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Dataset results
72 results for “SOLAR IRRADIANCE”
Influence of long-term changes in solar irradiance forcing on the Southern Annular Mode
<p>This dataset accompanies Wright et al. (2022): Influence of long-term changes in solar irradiance forcing on the Southern Annular Mode, Climate of the Past.</p> <p>This dataset contains:</p> <ul> <li><strong>Solar constant experiments</strong>: monthly files for sea level pressure (psl), surface stress east (tax), surface stress north (tay), screen temperature (tsc), and temperature at X pressure (t[0-18]) for solar constant experiments, specifically <ul> <li>control</li> <li>S+1</li> <li>S+3</li> <li>S+7</li> <li>S+35</li> <li>S-3</li> <li>S-7</li> <li>S-15</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Transient experiments</strong>: sea level pressure (psl) and screen temperature (tsc) files covering 1-2000 CE using: <ul> <li>Steinhilber_x2 solar forcing (monthly files)</li> <li>Shapiro solar forcing (monthly files)</li> </ul> </li> </ul> <p>These transient experiments are run as an Orbital-Greenhouse gases-Solar forcing experiment, and complement Phipps et al. (2013) (https://zenodo.org/record/3908927)</p> <p> </p>
A Missing Piece of the E-Region Puzzle: High-Resolution Photoionization Cross Sections and Solar Irradiances in Models
<p>Dataset corresponding to the associated publication, "A Missing Piece of the E-Region Puzzle: High-Resolution Photoionization Cross Sections and Solar Irradiances in Models." The dataset includes high-resolution photoionization and photoabsorption cross section for O and N<sub>2</sub> as well as high-resolution solar spectrum. Photoionization rates from model runs obtained from AURIC and the Meier photoionization code are also included. Please refer to the readme for information on the data structure.</p> <p><strong>***Please note that the paper is under review and has not been accepted yet.***</strong></p>
Solar spectral irradiance measurements above and in-canopy (SLOCS and CloudRoots Amazonia, 2022)
<p> </p> <p><strong>Shedding Light On CloudRoots</strong></p> <p>Solar spectral irradiance measurements made with the sensors produced within the Shedding Light On Cloud Shadows (SLOCS) project, deployed at the CloudRoots Amazonia 2022 campaign. </p> <p><strong>Dataset contents</strong></p> <ul> <li>Level 0 (raw): the raw data as it comes from the instruments</li> <li>Level 1 (L1): data in NetCDF format with metadata, quality control, homogenized factory calibration (counts bin-1 dt-1)</li> <li>Level 2 (L2): calibrated L1 data in W m-2 nm-1</li> <li>extras: this folder includes reference calibration spectra and data quality quicklooks</li> </ul> <p>Data is available at 1 Hz (resampled) and 10 Hz (native) resolution. 10 Hz resolution is compressed using NetCDF compression with gzip level 5 (uncompressed is 1.13 GB per date).</p> <p><strong>Data quality and uncertainty<br></strong></p> <p>Please note this dataset is in version 0.1.0, meaning you should use the dataset with caution. Not all unphysical data may have been flagged as such, and spectral calibration is an estimate based on a simple modelled spectrum. This modelled spectrum is a standard tropical atmosphere without aerosols, and is not run with observed profiles except an ERA5 estimate of total column water vapour. Please refer to 'extras' for technical validation of the spectral calibration method, and LibRadtran input/output files.</p> <p>A production (1.0) version will be released as soon data is fully validated.</p> <p>Lower-end uncertainty can be estimated by looking at the sensor to sensor spread at wavelength level during the calibration measurements. In the calibration phase, all sensors were co-located and homogenized at wavelength level. The 13:50 to 14:10 UTC time on August 7 is the reference frame for spectral calibration. </p> <p>Other sources of uncertainty are difficult to quantify due to measurements taking place in a very heteregeneous forest. These uncertainties relate primarily to the less-than-perfect placement of sensors on the towers in comparison to the reference calibration phase. </p> <p>Sensor 18 is only available in raw data or calibrated data. Precalibration (homogenizing) is not possible given its deviating spectral filter set compared to the others (sensor version 3b vs. 3a). </p> <p><strong>Technical information</strong></p> <ul> <li>The NetCDF files comply with CF1.7 where applicable.</li> <li>Metadata include sensor location (altitude relative to ground and sea level, lat, lon). </li> <li>Code for processing raw data to NetCDF available at <a href="../records/10159129">https://zenodo.org/records/10159129</a></li> <li>Calibration of raw sensor units to spectral irradiance is done using a reference clear-sky spectrum simulated with LibRadtran. Settings and output is included in "extras".</li> </ul> <p><strong>More information</strong></p> <ul> <li><a href="https://chiel.ghost.io/slocs">SLOCS project homepage</a></li> <li><a href="https://cloudroots.wur.nl/">CloudRoots project homepage</a></li> <li>2022 campaign reference paper is in preparation</li> <li>See 'related works' for the instrument reference paper </li> </ul>
