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The EU funded the FLARECAST project, that ran from Jan 2015 until Feb 2018. FLARECAST had a R2O focus, and introduced several innovations into the discipline of solar flare forecasting. FLARECAST innovations were: first, the treatment of hundreds of physical properties viewed as promising flare predictors on equal footing, extending multiple previous works; second, the use of fourteen (14) different ML techniques, also on equal footing, to optimize the immense Big Data parameter space created by these many predictors; third, the establishment of a robust, three-pronged communication effort oriented toward policy makers, space-weather stakeholders and the wider public. FLARECAST pledged to make all its data, codes and infrastructure openly available worldwide. The combined use of 170+ properties (a total of 209 predictors are now available) in multiple ML algorithms, some of which were designed exclusively for the project, gave rise to changing sets of best-performing predictors for the forecasting of different flaring levels. At the same time, FLARECAST reaffirmed the importance of rigorous training and testing practices to avoid overly optimistic pre-operational prediction performance. In addition, the project has (a) tested new and revisited physically intuitive flare predictors and (b) provided meaningful clues toward the transition from flares to eruptive flares, namely, events associated with coronal mass ejections (CMEs). These leads, along with the FLARECAST data, algorithms and infrastructure, could help facilitate integrated space-weather forecasting efforts that take steps to avoid effort duplication. In spite of being one of the most intensive and systematic flare forecasting efforts to-date, FLARECAST has not managed to convincingly lift the barrier of stochasticity in solar flare occurrence and forecasting: solar flare prediction thus remains inherently probabilistic., Comment: 67 pages, 14 figures; submitted

Global EUV waves remain a controversial phenomenon more than 20 yr after their discovery by SOHO/EIT. Although consensus is growing in the community that they are most likely large-amplitude waves or shocks, the wide variety of observations and techniques used to identify and analyze them have led to disagreements regarding their physical properties and interpretation. Here, we use a 3D magnetohydrodynamic (MHD) model of the solar corona to simulate an EUV wave event on 2009 February 13 to enable a detailed validation of the various commonly used detection and analysis techniques of global EUV waves. The simulated event exhibits comparable behavior to that of a real EUV wave event, with similar kinematic behavior and plasma parameter evolution. The kinematics of the wave are estimated via visual identification and profile analysis, with both approaches providing comparable results. We find that projection effects can affect the derived kinematics of the wave, due to the variation in fast-mode wave speed with height in the corona. Coronal seismology techniques typically used for estimates of the coronal magnetic field are also tested and found to estimate fast-mode speeds comparable to those of the model. Plasma density and temperature variations of the wave front are also derived using a regularized inversion approach and found to be consistent with observed wave events. These results indicate that global waves are best interpreted as large-amplitude waves and that they can be used to probe the coronal medium using well-defined analysis techniques.

One essential component of operational space weather forecasting is the prediction of solar flares. With a multitude of flare forecasting methods now available online it is still unclear which of these methods performs best, and none are substantially better than climatological forecasts. Space weather researchers are increasingly looking towards methods used by the terrestrial weather community to improve current forecasting techniques. Ensemble forecasting has been used in numerical weather prediction for many years as a way to combine different predictions in order to obtain a more accurate result. Here we construct ensemble forecasts for major solar flares by linearly combining the full-disk probabilistic forecasts from a group of operational forecasting methods (ASAP, ASSA, MAG4, MOSWOC, NOAA, and MCSTAT). Forecasts from each method are weighted by a factor that accounts for the method's ability to predict previous events, and several performance metrics (both probabilistic and categorical) are considered. It is found that most ensembles achieve a better skill metric (between 5\% and 15\%) than any of the members alone. Moreover, over 90\% of ensembles perform better (as measured by forecast attributes) than a simple equal-weights average. Finally, ensemble uncertainties are highly dependent on the internal metric being optimized and they are estimated to be less than 20\% for probabilities greater than 0.2. This simple multi-model, linear ensemble technique can provide operational space weather centres with the basis for constructing a versatile ensemble forecasting system -- an improved starting point to their forecasts that can be tailored to different end-user needs., Comment: Accepted for publication in the Journal of Space Weather and Space Climate

We investigate the relation between characteristics of coronal mass ejections and parameterizations of the eruptive capability of solar active regions widely used in solar flare prediction schemes. These parameters, some of which are explored for the first time, are properties related to topological features, namely, magnetic polarity inversion lines (MPILs) that indicate large amounts of stored non-potential (i.e. free) magnetic energy. We utilize the Space Weather Database of Notifications, Knowledge, Information (DONKI) and the Large Angle and Spectrometric Coronograph (LASCO) databases to find flare-associated coronal mass ejections and their kinematic characteristics while properties of MPILs are extracted from Helioseismic and Magnetic Imager (HMI) vector magnetic-field observations of active regions to extract the properties of source-region MPILs. The correlation between all properties and the characteristics of CMEs ranges from moderate to very strong. More significant correlations hold particularly for fast CMEs, which are most important in terms of adverse space-weather manifestations. Non-neutralized currents and the length of the main MPIL exhibit significantly stronger correlations than the rest of the properties. This finding supports a causal relationship between coronal mass ejections and non-neutralized electric currents in highly sheared, conspicuous MPILs. In addition, non-neutralized currents and MPIL length carry distinct, independent information as to the eruptive potential of active regions. The combined total amount of non-neutralized electric currents and the length of the main polarity inversion line, therefore, reflect more efficiently than other parameters the eruptive capacity of solar active regions and the CME kinematic characteristics stemming from these regions., 31 pages, 14 figures

