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108 results for “wind modelling”
McMurdo Dry Valleys Glacier melt modeling: Wind Direction 1996-2011
This is the data and metatada for modeled Wind Direction - part of six modeled parameters that comprise the Taylor Valley Galcier Melt modeling Data contained and described in this document correspond to the physically-based surface energy balance model for the glaciers of Taylor Valley developed by the dataset owners. The spatial variability in ablation (ice melt and sublimation), runoff, and climate sensitivity of the glaciers was modeled using 16 years of meteorological and surface mass balance (the net mass gain or loss of ice on the surface of the glacier) observations collected in Taylor Valley (see figure).  An unusual aspect of the model is the inclusion of transmission of solar radiation into the ice and subsequent drainage of some subsurface melt .  Melt model was applied to the ablation zones of the glaciers of Taylor Valley, identified by colored areas. Mass balance stakes, meteorological stations, and stream gages shown for reference. This dataset package is part of a 6-pack multi-set, which you can find at http://mcmlter.org The input files, parameters and examples are found in this package: http://mcmlter.org/content/glacier-melt-modeling-inputs-and-example-m-file-reader
Datatset from "Coastal flooding in the Maldives induced by mean sea-level rise and wind-waves: from global to local coastal modelling"
<p>Resulting downscaled wave fields from the WaveWatch III simulations for the four main wave directions identified and six return periods (10, 20, 50, 100, 500, and 1000 years).</p>
CESM1.2 simulation output for: The role of westerly wind bursts during different seasons versus ocean heat recharge in the development of extreme El Niño in a climate model
<p>This is the subset of CESM1.2 model simulation output that was used for analysis and visualization of Yu and Fedorov [2020] (DOI:10.1029/2020GL088381). Please refer to README for details.</p>
Representation model of wind velocity fluctuations and saltation sand transport in aeolian sand flow
<p>The data of the figures in the article.</p>
Data from: "Genome-wide microsatellite marker development from next-generation sequencing of two non-model bat species impacted by wind turbine mortality: Lasiurus borealis and L. cinereus (Vespertilionidae)" in Genomic Resources Notes accepted 1 October 2013 to 30 November 2013
Tree-roosting bats in the genus Lasiurus are widespread, migratory species that have not been well characterized for population genetic diversity and structure due to a lack of genetic resources. Generating genetic resources in Lasiurus is made pressing by the need for conservation genetic assessments of demographic trends in this genus, which comprise a large percentage of bat mortalities at wind turbine sites across North America. We report on marker development from whole-genome Illumina sequencing of the red bat (Lasirus borealis) and the hoary bat (L. cinereus). We generated paired-end libraries for a single individual of each species, sequenced on the Illumina HiSeq platform. We mapped a total of 46.6 million reads to the Myotis lucifigus reference genome, and used bioinformatics searches to identify tends of thousands of simple sequence repeats (SSRs) distributed across the bat genome. We selected 48 candidate microsatellite loci to develop cross-species primer sequences for Lasiurus, assembled these into multiplex combinations, and tested for amplification and polymorphism levels in a sample of 23 individuals from each of L. borealis and L. cinereus. In total, we identified 42 highly polymorphic loci that could be robustly amplified and scored, the majority of which (39) were also combinable into highly multiplexed assays of 4-8 loci each. The combination of new genomic sequence assemblies, a large set of highly polymorphic microsatellite loci, and the ability to efficiently multiplex represents a significant contribution to the genetic resources available for population and comparative genetic studies of bats.
