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342 results for “solar wind”
Solar and Wind Energy Drought Data for 15 BAs in the CONUS
<p><strong>Solar and wind energy drought data for 15 BAs in the CONUS</strong></p> <p>This dataset has 2 components, (1) physically consistent wind, solar and load data for 15 Balancing Authorities (BAs) in the CONUS and (2) pre-computed BA-level energy droughts for a variety of time scales from 1 hour to 5 days. The generation and load data is aggregated from plant level data based on EIA-860 2020 infrastructure. </p> <p>For more information please refer to Bracken et al. 2023, Standardized Benchmark of Historical Compound Wind and Solar Energy Droughts Across the Continental United States, in prep, or refer to the Github repository https://github.com/GODEEEP/energy-droughts</p> <p><strong>Wind, solar and load data</strong></p> <p>The data is broken up with one csv file per time scale, the available time scales are 1-hour, 4-hour, 12-hour, 1-day, 2-day, 3-day, and 5-day. Each file has the following columns</p> <ul> <li>ba - Abbreviated name for the BA </li> <li>year - The current year as an integer</li> <li>period - A unique integer for the current time step</li> <li>solar_gen_mwh - Aggregated solar generation in units of MWh</li> <li>solar_capacity_mwh - Aggregated solar plant capacity expresed as MWh </li> <li>wind_gen_mwh - Aggregated wind generation in units of MWh</li> <li>wind_capacity_mwh - Aggregated wind plant capacity expresed as MWh </li> <li>load_mwh - BA load in MWh</li> <li>load_max_mwh - The maximum BA load over the entire historical period</li> <li>datetime_utc - Time stamp for the current time step, in UTC, All time stamps are beginning of period. </li> <li>timezone - The predominant time zone for the BA</li> <li>wind_cf - Wind capacity factor, wind_gen_mwh/wind_capacity_mwh</li> <li>solar_cf - Solar capacity factor, wind_gen_mwh/wind_capacity_mwh </li> <li>load_cf - Load "capacity factor", expresed as a fraction of the maximum BA load, load_mwh/load_max_mwh </li> </ul> <p><strong>Energy drought data</strong></p> <p>Several kinds of energy droughts are available</p> <ul> <li><strong>lws</strong> - Load, wind, and solar compound droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>rl</strong> - Residual load (load minus wind and solar gen) droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>solar</strong> - Solar only droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>solar_fixed</strong> - Solar only droughts defined using a single 10th percentile threshold </li> <li><strong>wind</strong> - Wind only droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>wind_fixed</strong> - Wind only droughts defined using a single 10th percentile threshold </li> <li><strong>ws</strong> - Wind only droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>ws_fixed</strong> - Wind and solar droughts defined using a single 10th percentile threshold </li> </ul> <p>Each drought type and time scale is in a csv file with the following columns (not all columns are available for every drought type)</p> <ul> <li>ba - Abbreviated name for the BA </li> <li>run_id - unique id for each drought event</li> <li>datetime_utc - Date stamp for the start of the drought, in UTC</li> <li>timezone - The predominant time zone for the BA</li> <li>run_length - The length of a drought in time steps</li> <li>run_length_days - The length of the drought in days</li> <li>severity_ws - Drought severity for wind and solar droughts, computed using the compound drought magnitude metric</li> <li>severity_lws - Drought severity load, wind, and solar droughts, computed using the compound drought magnitude metric</li> <li>severity_mwh - Drought severity expressed as MWh</li> <li>zero_prob - For solar, this value indicates if the timestep has zero probability of solar production, i.e. night time</li> <li>year - The year of the timestep</li> <li>month - The month of the timestep </li> <li>hour - The hour of the timestep </li> <li>wind_cf - Wind capacity factor for the drought</li> <li>solar_cf - Solar capacity factor for the drought </li> <li>srepi_solar - Standardized renewable energy production index for solar</li> <li>srepi_wind - Standardized renewable energy production index for wind</li> </ul> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p> <p> </p>
Wind and Solar Candidate Project Areas for Princeton REPEAT
