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31 results for “Solar cycle”
EUV-ML solar physics dataset from STEREO + SOHO, 2 solar cycles
<p>A unified ML-ready dataset of all SOHO EIT and STEREO EUVI EUV images, aligned and set to 512x512 pixels, are available via the NASA TOPS ODR in HelioCloud at s3://gov-nasa-hdrl-data1/contrib/euvml/ (inside of AWS, and with free egress via https). We also provide the software used to create it via a public github repository, and a sample Python Jupyter Notebook (in this Zenodo link, and in the HelioCloud sample tutorials) for accessing them via the 'cloudcatalog' Python client.We</p> <p>We created a full set of ML-ready EUV data from 1995 to present, accessible via the cloud, by bringing in historical restoration of the STEREO/SOHO era into a machine-learning (ML) -ready dataset. This work will enable research on events, evolution of solar irradiance, segmentation approaches, 360 degree maps of the sun, and other research topics as well as for use with space weather. The reduced dataset is 6TB in size. The data will be freely available to scientists both within the AWS cloud and for downloading for local use on their laptops. The creation of this dataset had several steps, starting with the mechnical ingest stage (get the raw data), an analysis of best approach to create the ML-ready set, bulk processing, uploading the cloud, and dissemination and promotion. The task also required determination best approaches for cadence matching, interpolation, and effective spans of contiguous data for ML applications.</p> <p>Work was funded under NASA Heliophysics 21-LWSTM21_2-0018, award number 80NSSC22K0643.</p>
The effects of solar cycle variability on nanodust dynamics in the inner heliosphere: Predictions for future STEREO A/WAVES measurements
<p>This dataset contains results from the associated manuscript in JGR Space Physics. The dataset consists of two-dimensional nanodust grain fluxes in the HEEQ equatorial plane for various specified Carrington Rotations (CRs), as specified in the parent manuscript.</p>
Solar cycle and long-term trends in the observed peak of the meteor altitude distributions by meteor radars
<p>The datasets here correspond to a paper by Dawkins et al., “Solar cycle and long-term trends in the observed peak of the meteor altitude distributions by meteor radars”, originally submitted in November 2022.</p> <p>The following datasets are sufficient to produce Figure 2 and 3 in the main manuscript.</p> <p>Figure 2:</p> <ul> <li>Please use the 12 individual files with filenames, “Dawkins_et_al_2022__meteor_peak_altitude__*_data.txt”. Here the asterisk should be replaced by one of the following station abbreviations: CAR, COL, CPa, DAV, KIR, KSS, ROT, SMa, SOD, SVA, TdF, and TRO.</li> <li>Each file contains 5 columns: Column 1 is year (from 1999 to 2022), Column 2 is the time series of the annual peak altitude residuals (no units), Column 3 is the corresponding standard error, Column 4 is the multilinear model fit, and Column 5 is the normalized annual solar flux (F10.7) in arbitrary units.</li> </ul> <p>Figure 3:</p> <ul> <li>Please use “Dawkins_et_al_2022__meteor_peak_altitude_trends.txt”. For ease, a description of the different columns is included within this file.</li> </ul> <p> </p>
Supporting Information for "Electron and proton peak intensities as observed by a five-spacecraft fleet in solar cycle 25"
<p>Table of parameters employed in the study "Electron and proton peak intensities as observed by a five-spacecraft fleet in solar cycle 25". The table contains the original peak intensities directly taken from the <em>SERPENTINE SEP event catalog</em>, without any scaling or inter-calibration factors that are applied in the study. All information provided in the table is based on the <em>SERPENTINE SEP event catalog</em> and <em>SERPENTINE CME and coronal shocks catalog</em><strong>,</strong> only limiting the variables to those used in this study. The dataset is in CSV format.