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342 results for “Solar wind”
Hourly wind and solar generation profiles for every EIA 2020 plant in the CONUS
<p>Historical hourly time series of wind and solar generation profiles for every plant within the United States (US) that is part of the Energy Information Administration (EIA) 2020 dataset for the years 1980 through 2022. The data uses regional atmospheric climate model simulations and 2020 wind and solar power plant configurations across the entire contiguous US. This data is designed to be be aggregated to the Balancing Authority (BA) scale, or to other scales such as to the nodes of a production cost model which would allow the data to be used to perform reliability assessments and evaluations of technology innovation. There are ongoing efforts to extend this dataset for future climate projections, which additionally require the projection of future infrastructure under a wide range of uncertainties. This historical dataset is a benchmark for those projections and can be used to understand sensitivity to historical inter-annual variability, seasonality, and recent extreme events.</p> <p>For more information please refer to the <a href="https://www.nature.com/articles/s41597-024-03894-w">Scientific Data paper</a> and the <a href="https://github.com/GODEEEP/tgw-gen">code repository</a>.</p> <p>The dataset consists of two components:</p> <ul> <li>Plant configuration files - The plant configuration files (<code>eia_solar_configs.csv</code> and <code>eia_wind_configs.csv</code>) contain all the plant data that is relevant to a generation model, derived from EIA 860 2020 data. Each row corresponds to a single logical plant. In some cases actual plants were split into two logical plants for modeling purposes.</li> <li>Generation data files - The generation data resides in the <code>solar/</code> and <code>wind/</code> directories and consists of one file per year. Each year contains an 8760 (hourly) profile of generation for every plant. The first column in each csv file is the datetime in Coordinated Universal Time (UTC), and the subsequent columns correspond to the <code>plant_code_unique</code> column in the configuration files. Data in these generation files is expressed as a capacity factor, which is generation divided by the plant capacity. To obtain the actual Megawatts (MW) generated, multiply the capacity factor for a plant by the value in the <code>system_capacity</code> column from the respective configuration file.</li> </ul> <p>This research was supported under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL). 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>Version 1.1.0 adds bias corrected solar and extends the data through 2022</p> <p>Version 1.1.1 adds missing wind years 2021 and 2022</p> <p>Version 1.2.0 Extends the data through 2024</p> <p>Corresponding Author:</p> <ul> <li>Cameron Bracken, cameron.bracken@pnnl.gov</li> </ul>
Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution
<h2>Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution</h2><p><br>This dataset uses regional atmospheric climate model output to simulate wind and solar power generation across the entire contiguous US. This data is particularly useful for obtaining power production profiles at new locations or locations where only short records exist, or to study climate change impacts. We assume that a generic power plant exists at each grid cell and model the power output for solar at the surface and wind at 80, 100 and 125 meter hub heights. The data consists of a historical period 1980-2022 and several future scenarios that extend from 2020-2099.</p><p><br>- Code: <a href="https://github.com/GODEEEP/tgw-gen/">https://github.com/GODEEEP/tgw-gen/</a><br>- Underlying climate data and description of the future scenarios: <a href="https://tgw-data.msdlive.org/">https://tgw-data.msdlive.org/</a><br>- The NREL reV model was used to produce these profiles: <a href="https://github.com/NREL/reV">https://github.com/NREL/reV</a></p><h3>Data</h3><p>The dataset consists of a series of netcdf files, one per year, grouped together based on resource type, climate scenario, and year range. The data is available as capacity factors which can be scaled to any desired plant size.</p><p> </p><p>Please see the data directory below to find the appropriate record for your purposes.</p><ul><li>solar<ul><li>historical<ul><li><a href="https://doi.org/10.5281/zenodo.10138040">solar historical 1980-2022</a></li></ul></li><li>rcp45cooler<ul><li><a href="https://doi.org/10.5281/zenodo.10138850">solar rcp45cooler 2020-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10139076">solar rcp45cooler 2060-2099</a></li></ul></li><li>rcp45hotter<ul><li><a href="https://doi.org/10.5281/zenodo.10139819">solar rcp45hotter 2020-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10139994">solar rcp45hotter 2060-2099</a></li></ul></li><li>rcp85cooler<ul><li><a href="https://doi.org/10.5281/zenodo.10140410">solar rcp85cooler 2020-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10140517">solar rcp85cooler 2060-2099</a></li></ul></li><li>rcp85hotter<ul><li><a href="https://doi.org/10.5281/zenodo.10140685">solar