<p>This data set contains the GIS data for solar, land-based wind, and offshore wind Candidate Project Areas (CPAs) under base (BLUA). Each record in this dataset represents a “Candidate Project Area” with attributes such as nameplate capacity, annual generation, model region, distance to transmission, etc. The Lawrence Berkeley National Lab MAPRE tools (<a href="https://mapre.lbl.gov/gis-tools/">https://mapre.lbl.gov/gis-tools/</a>) were used to create this dataset, along with input assumptions adapted from Wu et al 2020 (Grace C Wu <em>et al</em> 2020 <em>Environ. Res. Lett.</em> 15 074044) and E. Larson, C. Greig, J. Jenkins, E. Mayfield, A. Pascale, C. Zhang, J. Drossman, R. Williams, S. Pacala, R. Socolow, EJ Baik, R. Birdsey, R. Duke, R. Jones, B. Haley, E. Leslie, K. Paustian, and A. Swan, Net-Zero America: Potential Pathways, Infrastructure, and Impacts, interim report, Princeton University, Princeton, NJ, December 15, 2020.</p> <p><br> What's new in version 2: </p> <ul> <li>Solar: <ul> <li>Prime farmland eligible for utility scale solar development throughout the study area.</li> <li>Solar configuration: single-axis tracking solar assumed per VCE data</li> <li>Additional attributes calculated: <ul> <li>Aspect indicates the compass direction toward which a slope faces, measured in degrees from North in a clockwise direction from 0 to 360. Surface is flat where aspect_min = -1 (Source: https://www.sciencebase.gov/catalog/item/5540ec40e4b0a658d7939628)</li> </ul> </li> </ul> </li> <li>Wind: additional airspace and military exclusions applied <ul> <li>Airfields with arrivals and departures > 1000 buffered by 5 mi</li> <li>Weather radar stations buffered 5 mi</li> <li>NEXRAD radar buffered 3 km</li> <li>Military installations, ranges and training areas buffered 5 mi</li> </ul> </li> <li>Wind: additional attributes calculated: <ul> <li>VFR navigation landmarks (buffered 2 nautical mi)</li> <li>Navaid systems (buffered 8 nautical mi)</li> <li>Special Use Airspace (floor <=1000 ft)</li> </ul> </li> <li>Additional attribute for both solar and wind: <ul> <li>m_landcover indicates landcover type (per USGS NLCD 2016. https://www.mrlc.gov/data/legends/national-land-cover-database-2016-nlcd2016-legend)</li> </ul> </li> </ul> <p>What's new in version 3:</p> <ul> <li>Geographic extent of offshore wind increased farther west</li> </ul>
Data and Code for "Study of Solar Wind and Interplanetary Magnetic Field Features Associated with Geomagnetic Storms: The Cross Wavelet Approach"
<p>These files are the supplementary information, including dataset, codes and plots for the research work entitled "Study of Solar Wind and Interplanetary Magnetic Field Features Associated with Geomagnetic Storms: The Cross Wavelet Approach".</p>
Supplementary data for: Does Phobos reflect solar wind protons? Mars Express special flyby operations with and without the presence of Phobos
<p>Supplementary data to reproduce figures for "Does Phobos reflect solar wind protons? Mars Express special flyby operations with and without the presence of Phobos".</p>
Helios fast solar wind stream observation times
<p>This record contains the dates, times and observing probe of fast solar wind streams observed by the two Helios spacecraft, used in a study of the radial variation of alpha particles in the solar wind</p> <p>stream_times.csv contains following columns:</p> <p>Start: Start time of each observed structure (UT)<br> End : End time of each observed structure (UT)<br> Probe : Observing probe of each structure (1 for Helios 1, 2 for Helios 2)</p>
Data for the manuscript named 'Altitude-dependence response near cusp region after solar wind variations'
<p>Data for the manuscript named 'Altitude-dependence response near cusp region after solar wind variations</p> <p>1) cluster data, observation from cluster mission</p> <p>2) MAD6400_2001-10-12_tau1a_59.4@vhfa_041292, EISCAT radar data</p> <p>3) OMNI: solar wind and IMF data</p> <p>4) p_shell_5.7Re_011012 is thermal pressure at 5.7 Re-shell, which was carried out in real-time solar wind conditions during 08:30-10:00UT (real-time-density run)</p> <p>5) p_shell_5.7Re_011012cd is thermal pressure at 5.7 Re-shell, which was carried out in real-time solar wind conditions except for solar wind density during 08:30-10:00UT (no-density-increasing run) </p> <p>“1_5.7Rshell_out.txt”time_latitude_out</p> <p>“1P_shell_5.7Re.csv”time_shell_5.7Re</p> <p>data range: MLT 08:00~16:00, latitude -90°~ -30°</p>
Supplementary Data for "Prediction of solar wind speed by applying convolutional neural network to potential field source surface (PFSS) magnetograms"
<p>These are supplementary data for the paper "Prediction of solar wind speed by applying convolutional neural network to potential field source surface (PFSS) magnetograms". They are:</p> <p>- Python code to construct a neural network model</p> <p>- Saved optimal models (for 8-fold validation)</p> <p>- Selected y-label data (solar wind speed) and corresponding dates, which we eliminate the data identified as ICME</p>
Nighttime geomagnetic response to jumps of solar wind dynamic pressure: A possible cause of Québec blackout in March 1989