</p> <p>For more information, and if you use this table, please refer to the corresponding publication:</p> <blockquote> <div> <p>Electron and proton peak intensities as observed by a five-spacecraft fleet in solar cycle 25<br>G. U. Farwa, N. Dresing, J. Gieseler, L. Vuorinen, I. G. Richardson, C. Palmroos, S. Valkila, B. Heber, S. Jensen, P. Kühl, L. Rodríguez-García and R. Vainio<br>A&A, 693 (2025) A198<br>DOI: <a href="https://doi.org/10.1051/0004-6361/202450945">10.1051/0004-6361/202450945</a></p> </div> </blockquote> <div> <div> </div> </div> <p><strong>Field descriptions</strong></p> <ul> <li>id: ID</li> <li>date: Event date [UTC]</li> <li>flare_time: Flare time [UTC]</li> <li>flare_lat: Flare Carrington latitude [deg]</li> <li>flare_lon: Flare Carrington longitude [deg]</li> <li>flare_class: Flare class (GOES)</li> <li>flare_comments: Flare Comments</li> <li>radio_type2: Radio type II bursts</li> <li>decametric_type2_start: Decametric type II burst start time [UT]</li> <li>decametric_type2_stop: Decametric type II burst end time [UT]</li> <li>radio_type2_start: Metric radio type II burst start time [UT]</li> <li>radio_type2_stop: Metric radio type II burst end time [UT]</li> <li>solar_mach_link: Solar-Mach link</li> <li>S/C codes <ul> <li>BepiC: BepiColombo</li> <li>L1: L1 (SOHO/Wind)</li> <li>PSP: Parker Solar Probe</li> <li>STA: STEREO A</li> <li>SolO: Solar Orbiter</li> </ul> </li> <li>S/C related field descriptions <ul> <li>{sc}_sc_lat: S/C Carrington latitude [deg]</li> <li>{sc}_sc_lon: S/C Carrington longitude [deg]</li> <li>{sc}_dist: S/C radial distance [au]</li> <li>{sc}_p25MeV_onset_date: S/C protons 25 MeV onset date [UTC]</li> <li>{sc}_p25MeV_onset_time: S/C protons 25 MeV onset time [UTC]</li> <li>{sc}_p25MeV_onset_time_formatted: S/C protons 25 MeV onset time [UTC] (Formatted)</li> <li>{sc}_p25MeV_onset_averaging: S/C protons 25 MeV averaging used for onset [min]</li> <li>{sc}_p25MeV_onset_sector: S/C protons 25 MeV sector used for onset</li> <li>{sc}_p25MeV_peak_date: S/C protons 25 MeV peak date [UTC]</li> <li>{sc}_p25MeV_peak_time: S/C protons 25 MeV peak time [UTC]</li> <li>{sc}_p25MeV_peak_time_formatted: S/C protons 25 MeV peak time [UTC] (Formatted)</li> <li>{sc}_p25MeV_peak_flux: S/C protons 25 MeV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1]</li> <li>{sc}_p25MeV_peak_flux_formatted: S/C protons 25 MeV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1] (Formatted)</li> <li>{sc}_p25MeV_peak_averaging: S/C protons 25 MeV averaging used for peak [min]</li> <li>{sc}_p25MeV_peak_sector: S/C protons 25 MeV sector used for peak</li> <li>{sc}_p25MeV_injection_date: S/C protons 25 MeV inferred injection date [UTC]</li> <li>{sc}_p25MeV_injection_time: S/C protons 25 MeV inferred injection time [UTC]</li> <li>{sc}_p25MeV_sw_speed: S/C protons 25 MeV onset solar wind speed [km/s]</li> <li>{sc}_p25MeV_comments: S/C protons 25 MeV comments</li> <li>{sc}_e100keV_onset_date: S/C electrons 100 keV onset date [UTC]</li> <li>{sc}_e100keV_onset_time: S/C electrons 100 keV onset time [UTC]</li> <li>{sc}_e100keV_onset_time_formatted: S/C electrons 100 keV onset time [UTC] (Formatted)</li> <li>{sc}_e100keV_onset_averaging: S/C electrons 100 keV averaging used for onset [min]</li> <li>{sc}_e100keV_onset_sector: S/C electrons 100 keV sector used for onset</li> <li>{sc}_e100keV_peak_date: S/C electrons 100 keV peak date [UTC]</li> <li>{sc}_e100keV_peak_time: S/C electrons 100 keV peak time [UTC]</li> <li>{sc}_e100keV_peak_time_formatted: S/C electrons 100 keV peak time [UTC] (Formatted)</li> <li>{sc}_e100keV_peak_flux: S/C electrons 100 keV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1]</li> <li>{sc}_e100keV_peak_flux_formatted: S/C electrons 100 keV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1] (Formatted)</li> <li>{sc}_e100keV_peak_averaging: S/C electrons 100 keV averaging used for peak [min]</li> <li>{sc}_e100keV_peak_sector: S/C electrons 100 keV sector used for peak</li> <li>{sc}_e100keV_injection_date: S/C electrons 100 keV inferred injection date [UTC]</li> <li>{sc}_e100keV_injection_time: S/C electrons 100 keV inferred injection time [UTC]</li> <li>{sc}_e100keV_sw_speed: S/C electrons 100 keV onset solar wind speed [km/s]</li> <li>{sc}_e100keV_comments: S/C electrons 100 keV comments</li> <li>{sc}_e1MeV_onset_date: S/C electrons 1 MeV onset date [UTC]</li> <li>{sc}_e1MeV_onset_time: S/C electrons 1 MeV onset time [UTC]</li> <li>{sc}_e1MeV_onset_time_formatted: S/C electrons 1 