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68 results for “Solar activity”
Parker Solar Probe Filtered Ion Scale Wave Activity for Encounters 8 to 16
<p>The following datasets are the result of filtering algorithm applied to a wave analysis of Parker Solar Probe data from Encounters 8 to 16. The wave analysis was conducted by Kristoff Paulson using a Short-Time Fourier Transform (STFT) approach based on polarization techniques derived by Means, 1972 (DOI: <a href="http://doi.org/10.1029/JA077i028p05551">10.1029/JA077i028p055511135</a>). Included is a jupyter notebook containing the filtering algorithm, the results of the filtering, and a demonstration of how to best open the files. The dataset for each encounter contains 9 columns that correspond with:</p> <ol> <li>Date in CDF epoch</li> <li>Left-handed (LH) Integrated Wave Power (nT^2) where integration is over frequency space (0-32 Hz) of filtered activity</li> <li>Right-handed (RH) Integrated Wave Power (nT^2)</li> <li>LH median ellipticity where median is over frequency space</li> <li>RH median ellipticity</li> <li>LH median coherency</li> <li>RH median coherency</li> <li>LH median wave normal angle (deg)</li> <li>RH median wave normal angle (deg)</li> </ol> <p>In all cases, ellipticity is measured in the Parker Solar Probe spacecraft frame. Ellipticity measures the ellipticity of the polarization ellipse and takes on values between -1 and 1. Values of 1 correspond with RH circular polarization and -1 with LH circular polarization. Coherency takes on values between 0 and 1. It measures how interrelated fluctuations are where 0 represents noise and 1 represents coherent fluctuations. The wave normal angle is the angle between the wave vector, k, and the local mean magnetic field, B. Since there are inherent ambiguities in the direction of the wave vector for single spacecraft measurements, the wave normal angle is calculated such that it takes on angles from 0 to 90 degrees. The filtering algorithm selects activity in which coherency is above 0.8, absolute value of ellipticity is above 0.5, and wave normal angle is below 45 degrees such that coherent, circularly polarized, near parallel propagating wave activity on ion scales is selected. <strong>If wave power for a given time has value of 0.0, then no fluctuations in the magnetic field data passed the required filters at that time.</strong></p> <p>:</p>
CHAMP and Swarm solar activity- and height-scaled polar cap plasma density measurements
<p>Solar activity- and height-adjusted plasma density measurements in the polar cap (i.e., above 80° latitude in Modified Apex<sub>110</sub> coordinates) from the Swarm and CHAMP satellites. covering the entire CHAMP mission period (2002–2009) and the Swarm mission period from launch through February 2020.</p> <p>Plasma density measurements are scaled to a nominal solar activity level of <<em>F</em>10.7><sub>27</sub> = 80 sfu, and an altitude of 500 km, as described in Hatch et al. (submitted to JGR: Space Physics; <a href="https://www.essoar.org/doi/abs/10.1002/essoar.10502854.1">ESSOAr pre-print</a>) </p> <p>This dataset was prepared as a part of the "Swarm+ Coupling High-Low Atmosphere Interactions: Ion Outflow" project (<a href="https://swarmoutflow.w.uib.no/">project website</a>) (<a href="https://eo4society.esa.int/projects/swarm-coupling-high-low-atmosphere-interactions-ion-outflow/">ESA website</a>), and is funded by European Space Agency Contract #4000126731.</p> <p>Data are stored in HDF5 format as a Python Pandas dataframe. They can be loaded into Python via the following.</p> <pre><code class="language-python">import pandas as pd df = pd.read_hdf('CHAMP_Swarm_polarcap_adjDensity.hdf',key='df')</code></pre> <p>The data columns are</p> <ul> <li>'NeAdj' : Solar activity- and height-adjusted plasma density (cm<sup>-3</sup>)</li> <li>'a110lat' : Modified Apex<sub>110</sub> latitude (deg)</li> <li>'a110lon' : Modified Apex<sub>110</sub> longitude (deg)</li> <li>'mlt' : Modified Apex<sub>110</sub> magnetic local time</li> <li>'h_km' : satellite altitude (km)</li> <li>'gclat' : geocentric latitude (deg)</li> <li>'gclon' : geocentric longitude (deg)</li> <li>'sat' : satellite identifier (string, one of 'A', 'B',' 'C', or 'CHAMP')</li> </ul>