CAELUS: Classification of sky conditions from 1-min time series of global solar irradiance using variability indices and dynamic thresholds
<p>CAELUS, a novel classification algorithm that relies on various thresholds to separate all possible sky conditions into six classes, is presented in Ruiz-Arias and Gueymard (2023, doi: <a href="https://doi.org/10.1016/j.solener.2023.111895">10.1016/j.solener.2023.111895</a>).</p> <p>This dataset was used to develop, validate and benchmark CAELUS. It is made up by 1-min quality-assured observations of global horizontal irradiance (GHI) and diffuse horizontal irradiance at 54 stations of the Baseline Surface Radiation Network (BSRN) archive, which is publicly available (see download instructions in https://bsrn.awi.de/data). The dataset includes 5 years of data per station, except in two of them (Petrolina, Brazil, and Solar Village, Saudi Arabia), combined with other variables that are required to run CAELUS, namely: solar zenith angle (sza), extraterrestrial horizontal solar irradiance (eth), clear-sky GHI (ghics) and GHI in a clean and dry atmosphere (ghicda). In addition, the dataset also provides the sky classification obtained with CAELUS.</p> <p>Further details about CAELUS and the dataset compilation is available in Ruiz-Arias and Gueymard (2023, doi: <a href="https://doi.org/10.1016/j.solener.2023.111895">10.1016/j.solener.2023.111895</a>). A Python implementation of CAELUS is available in https://github.com/jararias/caelus.</p>
Supplementary data for "Mechanism of surface solar irradiance variability under broken cloud cover"
<p>Open Data for manuscript to be submitted in ACP: "Mechanisms of surface solar irradiance variability under broken clouds". Refer to the README for details. Most (larger) files within the .zip archives are gzipped. Use `gunzip` to decompress.</p>
SolarStations.Org - A global catalog of solar irradiance monitoring stations
<p>The SolarStations.Org catalog provides a global overview of multi-component solar irradiance monitoring stations with the aim of streamlining the identification of relevant stations. The list of stations and their metadata are stored in a single CSV file with the following columns: station name, location, elevation, owner, network, period of operation, data availability, instrumentation, and climate zone. The station catalog and an interactive map are available at <a title="SolarStations.Org website" href="https://SolarStations.Org" target="_blank" rel="noopener">SolarStations.Org</a>. As of April 2025, the catalog contains information on 808 stations, of which 440 are active. The website and catalog are developed openly on GitHub and welcome community contributions.</p>
Data used for developing a parameterization for spatial distribution of solar irradiance over rugged terrain
<p>This dataset includes data and results produced while developing a parameterization for the spatial distribution of solar radiation over mountainous terrain. The method is designed for applications in Earth System Models.</p> <p>The four items included in this dataset are the following:</p> <p><strong>GFDL_preproc_dems</strong> includes digital elevations maps derived from the Shuttle Radar Topography Mission (SRTM) for three sample domains.</p> <p><strong>single-time-step </strong>Includes atmospheric optical properties used as input for Monte Carlo simulations.</p> <p><strong>gmd_2021_grids_light_*</strong> Include maps of terrain parameters derived from SRTM elevation data, and the partition of the domains in homogeneous tiles (sub-grid units) for different number and types of of land units.</p> <p><strong>rmc_simul_res</strong> results of the Monte Carlo simulations, consisting of maps of simulated solar radiation for different domains and solar angles.</p>
Dataset of Paper "Material selection and prediction of solar irradiance in plastic devices for application of solar water disinfection (SODIS) to inactivate viruses, bacteria and protozoa"
<p>Datasets of Paper “Predictive evaluation of solar irradiance in solar disinfection water plastic containers”.</p> <p>Data of the transmission spectra of the polymers: PMMA, PET, PC and PP.</p> <p>Data of the extinction coefficient spectra of the polymers: PMMA, PP, PC and PET.</p> <p>Data of the spectral incident radiation as a function of the thickness for PMMA, PET, PC and PP containers.</p> <p>Data of the spectral incident radiation required for inactivation of <em>MS2</em> virus, <em>E. coli</em> bacteria and <em>C. parvum</em> protozoa in a PMMA, PET, PC and PP containers.</p>
Simulated top-of-atmosphere (120 km) downward and upward solar and thermal-infrared irradiances and ice cloud optical thickness; calculated solar, TIR and net cloud radiative effect. Simulated with ice crystal properties for aggregates, droxtals, and plates based on Yang (2013).