rcp85hotter 2020-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10140758">solar rcp85hotter 2060-2099</a></li></ul></li></ul></li><li>wind<ul><li>historical<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10182689">wind 80m historical 1980-2000</a></li><li><a href="https://doi.org/10.5281/zenodo.10182848">wind 80m historical 2001-2022</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10143911">wind 100m historical 1980-2000</a></li><li><a href="https://doi.org/10.5281/zenodo.10144158">wind 100m historical 2001-2022</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10161120">wind 125m historical 1980-2000</a></li><li><a href="https://doi.org/10.5281/zenodo.10161276">wind 125m historical 2001-2022</a></li></ul></li></ul></li><li>rcp45cooler<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10183152">wind 80m rcp45cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10183308">wind 80m rcp45cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10183360">wind 80m rcp45cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10192154">wind 80m rcp45cooler 2080-2099</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10145097">wind 100m rcp45cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10145630">wind 100m rcp45cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10145720">wind 100m rcp45cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10145987">wind 100m rcp45cooler 2080-2099</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10161377">wind 125m rcp45cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10161494">wind 125m rcp45cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10161683">wind 125m rcp45cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10162136">wind 125m rcp45cooler 2080-2099</a></li></ul></li></ul></li><li>rcp45hotter<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10193740">wind 80m rcp45hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10198849">wind 80m rcp45hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10198912">wind 80m rcp45hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10198962">wind 80m rcp45hotter 2080-2099</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10146056">wind 100m rcp45hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10146108">wind 100m rcp45hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10146118">wind 100m rcp45hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10146159">wind 100m rcp45hotter 2080-2099</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10162368">wind 125m rcp45hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10162463">wind 125m rcp45hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10162561">wind 125m rcp45hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10162689">wind 125m rcp45hotter 2080-2099</a></li></ul></li></ul></li><li>rcp85cooler<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10199074">wind 80m rcp85cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10199103">wind 80m rcp85cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10199362">wind 80m rcp85cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10199411">wind 80m rcp85cooler 2080-2099</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10146346">wind 100m rcp85cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10150299">wind 100m rcp85cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10152763">wind 100m rcp85cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10152830">wind 100m rcp85cooler 2080-2099</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10162728">wind 125m rcp85cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10163251">wind 125m rcp85cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10171504">wind 125m rcp85cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10178654">wind 125m rcp85cooler 2080-2099</a></li></ul></li></ul></li><li>rcp85hotter<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10199444">wind 80m rcp85hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10199543">wind 80m rcp85hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10199587">wind 80m rcp85hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10199638">wind 80m rcp85hotter 2080-2099</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10152864">wind 100m rcp85hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10152945">wind 100m rcp85hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10160579">wind 100m rcp85hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10160776">wind 100m rcp85hotter 2080-2099</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10182205">wind 125m rcp85hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10182349">wind 125m rcp85hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10182514">wind 125m rcp85hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10182574">wind 125m rcp85hotter 2080-2099</a></li></ul></li></ul></li></ul></li></ul><p> </p><p>This research was supported by the <a href="https://godeeep.pnnl.gov">Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP)</a> 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>
Magnetic field enhancements in the interplanetary solar wind: Diverse processes manifesting a uniform observation type?