Dataset for "Helicity proxies from linear polarisation of solar active regions"
<p>The <span class="math-tex">\(\alpha\)</span> effect is believed to play a key role in the generation of the solar magnetic field. A fundamental test for its significance in the solar dynamo is to look for magnetic helicity of opposite signs in the two hemispheres, and at small and large scales. However, measuring magnetic helicity is compromised by the inability to fully infer the magnetic field vector from observations of solar spectra, caused by what is known as the <span class="math-tex">\(\pi\)</span> ambiguity of spectropolarimetric observations. We decompose linear polarisation into parity-even and parity-odd <em>E</em> and <em>B</em> polarisations, which are not affected by the <span class="math-tex">\(\pi \)</span> ambiguity. Furthermore, we study whether the correlations of spatial Fourier spectra of <em>B</em> and parity-even quantities such as <em>E </em>or temperature <em>T</em> are a robust proxy for magnetic helicity of solar magnetic fields. We analyse polarisation measurements of active regions observed by the Helioseismic and Magnetic Imager on board the Solar Dynamics observatory. Theory predicts the magnetic helicity of active regions to have, statistically, opposite signs in the two hemispheres. We then compute the parity-odd <em>EB</em> and <em>TB</em> correlations, and test for systematic preference of their sign based on the hemisphere of the active regions. We find that: (i) <em>EB</em> and <em>TB</em> correlations are a reliable proxy for magnetic helicity, when computed from linear polarisation measurements away from spectral line cores, and (ii) <em>E</em> polarisation reverses its sign close to the line core. Our analysis reveals Faraday rotation to not have a significant influence on the computed parity-odd correlations. The <em>EB</em> decomposition of linear polarisation appears to be a good proxy for magnetic helicity independent of the <span class="math-tex">\(\pi\)</span> ambiguity. This allows us to routinely infer magnetic helicity directly from polarisation measurements.</p> <p>The full article can be found at https://arxiv.org/abs/2001.10884</p>
Arbitrary and active colouring of solar cells with negligible loss of efficiency
<p>This is all the data associated with the journal article. The dataset is organized on a figure-by-figure basis within a compressed ZIP file for ease of access.</p> <ul> <li><strong>Graph Data</strong>: Available in <code>.txt</code> and <code>.xlsx</code> formats, providing raw and processed data used to generate the figures.</li> <li><strong>Images</strong>: All images included in the article are provided in <code>.jpg</code> format.</li> <li><strong>Figure Graphs</strong>: All complete figure graphs are supplied as <code>.pdf</code> files.</li> </ul>
Variations in Solar Activity Across the Sporer Minimum Based on Radiocarbon in Danish Oak (dataset)
<p>This dataset (AARAMS_Radiocarbon_1432_1578_ver2) provides radiocarbon ages of LW tree rings from Danish oak from the period AD 1432 to 1578. The measurements were conducted at the Aarhus AMS Centre (AARAMS) at Aarhus University, Denmark. Ring widths of the two wood samples (SchB and Gr02) used for the measurements are found in the two files: TableS1_SchB_Ringwidths and TableS2_Gr02_Ringwidths. These data are presented in a paper submitted to Geophysical Research Letters with the title: Variations in Solar Activity Across the Sporer Minimum Based on Radiocarbon in Danish Oak.</p>
Simulated Solar Spectra for Testing Stellar Activity Mitigation Strategies