<p>This dataset consists of three .nc files for ice crystal shapes of aggregates, plates, and droxtals. The files include ice cloud optical thickness <span class="math-tex">\(\tau\)</span> (550nm), the simulated upward and downward irradiances <span class="math-tex">\(F\)</span> at the top-of-atmosphere (with and without the presence of the ice cloud), and the calculated ice cloud radiative effect <span class="math-tex">\(\Delta F\)</span> (solar [0.3-3.5 <span class="math-tex">\(\mu\)</span>m], thermal-infrared [3.5-75 <span class="math-tex">\(\mu\)</span>m], and net). The data set allows the user to extract <span class="math-tex">\(\Delta F\)</span> values for their parameter combinations. The available cloudy and cloud-free irradiances further allow to calculate the cirrus radiative effect (RE) by scaling the 'cloudy' RE with the required cloud cover. This serves as a first-approximation because, as 3D effects are neglected.</p>
PFR AOD and solar irradiance data aquired during the MAPP project.
<p>Aerosol optical depth and solar irradiance data acquired in the frame of the JRP project MAPP, Metrology for Aerosol Optical Properties 19ENV04 with the Precision FilterRadiometer PFR-98-N-001. </p> <ul> <li>Monitoring period at Davos, 2021 -2022</li> <li>Rome Campaign – Sep.2021</li> <li>Izaña Campaign -Sep.2022</li> </ul> <p>for the dataset of version 1.0 the calibration of the PFR is against the PFR-Triad has been used. For the SI-traceable AOD retrieval the PTB (Physikalisch-Technische Bundesanstalt, Braunschweig, Germany) calibration (Kouremeti, Nevas et al. 2022) has been used in combination with QASUMEFTS (Gröbner, Kröger et al. 2017) for 368 nm and 412 nm, and TSIS-1 (Coddington, Richard et al. 2021) for 501 nm and 862 nm.</p> <p><strong>Calibration Record </strong></p> <p>The calibration record given as Top-of-Atmosphere PFR signal (<em>V<sub>0</sub></em>) and the expanded combined uncertainty of <em>V<sub>0</sub></em> (<em>U) </em>are given in the following table in Volts .</p> <table align="center"> <tbody> <tr> <td> <p> </p> </td> <td> <p><strong>PFR-TRIAD (2021)</strong></p> </td> <td> <p><strong>PTB</strong></p> </td> <td> <p><strong>Langley Izaña</strong></p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p><em>V<sub>0 </sub></em>(V)</p> </td> <td> <p><strong><em>U </em></strong>(V)</p> </td> <td> <p><em>V<sub>0 </sub></em>(V)</p> </td> <td> <p><strong><em>U </em></strong>(V)</p> </td> <td> <p><em>V<sub>0 </sub></em>(V)</p> </td> <td> <p><strong><em>U </em></strong>(V)</p> </td> </tr> <tr> <td> <p>862nm</p> </td> <td> <p>3.380</p> </td> <td> <p>0.017</p> </td> <td> <p>3.3602</p> </td> <td> <p>0.006</p> </td> <td> <p>3.376</p> </td> <td> <p>0.003</p> </td> </tr> <tr> <td> <p>501nm</p> </td> <td> <p>3.717</p> </td> <td> <p>0.010</p> </td> <td> <p>3.7326</p> </td> <td> <p>0.006</p> </td> <td> <p>3.712</p> </td> <td> <p>0.004</p> </td> </tr> <tr> <td> <p>412nm</p> </td> <td> <p>3.503</p> </td> <td> <p>0.010</p> </td> <td> <p>3.5368</p> </td> <td> <p>0.010</p> </td> <td> <p>3.493</p> </td> <td> <p>0.005</p> </td> </tr> <tr> <td> <p>368nm</p> </td> <td> <p>4.011</p> </td> <td> <p>0.012</p> </td> <td> <p>4.0438</p> </td> <td> <p>0.026</p> </td> <td> <p>4.006</p> </td> <td> <p>0.007</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Data description</strong> </p> <p>The data are daily files in matlab table format (table name PFR)</p> <p>The table contains 5 variables: 'Station' , 'Instrument' , 'Date' , 'Data' , 'MetaData_Flags'</p> <p>The variables of each sub-table and theirs units can be seen using e.g. the following : </p> <p>PFR.Data{1}.Properties.VariableNames, PFR.Data{1}.Properties.VariableUnits</p> <p>The signal, irradiance and atmospheric transmittance have been corrected for the Sun-Earth distance and are given at 1AU. </p> <p><em>Measurements, calibration and aanlysis performed by NK </em></p> <p><strong>References </strong></p> <p>Coddington, O. M., E. C. Richard, D. Harber, P. Pilewskie, T. N. Woods, K. Chance, X. Liu and K. Sun (2021). "The TSIS-1 Hybrid Solar Reference Spectrum." Geophysical Research Letters <strong>48</strong>(12): e2020GL091709.