<p>Interlaced magnetic flux ropes are a subject of investigation in various space environments, including the solar corona, interplanetary solar wind, and magnetosheath. In this study, we utilize a Hall Magnetohydrodynamics (MHD) model to replicate these interactions within selected regions. By employing plasma flow to propel two flux ropes of various parameters towards each other, we examine the effect of their relative helicity on the ensuing process. Our simulations, coupled with vector analysis, found the existence of flux ropes with opposing helicity along their axes (counter-helical) in some of the resultant interactions. These findings underscore the need to identify and study counter-helical flux ropes in space. The study presented here contributes new comprehension of space plasma dynamics and new insights into the solar wind in control of space weather.</p>
India Onshore Wind Energy Atlas Accounting for Altitude and Land Use Restrictions and Co-Located Solar
<p>India faces the simultaneous challenges of meeting rising energy demand and reducing carbon emissions. To address these, India must transition to renewable energy sources. These high-resolution maps are used to quantify available areas for wind farms, after accounting for restrictions, including airports, buildings, protected land use, military zones, railways, roads, water bodies, waterways, wildlife and nature, high elevation and slope, and existing solar farms, to which policy-informed setback distances are applied. This study finds the wind and solar potential within available areas considering three altitudes (100 m, 150 m, 200 m) and four wind speed thresholds (5-8 m/s), and modern wind turbine and solar array dimensions. The raster files included here indicate available areas after aggregating restrictions for different combinations of altitude and wind speed threshold. Availability is indicated with a binary system in which available land is designated with a value of zero and restricted land is designated with a value of one.</p>
CNN-Based Forecasting of Pitch Angle-Resolved Energetic Electron Flux at MEO Using Solar Wind and Geomagnetic Data
<div> <p> CNN-Based Forecasting of Pitch Angle-Resolved Energetic Electron Flux at MEO Using Solar Wind and Geomagnetic Data. The model and test set data are provided here. Data used for training, validating, and testing the 1.8MeV channel model is also offered as an example.</p> </div> <p><strong>initial_data: </strong>The test set data has been normalized and can be used as model input.</p> <p><strong>norm para: </strong>The normalization parameters used for data processing</p> <p><strong>model_test_dataset_performance.py: </strong>The script to obtain the outputs of the models at different energy levels on the test set. Before running it, unzip “initial_data.rar” and "norm_para.rar"</p> <p><strong>full_dataset_for_rept_ch0: </strong>Data used for training, validating, and testing the 1.8MeV channel model. It's not essential for model_test_dataset_performance.py</p>
Wind and solar capacity factor time series by year and grid cell over the contiguous U.S.
<p>This data set contains hourly capacity factor time series of wind and solar resources over the contiguous U.S.</p> <p> </p> <p>The included time series cover four individual years and 2,586 grid cells. The years range from 2016 to 2019. The grid cells correspond to the grid cells of the NASA's MERRA-2 reanalysis data set into which the contiguous U.S. is subdivided. The grid cells have a spatial resolution of 0.5° latitude x 0.625° longitude with dimensions ranging from about 55 km x 45 km to 55 km x 62 km.</p> <p> </p> <p>This data set is used to generate the results of the following journal article:</p> <p>Enrico G. A. Antonini, Tyler H. Ruggles, David J. Farnham, Ken Caldeira, "The quantity-quality transition in the value of expanding wind and solar power generation", iScience 25 (4), 104140, 2022.</p> <p> </p> <p>Code and instructions required to reproduce the results reported in the above paper are available in the GitHub repositories at <a href="https://github.com/eantonini/Distributed_wind_and_solar_generation">https://github.com/eantonini/Distributed_wind_and_solar_generation</a> and <a href="https://github.com/carnegie/MEM_public/tree/Antonini_et_al_2022">https://github.com/carnegie/MEM_public/tree/Antonini_et_al_2022</a>.</p>
Molecular Dynamics Simulation of Solar Wind Implantation in the Permanently Shadowed Regions on the Lunar Surface
<p>Supporting data for "Molecular Dynamics Simulation of Solar Wind Implantation in the Permanently Shadowed Regions on the Lunar Surface"</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>
Numerical Simulations on Unconventional Surface Charging within Deep Cavities in the Solar Wind Plasma.
<p>Numerical simulation data presented in Nakazono and Miyake (2022): Unconventional Surface Charging within Deep Cavities in the Solar Wind Plasma. The format of the dataset is described in the PDF document (2022JA_supporting_information.pdf).</p>
Dynamics of the Young Solar Wind: Weakened Magnetization and Onset of Large Scale Turbulence (Simulation Data)