A workshop was recently held at Nagoya University (31 October - 02 November 2017), sponsored by the Center for International Collaborative Research, at the Institute for Space-Earth Environmental Research, Nagoya University, Japan, to quantitatively compare the performance of today's operational solar flare forecasting facilities. Building upon Paper I of this series (Barnes et al. 2016), in Paper II (Leka et al. 2019) we described the participating methods for this latest comparison effort, the evaluation methodology, and presented quantitative comparisons. In this paper we focus on the behavior and performance of the methods when evaluated in the context of broad implementation differences. Acknowledging the short testing interval available and the small number of methods available, we do find that forecast performance: 1) appears to improve by including persistence or prior flare activity, region evolution, and a human "forecaster in the loop"; 2) is hurt by restricting data to disk-center observations; 3) may benefit from long-term statistics, but mostly when then combined with modern data sources and statistical approaches. These trends are arguably weak and must be viewed with numerous caveats, as discussed both here and in Paper II. Following this present work, we present in Paper IV a novel analysis method to evaluate temporal patterns of forecasting errors of both types (i.e., misses and false alarms; Park et al. 2019). Hence, most importantly, with this series of papers we demonstrate the techniques for facilitating comparisons in the interest of establishing performance-positive methodologies., Comment: 23 pages, 6 figures, accepted for publication in The Astrophysical Journal

Solar flares are extremely energetic phenomena in our Solar System. Their impulsive, often drastic radiative increases, in particular at short wavelengths, bring immediate impacts that motivate solar physics and space weather research to understand solar flares to the point of being able to forecast them. As data and algorithms improve dramatically, questions must be asked concerning how well the forecasting performs; crucially, we must ask how to rigorously measure performance in order to critically gauge any improvements. Building upon earlier-developed methodology (Barnes et al, 2016, Paper I), international representatives of regional warning centers and research facilities assembled in 2017 at the Institute for Space-Earth Environmental Research, Nagoya University, Japan to - for the first time - directly compare the performance of operational solar flare forecasting methods. Multiple quantitative evaluation metrics are employed, with focus and discussion on evaluation methodologies given the restrictions of operational forecasting. Numerous methods performed consistently above the "no skill" level, although which method scored top marks is decisively a function of flare event definition and the metric used; there was no single winner. Following in this paper series we ask why the performances differ by examining implementation details (Leka et al. 2019, Paper III), and then we present a novel analysis method to evaluate temporal patterns of forecasting errors in (Park et al. 2019, Paper IV). With these works, this team presents a well-defined and robust methodology for evaluating solar flare forecasting methods in both research and operational frameworks, and today's performance benchmarks against which improvements and new methods may be compared., 26 pages, 5 figures, accepted for publication in the Astrophysical Journal Supplement Series

Solar flares originate from magnetically active regions but not all solar active regions give rise to a flare. Therefore, the challenge of solar flare prediction benefits by an intelligent computational analysis of physics-based properties extracted from active region observables, most commonly line-of-sight or vector magnetograms of the active-region photosphere. For the purpose of flare forecasting, this study utilizes an unprecedented 171 flare-predictive active region properties, mainly inferred by the Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) in the course of the European Union Horizon 2020 FLARECAST project. Using two different supervised machine learning methods that allow feature ranking as a function of predictive capability, we show that: i) an objective training and testing process is paramount for the performance of every supervised machine learning method; ii) most properties include overlapping information and are therefore highly redundant for flare prediction; iii) solar flare prediction is still - and will likely remain - a predominantly probabilistic challenge.

A crucial challenge to successful flare prediction is forecasting periods that transition between "flare-quiet" and "flare-active". Building on earlier studies in this series (Barnes et al. 2016; Leka et al. 2019a,b) in which we describe methodology, details, and results of flare forecasting comparison efforts, we focus here on patterns of forecast outcomes (success and failure) over multi-day periods. A novel analysis is developed to evaluate forecasting success in the context of catching the first event of flare-active periods, and conversely, of correctly predicting declining flare activity. We demonstrate these evaluation methods graphically and quantitatively as they provide both quick comparative evaluations and options for detailed analysis. For the testing interval 2016-2017, we determine the relative frequency distribution of two-day dichotomous forecast outcomes for three different event histories (i.e., event/event, no-event/event and event/no-event), and use it to highlight performance differences between forecasting methods. A trend is identified across all forecasting methods that a high/low forecast probability on day-1 remains high/low on day-2 even though flaring activity is transitioning. For M-class and larger flares, we find that explicitly including persistence or prior flare history in computing forecasts helps to improve overall forecast performance. It is also found that using magnetic/modern data leads to improvement in catching the first-event/first-no-event transitions. Finally, 15% of major (i.e., M-class or above) flare days over the testing interval were effectively missed due to a lack of observations from instruments away from the Earth-Sun line., Comment: 33 pages, 13 figures, accepted for publication in ApJ