<p>This contains the original simulation data used by the paper 'Nighttime geomagnetic response to jumps of solar wind dynamic pressure: A possible cause of Québec blackout in March 1989', which will appear in Space Weather. The data were obtained by the global MHD simulation (REPPU).</p> <p>Each sav file (IDL saveset format) contains the physical variables in the ionosphere at each grid point (mlt*mlat: 321*221, starting at 0 MLT and -90 MLAT) for Run 02n, 30n, 30s, 33n, and 33s, including</p> <ul> <li>MHD time series ('time' in minutes),</li> <li>field-aligned current ('fac1' in A/m2),</li> <li>electric potential ('pot' in V),</li> <li>ionospheric conductivities ('ss11', 'ss12', and 'ss22' in S).</li> </ul> <p>The sav file can be opened by IDL. By typing, for example,<br> IDL> restore,'run02n.sav'<br> you can load the file to the memory of IDL. Type 'help' to confirm the variables that are loaded.</p>
EMHIRES dataset: wind and solar power generation
<p><strong>EMHIRES Wind</strong></p> <p>The first version of EMHIRES dataset releases four different files about the wind power generation hourly time series during 30 years (1986-2015), taking into account the existing wind fleet at the end of 2015, for each country (onshore and offshore), bidding zone and by NUTS 1 and NUTS 2 region. The time series are given as capacity factors. The installed capacity used accounted for calculating the capacity factors are summarised in the annexes of the report.</p> <p>https://setis.ec.europa.eu/emhires-dataset-part-i-wind-power-generation_en</p> <p><strong>EMHIRES Solar</strong></p> <p>EMHIRES provides RES-E generation time series for the EU-28 and neighbouring countries. The solar power time series are released at hourly granularity and at different aggregation levels: by country, power market bidding zone, and by the European Nomenclature of territorial units for statistics (NUTS) defined by EUROSTAT; in particular, by NUTS 1 and NUTS 2 level. The time series provided by bidding zones include special aggregations to reflect the power market reality where this deviates from political or territorial boundaries.</p> <p>The overall scope of EMHIRES is to allow users to assess the impact of meteorological and climate variability on the generation of solar power in Europe and not to mime the actual evolution of solar power production in the latest decades. For this reason, the hourly solar power generation time series are released for meteorological conditions of the years 1986-2015 (30 years) without considering any changes in the solar installed capacity. Thus, the installed capacity considered is fixed as the one installed at the end of 2015. For this reason, data from EMHIRES should not be compared with actual power generation data other than referring to the reference year 2015.</p> <p>https://setis.ec.europa.eu/emhires-dataset-part-ii-solar-power-generation_en</p>
Artificial space weathering to mimic solar wind enhances the toxicity of lunar dust simulants in human lung cells
<p>During NASA's Apollo missions, inhalation of dust particles from lunar regolith was identified as a potential occupational hazard for astronauts. These fine particles adhered tightly to spacesuits and were unavoidably brought into the living areas of the spacecraft. Apollo astronauts reported that exposure to the dust caused intense respiratory and ocular irritation. This problem is a potential challenge for the Artemis Program, which aims to return humans to the Moon for extended stays in this decade. Since lunar dust is "weathered" by space radiation, solar wind, and the incessant bombardment of micrometeorites, we investigated whether treatment of lunar regolith simulants to mimic space weathering enhanced their toxicity. Two such simulants were employed in this research, Lunar Mare Simulant-1 (LMS-1), and Lunar Highlands Simulant-1 (LHS-1), which were added to cultures of human lung epithelial cells (A549) to simulate lung exposure to the dusts. In addition to pulverization, previously shown to increase dust toxicity sharply, the simulants were exposed to hydrogen gas at high temperature as a proxy for solar wind exposure. This treatment further increased the toxicity of both simulants, as measured by the disruption of mitochondrial function, and damage to DNA both in mitochondria and in the nucleus. By testing the effects of supplementing the cells with an antioxidant (N-acetylcysteine), we showed that a substantial component of this toxicity arises from free radicals. It remains to be determined to what extent the radicals arise from the dust itself, as opposed to their active generation by inflammatory processes in the treated cells.</p>
Current sheet intervals for "Solar wind current sheets: MVA inaccuracy and recommended single-spacecraft methodology"
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Solar wind ENA precipitation in the Martian atmosphere: Monte Carlo simulation results
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Magnetic field enhancements in the solar wind: Diverse processes manifesting a uniform observation type?