</p> <p>Gröbner, J., I. Kröger, L. Egli, G. Hülsen, S. Riechelmann and P. Sperfeld (2017). "The high-resolution extraterrestrial solar spectrum (QASUMEFTS) determined from ground-based solar irradiance measurements." Atmos. Meas. Tech. <strong>10</strong>(9): 3375-3383.</p> <p>Kouremeti, N., S. Nevas, S. Kazadzis, J. Gröbner, P. Schneider and K. M. Schwind (2022). "SI-traceable solar irradiance measurements for aerosol optical depth retrieval." Metrologia <strong>59</strong>(4): 044001.</p> <p><strong>Acknowledgments</strong></p> <p>This data was obtained within the joint research project EMPIR 19ENV04 MAPP “Metrology for aerosol optical properties” which has been supported by the European Metrology Program for Innovation and Research (EMPIR) . The EMPIR is jointly funded by the EMPIR participating countries within EURAMET and the European Union.</p>
Level 2 spectra of the 290 - 500 nm solar irradiance measured at Aosta - Saint Christophe by the Bentham DTMc300 with serial number 5541 in 2006 - 2019
<p>The provided dataset includes the Level 2 spectral measurements (in Watt/m<sup>2</sup>) of the solar irradiance in the range 290 - 500 nm, performed by the Bentham DTMc300 spectroradiometer with serial number 5541. The particular instrument performs automated continuous, high quality measurements at Aosta - Saint Christophe, Italy (45.7° N, 7.4° E, 570 m a.s.l.) since 2006. The Level 2 spectra are re-evaluated and homogenized and are currently available for the period 24 July 2006 - 8 July 2019. Each file contains the spectra for one day. The time (in UTC) for the measurements at 290, 400, and 500 nm is also provided for each spectral scan.</p>
Girasol, a sky imaging and global solar irradiance dataset
<p>The energy available in Micro Grid (MG) that is powered by solar energy is tightly related to the weather conditions in the moment of generation. Very short-term forecast of solar irradiance provides the MG with the capability of automatically controlling the dispatch of energy. We propose to achieve this using a data acquisition systems (DAQ) that simultaneously records sky imaging and Global Solar Irradiance (GSI) measurements, with the objective of extracting features from clouds and use them to forecast the power produced by a Photovoltaic (PV) system. The DAQ system is nicknamed as the <em>Girasol Machine</em> (Girasol means Sunflower in Spanish). The sky imaging system consists of a longwave infrared (IR) camera and a visible (VI) light camera with a fisheye lens attached to it. The cameras are installed inside a weatherproof enclosure that it is mounted on an outdoor tracker. The tracker updates its pan an tilt every second using a solar position algorithm to maintain the Sun in the center of the IR and VI images. A pyranometer is situated on a horizontal support next to the DAQ system to measure GSI. The dataset, composed of IR images, VI images, GSI measurements, and the Sun's positions, has been tagged with timestamps.</p>
Data belonging to Record high solar irradiance in Western Europe during first COVID-19 lockdown largely due to unusual weather,
<p>This data belongs to the paper Record high solar irradiance in Western Europe during first COVID-19 lockdown largely due to unusual weather, (soon to be) published in Communications Earth & Environment.</p>
WRF-Solar AOD550 & Clear-Sky Irradiance Forecasts & Verifying Observations over CONUS