CFD Modeling Results and Related Data and Codes for Plotting of "A Mesoscale-to-LES Modeling of Tornado-like Vortex and Associated Local Strong Winds in Urban Area"
<p>The CFD modeling outputs, derived maximum wind fields in the analysis area, the topography data, the Python codes used to produce the figures, as we as the namelist of WRF simulation are available. The CFD modeling outputs are in binary format. The ctl. files of corresponding binary data (or dataset if ordered chronologically) are available in each directory (named after each experiment in our study).</p>
A one-year-long evaluation of a wind-farm parameterisation in HARMONIE-AROME -- Model data
<p>This file contains supporting data for the manuscript "A one-year-long evaluation of a wind-farm parameterisation in HARMONIE-AROME" by van Stratum et al. 2022 in JAMES. </p> <p>- model_output: contains NetCDF files with HARMONIE-AROME for specific columns for the lidar locations, only for the WIPAFF flight comparison the full 3D hourly fields are given for one day. Files containing "DOWA_40h12tg2_fERA5_ptE" are the reference simulations and files containing "DOWA_40h12tg2_fERA5_WF2019_fix" are output from the wind farm parameterisation simulations. <br> - input_HARMONIE_WFP: contains the input files used for the wind farm parameterisation, where wind_turbine_coordinates.tab contains the locations of all wind turbines and the turbine type, and wind_turbine_0XX.tab the cp/ct curves, radius and hub height for each turbine type. </p> <p>The measurements used in the manuscript are from various external sources and should be downloaded separately.</p>
Plots for the publication "Lidar-assisted model predictive control of wind turbine fatigue via online rainflow-counting considering stress history"
<p>These are the raw plot files from the publication "Lidar-assisted model predictive control of wind turbine fatigue via online rainflow-counting considering stress history".</p> <p>The files have been created with MATLAB 2019, and labeled according to their corresponding figure number(s) in the publication.</p>
Input data and model output for study about wind changes and impact on the Subtropical Front
<p>This dataset contains:</p> <p>Model data for the CONTROL simulation (CONTROL.gz)</p> <p>Model data for the SHIFT simulation (SHIFT.gz) where the westerly winds have been shifted by 1degree per decade</p> <p>Model data for the INCREASE simulation (INCREASE.gz) where the westerly winds have been incresaed by 1 percent per decade</p> <p>Reference dataset are provided (Argo.gz and Modiz.gz)</p>
Data for "SPH modelling of AGB wind morphology in hierarchical triple systems & comparison to observation of R Aql"
<div> <p>Additional material to Malfait et al. 2024, subm. "SPH modelling of AGB wind morphology in hierarchical triple systems & comparison to observation of R Aql"</p> <p>This contains input files and final output dumps of the Phantom simulations of this paper.</p> <p>The code used to perform the simulations is available at: <a href="https://github.com/danieljprice/phantom">https://github.com/danieljprice/phantom.</a></p> <p>Splash (<a href="https://github.com/danieljprice/splash">https://github.com/danieljprice/splash</a> ) and Plons (<a href="https://github.com/Ensor-code/plons">https://github.com/Ensor-code/plons</a> ) were used to create figures and plots from this data.</p> <p> </p> </div>
Solar Wind - Venus Interaction during the Solar Maximum & Solar Minimum 1 Periods: A Newly Developed Multi-Fluid MHD Model
Open the record for dataset details and reuse information.
Study on the initiation of saltation in the model of wind-blown sand transport considering the effect of turbulence
<p>Dataset of "Study on the initiation of saltation in the model of wind-blown sand transport considering the effect of turbulence"</p>
Investigating the "Too Bright" Issue Pertaining to Non-PBL Clouds over the South Pacific Trade-Wind Region in CMIP6 Global Climate Models
<p><a href="../api/records/13314147/draft/files/f09.C6.B-hist.SON_ANN.tar.gz/content" target="_blank" rel="noopener noreferrer">f09.C6.B-hist.SON_ANN.tar.g</a>z</p> <p>CESM2-CAM6 with falling ice radiative effects (FIREs), fully coupled run folloing CMIP6 historical run, same as CESM2-CAM6 in CMIP6 data port.</p> <p> </p> <p>The data includes with netcdf self description.</p> <p>f09.C6.B-hist.h01_AWNC_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CLDHGH_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLDLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CLDLOW_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLDMED_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLDTOT_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLOUD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CLOUDFRAC_CLUBB_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CONCLD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_FREQL_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_ICWMR_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_NUMLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_OMEGA_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_PRECC_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_PRECL_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_SST_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_tauy_ANN_climo-CDO.nc</p> <p><a href="../api/records/13314147/draft/files/f09.C6.B-hist.SON_ANN.tar.gz/content" target="_blank" rel="noopener noreferrer">f09.C6.B-hist.NOS_ANN.tar.g</a>z</p> <p>CESM2-CAM6 without falling ice radiative effects (FIREs), fully coupled run folloing CMIP6 historical run, same as CESM2-CAM6 in CMIP6 data port.</p> <p><br>f09.C6.B-hist.nos81_AWNC_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CDNUMC_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDHGH_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CLDLOW_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDMED_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDTOT_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLOUD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CLOUDFRAC_CLUBB_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CONCLD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_FREQL_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_ICWMR_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_NUMLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_OMEGA_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_PRECC_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_PRECL_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_SST_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_taux_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_tauy_ANN_climo-CDO.nc</p>