<p>Recent and upcoming stabilized spectrographs are pushing the frontier for Doppler spectroscopy to detect and characterize low-mass planets. Specifications for these instruments are so impressive that intrinsic stellar variability is expected to limit their Doppler precision for most target stars (Fischer et al. 2016). In order to realize their full potential, astronomers must develop new strategies for distinguishing true Doppler shifts from intrinsic stellar variability. Stellar variability due to starspots, faculae, and other rotationally-linked variability is particularly concerning, as the stellar rotation period is often included in the range of potential planet orbital periods. In order to robustly detect and accurately characterize low-mass planets via Doppler planet surveys, the exoplanet community must develop statistical models capable of jointly modeling planetary perturbations and intrinsic stellar variability. Towards this effort, we present simulations of extremely high-resolution solar-like spectra created with SOAP 2.0(Dumusque et al. 2014) that includes multiple evolving starspots to aid in developing and testing future statistical methods.</p>
FIGURE 3 in A new solar powered species of the genus Phyllodesmium Ehrenberg, 1831 (Mollusca: Nudibranchia: Aeolidoidea) from Indonesia with analysis of its photosynthetic activity and notes on biology
FIGURE 3: Phyllodesmium jakobsenae, hard structures in digestive system: A: Right jaw seen from the outside. B: Left jaw from the inside, denticles at the masticatory border seen from the inside. C: Distal part of radula of specimen No. 5. D: Closeup of distal part of radula of specimen No. 5; note the worn denticles on the edge of the rhachidian teeth .. E: Distal part of radula of specimen No. 3. F: Different angle of view of rhachidian cusps from No. 3. G: Part of the radula of specimen No. 3.
FIGURE 4 in A new solar powered species of the genus Phyllodesmium Ehrenberg, 1831 (Mollusca: Nudibranchia: Aeolidoidea) from Indonesia with analysis of its photosynthetic activity and notes on biology
FIGURE 4: Phyllodesmium jakobsenae, histology: A: View of ceras from side orientated to light. Note the dense branches along the whole ceras. B: View of ceras from side orientated away from light. Note the main branch of digestive gland and the fewer ramifications. C: Cnidosac of large ceras with no nematocysts. D: Digestive glandular branches beneath epidermis in large ceras. Note the many zooxanthellae especially in the digestive glandular tissue. E: Longitudinal section of small ceras with cnidosac. The digestive glandular duct is not branching, and shows glandular cells. The cnidosac is filled with many tiny nematocysts. The epidermis shows many glandular cells, beneath the epidermis a layer of “ cellules spéciales ” can be seen. F: Section trough eye and statocyst with one statolith. G: Small part of the oral gland with outleading duct. Abbreviations: cn cnidosac, cs “ cellules spéciales ”, e eye, mc mucous cells, st statocyst.
FIGURE 1 in A new solar powered species of the genus Phyllodesmium Ehrenberg, 1831 (Mollusca: Nudibranchia: Aeolidoidea) from Indonesia with analysis of its photosynthetic activity and notes on biology
FIGURE 1: Phyllodesmium jakobsenae, living animals from North Sulawesi: A: Specimen laying eggs in an aquarium. B: Two specimens sitting in their food coral Xenia: on the right side a specimen with more brownish cerata and to the left a bigger specimen with more whitish cerata. Polyps of Xenia surround both individuals. C: Bigger specimen from B; please note the smaller cerata in the anterior part of body and the oral tentacles stretched to the lateral sides. D: Animal sitting inactive and mimicking Xenia polyps. E: Specimen starting to crawl.
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>
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>
Figure 1 in The Solar Activity Cycles and the Outbreaks of the Gypsy Moth - Lymantria dispar L. (Lepidoptera: Lymantriidae) in Serbia
Figure 1. Gypsy moth outbreaks and the solar flux at 2.8 GHz in the period 1948–2016 (the solar flux at 2.8 GHz data source: http://www.esrl.noaa.gov/psd/data/correlation/solar.data)
Magnetic Proxy for Solar Activity
<p>Proxies for solar activity are often used to extend solar irradiance measurements in time or forecast future variability. We calculate images of the solar photospheric magnetic field from a flux transport simulation and create a multicomponent proxy for solar activity.</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>
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