<p>This dataset is fully described in and is a companion of Lee et al. (2022):</p> <p>Lee, J. A., P. A. Jiménez, J. Dudhia, and Y.-M. Saint-Drenan, 2023: Impacts of the aerosol representation in WRF-Solar clear-sky irradiance forecasts over CONUS. J. Appl. Meteor. Climatol., 62, 227–250, https://doi.org/10.1175/JAMC-D-22-0059.1.</p> <p>Aerosol optical depth (AOD) is a primary source of solar irradiance forecast error in clear-sky conditions. Improving the accuracy of AOD in NWP models like WRF will thus reduce error in both direct normal irradiance (DNI) and global horizontal irradiance (GHI), which should improve solar power forecast errors, at least in cloud-free conditions. In this study clear-sky GHI and DNI was analyzed from four configurations of the WRF-Solar model with different aerosol representations: 1) The default Tegen climatology; 2) Imposing AOD forecasts from the GEOS-5 model; 3) Imposing AOD forecasts from the Copernicus Atmosphere Monitoring Service (CAMS) model; and 4) The Thompson-Eidhammer aerosol-aware water/ice-friendly aerosol climatology. Over eight months of these 15-min output forecasts are compared against high-quality irradiance observations at NOAA SURFRAD and Solar Radiation (SOLRAD) stations located across CONUS. In general, WRF-Solar with GEOS-5 AOD had the lowest errors in clear-sky DNI, while WRF-Solar with CAMS AOD had the highest errors, higher even than the two aerosol climatologies, which is consistent with validation of the four AOD550 datasets against AERONET stations. For clear-sky GHI, the statistics differed little between the four models, as expected due to the lesser sensitivity of GHI to aerosol loading. Hourly-average clear-sky DNI and GHI was also analyzed, and additionally compared with CAMS model output directly. CAMS irradiance performed competitively with the best WRF-Solar configuration (with GEOS-5 AOD). The markedly different performance of CAMS versus WRF-Solar with CAMS AOD indicates that CAMS is apparently less sensitive to AOD550 than WRF-Solar is.</p> <p>wrf_aeronet_aod550_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 317 MB</p> <p>WRF-Solar AOD550 spatially interpolated to AERONET stations and AERONET observed AOD550. Organized in arrays sorted by model cycle time (once daily at 09 UTC from 20191119 to 20200730) and model lead time (every 15 min to 45 h). The four WRF-Solar experiments correspond to the four from Lee et al. (2022).</p> <p>wrf_surfrad_solrad_inst_dni_ghi_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 102 MB</p> <p>WRF-Solar clear-sky DNI and clear-sky GHI interpolated to SURFRAD & SOLRAD stations, and SURFRAD & SOLRAD observed clear-sky DNI and clear-sky GHI. Organized in arrays sorted by model cycle time (once daily at 09 UTC from 20191119 to 20200730) and model lead time (every 15 min to 45 h). The four WRF-Solar experiments correspond to the four from Lee et al. (2022).</p> <p>wrf_surfrad_solrad_hrly_dni_ghi_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 26 MB</p> <p>WRF-Solar clear-sky DNI and clear-sky GHI interpolated to SURFRAD & SOLRAD stations, and SURFRAD & SOLRAD observed clear-sky DNI and clear-sky GHI. All values are averaged to time-ending 1-hourly averages. Organized in arrays sorted by model cycle time (once daily at 09 UTC from 20191119 to 20200730) and model lead time (every 1 h to 45 h). The four WRF-Solar experiments correspond to the four from Lee et al. (2022).</p> <p>cams_surfrad_solrad_hrly_dni_ghi_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 26 MB</p> <p>CAMS clear-sky DNI and clear-sky GHI interpolated to SURFRAD & SOLRAD stations, and SURFRAD & SOLRAD observed clear-sky DNI and clear-sky GHI. All values are averaged to time-ending 1-hourly averages. Organized in arrays sorted by model cycle time (once daily at 00 UTC from 20191119 to 20200730) and model lead time (every 1 h to 54 h).</p>
"Nowcasting Solar EUV Irradiance with Photospheric Magnetic Fields and the MgII Index" Figures, Scripts, and Data
<p>These tar files, scripts, and datasets were used in the paper "Nowcasting Solar EUV Irradiance with Photospheric Magnetic Fields and the MgII Index," submitted for publication to the Space Weather Journal. More information on what is included in this archive can be found the the ReadMe file. </p>
Data of manuscript "Forecasting day-ahead 1-minute irradiance variability from Numerical Weather Predictions" submitted to Solar Energy
