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51 results for “Solar Flares”
Datasets and results from: "Random Forest Classification and Solar Flares Data: Analysis and Validation"
<p><strong>Instructions for the data and code repository</strong></p> <p>Results, post-processing workflow, and datasets for the research paper titled "Random Forest Classification and Solar Flares Data: Analysis and Validation".</p> <p>The folder contains three .csv files: the complete dataset (dataset.csv), the balanced training dataset (train_dataset.csv), and the testing dataset (test_dataset.csv).</p> <p>The folder also contains the result files from the research (.csv output files with predictions and .html files with evaluation metrics, etc.) exported from the JASP software. The number in each file name corresponds to the number of trees utilized in Random Forest modelling.</p> <p>In addition, the Python script for the post-processing workflow is provided, with comments located in the script.</p> <p>The soft range X-ray irradiance and VLF amplitude data were obtained from:<br> National Centers for Environmental Information (NCEI) Available online: https://www.ncei.noaa.gov/. Accessed on: 24th June 2023. <br> Worldwide archive of low-frequency data and observations (WALDO) Available online: https://waldo.world/. Accessed on: 24th June 2023.</p>
Images of solar flares in 1600 Angstrom wavelenght
<p>This dataset contains solar flares in classes B, C, M, and X at 1600 Angstrom wavelength. The images were obtained from the Solar Dynamics Observatory by the Atmospheric Imaging Assembly instrument. Besides, the dataset contains labels to indicate the active regions in each image.</p>
Images of solar flares in HMI Continuum
<p>This dataset contains solar flares in classes B, C, M, and X at HMI Continuum. The images were obtained from the Solar Dynamics Observatory by the Atmospheric Imaging Assembly instrument. Besides, the dataset contains labels to indicate the active regions in each image.</p>
Images of solar flares in 1700 Angstrom wavelength
<p>This dataset contains solar flares in classes B, C, M, and X at 1700 Angstrom (Å) wavelength. The images were obtained from the Solar Dynamics Observatory by the Atmospheric Imaging Assembly instrument. Besides, the dataset contains labels to indicate the active regions in each image.</p>
Solar flare forecasting based on magnetogram sequences learning with MViT and data augmentation
<p><strong>Source codes and dataset of the research "Solar flare forecasting based on magnetogram sequences learning with MViT and data augmentation".</strong></p><p>Our work employed PyTorch, a framework for training Deep Learning models with GPU support and automatic back-propagation, to load the MViTv2 s models with Kinetics-400 weights. To simplify the code implementation, eliminating the need for an explicit loop to train and the automation of some hyperparameters, we use the PyTorch Lightning module. The inputs were batches of 10 samples with 16 sequenced images in 3-channel resized to 224 × 224 pixels and normalized from 0 to 1.</p><p>Most of the papers in our literature survey split the original dataset chronologically. Some authors also apply k-fold cross-validation to emphasize the evaluation of the model stability. However, we adopt a hybrid split taking the first 50,000 to apply the 5-fold cross-validation between the training and validation sets (known data), with 40,000 samples for training and 10,000 for validation. Thus, we can evaluate performance and stability by analyzing the mean and standard deviation of all trained models in the test set, composed of the last 9,834 samples, preserving the chronological order (simulating unknown data).</p><p>We develop three distinct models to evaluate the impact of oversampling magnetogram sequences through the dataset. The first model, Solar Flare MViT (SF MViT), has trained only with the original data from our base dataset without using oversampling. In the second model, Solar Flare MViT over Train (SF MViT oT), we only apply oversampling on training data, maintaining the original validation dataset. In the third model, Solar Flare MViT over Train and Validation (SF MViT oTV), we apply oversampling in both training and validation sets.</p><p>We also trained a model oversampling the entire dataset. We called it the "SF_MViT_oTV Test" to verify how resampling or adopting a test set with unreal data may bias the results positively.