JournalofGeophysicalResearch: SpacePhysics

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1 JournalofGeophysicalResearch: SpacePhysics RESEARCH ARTICLE Special Section: The Causes and Consequences of the Extended Solar Minimum between Solar Cycles 23 and 24 Key Points: Complexity of auroral structures can be described quantitatively Auroral occurrence shows a clear solar cycle behavior Complex aurora increases during solar active years Correspondence to: N. Partamies, noora.partamies@fmi.fi Citation: Partamies, N., D. Whiter, M. Syrjäsuo, and K. Kauristie (2014), Solar cycle and diurnal dependence of auroral structures, J. Geophys. Res. Space Physics, 119, , doi:. Received 22 NOV 2013 Accepted 9 SEP 2014 Accepted article online 12 SEP 2014 Published online 1 OCT 2014 Solar cycle and diurnal dependence of auroral structures N. Partamies 1, D. Whiter 1, M. Syrjäsuo 2, and K. Kauristie 1 1 Arctic Research, Finnish Meteorological Institute, Helsinki, Finland, 2 School of Electrical Engineering, Aalto University, Espoo, Finland Abstract In order to facilitate usage of optical data in space climate studies, we have developed an automated algorithm to quantify the complexity of auroral structures as they appear in ground-based all-sky images. The image analysis is based on a computationally determined arciness value, which describes how arc like the auroral structures in the image are. With this new automatic method we have analyzed the type of aurora in about 1 million images of green aurora (λ = nm) captured at five camera stations in Finnish and Swedish Lapland in We found that highly arc like structures can be observed in any time sector and their portion of the auroral structures varies much less than the fraction of more complex forms. The diurnal distribution of arciness is in agreement with an earlier study with high arc occurrence rate in the evening hours and steadily decreasing toward the late morning hours. The evolution of less arc-like auroral structures is more dependent on the level of geomagnetic activity and solar cycle than the occurrence of arcs. The median arciness is higher during the years close to the solar minimum than during the rest of the solar cycle. Unlike earlier proposed, the occurrence rate of both arcs and more complex auroral structures increases toward the solar maximum and decreases toward the solar minimum. The cyclic behavior of auroral structures seen in our data is much more systematic and clear than previously reported visual studies suggest. The continuous arciness index describing the complexity of auroral structures can improve our understanding on auroral morphology beyond the few commonly accepted structure classes, such as arcs, patches, and omega bands. Arciness can further be used to study the relationship of auroral structures at different complexity levels and magnetospheric dynamics. 1. Introduction Morphological evolution of aurora during substorms was introduced by Akasofu [1964] as a local time template including a set of simple auroral forms. The evening and premidnight sector is dominated by quiet arcs or substorm growth phase arcs, which are described as east-west elongated stable structures. During the substorm growth phase an equatorward motion of an arc or arc system is expected. Substorm onset and expansion phase auroras comprise bright, dynamic, fast-moving, and fast-evolving forms, which typically take place in the midnight sector. Morning sector or substorm recovery phase aurora is characterized by fading brightness and diffuse, broken, and patchy structures. This overview, based on visual inspection of ground-based auroral images during the International Geophysical Year in , is still referred to as the ground truth and a reference frame for auroral evolution and substorm studies [e.g., Liang et al., 2005]. However, it is based on ground-based images of substorm activity and is, in fact, a combination of local time, temporal, and geomagnetic activity evolution of aurora. The classes of auroral structures are traditionally based on visual inspection of ground-based auroral all-sky camera (ASC) images, as in the case of the above mentioned study. Apart from auroral arcs [e.g., Knudsen et al., 2001; Partamies et al., 2010] and spirals [Partamies et al., 2001], mesoscale (about km) auroral forms have been examined in detail only in event studies. Their average behavior is extrapolated from the reported case studies without proper statistical analyses. This is largely due to the lack of automatic identification routines and the fast data rate of modern imaging instruments. Auroral images can provide versatile information about solar wind magnetosphere ionosphere coupling processes and on the related changes in the magnetospheric topology, and thus, efficient harvesting of the continuously accumulating image data is desirable. From the morphology of auroral structures it is possible to distinguish sequences of different magnetospheric responses to solar wind driving. As the first approximation one can associate arc-like structures with loading phase, where energy, mass, and momentum accumulate from the solar wind to the magnetosphere. More complicated auroral structures, on the other hand, would be more PARTAMIES ET AL American Geophysical Union. All Rights Reserved. 8448

