlation with LT when comparing substorm lists with varying spatial constraints on the deriving instruments further show that the LT of remote AKR observations can be related statistically to the MLT of ionospheric phenomena. More work is needed to quantify this when no direction finding capabilities are available for the instruments observing AKR. Overall, this supports the use of remote AKR observations to provide a proxy of active acceleration regions in the auroral zone. As seen here, they can act alone as an indicator of substorm activity. This study opens questions for the possibility of analyzing AKR source locations with a statistical, theoretical, or combined framework of radio observations, which could in turn be useful to identify active acceleration regions with future observations when paired with others in the magnetosphere or ionosphere.

## Data Availability Statement

The Wind ephemeris was accessed from https://sscweb.gsfc.nasa.gov/cgi-bin/Locator.cgi. The list of AKR bursts used in this study can be found at https://doi.org/10.25935/azyp-1833 (Fogg et al., 2022). The substorm timing lists identified by the Newell and Gjerloev technique (Newell & Gjerloev, 2011) (N11 in this paper) and identified by the Ohtani and Gjerloev technique (Ohtani & Gjerloev, 2020) (O20 in this paper) were both accessed from the SuperMAG website (https://supermag.jhuapl.edu/substorms/). The F04 (Frey et al., 2004) and C15 (Chu et al., 2015) substorm timing lists were accessed from the supporting information of their respective publications. IMAGE/WIC quality parameter used for window selection of F04 was accessed from CDAWeb (https://cdaweb.gsfc.nasa.gov/). IGRF 14 magnetic field strength measurements, used to estimate the altitude of AKR sources of a given frequency, were taken from https://geomag.bgs.ac.uk/data_service/models_compass/igrf_CALC.html.

## Acknowledgments

JEW and LL acknowledge support from CNES and CNRS/INSU programs of planetology and heliophysics, as well as from the LIRA Wind/WAVES team. JCC was supported by Science and Technology Facilities Council (STFC) Ernest Rutherford Fellowship ST/V004883/1. Work at DIAS was supported by Taighde Éireann–Research Ireland award 18/FRL/ 6199 and Laureate Consolidator award SOLMEX to CMJ. CJL is funded by the Science and Technology Facilities Council (STFC) Studentship Grant 2573668. We acknowledge the substorm timing list identified by the Newell and Gjerloev technique (Newell & Gjerloev, 2011), the substorm timing list identified by the Ohtani and Gjerloev technique (Ohtani & Gjerloev, 2020), the SMU and SML indices (Newell & Gjerloev, 2011), and the SuperMAG collaboration (Gjerloev, 2012).

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