Solar active regions (ARs) that produce major flares typically exhibit strong plasma shear flows around photospheric magnetic polarity inversion lines (MPILs). It is therefore important to quantitatively measure such photospheric shear flows in ARs for a better understanding of their relation to flare occurrence. Photospheric flow fields were determined by applying the Differential Affine Velocity Estimator for Vector Magnetograms (DAVE4VM) method to a large data set of 2,548 co-aligned pairs of AR vector magnetograms with 12-min separation over the period 2012-2016. From each AR flow-field map, three shear-flow parameters were derived corresponding to the mean (), maximum (S_max) and integral (S_sum) shear-flow speeds along strong-gradient, strong-field MPIL segments. We calculated flaring rates within 24 hr as a function of each shear-flow parameter, and also investigated the relation between the parameters and the waiting time ({\tau}) until the next major flare (class M1.0 or above) after the parameter observation. In general, it is found that the larger S_sum an AR has, the more likely it is for the AR to produce flares within 24 hr. It is also found that among ARs which produce major flares, if one has a larger value of S_sum then {\tau} generally gets shorter. These results suggest that large ARs with widespread and/or strong shear flows along MPILs tend to not only be more flare productive, but also produce major flares within 24 hr or less., Comment: 19 pages, 8 figures, accepted for publication in Solar Physics

Large and magnetically complex sunspot groups are known to be associated with flares. To date, the Mount Wilson scheme has been used to classify sunspot groups based on their morphological and magnetic properties. The most flare prolific class, the delta sunspot-group, is characterised by opposite polarity umbrae within a common penumbra, separated by less than 2 degrees. In this article, we present a new system, called the Solar Monitor Active Region Tracker - Delta Finder (SMART-DF), that can be used to automatically detect and classify magnetic deltas in near-realtime. Using continuum images and magnetograms from the Helioseismic and Magnetic Imager (HMI) onboard NASA's Solar Dynamics Observatory (SDO), we first estimate distances between opposite polarity umbrae. Opposite polarity pairs having distances of less that 2 degrees are then identified, and if these pairs are found to share a common penumbra, they are identified as a magnetic delta configuration. The algorithm was compared to manual delta detections reported by the Space Weather Prediction Center (SWPC), operated by the National Oceanic and Atmospheric Administration (NOAA). SMART-DF detected 21 out of 23 active regions (ARs) that were marked as delta spots by NOAA during 2011 - 2012 (within +/- 60 degrees longitude). SMART-DF in addition detected five ARs which were not announced as delta spots by NOAA. The near-relatime operation of SMART-DF resulted in many deltas being identified in advance of NOAA's daily notification. SMART-DF will be integrated with SolarMonitor (www.solarmonitor.org) and the near-realtime information will be available to the public., Comment: 14 pages, 5 figures, accepted for publication in Solarphysics

We study the predictive capabilities of magnetic feature properties (MF) generated by Solar Monitor Active Region Tracker (SMART) for solar flare forecasting from two datasets: the full dataset of SMART detections from 1996 to 2010 that has been previously studied by Ahmed et al. (2011) and a subset of that dataset which only includes detections that are NOAA active regions (ARs). Main contributions: we use marginal relevance as a filter feature selection method to identify most useful SMART MF properties for separating flaring from non-flaring detections and logistic regression to derive classification rules to predict future observations. For comparison, we employ a Random Forest, Support Vector Machine and a set of Deep Neural Network models, as well as Lasso for feature selection. Using the linear model with three features we obtain significantly better results (TSS=0.84) to those reported by Ahmed et al.(2011) for the full dataset of SMART detections. The same model produced competitive results (TSS=0.67) for the dataset of SMART detections that are NOAA ARs which can be compared to a broader section of flare forecasting literature. We show that more complex models are not required for this data., Comment: Accepted for publication in Solar Physics. 22 pages, 6 figures, 8 tables

We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) mission. In particular, we use the Space-weather HMI Active Region Patches (SHARP) product that facilitates cut-out magnetograms of solar active regions (AR) in the Sun in near-real-time (NRT), taken over a five-year interval (2012 - 2016). Our approach utilizes a set of thirteen predictors, which are not included in the SHARP metadata, extracted from line-of-sight and vector photospheric magnetograms. We exploit several Machine Learning (ML) and Conventional Statistics techniques to predict flares of peak magnitude >M1 and >C1, within a 24 h forecast window. The ML methods used are multi-layer perceptrons (MLP), support vector machines (SVM) and random forests (RF). We conclude that random forests could be the prediction technique of choice for our sample, with the second best method being multi-layer perceptrons, subject to an entropy objective function. A Monte Carlo simulation showed that the best performing method gives accuracy ACC=0.93(0.00), true skill statistic TSS=0.74(0.02) and Heidke skill score HSS=0.49(0.01) for >M1 flare prediction with probability threshold 15% and ACC=0.84(0.00), TSS=0.60(0.01) and HSS=0.59(0.01) for >C1 flare prediction with probability threshold 35%., Accepted for publication by Solar Physics