2 typically signs of unloading phases and associated with energy release from the magnetosphere to the ionosphere and to the interplanetary space as plasmoids. Stable or quiet arcs are the simplest and probably most studied auroral forms [e.g., Borovsky, 1993; Kauristie et al., 2001; Knudsen et al., 2001]. In addition to their east-west alignment, they are typically defined as forms stretching over the entire ASC field of view, or a large part of it, with a width of the order of some kilometers to several tens of kilometers at auroral altitudes. Arcs are expected to be present in any local time sector and at all latitudes of the auroral oval [e.g., Stringer and Belon, 1967], but they are most often associated with the evening sector. However, the smooth appearance of auroral arcs may be partly due to the limited spatial resolution of traditional ASCs (down to about 1 km), which have provided the data for most arc studies in the past. To facilitate statistical studies of auroral structures, some automatic algorithms have been developed in the past. One attempt for automatic identification of auroral forms was described by Syrjäsuo and Donovan [2004]. They classified 350,000 Canadian all-sky camera images from Gillam station (at geographic and geomagnetic latitude) captured in Their study included five mutually exclusive classes: (1) No Aurora, (2) Arcs, (3) Patchy Aurora (irregular patchy shapes), (4) Omega bands (Ω-shaped structures), and (5) Other (e.g., diffuse or complex auroral structures). Excluding the first class left the shape analysis with 220,000 images containing aurora. Classification by supervised learning means similarity analysis based on a comparison between unseen data and preselected samples of certain data type (a training set), where the sample images can be divided to different classes by using numeric values calculated for each image ( features as described by, e.g., Therrien [1989]). The study by Syrjäsuo and Donovan [2004] was based on brightness, alignment, and multiscale texture-related features of auroral images. The training set included some tens to about 130 carefully selected samples per structure class. The automatic classification program (classifier) detected about 17,000 arcs, 9700 patchy auroras, and 600 omega bands, i.e., about 27,000 preselected structures out of the total number of 220,000 images containing aurora. About 60% of these successfully identified structures (classes 2 4) were arcs. About 193,000 images were classified as Other, which is a large amount of unidentified auroral structures. Thus, less than 10% of all 220,000 images containing aurora (classes 2 5) were Arcs. According to these results, the largest class of aurora is Others (class 5), i.e., something more complex and ambiguous than a clean Arc (class 2), Patch (class 3), or Omega (class 4). In a more recent study Wang et al. [2010] classified discrete dayside auroral forms into four different categories using spatial texture, intensity, and shape features of the images. Their training data consisted of about 8000 manually labeled images taken in The classification was then performed for data collected at Ny-Ålesund station (at geographic and magnetic latitude) on Svalbard in Images which did not contain aurora were first manually excluded, and the remaining data set for the automatic classification included about 70,000 unseen images. Dayside auroral imaging is limited to high-latitude stations and a time period from November to February. Dayside auroral structures and dynamics are somewhat different from the nightside ones, but one of the four classes was auroral arcs, which contained about 37% of the manually labeled images and about 33% of the automatically classified data. The diurnal distribution of the dayside auroral structures shows that arcs dominate the dusk/evening sector with 60 70% occurrence rate (number of arcs per total number of images) at around 17 magnetic local time (MLT). The longest time series of auroral observations extends over several solar cycles and has been analyzed by Nevanlinna and Pulkkinen [1998, 2001]. Based on visual inspection of images from eight camera stations, they concluded that there was no obvious correlation between the auroral occurrence rate and the solar cycle in but rather a slowly increasing long-term trend observed in auroral occurrence, magnetic activity, and sunspot number. The study by Nevanlinna and Pulkkinen [1998] further divided auroral structures into active and quiet auroral forms. This division was based on quick look table descriptions of the auroral images (available online at Visual classification of each 15 min interval in gives an average behavior of the aurora. The dominant description in the images has been divided to classes of (0) no observations, (1) homogeneous auroral arcs, (2) rayed aurora, (3) diffuse patches, (4) other auroral forms, (5) clear skies but no aurora, and (6) cloudy. The solar cycle dependence of quiet arcs (class 1) and active auroral forms (classes 2 4) suggested that PARTAMIES ET AL American Geophysical Union. All Rights Reserved. 8449

3 active auroral forms follow geomagnetic activity somewhat better while the behavior of quiet auroral arcs resembles more of an anticorrelation with the solar cycle, with more quiet arcs during the solar minimum years than during the solar maximum years. A more recent report on the auroral occurrence in Fennoscandia and Svalbard in showed a clear maximum in 2003 during the declining phase of the solar cycle 23 and a minimum during the solar quiet years [Pulkkinen et al., 2011]. In this study the latitude variation in the auroral occurrence was also examined. While the auroral oval latitudes follow the solar cycle variation, the high-latitude stations on Svalbard experience much smaller changes in auroral occurrence. This reflects the fact that an active auroral oval during the years close to solar maximum is wide and auroral displays can be observed over a large range of latitudes from southern Fennoscandia to Svalbard. During the years close to the solar minimum the contracted auroral oval resides at higher latitudes. Thus, auroral displays frequently occur over Svalbard throughout the solar cycle. In this study we analyze the diurnal and solar cycle behavior of auroral arciness, i.e., how arc like the dominant auroral structures appear in the images. We seek to answer the question of the existence and occurrence rate of arcs in all local time sectors, as well as to examine the solar cycle evolution of arc occurrence as compared to the occurrence rate of more complex auroral structures. We further provide a numeric tool to characterize the complexity of auroral forms in a continuous manner. The tool will allow us to study the evolution of the more ambiguous structures not belonging to the generally accepted and well-defined structure classes. This will provide us with novel information about other auroral structures, which dominate