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51 results for “solar flare”
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 2489 through 2731
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 2489 through 2731 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Extra Dataset Images for ARs 1064 through 1527
<p>This dataset is the extra images associated with Dryad dataset <a href="https://doi.org/10.5061/dryad.qjq2bvqmj">https://doi.org/10.5061/dryad.qjq2bvqmj</a>. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1064 through 1527 in .fits format. These are images that were removed from the preconfigured dataset https://doi.org/10.5061/dryad.jq2bvq898.</p>
Active Region Magnetograms for Solar Flare Prediction: Extra Dataset Images for ARs 2470 through 2731
<p>This dataset is the extra images associated with Dryad dataset <a href="https://doi.org/10.5061/dryad.qjq2bvqmj">https://doi.org/10.5061/dryad.qjq2bvqmj</a>. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 2470 through 2731 in .fits format. These are images that were removed from the preconfigured dataset https://doi.org/10.5061/dryad.jq2bvq898.</p>
Active Region Magnetograms for Solar Flare Prediction: Extra Dataset Images for ARs 1981 through 2469
<p>This dataset is the extra images associated with Dryad dataset <a href="https://doi.org/10.5061/dryad.qjq2bvqmj">https://doi.org/10.5061/dryad.qjq2bvqmj</a>. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1981 through 2469 in .fits format. These are images that were removed from the preconfigured dataset https://doi.org/10.5061/dryad.jq2bvq898.</p>
Active Region Magnetograms for Solar Flare Prediction: Extra Dataset Images for ARs 1528 through 1980
<p>This dataset is the extra images associated with Dryad dataset <a href="https://doi.org/10.5061/dryad.qjq2bvqmj">https://doi.org/10.5061/dryad.qjq2bvqmj</a>. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1528 through 1980 in .fits format. These are images that were removed from the preconfigured dataset https://doi.org/10.5061/dryad.jq2bvq898.</p>
Active region magnetograms for solar flare prediction: Extra images dataset
<p>In this dataset, we provide a comprehensive collection of magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). The dataset incorporates data from three sources and provides SDO Helioseismic and Magnetic Imager (HMI) magnetograms of solar active regions as well as labels of corresponding flaring activity. This dataset will be useful for image analysis or solar physics research related to magnetic structure, its evolution over time, and its relation to solar flares. The dataset will be of interest to those researchers investigating automated solar flare prediction methods, including supervised and unsupervised machine learning (classical and deep), binary and multi-class classification, and regression. This dataset contains those images that were removed from the preconfigured datasets (see usage notes below).</p>
Active region magnetograms for solar flare prediction: Full resolution dataset
<p>In this dataset, we provide a comprehensive collection of magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). The dataset incorporates data from three sources and provides SDO Helioseismic and Magnetic Imager (HMI) magnetograms of solar active regions as well as labels of corresponding flaring activity. This dataset will be useful for image analysis or solar physics research related to magnetic structure, its evolution over time, and its relation to solar flares. The dataset will be of interest to those researchers investigating automated solar flare prediction methods, including supervised and unsupervised machine learning (classical and deep), binary and multi-class classification, and regression. This dataset is a minimally processed, user configurable dataset of consistently sized images of solar active regions that can serve as a benchmark dataset for solar flare prediction research. This dataset consists of full resolution images (see usage notes below).</p>
Active region magnetograms for solar flare prediction: Full resolution dataset
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Active region magnetograms for solar flare prediction: Reduced resolution dataset
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Active region magnetograms for solar flare prediction: Extra images dataset
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The First Fermi-LAT Solar Flare Catalog Appendix