Most solar flares originate in sunspot groups, where magnetic field changes lead to energy build-up and release. However, few flare-forecasting methods use information of sunspot-group evolution, instead focusing on static point-in-time observations. Here, a new forecast method is presented based upon the 24-hr evolution in McIntosh classification of sunspot groups. Evolution-dependent $\geqslant$C1.0 and $\geqslant$M1.0 flaring rates are found from NOAA-numbered sunspot groups over December 1988 to June 1996 (Solar Cycle 22; SC22) before converting to probabilities assuming Poisson statistics. These flaring probabilities are used to generate operational forecasts for sunspot groups over July 1996 to December 2008 (SC23), with performance studied by verification metrics. Major findings are: i) considering Brier skill score (BSS) for $\geqslant$C1.0 flares, the evolution-dependent McIntosh-Poisson method ($\text{BSS}_{\text{evolution}}=0.09$) performs better than the static McIntosh-Poisson method ($\text{BSS}_{\text{static}} = -0.09$); ii) low BSS values arise partly from both methods over-forecasting SC23 flares from the SC22 rates, symptomatic of $\geqslant$C1.0 rates in SC23 being on average $\approx$80% of those in SC22 (with $\geqslant$M1.0 being $\approx$50%); iii) applying a bias-correction factor to reduce the SC22 rates used in forecasting SC23 flares yields modest improvement in skill relative to climatology for both methods ($\mathrm{BSS}^{\mathrm{corr}}_{\mathrm{static}} = 0.09$ and $\mathrm{BSS}^{\mathrm{corr}}_{\mathrm{evolution}} = 0.20$) and improved forecast reliability diagrams., 21 pages, 9 figures

We calculated the temperature response of the 171 A passbands of the Sun Watcher using APS detectors and image Processing (SWAP) instrument onboard the PRoject for OnBoard Autonomy 2 (PROBA2) satellite. These results were compared to the temperature responses of the Extreme Ultraviolet Imaging Telescope (EIT) onboard the Solar and Heliospheric Observatory (SOHO), the Transition Region and Coronal Explorer (TRACE), the twin Extreme Ultraviolet Imagers (EUVI) onboard the Solar TErrestrial RElations Observatory (STEREO) A and B spacecraft, and the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO). Multiplying the wavelength-response functions for each instrument by a series of isothermal synthetic spectra and integrating over the range 165 – 195 A produced temperature-response functions for the six instruments. Each temperature response was then multiplied by sample differential emission-measure functions for four different solar conditions. For any given plasma condition (e.g. quiet Sun, active region), it was found that the overall variation with temperature agreed remarkably well across the six instruments, although the wavelength responses for each instrument have some distinctly different features. Deviations were observed, however, when we compared the response of any one instrument to different solar conditions, particularly for the case of solar flares.

Novel machine-learning and feature-selection algorithms have been developed to study: i) the flare-prediction-capability of magnetic feature (MF) properties generated by the recently developed Solar Monitor Active Region Tracker (SMART); ii) SMART’s MF properties that are most significantly related to flare occurrence. Spatiotemporal association algorithms are developed to associate MFs with flares from April 1996 to December 2010 in order to differentiate flaring and non-flaring MFs and enable the application of machine-learning and feature-selection algorithms. A machine-learning algorithm is applied to the associated datasets to determine the flare-prediction-capability of all 21 SMART MF properties. The prediction performance is assessed using standard forecast-verification measures and compared with the prediction measures of one of the standard technologies for flare-prediction that is also based on machine-learning: Automated Solar Activity Prediction (ASAP). The comparison shows that the combination of SMART MFs with machine-learning has the potential to achieve more accurate flare-prediction than ASAP. Feature-selection algorithms are then applied to determine the MF properties that are most related to flare occurrence. It is found that a reduced set of six MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties.

The kinematics of a globally propagating disturbance (also known as an ``EIT wave") is discussed using Extreme UltraViolet Imager (EUVI) data Solar Terrestrial Relations Observatory (STEREO). We show for the first time that an impulsively generated propagating disturbance has similar kinematics in all four EUVI passbands (304, 171, 195, and 284 A). In the 304 A passband the disturbance shows a velocity peak of 238+/-20 kms-1 within ~28 minutes of its launch, varying in acceleration from 76 ms-2 to -102 ms-2. This passband contains a strong contribution from a Si XI line (303.32 A) with a peak formation temperature of ~1.6 MK. The 304 A emission may therefore be coronal rather than chromospheric in origin. Comparable velocities and accelerations are found in the coronal 195 A passband, while lower values are found in the lower cadence 284 A passband. In the higher cadence 171 A passband the velocity varies significantly, peaking at 475+/-47 kms-1 within ~20 minutes of launch, with a variation in acceleration from 816 ms-2 to -413 ms-2. The high image cadence of the 171 A passband (2.5 minutes compared to 10 minutes for the similar temperature response 195 A passband) is found to have a major effect on the measured velocity and acceleration of the pulse, which increase by factors of ~2 and ~10, respectively. This implies that previously measured values (e.g., using EIT) may have been underestimated. We also note that the disturbance shows strong reflection from a coronal hole in both the 171 and 195 A passbands. The observations are consistent with an impulsively generated fast-mode magnetoacoustic wave., Comment: 4 pages 4 figures