in all auroral images (e.g., 88% of classified images in Syrjäsuo and Donovan [2004]). Because the complexity in the auroral morphology is an ionospheric signature of the magnetospheric dynamics, our database of arciness can be used to improve our understanding of magnetosphere-ionosphere coupling processes. By introducing the arciness concept, we hope to open new ways for auroral image analysis. Extracting new objective and numeric information from ASC images will gradually allow us to use ASC images in space climate studies in the same way as magnetic indices are used today, i.e., to characterize magnetospheric topology during different solar cycle phases. A short instrument network and data description is given in section 2, methods to prune the image data and to calculate auroral arciness are explained in section 3, and local time and solar cycle dependence of auroral morphology are discussed in sections 4 and 5, respectively, before the final conclusions and summary in section MIRACLE Auroral Camera Network As of 2013 the Magnetometers-Ionospheric Radars-All-sky Cameras Large Experiment (MIRACLE) [Syrjäsuo et al., 1998] instrument network includes nine auroral cameras of three different types [Sangalli et al., 2011; Partamies et al., 2007] located in Fennoscandia and Svalbard. During the years seven identical ASCs have been operated, five of which are located in Lapland with overlapping fields of view. Depending on the year, three to five cameras have been simultaneously in operation (see years of operation for individual stations in Table 1). The instruments include a filter wheel with optical filters for the main three auroral emissions (red λ = nm, green λ = nm, and blue λ = nm), an image intensifier, and fish-eye optics. The image cadence for green emission has been 20 s throughout the lifetime of the cameras, and the exposure time of the green line has mainly been 1 s. Imaging season in Lapland extends from September until April every winter. During that time images have been automatically captured when the Sun is more than 10 below the horizon to provide dark enough skies for auroral observations. The images stored in gray scale 8bit JPG format have a pixel resolution of , which gives an average spatial resolution of about 1 km at the ionospheric height of 110 km. These intensified charge-coupled device (ICCD) cameras produce several tens of thousands of images per station per year. The locations and the fields of view of the five Lapland stations used in this study (SOD, MUO, ABK, KIL, and KEV) can be seen in the map of Figure 1, and their coordinates are listed in Table Methods: Pruning of Image Data (Step 1) and Arciness Analysis (Step 2) 3.1. Step 1: Aurora Versus No Aurora All MIRACLE ASC data from the ICCD camera era ( ) have been automatically pruned into classes of Aurora and No Aurora by an algorithm developed by Syrjäsuo et al. [2001]. The images in the class Aurora have been stored on the server at the Finnish Meteorological Institute, while the images of the class No Aurora have been stored on CD/DVD disks and are not available online in order to save disk space. In PARTAMIES ET AL American Geophysical Union. All Rights Reserved. 8450

4 Table 1. Names, Geographic Coordinates, Corrected Geomagnetic Latitudes, and the Years of Operation for the Lapland ASC Stations (From South to North) Used in This Study a Station Abbreviation Glat Glon CGMlat Years of Operation Sodankylä SOD Muonio MUO Abisko ABK Kilpisjärvi KIL Kevo KEV a The magnetic midnight in Fennoscandia and Svalbard meridians is at about 21:30 UT. The auroral imaging season in Lapland extends from September until April every year. addition to the images in the Aurora class, images from the 5 min either side of midnight UT have been included in the Selected set on the server to allow monitoring the number of nights each camera has been in operation. The detection of the presence of aurora in this pruning procedure is based on thresholding: if the number of pixels above a local brightness threshold is sufficiently large, the image is assumed to contain aurora. However, as the ASCs have also operated during periods when the Moon is visible in the sky, an additional test is performed to ignore cases when the Moon is the only bright object in the image [Syrjäsuo, 2001]. Moonlit scattered or broken clouds may be confused as aurora if the total brightness is high enough. Images of overcast sky are classified as No Aurora. This pruning algorithm uses experimentally determined brightness parameters as thresholds implemented in the method. With these parameters the routine detects practically all cases where visual aurora is seen in summary plots (keograms). Although a formal accuracy estimate and validation of the classifier (test runs with a training set of manually labeled data) has not been carried out, its performance has been constantly monitored in a visual manner to make sure that no auroral events are being missed. Figure 1. Locations of the MIRACLE auroral camera stations in Lapland. Data from these stations have been used in this study. Circles around the red station markers show the approximate fish-eye field of view with the diameter of about 600 km at the altitude of 110 km. The total number of pruned green line images (i.e., the number of images classified as Aurora) in the entire time period of 12 years is 623,539 from SOD; 450,099 from MUO; 305,279 from ABK; 856,279 from KIL; and 555,303 from KEV (in total images). Before using these data for calculating auroral arciness as described in the following section, we exclude (1) images within 5 min of UT midnight, (2) images for which the solar zenith angle is less than 101, and (3) images which do not have a corresponding image pair from another Lapland camera station taken at the same time. Because the ±5 min around UT midnight have always been saved for monitoring purposes, most of those data do not contain aurora and the data loss from excluding these images is negligible. Imaging starts PARTAMIES ET AL American Geophysical Union. All Rights Reserved. 8451