MeV onset time [UTC] (Formatted)</li> <li>{sc}_e1MeV_onset_averaging: S/C electrons 1 MeV averaging used for onset [min]</li> <li>{sc}_e1MeV_onset_sector: S/C electrons 1 MeV sector used for onset</li> <li>{sc}_e1MeV_peak_date: S/C electrons 1 MeV peak date [UTC]</li> <li>{sc}_e1MeV_peak_time: S/C electrons 1 MeV peak time [UTC]</li> <li>{sc}_e1MeV_peak_time_formatted: S/C electrons 1 MeV peak time [UTC] (Formatted)</li> <li>{sc}_e1MeV_peak_flux: S/C electrons 1 MeV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1]</li> <li>{sc}_e1MeV_peak_flux_formatted: S/C electrons 1 MeV original peak flux [cm^-2 s^-1 sr^-1 MeV^-1] (Formatted)</li> <li>{sc}_e1MeV_peak_averaging: S/C electrons 1 MeV averaging used for peak [min]</li> <li>{sc}_e1MeV_peak_sector: S/C electrons 1 MeV sector used for peak</li> <li>{sc}_e1MeV_injection_date: S/C electrons 1 MeV inferred injection date [UTC]</li> <li>{sc}_e1MeV_injection_time: S/C electrons 1 MeV inferred injection time [UTC]</li> <li>{sc}_e1MeV_sw_speed: S/C electrons 1 MeV onset solar wind speed [km/s]</li> <li>{sc}_e1MeV_comments: S/C electrons 1 MeV comments</li> <li>{sc}_ep_ratio: Ratio of Electrons (~1MeV) / Protons (25-40 MeV)</li> </ul> </li> <li>CME related descriptions <ul> <li>cme_id: CME ID</li> <li>L1_date: Date of CME identification at L1</li> <li>L1_time: Time of CME identification at L1</li> <li>L1_pos_speed: Plane of sky speed of CME measured at L1</li> </ul> </li> </ul> <div><strong>CHANGELOG:</strong></div> <div> <ul> <li>2025-06-12 <ul> <li>Updated peak fluxes and peak times of PSP 1 MeV electrons, as well as PSP's e/p ratios (the previous flux values are erroneous!)</li> </ul> </li> </ul> </div>
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>
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)
Daily solar data and sunspot region summary of 23-24 solar cycle
<p>This dataset contains records of daily solar data as well as data collected from magnetic classes...</p> <p>This dataset was assembled with data from ftp://ftp.swpc.noaa.gov/pub/warehouse/.</p> <p>The date the data was assembled is 2017-01-15 (yyyy-mm-dd).</p> <p>The original data source is provided by the Space Weather Prediction Center - SWPC, which is linked to the National Oceanic and Atmospheric Administration - NOAA from US Department of Commerce.</p> <p>Data description:</p> <ul> <li><strong>radio_flux_10.7cm</strong>: the solar radio flux at 10.7 cm (2800 MHz) is an indicator of solar activity. It is also called the F10.7 index and is one of the longest running records of solar activity. Radio emissions originate high in the chromosphere and low in the corona of the solar atmosphere.</li> <li><strong>sesc_sunspot_number</strong>: it refers to the number of sunspots computed on a given day. Also called Wolf's number of sunspots, it is given by R = k(10g + s), where k is a scalable factor indicating the combined effects of observation conditions, g is the number of active regions and s the number of sunspots in all these groups.</li> <li><strong>sunspot_area</strong>: it refers to the sum of the corrected area of all observed sunspots. It is measured in units of millionths of the solar hemisphere.</li> <li><strong>goes15_xray_bkgd_flux</strong>: it corresponds to the daily average background X-ray flux that is measured by the SWPC primary GOES satellite. To calculate this value, sensors register 24 X-ray measures for a given day, one for each hour. Then, the SWPC creates 3 groups of periods of 8 hours. For these groups, the SWPC registers the lowest values of flux, creating 3 minimal values, one for each group. Then, they calculate the average between the minimum values of the first and the third group. After the average calculation, they must compare this value to the minimal value of the second group. The minimum value from the last comparison gives the result of the X-ray background flux.</li> <li><strong>mwl_alpha</strong>: binary attribute indicating the presence of apha magnetic class in any observed spot.</li> <li><strong>mwl_beta</strong>: binary attribute indicating the presence of beta magnetic class in any observed spot.</li> <li><strong>mwl_gamma</strong>: binary attribute indicating the presence of gamma magnetic class in any observed spot.