The physical processes that trigger solar flares are not well understood and significant debate remains around processes governing particle acceleration, energy partition, and particle and energy transport. Observations at high resolution in energy, time, and space are required in multiple energy ranges over the whole course of many flares in order to build an understanding of these processes. Obtaining high-quality, co-temporal data from ground- and space- based instruments is crucial to achieving this goal and was the primary motivation for starting the Max Millennium program and Major Flare Watch (MFW) alerts, aimed at coordinating observations of all flares >X1 GOES X-ray classification (including those partially occulted by the limb). We present a review of the performance of MFWs from 1 February 2001 to 31 May 2010, inclusive, that finds: (1) 220 MFWs were issued in 3,407 days considered (6.5% duty cycle), with these occurring in 32 uninterrupted periods that typically last 2-8 days; (2) 56% of flares >X1 were caught, occurring in 19% of MFW days; (3) MFW periods ended at suitable times, but substantial gain could have been achieved in percentage of flares caught if periods had started 24 h earlier; (4) MFWs successfully forecast X-class flares with a true skill statistic (TSS) verification metric score of 0.500, that is comparable to a categorical flare/no-flare interpretation of the NOAA Space Weather Prediction Centre probabilistic forecasts (TSS = 0.488)., 19 pages, 2 figures, accepted for publication in Solar Physics

Two sequences of solar images obtained by the Transition Region and Coronal Explorer in three UV passbands are studied using wavelet and Fourier analysis and compared to the photospheric magnetic flux measured by the Michelson Doppler Interferometer on the Solar Heliospheric Observatory to study wave behaviour in differing magnetic environments. Wavelet periods show deviations from the theoretical cutoff value and are interpreted in terms of inclined fields. The variation of wave speeds indicates that a transition from dominant fast-magnetoacoustic waves to slow modes is observed when moving from network into plage and umbrae. This implies preferential transmission of slow modes into the upper atmosphere, where they may lead to heating or be detected in coronal loops and plumes., 8 pages, 6 figures (4 colour online only), accepted for publication in The Astrophysical Journal

High-cadence Halpha blue wing observations of a C9.6 solar flare obtained at Big Bear Solar Observatory using the Rapid Dual Imager are presented. Wavelet and time-distance methods were used to study oscillatory power along the ribbon, finding periods of 40 - 80 s during the impulsive phase of the flare. A parametric study found statistically significant intensity oscillations with amplitudes of 3% of the peak flare amplitude, periods of 69 s (14.5 mHz) and oscillation decay times of 500 s. These measured properties are consistent with the existence of flare-induced acoustic waves within the overlying loops.

A new application of wavelet analysis is presented that utilizes the inherent phase information residing within the complex Morlet transform. The technique is applied to a weak solar magnetic network region, and the temporal variation of phase difference between TRACE 1700 A and SOHO/SUMER C II 1037 A intensities is shown. We present, for the first time in an astrophysical setting, the application of wavelet phase coherence, including a comparison between two methods of testing real wavelet phase coherence against that of noise. The example highlights the advantage of wavelet analysis over more classical techniques, such as Fourier analysis, and the effectiveness of the former to identify wave packets of similar frequencies but with differing phase relations is emphasized. Using cotemporal, ground-based Advanced Stokes Polarimeter measurements, changes in the observed phase differences are shown to result from alterations in the magnetic topology.

High-cadence multiwavelength optical observations were taken with the Dunn Solar Telescope at the National Solar Observatory, Sacramento Peak, accompanied by Advanced Stokes Polarimeter vector magnetograms. A total of 11 network bright points (NBPs) have been studied at different atmospheric heights using images taken in wave bands centered on Mg I b1 - 0.4 A, Hα, and Ca II K3. Wavelet analysis was used to study wave packets and identify traveling magnetohydrodynamic waves. Wave speeds were estimated through the temporal cross-correlation of signals, in selected frequency bands of wavelet power, in each wavelength. Four mode-coupling cases were identified, one in each of four of the NBPs, and the variation of the associated Fourier power with height was studied. Three of the detected mode-coupling, transverse-mode frequencies were observed in the 1.2-1.6 mHz range (mean NBP apparent flux density magnitudes over 99-111 Mx cm-2), with the final case showing 2.0-2.2 mHz (with 142 Mx cm-2). Following this, longitudinal-mode frequencies were detected in the range 2.6-3.2 mHz for three of our cases, with 3.9-4.1 mHz for the remaining case. After mode coupling, two cases displayed a decrease in longitudinal-mode Fourier power in the higher chromosphere.

Quiet-Sun oscillations in the four Transition Region and Coronal Explorer (TRACE) ultraviolet passbands centered on 1700, 1600, 1216, and 1550 A are studied using a wavelet-based technique. Both network and internetwork regions show oscillations with a variety of periods and lifetimes in all passbands. The most frequent network oscillation has a period of 283 s, with a lifetime of 2-3 cycles in all passbands. These oscillations are discussed in terms of upwardly propagating magnetohydrodynamic wave models. The most frequent internetwork oscillation has a period of 252 s, again with a lifetime of 2-3 cycles, in all passbands. The tendency for these oscillations to recur in the same position is discussed in terms of "persistent flashers." The network contains greater oscillatory power than the internetwork at periods longer than 300 s in the low chromosphere. This value is shown to decrease to 250 s in the high chromosphere. The internetwork also displays a larger number of short-lifetime, long-period oscillations than the network, especially in the low chromosphere. Both network and internetwork regions contain a small number of nonrecurring long-lifetime oscillations.