<p>We present the appendix of the First Fermi-Large Area Telescope (LAT) Solar flare catalog covering the 24thSolar cycle. The catalog (published in ApJS) contains 45 Fermi-LAT Solar flares (FLSFs) with emission in theγ-ray energy band (30 MeV -10 GeV) detected with a significance ≥5σ over the years 2010-2018. A subsample containing 37 of these flares exhibit delayed emission beyond the prompt-impulsive hard X-ray phase with 21 flares showing delayed emission lasting more than two hours. No prompt-impulsive emission is detected in four of these flares. We also report the first time observations of GeV emission from 3 flares originating from Active Regions located behind the limb (BTL) of the visible Solar disk. We report the light curves, spectra, best proton index and localization (when possible) for all the FLSFs and correlations with Solar multi-wavelength phenomena. The gamma-ray spectra is consistent with the decay of pions produced by >300 MeV protons. The work presented in the First Fermi Solar Flare Catalog contains the largest sample of high-energy gamma-ray flares ever reported and provides the unique opportunity to perform population/correlation studies on the different phases of the flare and thus allowing to open a new window in solar physics.</p> <p>This work is on behalf of the Fermi Large Area Telescope Collaboration. The First Fermi LAT Solar Flare Catalog has been accepted for publication on ApJS.</p>
Ionosphere-Thermosphere Data Published in "Responses of the Thermosphere and Ionosphere System to Concurrent Solar Flares and Geomagnetic Storms"
<p>This dataset supports the Journal of Geophysical Research publication "Responses of the Thermosphere and Ionosphere System to Concurrent Solar Flares and Geomagnetic Storms" by Qian et al., 2019. The data files are selected output and related analyses from the thermosphere-ionosphere-electrodynamics general circulation model (TIEGCM). The format of the data files are either IDL save files or NetCDF files or ASCII.</p>
Supplementary material for "Stellar flares" (Living Reviews in Solar Physics)
<p>The file supplementary.tar.gz contains a Jupyter notebook (Figs.ipynb) and supplementary material for recreating Figures 13, 35, 38, and 40 in Kowalski, A.F. (2024), "Stellar flares", <em>Living Reviews in Solar Physics</em> 24, 1 (<a href="10.1007/s41116-024-00039-4">https://doi.org/10.1007/s41116-024-00039-4</a>).</p>
Solar Flare List for Pre-Flare Emission Variability Study
<p>Flare file: dates, GOES flare class, cutout coordinates used, and eruptivity/131 spike flags for pre-flare emission study</p> <p>Quiet file: dates, NOAA AR designation for non-flaring control cases</p>
The data for Prediction of Large Solar Flares Based on SHARP and HED Magnetic Field Parameters
<p>The <span>repository</span> includes HED and SHARP Datasets, along with descriptions of the <span>d</span>ata <span>p</span>rocessing <span>procedures</span>, and the experimental code u<span>tilized</span> in <span>our</span> study.</p> <p><span>The files named "HED parameters.csv" and "SHARP parameters.csv" </span><span>represent the raw data of HED and SHARP Datasets</span><span>, respectively. Each dataset consists of 286 ARs, specifically, each dataset includes 189 C-class flaring ARs, 86 M-class flaring ARs, and 11 X-class flaring ARs.</span> <span>The column name "AR" represents the NOAA AR numbers, the column name "time" represents the time at which the AR samples were acquired, and the column name "level" represents the flare class of the AR. Moreover,</span> <span>i</span>n the HED dataset, the column name "energy" represents the "E_{free}", "shear_mean" represents the "\Psi", "uj_mean" represents the "J_{Z}", "uhc_mean" represents the "H_{C}", "GBH_MEAN" represents the "B_{h}", <span>and </span>"ALP_MEAN" represents the "\alpha". In the <span>SHARP</span> dataset<span>, </span>the column names of the other columns correspond to the names of various parameters.</p> <p><span>The files named </span>"SHARP_CV<span>.zip</span>" and "HED_CV<span>.zip</span>" represent the ten cross-validation sets that have been normalized and divided using an AR-based cross-validation method.</p> <p><span>T</span>he <span>d</span>ata <span>p</span>rocessing <span>procedures are as follows. Si</span><span>nce the SHARP and HED parameters have distinct scales and units, they </span><span>are </span><span>individually normalized using mean-standard deviation</span><span>.</span> <span>Subsequently, we set the labels for C-class flaring </span><span>AR</span><span>s to </span><span>the negative class</span> <span>which are equal to 0</span><span>, and the labels for M/X-class flaring </span><span>AR</span><span>s</span> to <span>positive class which are equal to 1</span>. <span>We employ an AR-based cross-validation (CV) method to partition the SHARP and HED datasets with the distribution of the training, validation, and testing sets at a ratio of 60%, 20%, and 20%, respectively.