</li> <li><strong>mwl_beta_gamma</strong>: binary attribute indicating the presence of beta-gamma magnetic class in any observed spot.</li> <li><strong>mwl_delta</strong>: binary attribute indicating the presence of delta magnetic class in any observed spot.</li> <li><strong>mwl_beta_delta</strong>: binary attribute indicating the presence of beta-delta magnetic class in any observed spot.</li> <li><strong>mwl_beta_gamma_delta</strong>: binary attribute indicating the presence of beta-gamma-delta magnetic class in any observed spot.</li> <li><strong>mwl_gamma_delta</strong>: binary attribute indicating the presence of gamma-delta magnetic class in any observed spot.</li> <li><strong>c_class_flares</strong>: number of c class flares observed.</li> <li><strong>m_class_flares</strong>: number of m class flares observed.</li> <li><strong>x_class_flares</strong>: number of x class flares observed.</li> </ul> <p>The data collected refer to the period between january 01, 1997 to january 15, 2017.</p> <p> </p>
Improvements to stratospheric chemistry scheme in the UM-UKCA (v10.7) model: solar cycle and heterogeneous reactions
<p>These are the data and the Python notebooks required to create the figures from the paper.</p> <p> </p> <p>Abstract:</p> <p>Improvements are made to two areas of the United Kingdom Chemistry and Aerosol (UKCA) module, which forms part of the Met Office Unified Model (UM) used for weather and climate applications. Firstly, a solar cycle is added to the photolysis scheme. The effect on total column ozone of this addition was found to be around 1-2% in mid-latitude and equatorial regions in phase with the solar cycle. Secondly, reactions occurring on the surfaces of polar stratospheric clouds and sulfate aerosol are updated and extended by modification of the uptake coefficients of five existing reactions and the addition of a further eight reactions involving bromine species. These modifications are shown to reduce the overabundance of modeled total-column ozone in the Arctic during October to February, southern mid-latitudes during August, and the Antarctic during September. Antarctic springtime ozone depletion is shown to be enhanced by 25 DU on average, which now causes the ozone hole to be somewhat too deep compared to observations. We show that this is in part due to a cold bias of the Antarctic polar vortex in the model.</p>
Short-term solar data stream of 23-24 cycle
<p>These datasets contain records of daily solar data as well as data collected from magnetic classes.</p> <p>The datasets were assembled with data from ftp://ftp.swpc.noaa.gov/pub/warehouse/.</p> <p>The date the data was assembled is 2017-01-15 (yyyy-mm-dd).</p> <p>The original data source is provided by the Space Weather Prediction Center - SWPC, which is linked to the National Oceanic and Atmospheric Administration - NOAA from US Department of Commerce.</p> <p>The data collected refer to the period between january 01, 1997 to january 15, 2017.</p> <p>Features included:</p> <ul> <li><strong>radio_flux_10.7cm</strong>: the solar radio flux at 10.7 cm (2800 MHz) is an indicator of solar activity. It is also called the F10.7 index and is one of the longest running records of solar activity. Radio emissions originate high in the chromosphere and low in the corona of the solar atmosphere.</li> <li><strong>sesc_sunspot_number</strong>: it refers to the number of sunspots computed on a given day. Also called Wolf's number of sunspots, it is given by R = k(10g + s), where k is a scalable factor indicating the combined effects of observation conditions, g is the number of active regions and s the number of sunspots in all these groups.</li> <li><strong>sunspot_area</strong>: it refers to the sum of the corrected area of all observed sunspots. It is measured in units of millionths of the solar hemisphere.</li> <li><strong>goes15_xray_bkgd_flux</strong>: it corresponds to the daily average background X-ray flux that is measured by the SWPC primary GOES satellite. To calculate this value, sensors register 24 X-ray measures for a given day, one for each hour. Then, the SWPC creates 3 groups of periods of 8 hours. For these groups, the SWPC registers the lowest values of flux, creating 3 minimal values, one for each group. Then, they calculate the average between the minimum values of the first and the third group. After the average calculation, they must compare this value to the minimal value of the second group. The minimum value from the last comparison gives the result of the X-ray background flux.