Oscillations in network bright points (NBPs) are studied at a variety of chromospheric heights. In particular, the three-dimensional variation of NBP oscillations is studied using image segmentation and cross-correlation analysis between images taken in light of Ca II K3, Hα core, Mg I b2, and Mg I b1 - 0.4 A. Wavelet analysis is used to isolate wave packets in time and to search for height-dependent time delays that result from upward- or downward-directed traveling waves. In each NBP studied, we find evidence for kink-mode waves (1.3, 1.9 mHz), traveling up through the chromosphere and coupling with sausage-mode waves (2.6, 3.8 mHz). This provides a means for depositing energy in the upper chromosphere. We also find evidence for other upward- and downward-propagating waves in the 1.3-4.6 mHz range. Some oscillations do not correspond to traveling waves, and we attribute these to waves generated in neighboring regions.

Solar flares are known to originate in sunspot groups, with increasingly complex magnetic structure leading to more frequent occurrence and often larger magnitude flares. Previously, McIntosh white-light classifications of sunspot groups and their historical flare rates have been used to calculate Poisson probabilities for flare forecasting. Here, we examine the temporal evolution of McIntosh classifications and calculate average flare rates for the following 24-hour periods. The impact that these evolution-dependent flare rates have on the performance of flare forecasts will be presented.

The continuous stream of data available from the Atmospheric Imaging Assembly (AIA) telescopes onboard the Solar Dynamics Observatory (SDO) spacecraft has allowed a deeper understanding of the Sun. However, the sheer volume of data has necessitated the development of automated techniques to identify and analyse various phenomena. In this article, we describe the Coronal Pulse Identification and Tracking Algorithm (CorPITA) for the identification and analysis of coronal "EIT waves". CorPITA uses an intensity-profile technique to identify the propagating pulse, tracking it throughout its evolution before returning estimates of its kinematics. The algorithm is applied here to a data-set from February 2011, allowing its capabilities to be examined and critiqued. This algorithm forms part of the SDO Feature Finding Team initiative and will be implemented as part of the Heliophysics Event Knowledgebase (HEK). This is the first fully automated algorithm to identify and track the propagating "EIT wave" rather than any associated phenomena and will allow a deeper understanding of this controversial phenomenon., Comment: 20 pages, 8 figures, accepted for publication in Solar Physics

Cosmic rays and solar energetic particles may be accelerated to relativistic energies by shock waves in astrophysical plasmas. On the Sun, shocks and particle acceleration are often associated with the eruption of magnetized plasmoids, called coronal mass ejections (CMEs). However, the physical relationship between CMEs and shock particle acceleration is not well understood. Here, we use extreme ultraviolet, radio and white-light imaging of a solar eruptive event on 22 September 2011 to show that a CME-induced shock (Alfv\'en Mach number 2.4$^{+0.7}_{-0.8}$) was coincident with a coronal wave and an intense metric radio burst generated by intermittent acceleration of electrons to kinetic energies of 2-46 keV (0.1-0.4 c). Our observations show that plasmoid-driven quasi-perpendicular shocks are capable of producing quasi-periodic acceleration of electrons, an effect consistent with a turbulent or rippled plasma shock surface.

The Spectrometer Telescope for Imaging X-rays (STIX) is one of 10 instruments on-board Solar Orbiter mission of the European Space Agency (ESA) scheduled to be launched in 2017. STIX is aimed to provide imaging spectroscopy of solar thermal and non-thermal hard X-ray emissions from 4 keV to 150 keV using a Fourier-imaging technique. The instrument employs a set of tungsten grids in front of 32 pixelized CdTe detectors. These detectors are source of data collected and analyzed in real time by Instrument Data Processing Unit (IDPU). In order to support development and implementation of on-board algorithms a dedicated detector hardware simulator is designed and manufactured as a part of Electrical Ground Support Equipment (EGSE) for STIX instrument. Complementary to the hardware simulator is data analysis software which is used to generate input data and to analyze output data. The simulator will allow sending strictly defined data from all detectors’ pixels at the input of the IDPU for further analysis of instrument response. Particular emphasis is given here to the simulator hardware design.

Solar flares are powered by energy stored in the coronal magnetic field, a portion of which is released when the field reconfigures into a lower energy state. Investigation of sunspot magnetic field topology during flare activity is useful to improve our understanding of flaring processes. Here we investigate the deviation of the non-linear field configuration from that of the linear and potential configurations, and study the free energy available leading up to and after a flare. The evolution of the magnetic field in NOAA region 10953 was examined using data from Hinode/SOT-SP, over a period of 12 hours leading up to and after a GOES B1.0 flare. Previous work on this region found pre- and post-flare changes in photospheric vector magnetic field parameters of flux elements outside the primary sunspot. 3D geometry was thus investigated using potential, linear force-free, and non-linear force-free field extrapolations in order to fully understand the evolution of the field lines. Traced field line geometrical and footpoint orientation differences show that the field does not completely relax to a fully potential or linear force-free state after the flare. Magnetic and free magnetic energies increase significantly ~ 6.5-2.5 hours before the flare by ~ 10^31 erg. After the flare, the non-linear force-free magnetic energy and free magnetic energies decrease but do not return to pre-flare 'quiet' values. The post-flare non-linear force-free field configuration is closer (but not equal) to that of the linear force-free field configuration than a potential one. However, the small degree of similarity suggests that partial Taylor relaxation has occurred over a time scale of ~ 3-4 hours., Accepted for Publication in Astronomy & Astrophysics. 11 pages, 11 figures

Poster presented at the UK National Astronomy Meeting in Manchester, April 2012.