5 automatically when the solar zenith angle is larger than 100. Requiring an extra degree for the Sun below the horizon provides dark skies without prominent glow on the horizon which could be misinterpreted as north-south aligned aurora. Unpaired images were not relevant for auroral height analysis [Whiter et al., 2013] which searched for simultaneous images from two stations with overlapping fields of view, but paired data ensure that there is aurora in all images. This means that if three cameras were operated at the same time but two of the stations were cloudy, the data from the single station with clear skies have not been analyzed for arciness. Because our data come from two to five stations, using paired data is estimated to have a potentially significant effect on the number of analyzed images only in In the autumn 1996 only KIL and MUO cameras were in operation, and in 1996 the total number of images containing aurora is about 11,000, as compared to several tens of thousands of images in other years Step 2: Auroral Arciness Arciness value, describing how arc like auroral structures in an image are, is an additional parameter stored from a new method designed for automatically estimating the peak emission height of the aurora [Whiter et al., 2013]. The method was tested with synthetic images where the aurora was characterized by length, width, angle, and bendiness, which describes how straight the synthetic auroral structure is in the range from 0.05 (straight) to 0.25 (high curvature). For fast calculation of the arciness value, the images are binned to pixels (average spatial resolution about 2 km at the ionospheric heights). The median pixel value in the image corners outside the circular field of view is subtracted from all pixels to remove the dark current contribution. The routine then finds the pixels above a certain threshold, i.e., the brightest pixels in the image, and labels them into clusters such that all pixels in a given cluster are adjacent to another pixel (not diagonal) in the same cluster. Individual bright pixels far from other bright pixels are ignored. If there are more than 1000 pixels above 120 (on an 8bit scale from 0 to 255), the brightness threshold is 120 (typical median value of an auroral image). Otherwise, the threshold is decreased until more than 1000 pixels in the entire image exceed the brightness threshold. The 1000 pixel threshold for the total number of bright pixels in an image provides a sufficient coverage while still keeping the speed of the analysis process reasonably high. The ultimate minimum threshold value is 8 which is just above the dark-corrected intensity of clear and dark skies. This flexible and experimentally determined thresholding has been tested on a variety of images. It matched up with what an expert would visually have chosen as auroral structures in the image. The images which fail to meet the above criteria have been discarded from the analysis. The discarded images may have included some aurora which, however, was so dim and/or evenly distributed in the field of view that any clear structures could not be detected. Single clusters with fewer than 20 pixels are discarded as too small, while larger clusters are considered to be independent structures. The routine fits a polynomial of the order of log 10 (n) to each structure, where n is the number of pixels in the structure (e.g., 25 pixels equal a straight line, pixels a second-order polynomial, etc.). For each fit the error is described by the chi-square goodness of fit (χ 2 ). We sum the values of χ 2 over all structures and normalize to the total number of pixels contained in the structures to describe the distribution of the bright pixels with respect to the polynomial fit line: χ 2 M = (1) n This describes how well the structure shapes are approximated by polynomials. A wide auroral structure has a higher M value than a thin one due to the pixels being geometrically farther away from the fit line. We then define a measure of the weighted number of structures as [ ] ( 1 c n N = ) c (2) n where c is the number of structures and the sums are over all independent structures. The power law dependence gives more weight to the main structure (largest pixel area) in the image than others. For images where all the brightest pixels are in one single structure N = 1. For images where the brightest pixels are spread equally between two structures N = 2, while higher N corresponds to increasing number of structures in the image. Finally, arciness A is defined as [ ] 3.0 A = min ln(nm), 1.0 (3) PARTAMIES ET AL American Geophysical Union. All Rights Reserved. 8452

6 Figure 2. Examples of clustering and fitting the bright pixels for arciness calculation. (a) Original auroral images with an inverted gray scale (A is the arciness value of the image). (b) Color-coded clusters of brightest pixels overlaid on the original images (N is the weighed number of structures in the image). Each cluster in the image has a different color. (c) Polynomial fits on the top of each cluster that was large enough for an auroral structure (M is a sum of the fitting errors over the image). This number is experimentally scaled to range from 0 to 1 so that the highest value 1 corresponds to an image where the dominant feature is an arc. The image-specific arciness value decreases with increasing number of structures N or with the polynomial fits turning less reliable (effect of large χ 2 values on M in equation (1)). Truncating the tail of the arciness distribution by including the minimum function makes A values suitably insensitive to small twists and width differences on arc-like structures. This guarantees reliable arc detection, grouping similar structures, likely to be caused by the same physics, to the class of auroral arcs with A = 1. This routine is a stand-alone procedure, which could be tuned to run on station computers to flag the auroral images in near real time. The clustering and fitting of auroral structures in the images is illustrated in Figure 2. Figure 2a displays the original auroral images, Figure 2b includes color-coded clusters of the brightest pixels on the top of the same images, and Figure 2c displays the polynomial fit lines. Images are displayed in an inverted gray scale, and values for A, N, and M are given for each sample image. From these examples it is obvious that simple arcs are easily detected even when the Moon is visible. Multiple structures may be problematic if their brightness distributions are not clearly separated from each other, and diffuse aurora (or aurora seen through the clouds) distributes the brightness to such a large area that the polynomial fits become poor resulting in low arciness values. The number of images for which arciness has been successfully calculated is 220,420 from SOD; 204,629 from MUO; 103,184 from ABK; 321,804 from KIL; and 242,684 from KEV station (in total 10 6 images). The PARTAMIES ET AL American Geophysical Union. All Rights Reserved. 8453