</li> <li><strong>mwl_alpha</strong>: binary attribute indicating the existence of apha magnetic class in any observed spot.</li> <li><strong>mwl_beta</strong>: binary attribute indicating the existence of beta magnetic class in any observed spot.</li> <li><strong>mwl_gamma</strong>: binary attribute indicating the existence of gamma magnetic class in any observed spot.</li> <li><strong>mwl_beta_gamma</strong>: binary attribute indicating the existence of beta-gamma magnetic class in any observed spot.</li> <li><strong>mwl_beta_delta</strong>: binary attribute indicating the existence of beta-delta magnetic class in any observed spot.</li> <li><strong>mwl_beta_gamma_delta</strong>: binary attribute indicating the existence of beta-gamma-delta magnetic class in any observed spot.</li> <li><strong>mwl_gamma_delta</strong>: binary attribute indicating the existence of gamma-delta magnetic class in any observed spot.</li> <li><strong>mwl_delta</strong>: binary attribute indicating the existence of delta magnetic class in any observed spot.</li> </ul> <p>We performed missing data imputation using k-NN over all features. The k-NN used the Gower's distance as its distance coefficient. In addition, we also performed z-score standardization in all features.</p> <p>We designed the data into a sliding time window stream. In other words, we designed the data stream regarding four days before a<em> </em><em>t1 </em>instant (i.e. <em>t5, t4, t3,</em> and <em>t2</em>). Hence, new features were created considering the evolution of data along five days:</p> <ul> <li><em>radio_flux_10.7cm_[t5, t4, t3, t2, t1];</em></li> <li><em>sesc_sunspot_number_[t5, t4, t3, t2, t1];</em></li> <li><em>sunspot_area_[t5, t4, t3, t2, t1];</em></li> <li><em>goes15_xray_bkgd_flux_[t5, t4, t3, t2, t1];</em></li> <li><em>mwl_alpha_[t5, t4, t3, t2, t1];</em></li> <li><em>mwl_beta_[t5, t4, t3, t2, t1];</em></li> <li><em>mwl_gamma_[t5, t4, t3, t2, t1];</em></li> <li><em>mwl_beta_gamma_[t5, t4, t3, t2, t1];</em></li> <li><em>mwl_beta_delta_[t5, t4, t3, t2, t1];</em></li> <li><em>mwl_beta_gamma_delta_[t5, t4, t3, t2, t1];</em></li> <li><em>mwl_gamma-delta_[t5, t4, t3, t2, t1];</em></li> <li><em>mwl_delta_[t5, t4, t3, t2, t1].</em></li> </ul> <p>We designed our target variable as being the occurrence of at least one flare phenomenon of M or X class in the next 24, 24-48, and 48-72 hours ahead of the <em>t1</em> instant:</p> <ul> <li><em>flare_t1d</em>: occurrence of at least one flare of class M or X in the next 24 hours ahead of the <em>t1 </em>instant;</li> <li><em>flare_t2d</em>: occurrence of at least one flare of class M or X 24-48 hours ahead of the <em>t1 </em>instant;</li> <li><em>flare_t3d</em>: occurrence of at least one flare of class M or X 48-72 hours ahead of the <em>t1 </em>instant.</li> </ul>
Occurrence characteristics of electromagnetic ion cyclotron waves at Maitri during solar cycle 24
<p>This data is of Induction Coil Magnetometer installed at the Indian Antarctic station- Maitri (geographic 70.7oS, 11.8oE, L = 5). It belongs to the Indian Institute of Geomagnetism, Navi Mumbai. The data is from January 2011 to December 2017. It contains the information about the EMIC wave ground observation at L~5.</p>
Short-term solar data stream of 23-24 cycle
<p>These datasets contain records of daily solar data as well as data collected from magnetic classes.</p> <p>The datasets were assembled with data from ftp://ftp.swpc.noaa.gov/pub/warehouse/.</p> <p>The date the data was assembled is 2017-01-15 (yyyy-mm-dd).</p> <p>The original data source is provided by the Space Weather Prediction Center (SWPC) linked to the National Oceanic and Atmospheric Administration (NOAA) from US Department of Commerce.</p> <p>The data collected refer to the period between january 01, 1997 to january 15, 2017.