Solar flares occur due to the sudden release of energy stored in active-region magnetic fields. To date, the pre-cursors to flaring are still not fully understood, although there is evidence that flaring is related to changes in the topology or complexity of an active region's magnetic field. Here, the evolution of the magnetic field in active region NOAA 10953 was examined using Hinode/SOT-SP data, over a period of 12 hours leading up to and after a GOES B1.0 flare. A number of magnetic-field properties and low-order aspects of magnetic-field topology were extracted from two flux regions that exhibited increased Ca II H emission during the flare. Pre-flare increases in vertical field strength, vertical current density, and inclination angle of ~ 8degrees towards the vertical were observed in flux elements surrounding the primary sunspot. The vertical field strength and current density subsequently decreased in the post-flare state, with the inclination becoming more horizontal by ~7degrees. This behaviour of the field vector may provide a physical basis for future flare forecasting efforts., Accepted for Publication in Solar Physics. 16 pages, 4 figures

The solar surface and atmosphere are highly dynamic plasma environments, which evolve over a wide range of temporal and spatial scales. Large-scale eruptions, such as coronal mass ejections, can be accelerated to millions of kilometers per hour in a matter of minutes, making their automated detection and characterisation challenging. Additionally, there are numerous faint solar features, such as coronal holes and coronal dimmings, which are important for space weather monitoring and forecasting, but their low intensity and sometimes transient nature makes them problematic to detect using traditional image processing techniques. These difficulties are compounded by advances in ground- and space- based instrumentation, which have increased the volume of data that solar physicists are confronted with on a minute-by-minute basis; NASA’s Solar Dynamics Observatory for example is returning many thousands of images per hour (~1.5 TB/day). This chapter reviews recent advances in the application of images processing techniques to the automated detection of active regions, coronal holes, filaments, CMEs, and coronal dimmings for the purposes of space weather monitoring and prediction.

The interaction of open and closed field lines at coronal hole boundaries is widely accepted to be due to interchange magnetic reconnection. To date, it is unclear how the boundaries vary on short timescales and at what velocity this occurs. Here, we describe an automated boundary tracking method used to determine coronal hole boundary displacements on short timescales. The bound- ary displacements were found to be isotropic and to have typical expansion/contraction speeds of \leq2 km s^-1, which indicate magnetic reconnection rates of \leq 3 \times 10^-3. The observed displacements were used in conjunction with the interchange reconnection model to derive typical diffusion coeffi- cients of \leq 3 \times 10^13 cm^2 s^-1. These results are consistent with an interchange reconnection process in the low corona driven by the random granular motion of open and closed fields in the photosphere.

One of the main features of the quiet solar chromosphere is the Network Bright Points (NBP), formed by the emerging magnetic flux, at the boundaries of supergranular cells. Triggered by the motions of magnetic loop foot‐points, at the top of the convection zone, MHD waves propagate inside the NBP’s. Using SSW IDL routines and wavelet analysis of series of images in four bandpasses (CaII K3, Mgb1‐0.4, Mgb2 and Ha core) we detected these MHD wave modes. The observations have been analyzed using cross‐correlation techniques and we have drawn conclusions on wave propagation and mode coupling.

Context. Super-Alfvénic shocks associated with coronal mass ejections (CMEs) can produce radio emission known as Type II bursts. In the absence of direct imaging, accurate estimates of coronal electron densities, magnetic field strengths, and Alfvén speeds are required to calculate the kinematics of shocks. To date, 1D radial models have been used, but these are not appropriate for shocks propagating in non-radial directions.\ud \ud Aims. Here, we study a coronal shock wave associated with a CME and Type II radio burst using 2D electron density and Alfvén speed maps to determine the locations that shocks are excited as the CME expands through the corona.\ud \ud Methods. Coronal density maps were obtained from emission measures derived from the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamic Observatory (SDO) and polarized brightness measurements from the Large Angle and Spectrometric Coronagraph (LASCO) on board the Solar and Heliospheric Observatory (SOHO). Alfvén speed maps were calculated using these density maps and magnetic field extrapolations from the Helioseismic and Magnetic Imager (SDO/HMI). The computed density and Alfvén speed maps were then used to calculate the shock kinematics in non-radial directions.\ud \ud Results. Using the kinematics of the Type II burst and associated shock, we find our observations to be consistent with the formation of a shock located at the CME flanks where the Alfvén speed has a local minimum.\ud \ud Conclusions. The 1D density models are not appropriate for shocks that propagate non-radially along the flanks of a CME. Rather, the 2D density, magnetic field and Alfvén speed maps described here give a more accurate method for determining the fundamental properties of shocks and their relation to CMEs.