7 images which contain aurora but have not obtained an arciness value are very dim lacking a good contrast for identifying auroral structures (clusters of bright pixels). Thus, some of the excluded images would contribute to the low values of the arciness spectrum. A set of sample images of aurora with different arciness values are shown in Figure 3. In the left column images with auroral arcs (arciness = 1) have been displayed, one from each station (KEV, KIL, ABK, MUO, and SOD from top (north) to bottom (south)). The KEV sample image in the top left corner shows multiple arcs. Due to the truncated arciness values, the A = 1 class includes both single and multiple arc structures. More complex auroral structures with arciness between 0.6 and 0.8 have been chosen for the middle column, and sample images with low arciness of are shown in the right column. The examples show that for clear auroral arc structures arciness is indeed one. For lower arciness values it is not clear how the structures are organized, but they include diffuse aurora, broken and complex structures of active aurora, and wiggly and folded arcs when the magnetic activity is changing from quiet to moderate and active. Arciness is also low when aurora is seen through broken cloud cover. The lowest value of arciness (0.4) corresponds to the sky full of diffuse aurora where the bright pixels are spread over the entire field of view (high M), but there is still only one big structure in the image (N = 1). While images containing the Moon as the only bright object have been discarded by the preprocessing software (section 3.1), some images of strongly moonlit clouds may have passed the pruning. These images may also have obtained an arciness value. The brightness distribution of moonlit clouds would be very scattered and thus would only contribute to the very low arciness values. The number of images with clear auroral arcs (A = 1) is not affected by clouds. In the images of intense aurora the Moon does not have an affect on arciness, as can be seen from the sample images in Figure 3. In clear-sky images the moon is such a small concentration of high brightness values that it does not dominate over the auroral forms. Sample images where clouds and/or the moonlight affect the arciness are shown in Figure 4. However, it is obvious that the pruning software cannot distinguish between the different sources of brightness in the images, but these misclassified cases are a clear minority. The local time and solar cycle analysis described in the following sections has been performed on a subset of image data in addition to the full data set. The subset includes 0.5 million ASC images for which the auroral peak emission height has been successfully calculated [Whiter et al., 2013]. Because the successful height analysis requires the same clear auroral form to be seen simultaneously from two stations, this means that the subset only contains data where auroral structures are closer to the zenith than the horizon in the camera field of view, and neither clouds nor moonlight dominates the view. The subset thus consists of higher-quality image data. The results of the high-quality subset are essentially identical to the results of the full data set. Therefore, we only discuss the analysis of the full data set of about 1 million ASC images in the following sections. 4. Local Time Evolution of Auroral Structures The magnetic local time (MLT) distribution of median arciness is displayed in Figure 5 (middle). It shows hourly median values of arciness with the standard deviation bars as a measure of the range of variation in each MLT bin over all years. Because A values cannot be larger than unity, the standard deviation bars have also been limited to a maximum value of 1 to be consistent with the definition of arciness. The most arc-like structures have been captured in the evening and premidnight hours, while the lowest arciness values have been observed during the morning hours. There is a lot of scatter, but the trend is clear. The evolution of auroral structures from arc like in the early evening and premidnight to more complex and broken forms in the postmidnight and morning hours is in agreement with the diurnal development of auroral and magnetic activity described by Akasofu [1964]. The earliest and latest time bins contain thousands of images, while the hours around magnetic midnight (±6 7 h) comprise some tens of thousands of images. Thus, the early afternoon and late morning bins are less reliable than the midnight bins where the decreasing arciness trend is most obvious (from A 1at17MLTtoA 0.7 at 7 MLT) and when all Lapland stations are situated within the auroral oval. The lowest arciness value found in our data set is 0.4. Thus, the median value trend over midnight experiences half of the range of A. Figure 5 (top) shows the MLT variation of very arc like structures (A = 1, solid line) and more complex structures (A < 0.9, dashed line). The total number of images for which arciness equals one (Arcs) is about 350,000, while the total number of more complex structures (Others) is about 600,000 (138,000 images PARTAMIES ET AL American Geophysical Union. All Rights Reserved. 8454