<p>Simulation data used in "Dynamics of the Young Solar Wind: Weakened Magnetization and Onset of Large Scale Turbulence", submitted to Geophysical Research Letters.</p>
Model generated open-closed field topology maps produced by ISSI team "Magnetic Open Flux And Solar Wind Structuring Of Interplanetary Space"
<p>Open and closed magnetic field topologies of the solar corona generated using four PFSS based coronal models (EUHFORIA, WSA, MULTI-VP, PSI-PFSS) and one full MHD coronal model (PSSI-MHD) using two different types of HMI-ADAPT magnetic field maps (with and without Active Regions (AR) added retrospectively). The magnetograms used are accessible here <a href="https://doi.org/10.5281/zenodo.10211762" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10211762</a> (Henney, Carl. ‘ADAPT Global Solar Magnetic Maps - 2010 Sep 18-20 (w/ & W/o Farside Active Region Input)’. Zenodo, 28 November 2023).</p> <p>These model outputs were generated by the ISSI team "Magnetic Open Flux And Solar Wind Structuring Of Interplanetary Space" and were used for the paper accessible on Arxiv via this link: https://arxiv.org/abs/2311.04024</p>
Mars nightside ionospheric response during the disappearing solar wind event: First Results
<p>The impact of a rarest solar wind phenomenon [disappearing solar wind (DSW) event during 26-28 December 2022] on the Martian nightside ionosphere is investigated.</p>
Solar wind access to grains in the upper layer of regolith
<p>The directory contains the scripts and output files from running COUPI DEM for determining the distribution of protons in upper layer of regolith. This is the model data for the paper "Solar wind access to grains in the upper layer of regolith" accepted to JGR Planets.</p> <p>Each case represents different packing density. The cases are described by Paul Duvoy in file Cases_paul_duvoy.xlsx.</p> <p>Most scripts to extract and process modeled data are located in utils and<br> gnuplot directory.</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>
Data for the manuscript named 'Soft X-ray imaging of the magnetosheath and cusps under different solar wind conditions: MHD simulations'
<p> This is the data used by the manuscript named 'Soft X-ray imaging of the magnetosheath and cusps under different solar wind conditions: MHD simulations'.</p> <p> The uploaded data is the X-ray intensity data for all the five cases studied in the manuscritpt. 'Casen' (n=1, 2, 3, 4, and 5) in the name of each data file indicates the case number, and 'sat pointX' (X=A, B, C, D) show the satellite positions analyzed in the manuscript. </p> <p> The data can be read by IDL using the following program statments:</p> <p>openr,lun,datai,/get_lun<br> xgse=0. & ygse=0. & zgse=0.<br> readf,lun,xgse,ygse,zgse ;;;;(satellite position in the GSE coordinate)<br> xsat=0. & ysat=0. & zsat=0.<br> readf,lun,xsat,ysat,zsat ;;;;(satellite position in the GSM coordinate)<br> xpoint=0. & ypoint=0. & zpoint=0.<br> readf,lun,xpoint,ypoint,zpoint ;;;;(satellite pointing of SXI, aim point)<br> nthtmax=0L & nphimax=0L<br> readf,lun,nthtmax,nphimax ;;;;(number of the tht and phi grids)<br> thti=fltarr(nthtmax) & phii=fltarr(nphimax)<br> readf,lun,thti,format='(e14.6)' ;;;;(the tht grids)<br> readf,lun,phii,format='(e14.6)' ;;;;(the phi grids)<br> Pxraytp=fltarr(nthtmax,nphimax)<br> readf,lun,Pxraytp,format='(e14.6)' ;;;;(X-ray intensity)<br> close,lun<br> free_lun,lun</p>
Data Intervals To Study Statistics of Whistler Waves in the Solar Wind
<p>This dataset contains a list of intervals that were used to study the statistics of whistler waves in the solar wind. It is created in companion to a manuscript submitted to ApJ.</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>
Cluster merged fluxgate/search coil data for a solar wind interval occuring on 15/02/2015 21:25:00-22:40:00
<p>Cluster merged fluxgate/search coil data for a solar wind interval occuring on 15/02/2015 21:25:00-22:40:00</p>
Venus Express IMA solar wind data for VeRa radio science observations
<p>The Venus Express (VEX) Analyser of Space Plasmas and Energetic Atoms (ASPERA-4)<br>ion mass spectrometer (IMA) observed the the pristine solar wind parameters<br>at Venus between 2006 and 2014. Details can be found in<br>Barabash et al. (2007) Planetary and Space Science, Vol. 55 (12), pp. 1772-1792<br>The Analyser of Space Plasmas and Energetic Atoms (ASPERA-4) for the <br>Venus Express mission.</p> <p>This file is based on the VEX_IMA_SW_NVP_20060101.cef data file provided<br>by M. Fränz (MPI for Solar System Research, Goettingen, Germany) and contains only those ASPERA-4<br>solar wind observations used in the publication Peter et al. (2024) Icarus<br>The variability of the topside ionospheres of Venus and Mars<br>as seen by recent radio science observations (Appendix A4 Fig. 2).</p> <p>Data are based on hourly average IMA spectra obtained when VEX was at larger distance than<br>1 Venus radius from nominal bow shock (Martinecz 2009, PhD). Density and total velocity<br>are obtained by integration over the IMA_EXTRA proton spectrum. Dynamic pressure<br>by product of density and velocity. This file contains only orbits used for VERA analyis.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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