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Artificial space weathering to mimic solar wind enhances the toxicity of lunar dust simulants in human lung cells
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Data from: Multi-fluid MHD study of the disappearing solar wind event observed by MAVEN: Effects of solar wind density
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The effects of the upper atmosphere and corona on the solar wind interaction with Venus
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Measurements of the solar wind propagation delay for L1 to Earth based on ACE and ground-based magnetometer data
<p>This database is the basis for the analysis described in the manuscript</p> <p>'Timing of the solar wind propagation delay between L1 and Earth based on machine learning'</p> <p>published in Journal of Space Weather and Space Climate.</p> <p> </p> <p><a href="https://doi.org/10.1051/swsc/2021026">https://doi.org/10.1051/swsc/2021026</a></p> <p> </p> <p>The database contains the times of 380 interplanetary shocks detected at ACE (T_ACE) which also caused a sudden impulse (T_SI) in the magnetosphere based on ground-based magnetometer data. This information can be found in 'measurement_SW_propagation.txt'.</p> <p>final_learningset_SWdelay.pickle contains the corresponding ACE data (solar wind speed, ACE position) at time T_ACE for each of the 380 cases.</p> <p>The datafile can be loaded with Python as follows:</p> <p>import pickle<br> with open('final_learningset_SWdelay.pickle', 'rb') as f:<br> [learnvector_o,learnvector_m,learnvector_s,learnvector,timevector]=pickle.load(f)</p> <p> </p> <p>The content is described as follows:</p> <p>learnvector_o - contains a list of ACE data in its original form, ordering ['rx','ry','rz','vx','vy','vz']</p> <p>learnvector_m - median of each feature</p> <p>learnvector_s - standard deviation of each feature</p> <p>learnvector - contains an array of the standardized data, which have been used to train the ML models.</p> <p>timevector - contains an array with the vector delay in seconds(first column), flat delay in seconds (second column), and measured solar wind propagation delay in seconds (third column)</p>
The impact of Solar wind variability on pulsar timing
<p>Timing models, ToA files and DM time series (UTC_start_date, UTC_center_date, MJD, DMvariation, DMvariation_error, SolarElongation) used in the article "The impact of Solar wind variability on pulsar timing" (published in A&A, part of the Veni project SOLTRAC, 016.Veni.192.086)</p> <p> </p>
Data from: Impact of solar and wind development on conservation values in the Mojave Desert
In 2010, The Nature Conservancy completed the Mojave Desert Ecoregional Assessment, which characterizes conservation values across nearly 130,000 km2 of the desert Southwest. Since this assessment was completed, several renewable energy facilities have been built in the Mojave Desert, thereby changing the conservation value of these lands. We have completed a new analysis of land use to reassess the conservation value of lands in two locations in the Mojave Desert where renewable energy development has been most intense: Ivanpah Valley, and the Western Mojave. We found that 99 of our 2.59-km2 planning units were impacted by development such that they would now be categorized as having lower conservation value, and most of these downgrades in conservation value were due to solar and wind development. Solar development alone was responsible for a direct development footprint 86.79 km2: 25.81 km2 of this was primarily high conservation value Bureau of Land Management lands in the Ivanpah Valley, and 60.99 km2 was privately owned lands, mostly of lower conservation value, in the Western Mojave. Our analyses allow us to understand patterns in renewable energy development in the mostly rapidly changing regions of the Mojave Desert. Our analyses also provide a baseline that will allow us to assess the effectiveness of the Desert Renewable Energy Conservation Plan in preventing development on lands of high conservation value over the coming decades.
Ionospheric Electron Densities, Neutral Temperature, Winds and Post-processed Output from TIEGCM Simulations in Support of Publication "Physical Processes Driving the Response of the F2-region Ionosphere to the 21 August 2017 Solar Eclipse at Millstone Hill"
This dataset is associated with the publication "Physical Processes Driving the Response of the F2-region Ionosphere to the 21 August 2017 Solar Eclipse at Millstone Hill". In particular, the NCAR- Community model: high-resolution thermosphere-ionosphere-electrodynamics general circulation model (TIEGCM) has been used to investigate the response of ionospheric F2-region electron density (Ne) at Millstone Hill (42.610N, 71.480W, maximum obscuration: 63%) to the Great American Solar Eclipse on 21 August 2017. Two sets of model runs were done, one with the eclipse and the other without. Model outputs of winds, temperatures and electron densities, as well as diagnostic variables of key chemical and physical processes that determined the ionosphere responses to the eclipse, were analyzed and used in producing a paper: Physical Processes Driving the Response of the F2-region Ionosphere to the 21 August 2017 Solar Eclipse at Millstone Hill.
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