8 Figure 3. Selection of auroral images from different stations with different arciness values. (left column) Images of auroral arcs (arciness = 1). (middle column) Images with arciness (right column) Images with arciness The rows from top to bottom are for different stations (north to south): KEV, KIL, ABK, MUO, and SOD. The bright spot in some of the images is the Moon. PARTAMIES ET AL American Geophysical Union. All Rights Reserved. 8455

9 Figure 4. Selection of auroral images where clouds/moonlight affect the arciness. From left to right the sample images contain (1) elongated aurora behind clouds (A = 0.7), (2) patches of moonlit clouds (A = 0.6), and (3) uneven moonlit cloud cover (or clouds illuminated by intense aurora) (A = 0.4). had arciness values between 0.9 and 1). The number of Arcs is about 32% of all the data for which arciness was successfully calculated, i.e., all clear auroral structures. This is in a very good agreement with one third obtained by Wang et al. [2010], which emphasizes the dominance of arciness in the evening sector. The numbers of Arcs and Others (Figure 5, top) have been divided by the total number of pruned images (images containing aurora). There is a decreasing trend in the occurrence of Arcs and an increasing trend in the number of Others during the night. As a comparison, Figure 5 (bottom) shows the reconstruction of the MLT occurrence of auroral arcs (blue), omegas (red), and patches (green) according to Syrjäsuo and Donovan [2004]. Each structure group has been normalized to its peak occurrence since the three event groups are unevenly populated. The highest Relative event number (%) Arcs Others Median arciness Relative number of events Arcs Patches Omegas MLT (hrs) Figure 5. Arciness distribution as a function of magnetic local time (MLT). (top) The relative numbers of Arcs (A = 1, solid line) and Others (A < 0.9, dashed line) in MLT have been normalized to the total number of images containing aurora. (middle) Hourly median arciness values along with the annual standard deviation bars to illustrate the range of variation of A. (bottom) Previously published distribution of the relative number of auroral arcs (blue), omegas (red), and patchy aurora (green) has been adapted from Syrjäsuo and Donovan [2004]. The vertical lines mark the local magnetic midnight, which is at about 21:30 UT in Fennoscandia and around 06:30 UT at Gillam. PARTAMIES ET AL American Geophysical Union. All Rights Reserved. 8456

10 number of automatically identified auroral arcs is seen at around 21 MLT, the number of omega bands maximize at about 02:30 MLT, and patchy aurora at around 6 MLT. Our arciness roughly agrees with the results of Syrjäsuo and Donovan [2004] with maximum arc occurrence in the evening and least arc-like structures late in the morning. At 2 4 MLT when the omega bands are most frequent, the decrease in the arciness trend is leveled. Since omegas are large-scale structures, they may appear as arc-like elongated bands in the ASC images before and after a full omega form is seen. This could thus be responsible for the stable median arciness during the morning transition from Arc-dominant to Omega-dominant hours. The omega bands defined by Syrjäsuo and Donovan [2004] were single images with aurora resembling a Greek letter omega. This is only true when the structure appears in the middle of the camera field of view, and in reality omega bands are somewhat more frequent than what their statistics suggest. The group of Arcs in the data by Syrjäsuo and Donovan [2004] is by far the largest and most accurately identified. Although the number of images in their data set is about one fifth of that in ours, the class of Arcs is the most reliable reference set. In addition to the similar temporal evolution as compared to our arciness and, in particular, the relative number of Arcs in Figure 5 (top), it also shows the presence of arcs in all local times ( all being limited by the hours of operation of the auroral cameras). To the extent that our time span overlaps with that of Wang et al. [2010], the evening sector shows a similar dominance of arcs. Although they focussed on daytime images, about one third of their data consist of dusk sector arcs. The time evolution of arciness is more consistent with this earlier maximum occurrence of arcs ( 17 MLT) than the maximum of premidnight arcs around 21 MLT by Syrjäsuo and Donovan [2004]. Instead of an occurrence rate of three preselected auroral forms, our analysis includes all structures. The high-median arciness values (around 0.8 or higher for almost all time bins) indicate that very arc like structures may be even more common in the auroral displays and less dependent on the local time than what the previous studies conclude. 5. Solar Cycle Evolution of Arciness An annual median value of arciness in Figure 6 shows higher values (bottom) suggesting more quiet time structures during the years close to the solar minima of 1996 and 2008 as compared to other years. The annual sunspot number (Figure 6, top) peaked in 2000 and stayed high until Variations in the magnetic activity in the MIRACLE sector are described by the annual average standard deviation of the local electrojet index (STD(IL) in Figure 6 (middle), calculated from the lower envelope curve of the magnetic north-south component measured by the MIRACLE network magnetometers). The largest magnetic deflections were observed in 2003, and the quietest years were 1997 and The variability of the arciness is illustrated by the standard deviation over all times for each year, drawn as bars bracketing the median A values (Figure 6, bottom). It should be noted that the standard deviation does not describe the statistical significance of the annual median values but provides a proxy for the range of variation of A for each year. The changes in the median arciness values during the solar active years are minor. The clearest features are the high arciness years around the solar minima. In particular, the increasing arciness toward the deep solar minimum of the cycle 23 is in a good agreement with the decreasing sunspot number and decreasing magnetic activity. The evolution in the occurrence of Arcs (A = 1, solid line) and Others (A < 0.9, dashed