</p> <p>Features included:</p> <ul> <li><strong>radio_flux_10.7cm</strong>: the solar radio flux at 10.7 cm (2800 MHz) is an indicator of solar activity. It is also called the F10.7 index and is one of the longest running records of solar activity. Radio emissions originate high in the chromosphere and low in the corona of the solar atmosphere.</li> <li><strong>sesc_sunspot_number</strong>: it refers to the number of sunspots computed on a given day. Also called Wolf's number of sunspots, it is given by R = k(10g + s), where k is a scalable factor indicating the combined effects of observation conditions, g is the number of active regions and s the number of sunspots in all these groups.</li> <li><strong>sunspot_area</strong>: it refers to the sum of the corrected area of all observed sunspots. It is measured in units of millionths of the solar hemisphere.</li> <li><strong>goes15_xray_bkgd_flux</strong>: it corresponds to the daily average background X-ray flux that is measured by the SWPC primary GOES satellite. To calculate this value, sensors register 24 X-ray measures for a given day, one for each hour. Then, the SWPC creates 3 groups of periods of 8 hours. For these groups, the SWPC registers the lowest values of flux, creating 3 minimal values, one for each group. Then, they calculate the average between the minimum values of the first and the third group. After the average calculation, they must compare this value to the minimal value of the second group. The minimum value from the last comparison gives the result of the X-ray background flux.</li> <li><strong>daily z component wmfr</strong>: daily McIntosh class z component weighted mean flare rate.</li> <li><strong>daily p component wmfr</strong>: daily McIntosh class p component weighted mean flare rate.</li> <li><strong>daily c component wmfr</strong>: daily McIntosh class c component weighted mean flare rate.</li> <li><strong>daily magnetic type wmfr</strong>: daily magnetic type weighted mean flare rate.</li> </ul> <p>We performed missing data imputation using k-NN over all features. In addition, we also performed z-score standardization in all features.</p> <p>We designed the data into a sliding time window stream. In other words, we designed the data stream regarding four days before a<em> </em><em>t1 </em>instant (i.e. <em>t5, t4, t3,</em> and <em>t2</em>). Hence, new features were created considering the evolution of data along five days:</p> <ul> <li><em>radio_flux_10.7cm_[t5, t4, t3, t2, t1];</em></li> <li><em>sesc_sunspot_number_[t5, t4, t3, t2, t1];</em></li> <li><em>sunspot_area_[t5, t4, t3, t2, t1];</em></li> <li><em>goes15_xray_bkgd_flux_[t5, t4, t3, t2, t1];</em></li> <li><em>z_component_wmfr_[t5, t4, t3, t2, t1];</em></li> <li><em>p_component_wmfr_[t5, t4, t3, t2, t1];</em></li> <li><em>c_component_wmfr_[t5, t4, t3, t2, t1];</em></li> <li><em>mag_type_wmfr_[t5, t4, t3, t2, t1].</em></li> </ul> <p>We designed our target variable as being the occurrence of at least one flare phenomenon of C-, M-, or X-class in the next 24, 24-48, and 48-72 hours ahead of the <em>t1</em> instant:</p> <ul> <li><em>flare_t1d</em>: occurrence of at least one flare of class C, M, or X in the next 24 hours ahead of the <em>t1 </em>instant;</li> <li><em>flare_t2d</em>: occurrence of at least one flare of class C, M, or X 24-48 hours ahead of the <em>t1 </em>instant;</li> <li><em>flare_t3d</em>: occurrence of at least one flare of class C, M, or X 48-72 hours ahead of the <em>t1 </em>instant.</li> </ul>
Supplement of "Algorithm for continual monitoring of fog life cycles based on geostationary satellite imagery as a basis for solar energy forecasting"
<p>The file uploaded here is an animation that visually illustrates the outputs of the a newly developed machine learning based FLS (<strong>F</strong>og and <strong>L</strong>ow <strong>S</strong>tratus) detection algorithm for the SEVIRI (<strong>S</strong>pinning <strong>E</strong>nhanced <strong>V</strong>isible and <strong>I</strong>nfra<strong>R</strong>ed <strong>I</strong>mager) instrument onboard the MSG (<strong>M</strong>eteosat <strong>S</strong>econd <strong>G</strong>eneration) geo-stationary satellites over the 24hr cycle of the day for the day of <strong>02/March/2021</strong> and compares them with the corresponding raw channel values observed by SEVIRI. The proposed algorithm classifies each