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EUV brightenings are small-scale magnetic reconnection events that consistently appear before and after solar flares. However, it is not well understood how EUV precursors might foreshadow flares and what the physical connection is between the EUV signatures and flares. We studied flare-active and inactive periods in three separate studies using the Detection and EUV Flare Tracking (DEFT) tool. In Study 1, EUV signatures were identified in 200 no-flare days, in Study 2 EUV signatures before 360 flares were analyzed, and in Study 3 close to 36,000 EUV signatures were detected, and their pre- and postflare distribution and trends were studied. Our key questions were as follows: do EUV signatures occur consistently before flares, do EUV signatures occur without flares, are there flares without EUV precursors, and is it possible to forecast different magnitude flares based on preceding EUV signature trends? Study 1 showed that in no-flare periods EUV signatures were only detected 4% of the time. Study 2 showed that EUV precursors were present 92% of the time within 6 hr before ≥C-class flares. Study 3 showed that over 90% of the signatures were associated with flares (≥B class), and over 50% of all signatures were associated with ≥M-class flares. A superposed epoch analysis showed precursor frequency peaks at  ∼70 and 100 minutes before M- and X-class flares, respectively, while B- and C-class flares had no notable precursor frequency peaks. These results demonstrate the close connection between EUV signatures and flares and the significant potential EUV signatures have in improving space weather forecasting. 

Abstract We present wave and turbulence observations from the DSCOVR spacecraft during the 2017 September solar flare and coronal mass ejection (CME) events. On September 4–12, the spectral index within the magnetic field power spectral density inertial range was consistent with Kolmogorov −5/3. This is despite the 9 days being composed of a complex mix of different features, including solar flares, solar energetic particle events, and CMEs. When analyzing shorter time periods, the spectral index varies. For two days where there were consecutive CMEs, the index follows Kraichnan–Iroshinikov −3/2, while on two quiet days, it was a mixture of −1, −3/2, and −2. The inertial range spectral index taken over the entire 9 days hides or averages out spectral features that occur over shorter time periods. We use a more realistic estimate of the amount of Doppler shifting into the spacecraft frame to show that the break frequencies on most days were located close to the H+ cyclotron frequency. We present evidence of wave–wave modulation and suggest that lower-frequency waves in the solar wind can modulate the growth rates/propagation of ion cyclotron waves, providing a method to transfer energy in the solar wind to smaller scales. Furthermore, we suggest that the indices in the inertial range can be explained by combining containment due to wave generation/propagation and stochastic Brownian motion in the solar wind. When these two phenomena are equal, they combine to create a −3/2 index. 

Abstract Solar flares have been linked to some of the most significant space weather hazards at Earth. These hazards, including radio blackouts and energetic particle events, can start just minutes after the flare onset. Therefore, it is of great importance to identify and predict flare events. In this paper we introduce the Detection and EUV Flare Tracking (DEFT) tool, which allows us to identify flare signatures and their precursors using high spatial and temporal resolution extreme-ultraviolet (EUV) solar observations. The unique advantage of DEFT is its ability to identify small but significant EUV intensity changes that may lead to solar eruptions. Furthermore, the tool can identify the location of the disturbances and distinguish events occurring at the same time in multiple locations. The algorithm analyzes high temporal cadence observations obtained from the Solar Ultraviolet Imager instrument aboard the GOES-R satellite. In a study of 61 flares of various magnitudes observed in 2017, the “main” EUV flare signatures (those closest in time to the X-ray start time) were identified on average 6 minutes early. The “precursor” EUV signatures (second-closest EUV signatures to the X-ray start time) appeared on average 14 minutes early. Our next goal is to develop an operational version of DEFT and to simulate and test its real-time use. A fully operational DEFT has the potential to significantly improve space weather forecast times. 

Abstract During thequadrature period(2010 December–2011 August) the STEREO-A and B satellites were approximately at right angles to the SOHO satellite. This alignment was particularly advantageous for determining the coronal mass ejection (CME) properties, since the closer a CME propagates to the plane of sky, the smaller the measurement inaccuracies are. Our primary goal was to study dimmings and their relationship to CMEs and flares during this time. We identified 53 coronal dimmings using STEREO/EUVI 195 Å observations, and linked 42 of the dimmings to CMEs (observed with SOHO/LASCO/C2) and 23 to flares. Each dimming in the catalog was processed with the Coronal Dimming Tracker which detects transient dark regions in extreme ultraviolet images directly, without the use of difference images. This approach allowed us to observefootpoint dimmings: the regions of mass depletion at the footpoints of erupting magnetic flux rope structures. Our results show that the CME mass has a linear, moderate correlation with dimming total EUV intensity change, and a monotonic, moderate correlation with dimming area. These results suggest that the more the dimming intensity drops and the larger the erupting region is, the more plasma is evacuated. We also found a strong correlation between the flare duration and the total change in EUV intensity. The correlation between dimming properties showed that larger dimmings tend to be brighter; they go through more intensity loss and generally live longer—supporting the hypothesis that larger transient open regions release more plasma and take longer to close down and refill with plasma.