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607 results for “wind data”
Figures: The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data
<p>Python figures in pickle format</p> <p>matplotlib version 3.5.1 </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>
Data for: Multi-LEO satellite stereo winds
The stereo-winds method follows trackable atmospheric cloud features from multiple viewing perspectives over multiple times, generally involving multiple satellite platforms. Multi-temporal observations provide information about the wind velocity and the observed parallax between viewing perspectives provides information about the height. The stereo-winds method requires no prior assumptions about the thermal profile of the atmosphere to assign a wind height, since the height of the tracked feature is directly determined from the viewing geometry. The method is well developed for pairs of Geostationary (GEO) satellites and a GEO paired with a Low Earth Orbiting (LEO) satellite. However, neither GEO-GEO nor GEO-LEO configurations provide coverage of the poles. In this paper, we develop the stereo-winds method for multi-LEO configurations, to extend coverage from pole to pole. The most promising multi-LEO constellation studied consists of Terra/MODIS and Sentinel-3/SLSTR. Stereo-wind products are validated using clear-sky terrain measurements, spaceborne LiDAR, and reanalysis winds for winter and summer over both poles. Applications of multi-LEO polar stereo winds range from polar atmospheric circulation to nighttime cloud identification. Low cloud detection during polar nighttime is extremely challenging for satellite remote sensing. The stereo-winds method can improve polar cloud observations in otherwise challenging conditions.
Replication data for: High-frequency variability induced in the Southern California Bight by a wind event in Sebastián Vizcaíno Bay, Baja California
<p>Replication data for Ramos-Musalem, K. , Gille, S. T., Cornuelle, B. D., & Mazloff, M. R. (2023) High-frequency variability induced in the Southern California Bight by a wind event in Sebastián Vizcaíno Bay, Baja California<em>, Journal of Geophysical Research: Oceans</em></p> <p><strong>Contents:</strong></p> <p><strong><code>1. Input/</code></strong>: Contains the necessary input files to re-run the MITgcm configuration provided here: <a href="https://github.com/anakarinarm/SVB_highFreqVar_paper/tree/main/MITgcm_config">https://github.com/anakarinarm/SVB_highFreqVar_paper/ </a>including the bathymetry, wind stress forcing, and initial temperature and salinity fields.</p> <p>MITgcmUV version: checkpoint67y</p> <p><strong>Sea surface height model results for 5 days of simulation in:</strong></p> <p><strong><code>2. 06_512x612x100_ORL_SVB/</code></strong>: Runs with Sebastian Vizcaino Bay.</p> <ul> <li><code>01_FebTS_SVB/</code>: Base run, February-like stratification</li> <li><code>02_barotropic_SVB/</code>: Constant T and S</li> <li><code>04_AugTS_SVB/</code> : August-like stratification</li> <li>grid_vars: grid variables (depth, land masks, horizontal and vertical spacing) </li> </ul> <p><strong><code>3. 06_512x612x100_ORL</code>:</strong> Runs without Sebastian Vizcaino Bay</p> <ul> <li><code>01_FebTS/</code>: Base run, February-like stratification</li> <li><code>02_barotropic/</code>: Constant T and S</li> <li><code>04_AugTS/</code>: August-like stratification</li> <li>grid_vars: grid variables (depth, land masks, horizontal and vertical spacing) </li> </ul> <p><strong>4. <code>saved_data</code>/:</strong> post-processed model results including calculations needed to reproduce the figures in the manuscript. The scripts used to obtain these files are available here: <a href="https://github.com/anakarinarm/SVB_highFreqVar_paper/tree/main/postprocessing">https://github.com/anakarinarm/SVB_highFreqVar_paper</a></p>
20th century winds, pressure, and temperature around Antarctica from single-proxy data assimilation
<p>Please cite <a href="https://doi.org/10.5194/tc-2023-16">O'Connor et al., 2023</a> (preprint available in The Cryosphere Discussions) when using these single-proxy reconstruction datasets. The reconstructions generated using all proxy data (O'Connor et al., 2021) are archived <a href="https://zenodo.org/record/5507607">here</a>.</p> <p>This dataset contains four sets of single-proxy reconstructions of annually resolved zonal surface wind (us), sea level pressure (psl), and surface temperature (tas) anomalies around Antarctica over the period 1900 to 2005 CE. The reconstructions were generated using the Last Millennium Reanalysis data assimilation framework (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2016JD024751">Hakim et al., 2016</a>), adapted for Antarctic atmospheric reconstructions following the methods from <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021GL095999">O'Connor et al., 2021</a>. The reconstructions were generated using the same proxy databases and methods as outlined in O'Connor et al., 2021, but we only assimilate proxies from ice cores or coral records in each reconstruction.</p> <p>This dataset contains four sets of reconstructions, based on two different climate model simulation priors and either ice cores or coral assimilated:</p> <ol> <li>CESM LM prior, ice cores only</li> <li>CESM LM prior, corals only</li> <li>PACE prior, ice cores only</li> <li>PACE prior, corals only</li> </ol> <p>The CESM LM prior is drawn from the iCESM Last Millennium Ensemble (<a href="https://doi.org/10.1029/2019PA003625">Stevenson et al., 2019</a>) and includes only natural forcings. The PACE prior is drawn from the CESM1 Pacific Pacemaker Ensemble (“PACE”; <a href="https://doi.org/10.1175/JCLI-D-16-0844.1">Deser et al., 2017</a>) and includes historical anthropogenic forcings. For each reconstruction, there are three netCDF files containing the ensemble mean (mean of 100 ensemble members) for each climate field. The anomaly reference period is 1961-1990.</p>
The Impact of Climate Change on Extreme Winds over Northern Europe According to CMIP6: data and codes