SEVIRI pixel as "clear-sky", "FLS", or "non-FLS-cloud" (identified with Khaki, Red, and Blue in the animation) based on the SEVIRI pixel values of BT12.0, BT8.7 - BT12.0, BT10.8 - BT12.0, and BT12.0 - BT13.4 plus the standard deviation of each of these variables in a spatial window sized 3x3 pixels with the central pixel being the target pixel. </p><p><br>In this animation, the left-hand panel shows a false-color RGB image constructed based on the SEVIRI raw channel data with the red, green, and blue channels being BT12.0- BT13.4, BT8.7 - BT12.0, and BT10.8 - BT12.0, respectively. In this panel, the green color represents the high clouds, and the light and dark red colors represent the clear-sky and FLS, respectively. The right-hand panel of this animation also shows the outputs of the ML FLS detection algorithm developed in the present study.</p>
Relative phase between tidal and solar cycles influences the heating of a coral atoll lagoon
<p>The residence time of water within reef-lagoon systems is of first-order importance to biogeochemical cycles and reef ecosystem functions. Circulation, mixing, and water renewal within these systems is highly dependent upon the geometry of the reef as well as wave, tide, and wind forcing. We are motivated to understand the physical driving a recent warming event as well as observed hypoxia-related fish kills and seagrass dieoff events in Dongsha Atoll lagoon, a 28 km wide coral atoll in the South China Sea. Though extensive work has been done on hypoxia events in temperate coastal systems, there remains a lack of understanding regarding the dynamics of these events in shallow tropical waters. Hydrographic observations were collected in summer 2019 to examine circulation and ocean-lagoon exchange in Dongsha Atoll lagoon. We construct a heat budget for the Dongsha Atoll lagoon to understand the physical processes governing lagoon circulation and ocean-lagoon exchange. We examine the influence of tides, winds, waves, advective exchange, and surface heating on lagoon heating and flushing time. Results show that surface heat fluxes drive rapid heating of the lagoon by approximately 1.5°C over the 22 day study period. Advective transport over the reef flat from tide- and wave-driven flows is an important component of oceanlagoon exchange that is balanced by a net outflow through the channels on subtidal timescales. Specifically, a near phase-locked interaction between diurnal solar forcing and near-diurnal tidal exchange drives persistent advective cooling over the reef flat. Estimated flushing times for the Dongsha Atoll lagoon range from 2.7 to 3.2 days for spring and neap tide, respectively.</p>
Dataset of Solar Wind Shocks and GICy-indices for Solar Cycles 23 and 24
<p>Dataset of solar wind shocks and GIC<em><sub>y</sub></em>-indices for solar cycles 23 and 24 (CFA-SC23 and CFA-SC24) obtained using the Harvard Centre for Astrophysics (CFA) solar wind shock database and geomagnetic field data from the Australian region. Columns 1-5 show the year, month, day, hour (UT), minute, of the solar wind shock; Columns 6-9 show the ground magnetic field change dB (nT) and GIC-index values for the x- and y-components respectively; Columns 10-12 show the pre-shock (minimum values), the post-shock (maximum values), and the calculated step change for solar wind IMF B<sub>z</sub> (nT); Columns 13-15 show the pre-shock (minimum values), the post-shock (maximum values), and the calculated step change for solar wind IMF B<sub>t</sub> (nT); Columns 16-18 show the pre-shock (minimum values), the post-shock (maximum values), and the calculated step change for solar wind V<sub>x</sub> (km/s); Columns 19-21 show the pre-shock (minimum values), the post-shock (maximum values), and the calculated step change for solar wind V<sub>t</sub> (km/s); Columns 22-24 show the pre-shock (minimum values), the post-shock (maximum values), and the calculated step change for solar wind density (cm<sup>-3</sup>); Columns 25-27 show the pre-shock (minimum values), the post-shock (maximum values), and the calculated step change for calculated solar wind dynamic pressure Psw<sub>x</sub> (nPa); Columns 28-30 show the pre-shock (minimum values), the post-shock (maximum values), and the calculated step change for calculated solar wind dynamic pressure Psw<sub>t</sub> (nPa); Columns 31-36 provide the station abbreviation, geographic latitude and longitude, geomagnetic latitude and longitude, and declination; Columns 37-40 are observing satellite, shock normal angle, shock Mach number, shock type (all events in the table are Fast-Forward shocks); and Column 41 provides the calculated decimal time (hours), ∆t, of the geomagnetic station from local noon for each event.</p>