</p><p><strong>GitHub version</strong></p><p>The .zip hosted here contains all files from the project, including the checkpoint and the output files generated by the codes. We have a clean version hosted on GitHub (<a href="https://github.com/lfgrim/SFF_MagSeq_MViTs">https://github.com/lfgrim/SFF_MagSeq_MViTs</a>), without the magnetogram_jpg folder (which can be downloaded directly on <a href="https://tianchi-competition.oss-cn-hangzhou.aliyuncs.com/531804/dataset_ss2sff.zip">https://tianchi-competition.oss-cn-hangzhou.aliyuncs.com/531804/dataset_ss2sff.zip)</a> and the output and checkpoint files. Most code files hosted here also contain comments on the Portuguese language, which are being updated to English in the GitHub version.</p><p><strong>Folders Structure</strong></p><p>In the Root directory of the project, we have two folders: </p><ul><li>magnetogram_jpg: holds the source images provided by Space Environment Artificial Intelligence Early Warning Innovation Workshop through the link <a href="https://tianchi-competition.oss-cn-hangzhou.aliyuncs.com/531804/dataset_ss2sff.zip">https://tianchi-competition.oss-cn-hangzhou.aliyuncs.com/531804/dataset_ss2sff.zip. </a>It comprises 73,810 samples of high-quality magnetograms captured by HMI/SDO from 2010 May 4 to 2019 January 26. The HMI instrument provides these data (stored in hmi.sharp_720s dataset), making new samples available every 12 minutes. However, the images from this dataset were collected every 96 minutes. Each image has an associated magnetogram comprising a ready-made snippet of one or most solar ARs. It is essential to notice that the magnetograms cropped by SHARP can contain one or more solar ARs classified by the National Oceanic and Atmospheric Administration (NOAA).</li><li>Seq_Magnetogram: contains the references for source images with the corresponding labels in the next 24 h. and 48 h. in the respectively M24 and M48 sub-folders.<ul><li>M24/M48: both present the following sub-folders structure:<ul><li>Seqs16;</li><li>SF_MViT;</li><li>SF_MViT_oT;</li><li>SF_MViT_oTV;</li><li>SF_MViT_oTV_Test.</li></ul></li></ul></li></ul><p>There are also two files in root:</p><ul><li>inst_packages.sh: install the packages and dependencies to run the models.</li><li>download_MViTS.py: download the pre-trained MViTv2_S from PyTorch and store it in the cache.</li></ul><p>M24 and M48 folders hold reference text files (flare_Mclass...) linking the images in the magnetogram_jpg folders or the sequences (Seq16_flare_Mclass...) in the Seqs16 folders with their respective labels. They also hold "cria_seqs.py" which was responsible for creating the sequences and "test_pandas.py" to verify head info and check the number of samples categorized by the label of the text files. All the text files with the prefix "Seq16" and inside the Seqs16 folder were created by "criaseqs.py" code based on the correspondent "flare_Mclass" prefixed text files.</p><p>Seqs16 folder holds reference text files, in which each file contains a sequence of images that was pointed to the magnetogram_jpg folders.</p><p>All SF_MViT... folders hold the model training codes itself (SF_MViT...py) and the corresponding job submission (jobMViT...), temporary input (Seq16_flare...), output (saida_MVIT... and MViT_S...), error (err_MViT...) and checkpoint files (sample-FLARE...ckpt). Executed model training codes generate output, error, and checkpoint files. There is also a folder called "lightning_logs" that stores logs of trained models.</p><p><strong>Naming pattern for the files:</strong></p><ul><li>magnetogram_jpg: follows the format<i> </i>"hmi.sharp_720s.<SHARP-ID>.<date>.magnetogram.fits.jpg" and</li><li>Seqs16: follows the format "hmi.sharp_720s.<i><</i>SHARP-ID<i>></i>.<init-date>.to.<end-date>", where:<ul><li>hmi: is the instrument that captured the image</li><li>sharp_720s: is the database source of SDO/HMI.</li><li><SHARP-ID>: is the identification of SHARP region, and can contain one or more solar ARs classified by the (NOAA).</li><li><date>: is the date-time the instrument captured the image in the format yyyymmdd_hhnnss_TAI (y:year, m:month, d:day, h:hours, n:minutes, s:seconds).</li><li><init-date>: is the date-time when the sequence starts, and follow the same format of <date>.</li><li><end-date>: is the date-time when the sequence ends, and follow the same format of <date>.</li></ul></li><li>Reference text files in M24 and M48 or inside SF_MViT... folders follows the format "<prefix>flare_Mclass_<forecasting-horizon>_<dataset>.txt<over>", where:<ul><li><prefix>: is Seq16 if refers to a sequence, or void if refers direct to images.</li><li><forecasting-horizon>: "24h" or "48h".</li><li><dataset>: is "TrainVal<n>" or "Test". The <n> refers to the split of Train/Val.</li><li><over>: void or "_over" after the extension (...txt_over): means temporary input reference that was over-sampled by a training model.