We carry out a novel statistical test of the Neupert effect based on multifractal spectra. The multifractal spectrum is the number distribution of the strengths (i.e., the Holder exponents) of bursts in a signal. This is tested on simulations and carried out on RHESSI X-ray data from a well observed GOES X4.8 magnitude flare. The multifractal spectra is ideally suited to quantifying the relative smooth and bursty signals typically found in (thermal) soft X-ray and (non-thermal) hard X-ray data of solar flares. We show that light curves from all energies between 3 keV and 25 keV are statistically similar, suggesting that all these signals are dominated by the same (presumably thermal) emission. Emission lying between 25 keV and 100 keV probably contains some contribution from both thermal and non-thermal sources. The multifractal spectrum of a signal and that of its (cumulative) temporal integration are statistically similar (i.e., low residuals upon subtraction), but shifted by one in the peak Holder exponent. We find the pairs of 3-6 keV and 100-300 keV emissions, the 6-12 keV and 100-300 keV emissions and the 12-25 keV and 100-300 keV emissions are all consistent with the Neupert effect. The best agreement with the Neupert effect is between the 12-25 keV and 100-300 keV pair, although possibly with some secondary source of thermal emission present.

Solar flares occur in complex sunspot groups, but it remains unclear how the probability of producing a flare of a given magnitude relates to the characteristics of the sunspot group. Here, we use Geostationary Operational Environmental Satellite X-ray flares and McIntosh group classifications from solar cycles 21 and 22 to calculate average flare rates for each McIntosh class and use these to determine Poisson probabilities for different flare magnitudes. Forecast verification measures are studied to find optimum thresholds to convert Poisson flare probabilities into yes/no predictions of cycle 23 flares. A case is presented to adopt the true skill statistic (TSS) as a standard for forecast comparison over the commonly used Heidke skill score (HSS). In predicting flares over 24 hr, the maximum values of TSS achieved are 0.44 (C-class), 0.53 (M-class), 0.74 (X-class), 0.54 (>=M1.0), and 0.46 (>=C1.0). The maximum values of HSS are 0.38 (C-class), 0.27 (M-class), 0.14 (X-class), 0.28 (>=M1.0), and 0.41 (>=C1.0). These show that Poisson probabilities perform comparably to some more complex prediction systems, but the overall inaccuracy highlights the problem with using average values to represent flaring rate distributions., 7 pages, 1 figure

One of the most dramatic manifestations of solar activity are large-scale coronal bright fronts (CBFs) observed in extreme ultraviolet (EUV) images of the solar atmosphere. To date, the energetics and kinematics of CBFs remain poorly understood, due to the low image cadence and sensitivity of previous EUV imagers and the limited methods used to extract the features. In this paper, the trajectory and morphology of CBFs was determined in order to investigate the varying properties of a sample of CBFs, including their kinematics and pulse shape, dispersion, and dissipation. We have developed a semi-automatic intensity profiling technique to extract the morphology and accurate positions of CBFs in 2.5-10 min cadence images from STEREO/EUVI. The technique was applied to sequences of 171A and 195A images from STEREO/EUVI in order to measure the wave properties of four separate CBF events. Following launch at velocities of ~240-450kms^{-1} each of the four events studied showed significant negative acceleration ranging from ~ -290 to -60ms^{-2}. The CBF spatial and temporal widths were found to increase from ~50 Mm to ~200 Mm and ~100 s to ~1500 s respectively, suggesting that they are dispersive in nature. The variation in position-angle averaged pulse-integrated intensity with propagation shows no clear trend across the four events studied. These results are most consistent with CBFs being dispersive magnetoacoustic waves., Comment: 15 pages, 18 figures

Sunspot groups are the main source of solar flares, with the energy to power them being supplied by magnetic-field evolution (e.g. flux emergence or twisting/shearing). To date, few studies have investigated the statistical relation between sunspot-group evolution and flaring, with none considering evolution in the McIntosh classification scheme. Here we present a statistical analysis of sunspot groups from Solar Cycle 22, focusing on 24-hour changes in the three McIntosh classification components. Evolution-dependent >C1.0, >M1.0, and >X1.0 flaring rates are calculated, leading to the following results: (i) flaring rates become increasingly higher for greater degrees of upward evolution through the McIntosh classes, with the opposite found for downward evolution; (ii) the highest flaring rates are found for upward evolution from larger, more complex, classes (e.g. Zurich D- and E-classes evolving upward to F-class produce >C1.0 rates of 2.66 +/- 0.28 and 2.31 +/- 0.09 flares per 24 hours, respectively); (iii) increasingly complex classes give higher rates for all flare magnitudes, even when sunspot groups do not evolve over 24 hours. These results support the hypothesis that injection of magnetic energy by flux emergence (i.e. increasing in Zurich or compactness classes) leads to a higher frequency and magnitude of flaring., 31 pages, 11 figures

For almost 20 years the physical nature of globally propagating waves in the solar corona (commonly called "EIT waves") has been controversial and subject to debate. Additional theories have been proposed over the years to explain observations that did not fit with the originally proposed fast-mode wave interpretation. However, the incompatibility of observations made using the Extreme-ultraviolet Imaging Telescope (EIT) onboard the Solar and Heliospheric Observatory with the fast-mode wave interpretation was challenged by differing viewpoints from the twin Solar Terrestrial Relations Observatory spacecraft and higher spatial/temporal resolution data from the Solar Dynamics Observatory. In this article, we reexamine the theories proposed to explain "EIT waves" to identify measurable properties and behaviours that can be compared to current and future observations. Most of us conclude that "EIT waves" are best described as fast-mode large-amplitude waves/shocks that are initially driven by the impulsive expansion of an erupting coronal mass ejection in the low corona., Comment: 26 pages, 2 figures, accepted for publication in Solar Physics