line) has again been plotted separately in Figure 7 (top). The image numbers of Arcs and Others have been divided by the annual number of pruned images, i.e., images containing aurora, which is shown by the dots in Figure 7 (bottom). Only in the first bin (1996) the number of events is clearly less than 10,000. The occurrence of Arcs and Others shows more of a solar cycle evolution than the annual median value does. Both groups reach their maximum in 2002 and stay numerous until 2004 while Arc occurrence stays high until The simultaneous increase of Arcs and Others during the solar active years implies a relative decrease of undefined (dim) auroral structures (images rejected from the arciness analysis). The fact that more auroral images are included in the analysis during the solar maximum years is most likely due to more frequent occurrence of bright aurora and thus better contrast and more easily detectable structures. The number of images rejected from the arciness analysis varies much less than the number of Arcs and Others indicating that the structural changes are more dominant than the brightness changes over the solar cycle. The fraction of Arcs (solid line in Figure 7 (top)) ranges from about 5% to about 15% of all pruned auroral images over the solar cycle, which agrees with the relative portion of Arcs (10% during the declining phase of solar cycle 22) obtained by Syrjäsuo and Donovan [2004]. The portion of more complex structures varies between PARTAMIES ET AL American Geophysical Union. All Rights Reserved. 8457

11 Annual SSN STD(IL index) Median Arciness Year Figure 6. (bottom) Median arciness over the 12 year period from 1996 to 2007 is compared with the (top) annual sunspot number and (middle) standard deviation of the local auroral electrojet index IL. Annual median values for arciness have been plotted together with the standard deviation bars. about 7 and 30% of all pruned image data. The fraction of the more broken auroral forms also shows a more pronounced decrease during the declining phase of the solar cycle as compared to the decrease in the occurrence of Arcs. The decline of the more complex structures in is consistent with the increasing arciness in the previous figure. It also agrees with the decline seen in the auroral occurrence rate of Lapland stations [Pulkkinen et al., 2011, Figure 9], as well as the decline of the magnitude of the interplanetary magnetic field (IMF B) plotted in Figure 7 (middle). The annual median values of the solar wind drivers, IMF magnitude (blue), and the wind speed (V SW, green) show strong magnetic field in and a high solar wind speed in Powered by the solar wind, the evolution of auroral Arcs and Others follows the behavior of geomagnetic activity which lags the sunspot cycle variation by 1 2 years [e.g., Pulkkinen et al., 2011; Partamies et al., 2013]. The data in our study suggest that the relative amount of quiet aurora varies less than the number of more complex auroral structures over the solar cycle. Yet both structure classes are positively correlated with the solar activity as opposed to the anticorrelation of the quiet arcs and the sunspot number suggested by Nevanlinna and Pulkkinen [1998]. The different analysis methods between the previous and current studies may well give rise to some differences in the results. Since arcs are the simplest auroral structures and most reliably defined both visually and computationally, the results are meaningfully comparable even from different approaches. The previously reported negative correlation between the sunspot number and quiet aurora is likely to be related to the large latitude spread of the camera stations from southern Finland to Svalbard. As shown by Pulkkinen et al. [2011], the auroral occurrence varies much less in the poleward part of the auroral region (Svalbard) than farther south. At the Lapland latitudes the auroral occurrence experiences cyclic behavior, while in the southern part of the auroral region auroral occurrence only increases significantly during the years close to the solar maximum. Thus, combining data from all Fennoscandian and PARTAMIES ET AL American Geophysical Union. All Rights Reserved. 8458

12 Relative event number (%) A = 1 A < IMF B (nt) Vsw (km/s) Number of pruned images 4 x Year Figure 7. (top) Annual numbers of images with arciness (A) equaling 1 (solid) and arciness less than 0.9 (dashed) normalized by the total annual number of images containing aurora (%). (middle) Annual median values of the solar wind speed (green) and IMF magnitude (blue). The standard deviation of IMF magnitude is steadily about 3 nt and that of the solar wind speed is about 90 km/s. (bottom) The annual number of images containing aurora (pruned), i.e., the data set used for normalization. Svalbard latitudes into one auroral occurrence index, as was done by Nevanlinna and Pulkkinen [1998], may have resulted in the less obvious solar cycle correlation as compared to the results of our study. 6. Conclusions We have analyzed the arciness (how arc-like auroral structures appear) of about 1 million ground-based auroral all-sky images from five MIRACLE stations in northern Finland and Sweden: SOD, MUO, ABK, KIL, and KEV. These data extend over the past solar cycle (number 23) in Although an empirically defined parameter, the diurnal behavior of arciness agrees with earlier local time studies of auroral morphology. By visually examining images of different auroral forms, we conclude that the clearest auroral arcs systematically correspond to the highest arciness values (A = 1). The way arciness is organized for more complex structures is not equally clear and possibly not fully physically meaningful. Therefore, much of the analysis is performed for two structure classes of Arcs (A = 1) and Others (more complex structures, A < 0.9). We compiled the arciness analysis for a high-quality subset of about 0.5 million images in addition to the full data set of about 1 million auroral images. This subset of images, which is free of clouds and moonlight, produced the same results as the full data set, and thus, only the larger data set results have been shown and discussed. To the best of our knowledge, this is the first objective and automated study of auroral morphology over a period long enough to cover a full solar cycle. Our results suggest cyclic behavior of the aurora along the solar cycle. An advantage of this data set is that it is based on identical instrumentation at five stations all located in the auroral oval region, and the method does not require human classification of individual images. The subjective human concept of an auroral arc is first embedded in our experimental model, after which the automatic process guarantees objectivity. Yet much more detailed information of the evolution of auroral structures can be achieved from this data set by applying computer vision algorithms for PARTAMIES ET AL American Geophysical Union. All Rights Reserved. 8459