</span> This process is repeated ten times, resulting in ten cross-validation sets in both SHARP datasets (SHARP_CV) and HED datasets (HED_CV), respectively. The column named "level" in the SHARP_CV data and the column named "key" in the HED_CV data represent the labels for the flare classes of the <span>AR</span>s.</p> <p><span>The file named </span>"Parameter description and formula of SHARP and HED as well as data division process.pdf" <span>represents the </span>description and formula of SHARP and HED <span>p</span>arameter<span>s, as well as the procedure for generating 10-fold cross-validation set divisions for the SHARP/HED datasets.</span></p> <p><span>We design five solar flare prediction models leveraging five currently popular deep learning algorithms: Transformer, BiLSTM-Attention, BiLSTM, LSTM-Attention, and LSTM. Moreover, we use the NN model as the baseline model to compare with other deep learning model. </span>The solar flare prediction model used in this <span>paper</span> can be accessed via https://drive.google.com/drive/folders/1n6fXcQdCBogKt6L0aaPFagraFcXmX0fw?usp=drive_link.</p> <p> </p>
Dataset for "Comparative Analysis of Machine Learning Models to Forecast Flaring Capability of Solar Active Regions: A Parameter Based Approach"
<p>This CSV file contains the values of 14 selected magnetic features along with the active region class for all the regions used in our study. As discussed in the paper, these 14 magnetic features characterize the properties of active regions. All these magnetic features are obtained from the HMI SHARP data series which provides open-sourced vector magnetic field information of solar active regions. the column named 'AR_class' carries information about the class of active regions i.e., 1 for flaring regions and 0 for non-flaring regions.</p>
Data release for 'Ensemble Forecasting of Major Solar Flares: Methods for Combining Models'
<p>This is a release of the data that were used for validation in the paper 'Ensemble Forecasting of Major Solar Flares: Methods for Combining Models' by J. A. Guerra, S. A. Murray, D. S. Bloomfield, and P. T. Gallagher, that has been submitted to the Journal of Space Weather and Space Climate.</p> <p> </p> <ul> </ul> <p>The naming scheme for the files is in the format:</p> <pre><code>class_type_metric.dat</code></pre> <ul> <li>'class' denotes whether the forecast is for M- or X- class flares.</li> <li>'type' is what kind of forecast, i.e., the original ensemble members, an ensemble created from probabilistic validation metrics, or an ensemble created from categorical validation metrics.</li> <li>'metric' specifies the metric used to create the ensemble in the case of 'probabilistic' or 'categorical' types as above (see paper for further details), or in the case of the original ensemble members the name of the operational forecasting method.</li> </ul> <p> </p> <p>The data files are in the format:</p> <pre><code>obs,prob</code></pre> <ul> <li>'obs' denotes whether or not a flare was observed within 24 hours of the forecast issue time (1 for yes and 0 for no).</li> <li>'prob' gives the probabilistic forecast value (between 0.0 and 1.0).</li> </ul> <p> </p> <p>These data files can easily be read into the <a href="https://cran.r-project.org/web/packages/verification/verification.pdf">R verification package</a> to replicate the results presented in the paper.</p>
Electron concentration and ionization rate profiles during solar X-ray flares
<p>The files contain eletron concentration <em>Ne</em> and ionization rate <em>q</em> profiles during solar X-ray flares that occurred on 24-25 October 2013 and 9-10 June 2014. The altitude range is 50-90 km.</p> <p>Values of eletron concentration and ionization rate were calculated on six VLF paths: from European transmitters GBZ, ICV, FTA, GQD, DHO, TBB to Mikhnevo geophysical observatory (55°N 38°E).</p> <p>The data is presented as MATLAB files. Each .mat file contains data and variable "description" with data's structure information.</p>
WACCM-X Simulations of September 2005 Solar Flare
This dataset contains simulation output from specified dynamics WACCM-X simulations of the solar flare that occurred on September 7, 2005. Included are vertical profiles of the electron density, conductivity, zonal wind, and vertical plasma drift at 285E and 320E geographic longitude at a 5 minute cadence for simulations with and without the effects of the solar flare. The resolution of the model output is 0.25 scale heights vertical resolution; 1.9 degrees latitude resolution. The simulation output is in support of a publication submitted to Geophysical Research Letters.
Investigation on the impact of solar flares on the Martian atmospheric emissions in the dayside near-terminator region: Case studies.
<p><strong>Mars, Solar Flares, Martian Airglow Emissions, MAVEN, MAVEN/IUVS, EUVM, and SWEA.</strong></p>
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