<p>Data and codes for the paper submitted to WES "The Impact of Climate Change on Extreme Winds over Northern Europe According to CMIP6"</p> <p>Data:</p> <p>1. U50*.nc: 50-year wind with spectral correction method from 18 CMIP6 models</p> <p>2. annual_max*.nc: annual wind maxima from 18 CMIP6 models</p> <p>3. spe*.dat: example of power spectrum from the original wind speed time series from a CMIP6 model and power spectrum with spectral correction</p> <p>Codes:</p> <p>1. AnnualMax*.py : calculate extreme winds from 18 CMIP6 models</p> <p>2. XFuture-code-sharing.nb: Mathematca code for analysis of data on the effect of climate change, and pack data for plotting using MATLAB</p> <p>3. *.m: MATLAB codes for plotting figures on climate change </p> <p> </p> <p> </p>
Input and output data for the paper "Evaluating the German onshore wind auction programme: An analysis based on individual bids"
<p>Batz Liñeiro, T., Müsgens, F., (2023). Energy Policy</p> <p><a href="https://doi.org/10.1016/j.enpol.2022.113317">https://doi.org/10.1016/j.enpol.2022.113317</a></p> <p>ABSTRACT</p> <p>Auctions are a highly demanded policy instrument for the promotion of renewable energy sources. Their flexible structure makes them adaptable to country-specific conditions and needs. However, their success depends greatly on how those needs are operationalised in the design elements. Disaggregating data from the German onshore wind auction programme into individual projects, we evaluated the contribution of auctions to the achievement of their primary (deployment at competitive prices) and secondary (diversity) objectives and have highlighted design elements that affect the policy's success or failure. We have shown that, in the German case, the auction scheme is unable to promote wind deployment at competitive prices, and that the design elements used to promote the secondary objectives not only fall short at achieving their intended goals but create incentives for large actors to game the system.</p> <p>Description</p> <p>The data package offered in this publication comprises input, processing, and output files, accompanied by the corresponding R-codes used for data processing at different stages. Among the various data outputs, the "Auctions" sheet within the file "3 Auction Realizations Onshore Wind" holds particular significance for users. Within this sheet, users can identify the realized projects, their respective IDs, and the reported individual bid values (BV). However, it is recommended to refer to the attached publication to gain a comprehensive understanding of the bid-value identification process.</p> <p>For users seeking to update the results, the input files can be easily updated by referring to partner publications that share the same file names. These partner publications include the <a href="https://zenodo.org/record/7945029">UnitRegister</a>, <a href="https://zenodo.org/record/8010410">PaymentRegister</a>, and <a href="https://zenodo.org/record/8013071">TariffRegister </a>datasets.<br> </p>
Data from: The effect of initial vortex asymmetric structure on tropical cyclone intensity change in response to an imposed environmental vertical wind shear
<p>Previous studies have investigated how the environmental vertical wind shear (VWS) may trigger the asymmetric structure in an initially axisymmetric tropical cyclone (TC) vortex and how TC intensity changes in response. In this study, the possible effect of the initial vortex asymmetric structure on the TC intensity change in response to an imposed environmental VWS is investigated based on idealized full-physics model simulations. Results show that the effect of the asymmetric structure in the initial TC vortex can either enhance or suppress the initial weakening of the TC in response to the imposed environmental VWS. When the initial asymmetric structure is in phase of the VWS-induced asymmetric structure, the TC weakening will be enhanced and vice versa. Our finding calls for realistic representation of initial TC asymmetric structure in numerical weather prediction models and observations to better resolve the asymmetric structure in TCs.</p>
Data set used in article: On the Potential of Reduced Order Models for Wind Farm Control: A Koopman Dynamic Mode Decomposition Approach
<p>Step-wise pitch simulation of two wind turbines interacting using SOWFA. More information in the paper.</p>
Data from: Wind-driven emission of marine ice nucleating particles in the Scripps Ocean-Atmosphere Research Simulator (SOARS)
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Data from: Wind and rain compound with tides to cause frequent and unexpected coastal floods
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Data from: Probability of lateral instability while walking on winding paths
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Data for: Wind speed that can effect increasing COVID-19
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Data from: The influence of wind selectivity on migratory behavioral strategies
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Data for: Transient shifts in Bering Sea shelf phytoplankton size structure in response to wind-induced mixing
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Data from: Sensitivity analysis of collision risk at wind turbines based on flight altitude of migratory waterbirds
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Data from: Interplay between wind-driven advection and mixing of salt and dissolved oxygen in a microtidal estuary
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Data from: Effect of tower base painting on willow ptarmigan collision rates with wind turbines
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Data from: Applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events
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Data for: The influence of vegetation structure on secondary diaspore dispersal by wind
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.