Dataset of Solar Wind Shocks and GICy-indices for Solar Cycle 23
<p>Dataset of solar wind shocks and GIC<em><sub>y</sub></em>-indices for solar cycle 23 obtained using ACE satellite solar wind data and geomagnetic field measurements from the Australian region. Columns 1 and 2 show the date and decimal time (hours UT) of the solar wind shock. Columns 3-17 show the pre-shock (minimum values), the post-shock (maximum values), and the calculated step change for solar wind velocity (km/s), density (cm<sup>-3</sup>), temperature (°K), magnetic field strength (nT), and dynamic pressure (nPa) respectively. Columns 18-24 show the geomagnetic field observing station, geographic longitude and latitude, geomagnetic longitude and latitude, decimal time (hours UT), and GIC<em><sub>y</sub></em>-index values associated with the shock parameters of columns 1-17.</p>
Process simulation-based inventory data for the perovskite single-junction, Silicon (PERC) and four-terminal perovskite/silicon tandem solar photovoltaic system life cycles
<p>Process simulation-based inventory data (mass and energy balances) for the perovskite single-junction, silicon (PERC architecture), and four-terminal perovskite/silicon tandem solar photovoltaic system life cycles. The file "0 Overview of simulation flowsheets.xlsx" contains images of the 11 flowsheets that constitute the perovskite/silicon tandem simulation model, which encompasses the perovskite single-junction and silicon (PERC) simulation models. For each unit process shown in each of the flowsheet images, the corresponding Excel file in this repository (with the same name) contains the detailed mass and energy balances, as well as full compositions and thermochemical properties of all streams and the compounds in them. That is, streams are not assumed to consist of pure elements simply moving through the system together, but rather taking into account that streams consist of compounds in solution, which have different thermochemical properties than simple mixtures of the elements involved.</p> <p>Nine additional data files, the names of which start with "Inventory - " contain summarized inventory data for the production of 1000 perovskite single-junction, silicon (PERC), and silicon/perovskite tandem PV modules, each with no Si recycling (i.e. zero circularity), 50% Si recycling, and 100% Si recycling (i.e. full Si circularity).</p>
Solar Orbiter Cycle 25 Interplanetary Shock List
<p>This dataset contains a list of interplenatary shocks observed by Solar Orbiter during solar cycle 25. The catalog has been built as follows in the SERPENTINE project.</p> <p><a href="https://data.serpentine-h2020.eu/static/doc/Shock_Cycle25_Documentation.pdf">https://data.serpentine-h2020.eu/static/doc/Shock_Cycle25_Documentation.pdf</a></p> <p>Please refer to the SERPENTINE data centre for the latest (and best) version of the catalog.</p> <p><a href="https://data.serpentine-h2020.eu/catalogs/shock-sc25/">https://data.serpentine-h2020.eu/catalogs/shock-sc25/</a></p> <p> </p>
Solar Wind-Induced Water Cycle on the Moon
<p>Simulated OH maps for LR1 and LR2 models. Rows are latitudes and Columns are local lunar time. Intensity corresponds to fractional coverage.</p>
Effect of Solar Wind and Micrometeoroid Impact on the Lunar Water Cycle: A Molecular Dynamics Study
<ul> <li>.txt files contain the data used to plot the hydrogen distributions.</li> <li>.csv files contain the data used to plot the temperature distribution graphs.</li> </ul>
Data for the paper: Towards high solar contribution in hybrid CSP-combined cycle gas turbine plants
<p>Data used for the paper "Towards high solar contribution in hybrid CSP-combined cycle gas turbine plants".</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.