13 more detailed structural identification. This would further advance our understanding of the long-term and diurnal development and interchange of auroral forms. A parameter like arciness describes the change from one type of structure to another in a continuous manner without excluding complex structures, which may be a good concept also for future studies. According to previous automated classification by Syrjäsuo and Donovan [2004], only about 10% of all images containing aurora could be classified into predetermined classes of different auroral structures. In a more recent study by Syrjäsuo et al. [2006], two human experts agreed with each other in 70% of the images and were able to classify only about 50% of the images into predefined structure classes. This suggests that without computer-based attributes such as arciness, our ability to truly analyze and interpret the auroral forms is severely limited. Our findings justify an earlier assumption that auroral arcs form the basic element of auroral structures. Arcs can be observed in any time sector but are most common in the premidnight hours. The most infrequent occurrence rate of auroral arcs was found in the early morning hours when the relative number of more complex structures is much higher. We found that the annual portion of highly arc-like structures varies between 5 and 15% of all data containing aurora in The annual median arciness suggests that arc-like structures are more likely during the magnetically quiet years than during the magnetically active years in the vicinity of the solar maximum. Large scatter in the annual arciness shows that even weaker magnetic activity during the solar quiet years can locally generate a full spectrum of different structures from simple arcs to complex morphology. On the other hand, even the strongest magnetic storms can locally include a certain portion of auroral arcs. Auroral displays during the past solar minimum were found to be exceptionally arc dominated for several years, and the increasing arciness agrees with decreasing sunspot number and magnetic activity (IL index) toward the past solar minimum. A similar relationship was proposed by Nevanlinna and Pulkkinen [1998] based on visual inspection of auroral camera data from 1973 to However, the cyclic behavior was concluded unclear in this previous study. Their results resembled more of an anticorrelation between quiet structures and solar activity. Our findings show that although the relative number of quiet arcs varies little compared to the evolution in the occurrence of more complex structures, it is still positively correlated with the solar activity. The occurrence of the complex structures follows the solar activity more closely than the number of quiet arcs during the past solar cycle. This suggests that dynamic aurora is more directly driven by the geomagnetic activity without affecting much the occurrence of quiet aurora. Extending the analysis to include different image data types and homogenizing the arciness time series over the ongoing solar cycle will, by the time of the next solar minimum, give more detailed insights into the exceptional behavior of the auroral morphology during the past solar minimum. The fact that the number of successfully analyzed auroral structures (Arcs and Others) stays high for a few years after the solar maximum in 2000 results from the intense solar wind energy input delivered by the high IMF magnitude and fast wind speed in Hence, the energy dissipation from magnetosphere to the ionosphere in terms of precipitation remains high. Constantly high precipitation fluxes are responsible for enhanced ionospheric conductivities and thus help maintaining the enhanced level of geomagnetic activity in the years following the solar maximum. The exceptionally high average solar wind speed in 2003 is responsible for the very high auroral occurrence rate and electrojet activity, as shown by Pulkkinen et al. [2011]. Which solar wind drivers exactly control the structural changes in the aurora will remain as a topic for future studies. The complexity of the auroral structures is an ionospheric signature of the dynamics in the magnetosphere. Auroral arcs are typically related to inverted V-type particle acceleration, upward Region 1 currents in the evening sector and converging ionospheric electric fields, quiet magnetic conditions or substorm growth phases, or wave activity, such as Alfvén waves or field line resonances. The complex morphology refers to more intense magnetic activity, such as substorm expansion and recovery phases or more broken structures of the morning sector aurora. The level of complexity in the auroral morphology and how it maps to the magnetosphere is not well understood. As a numeric measure of the complexity, auroral arciness can be used to study the ionospheric end of the magnetosphere-ionosphere coupling, for instance, the temporal and spatial evolution of substorms. The transition from the quiet aurora in the growth phase to the active forms in the expansion phase will show the timing of the local breakup. Using auroral image data more efficiently will also help in understanding the relation between magnetic deflections and auroral signatures. PARTAMIES ET AL American Geophysical Union. All Rights Reserved. 8460