</li></ul></li><li>All SF_MViT...folders:<ul><li>Model training codes: "SF_MViT_<oversampling-type>_M+_<forecasting-horizon>_<split-type><gpu-type>", where:<ul><li><oversampling -type>: void or "oT" (over Train) or "oTV" (over Train and Val) or "oTV_Test" (over Train, Val and Test);</li><li><forecasting-horizon>: "24h" or "48h";</li><li><split-type>: "oneSplit" for a specific split or "allSplits" if run all splits.</li><li><gpu-type>: void is default to run 1 GPU or "2gpu" to run into 2 gpus systems;</li></ul></li><li>Job submission files: "jobMViT_<queue>", where:<ul><li><queue>: point the queue in Lovelace environment hosted on CENAPAD-SP (<a href="https://www.cenapad.unicamp.br/parque/jobsLovelace">https://www.cenapad.unicamp.br/parque/jobsLovelace</a>)</li></ul></li><li>Temporary inputs: "Seq16_flare_Mclass_<forecasting-horizon>_<dataset>.txt<over>:<ul><li><dataset>: train or val;</li><li><over>: void or "_over" after the extension (...txt_over): means temporary input reference that was over-sampled by a training model.</li></ul></li><li>Outputs: "saida_MViT_Adam_10-7<split>", where:<ul><li><split>: k0 to k4, means the correlated split of the output, or void if the output is from all splits.</li></ul></li><li>Error files: "err_MViT_Adam_10-7<split>", where:<ul><li><split>: k0 to k4, means the correlated split of the error log file, or void if the error file is from all splits.</li></ul></li><li>Checkpoint files: "sample-FLARE_MViT_S_10-7-epoch=<n-epoch>-valid_loss=<loss-value>-Wloss_k=<n-split>.ckpt", where:<ul><li><n-opoch>: epoch number of the checkpoint;</li><li><loss-value>: corresponding valid loss;</li><li><n-split>: 0 to 4.</li></ul></li></ul></li></ul>
Datasets for the article "The temperature and density of a solar flare kernel measured from extreme ultraviolet lines of O IV"
<p>This entry contains the following files:</p><p>20120309_030933_kernel_fe8_shift.save<br>20120309_030933_kernel_fe8_shift_fits.txt<br>20110814_055342_qs_offlimb_si10.save<br>20110814_055342_qs_offlimb_si10_fits.txt</p><p>The .save files are IDL save files that can be restored into IDL using the restore command.</p><p>The 20120309 save file contains:</p><p>swspec - An IDL structure containing a 1D spectrum of the flare kernel for the EIS short wavelength (SW) channel. The format is that returned by eis-mask-spectrum.pro.<br>lwspec - As above, but for the long-wavelength (LW) channel.<br>map185 - An IDL map structure containing the Fe VIII 185.21 image that was used to select the flare kernel.<br>mask185 - An IDL structure containing the pixel mask that is used as input to eis-mask-spectrum.pro.</p><p>The Gaussian fits to the spectra (as performed with the routine spec-gauss-eis.pro) are stored in 20120309_030933_kernel_fe8<i>s</i>hift_fits.txt. This file can be read with read_line_fits.pro in Solarsoft.</p><p>The 20110814 dataset is used to obtain an off-limb coronal spectrum for calibration purposes. The save file contains:</p><p>swspec - An IDL structure containing a 1D spectrum of the off-limb region for the EIS SW channel. The format is that returned by eis-mask-spectrum.pro.<br>lwspec - As above, but for the LW channel.<br>map - An IDL map structure containing the Si X 272 image that was used to select off-limb region.<br>mask - An IDL structure containing the pixel mask that is used as input to eis-mask-spectrum.pro.</p><p>The Gaussian fits to the spectra (as performed with the routine spec-gauss-eis.pro) are stored in 20110814_055342_qs_offlimb_si10_fits.txt. This file can be read with read_line_fits.pro in Solarsoft. </p><p> </p><p> </p><p> </p><p> </p><p> </p><p> </p><p> </p>
Supplementary AIA Movies for the Solar Flares described in "Investigating the Soft X-ray Spectra of Solar Flare Onsets"
<p>This repository contains flare movies of six solar flares described in the paper "Investigating the Soft X-ray Spectra of Solar Flare Onsets".</p> <p>For each flare both the standard AIA 193 Angstrom Solar Dynamics Observatory (SDO) Atmospheric Imager (AIA) Assembly Extreme Ultra-Violet (EUV) image animations, as well as the running difference animations are included. The format of the file names for the AIA 193 Angstrom images is ‘DOY_NNN_Col.mp4’, where NNN is denotes the Day of Year (for e.g., DOY_065_Col.mp4) and the suffix ‘Col’ denotes ‘Colored’. Similarly the format of the file names for the difference images is ‘DOY_NNN_Diff.mp4’ where the suffix ‘Diff’ denotes ‘Difference Image’. The timestamp of each image in the animations is also shown on the top of the image.</p> <p>The SDO-AIA images are courtesy of NASA/SDO and the AIA and HMI science teams, and have been accessed from <a href="http://jsoc.stanford.edu/">http://jsoc.stanford.edu/</a>.</p>
Dataset for "The Dependence of Solar Flare Magnitude on Sunspot Area During Activity Cycle 24"