<p>This is the data corresponding to manuscript "Forecasting day-ahead 1-minute irradiance variability from Numerical Weather Predictions" by Kreuwel et al., 2022, submitted to Solar Energy.</p> <p> </p> <p>The file `basic_stats.tar.gz` contains a broad set of standard statistics of surface meteorology and vertical profiles. The file `sw_flux_dn_xy.tar.gz` contains spatial cross sections of downwelling shortwave radiation.</p>
High resolution solar irradiance variability climatology dataset part 1: direct, diffuse, and global irradiance
<p><strong>Dataset paper</strong></p> <p>See the official dataset description paper (preprint) over at <a href="https://essd.copernicus.org/preprints/essd-2022-456/">Earth System Science Data</a>.</p> <p><strong>Dataset description</strong></p> <p>High resolution surface solar irradiance observations from the Baseline Surface Radiation Network (BSRN) of Cabauw, the Netherlands. This dataset spans 10 years, <em>from 2011-02 until 2020-12-31</em>.</p> <p>This dataset is the preprocessed 1 Hz observational record of direct, diffuse, and global horizontal irradiance, which is the basis for the official BSRN 1-minute dataset published at <a href="https://doi.pangaea.de/10.1594/PANGAEA.940531">PANGAEA</a>. Please refer to the official dataset for detailed metadata, instrument information, quality control flags, the full radiation balance and more (1 minute resolution). More information about the observational site Cabauw can be found at the <a href="https://ruisdael-observatory.nl/cabauw/">Ruisdael Observatory website</a>, and more general information about BSRN is <a href="https://essd.copernicus.org/articles/10/1491/2018/">published on ESSD</a>.</p> <p><strong>Part 1 of 2</strong></p> <p>This dataset is the basis for an analysis of surface solar irradiance variability, derived variables, supplementary meteorological data, and quicklooks, available in part 2 here: <a href="https://doi.org/10.5281/zenodo.7092058">https://doi.org/10.5281/zenodo.7092058</a></p> <p><strong>Usage disclaimer</strong></p> <p>While resampling this 1-Hz data to 1-minute, with the official BSRN quality flags, should reproduce an identical dataset to the official 1-minute BSRN dataset, this has not yet been validated for this version. When you require the most reliable version of the radiation measurements, where variability at a higher resolution than 1 minute is not of concern, please refer to the official BSRN dataset at PANGAEA.</p>
Global Catastrophic Effects on Future Climate due to Increasing Total Solar Irradiance. A General Atmospheric Circulation Analysis.
<p>10-yr CESM run with standard TSI (BGCN_T31_g37.cam.h0*)</p> <p>10-yr CESM run with TSI +10% (BGCN_T31_g37_TSI10p.cam.h0*)</p>
Analysis of thermal and dielectric loss features of lunar regolith considering real-time effect solar irradiance
<p>ESI data for "Analysis of thermal and dielectric loss features of lunar regolith considering real-time effect solar irradiance".</p>
Solar Irradiance (GHI and DNI) for Folsom, CA (lat=38.642N, lon=121.148W)
<p>Solar Irradiance (GHI and DNI) for Folsom, CA (lat=38.642N, lon=121.148W).<br> The file is in comma-separated values format.</p> <p>The file has three columns:<br> 1- Timestamp in UTC in the format yyyy-mm-dd HH:MM:SS<br> 2- GHI measurement in W/m<sup>2</sup><br> 3- DNI measurement in W/m<sup>2</sup></p> <p>The GHI and DNI data included in this data release are measured with a second-generation RSR (RSR-2) from Augustyn, Inc.<br> The RSR-2 consists of a main shadowband head unit and two Licor LI-200SZ pyranometers, which have a typical error of ±5% compared to an Eppley Precision Spectral Pyranometer (PSP)[https://www.licor.com/].<br> The primary pyranometer provides continuous measurement of GHI, while the secondary pyranometer and shadowband enable measurement of the diffuse horizontal irradiance (DHI).<br> DNI is computed directly from the GHI, DHI, and solar zenith angle (<span class="math-tex">\(\theta_z\)</span>).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.