<p>Dataset for of Will, Avallone, & Sun (2022), RNAAS, 6, 37 "The Dependence of Solar Flare Magnitude on Sunspot Area During Activity Cycle 24". </p> <p>This is a .cvs file containing the information of 412 solar active regions, including their NOAA numbers, sunspot area, sunspot classification, and the peak GOES soft X-ray flux of the largest flare it produced. The sunspot area data are measured using continuum images from SDO/HMI.</p>
Electron concentration profiles calculated using different plasma chemical models during solar X-ray flares
<p>The files contain electron concentration <em>Ne</em> profiles during solar X-ray flares that occurred on 24-25 October 2013 and 9-11 June 2014. The altitude range is 50-90 km.</p> <p>Values of electron concentration were calculated using four-, five- and eight-component models of the ionospheric D-region. Results are obtained on four VLF paths: from European transmitters ICV, FTA, GQD, DHO 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>
Samples of solar flares classes, active regions and time of occurrence
<p>This dataset contains samples of solar flares measurements of classes X, M, C and B.</p> <p>For clarification, the flares are classified as follows:</p> <ul> <li>Class X: flares <span class="math-tex">\(>10^{-4} watts/m^{2}\)</span></li> <li>Class M: <span class="math-tex">\(10^{-5} watts/m^{2} <\)</span>flares <span class="math-tex">\(<10^{-4} watts/m^{2}\)</span></li> <li>Class C: <span class="math-tex">\(10^{-6} watts/m^{2}<\)</span>flares<span class="math-tex">\(<10^{-5} watts/m^{2}\)</span></li> <li>Class B: <span class="math-tex">\(10^{-7} watts/m^{2}<\)</span>flares<span class="math-tex">\(<10^{-6} watts/m^{2}\)</span></li> </ul> <p>This dataset was assembled with data from https://www.spaceweatherlive.com/en/solar-activity/top-50-solar-flares</p> <p>The date (yyyy-mm-dd hh:mm:ss) the authors assembled the data is 2017-11-14 13:48:37</p> <p>The original data source is the National Oceanic & Atmospheric Administration (NOAA), U.S. Departement of Commerce.</p> <p>Data description:</p> <ul> <li><strong>Class</strong>: Class of the flare: X, M, C or B</li> <li><strong>Date</strong>: Date of occurence in yyyy-mm-dd format.</li> <li><strong>AR</strong>: Active Region ID attributed by NOAA.</li> <li><strong>Begin</strong>: time the flare begins in hh:mm:ss format.</li> <li><strong>Max</strong>: time the flare reaches its max value in hh:mm:ss format.</li> <li><strong>End</strong>: time the flare vanishes in hh:mm:ss format.</li> </ul> <p>The dataset has 2,256 tuples divided as follows:</p> <ul> <li>171 tuples with X class flares data (7.58%).</li> <li>572 tuples with M class flares data (25.35%).</li> <li>767 tuples with C class flares data (34%).</li> <li>746 tuples with B class flares data (33.07%).</li> </ul> <p>The data collected refer to the period between August 25, 1996 and May 29, 2017.</p>
Simulation data for Response of the Thermosphere-Ionosphere System to an X-Class Solar Flare: March 30, 2022 Case Study
<p>GITM simulation results for the research article titled "Response of the Thermosphere-Ionosphere System to an X-Class Solar Flare: March 30, 2022 Case Study" submitted to JGR: Space Weather </p>
Active Region Magnetograms for Solar Flare Prediction: Reduced Resolution Dataset Images
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.jq2bvq898. 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 reduced resolution (224x224 pixels) images in .png format.</p>
Active region magnetograms for solar flare prediction: Reduced 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 reduced resolution images (see usage notes below).</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1307 through 1505
<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) 1307 through 1505 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1064 through 1306
<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) 1064 through 1306 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1506 through 1707
<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) 1506 through 1707 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1708 through 1918
<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) 1708 through 1918 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1919 through 2103
<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) 1919 through 2103 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 2284 through 2488
<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) 2284 through 2488 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 2104 through 2283
<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) 2104 through 2283 in .fits format.</p>
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