What Can Generative Modelling Do for Interpolation of Extremely Sparse Wind Farm Seismic Data
<p>2024 Global energy transition abstract about diffusion model data interpolation. </p>
High-resolution modelling of uplift landscapes can inform micro-siting of wind turbines for soaring raptors
<p>Collision risk of soaring birds is partly associated with updrafts to which they are attracted. To identify risk-enhancing landscape features, a micro-siting tool was developed to model orographic and thermal updraft velocities from high-resolution remote sensing data. The tool was applied to the island of Hitra, and validated using GPS-tracked white-tailed eagles (<i>Haliaeetus albicilla</i>). Resource selection functions predicted that eagles preferred ridges with high orographic uplift, especially at flight altitudes within the rotor-swept zone (40-110 m). Flight activity was negatively associated with the widely distributed areas with high thermal uplift at lower flight altitudes (<110 m). Both the existing wind-power plant and planned extension are placed at locations rendering maximum orographic updraft velocities around the minimum sink rate for white-tailed eagles (0.75 m/s) but slightly higher thermal updraft velocities. The tool can contribute to improved micro-siting of wind turbines to reduce environmental impacts, especially for soaring raptors.</p>
The influence coefficients used in Wind Energy Science paper "A computationally efficient engineering aerodynamic model for swept wind turbine blades"
<p>The influence coefficients for the convective correction with full double-precision floating-point accuracy. This is the supplement for the research article: "A computationally efficient engineering aerodynamic model for swept wind turbine blades", submitted to Wind Energy Science journal.</p> <p>Code language: Fortran</p>
Processed model output used in 'The impact of winds on AMOC in a fully-coupled climate model'
<p>Processed model output from wind-nudging experiments used to investigate the Atlantic Meridional Overturning Circulation. </p> <p> </p> <p>For further details, see </p> <p>Roach, L. A, Blanchard-Wrigglesworth E. Ragen, S., Cheng, W., Armour, K. and Bitz, C. M.. (2022). The impact of winds on AMOC in a fully-coupled climate model. In review at Geophysical Research Letters</p>
Intermediate data belonging to "Process-based climate change assessment for European winds using EURO-CORDEX and global models"
<p>This dataset contains the intermediate results of Wohland (2022) that are needed to redo the analysis und produce the figures. It allows to bypass those steps that rely on access to the supercomputers at the German Climate Computing Centre (DKRZ). When using this data in academic work, please reference</p> <blockquote> <p>Jan Wohland, Process-based climate change assessment for European winds using EURO-CORDEX and global models, Environmental Research Letters (provisionally accepted on 28/11/2022), 2022</p> </blockquote> <p><strong>Using this data to reproduce results</strong></p> <p>The data can be used together with the code provided in https://github.com/jwohland/kliwist_modelchain</p> <p>In the above mentioned github repository, there is a `run_all.py` script that repeats the analysis presented in Wohland (2022). After downloading and extracting this data, you can ignore the steps under "calculations", and begin with "plots".</p> <p><strong>Underlying data</strong></p> <p>The dataset draws on output from the CMIP5, CMIP6 and EURO-CORDEX initiatives. I thank the climate modeling groups for making their data openly available. In particular, I acknowledge the World Climate Research Programme’s Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5. I also acknowledge the Earth System Grid Federation infrastructure an international effort led by the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison, the European Network for Earth System Modelling and other partners in the Global Organisation for Earth System Science Portals (GO-ESSP). I also acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6.</p> <p><strong>Funding</strong></p> <p>This work is part of the project "The influence of climate change on wind energy site assessments – KliWiSt" funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK).</p> <p><strong>References to raw data journal articles</strong></p> <blockquote> <p>Jacob, D. <em>et al.</em> EURO-CORDEX: new high-resolution climate change projections for European impact research. <em>Reg Environ Change</em> <strong>14</strong>, 563–578 (2014).</p> </blockquote> <blockquote> <p>Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An Overview of CMIP5 and the Experiment Design. <em>Bull. Amer. Meteor. Soc.</em> <strong>93</strong>, 485–498 (2012).</p> </blockquote> <blockquote> <p>Hurtt, G. C. <em>et al.</em> Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. <em>Climatic Change</em> <strong>109</strong>, 117–161 (2011).</p> </blockquote>
Database for "A New Four-Component L*-dependent Model for Radial Diffusion based on Solar Wind and Magnetospheric Drivers of ULF Waves"
<p>Database for <strong>"A New Four-Component L*-dependent Model for Radial Diffusion based on Solar Wind and Magnetospheric Drivers of ULF Waves" </strong>submitted to Space Weather by Murphy et al. </p> <p>The repository contains 2 datasets:</p> <ul> <li>The power spectral density of the compressional magnetic field from THEMIS, Van Allen Probes, and GOES along with accompanying position (MLT, L, L* TS05), solar wind, and geomagnetic data</li> <li>The power spectral density of the azimuthal electric field from THEMIS and Van Allen Probes along with accompanying position (MLT, L, L* TS05), solar wind, and geomagnetic data</li> </ul> <p>Both datasets are provided as an IDL save file and as an HDF5 file.</p> <p>The IDL data can be loaded using: </p> <pre><code>filename='electric_field_psd.sav' filename='magnetic_field_psd.sav' restore, filename, /verbose</code></pre> <p>The HDF5 file can be opened and investigate using (small changes will be required to store each variable):</p> <pre><code class="language-python">import h5py filename = 'magnetic_field_psd.h5' # magnetic field data filename = 'magnetic_field_psd.h5' # electric field data with h5py.File(filename, "r") as f: # loop through all keys (data) #print key and key attribute and size for i in f.keys(): print(f"{i} - {f[i].attrs['attributes']}, shape - {f[i].shape}") ds_obj = f[i] # returns as a h5py dataset object ds_arr = f[i][()] # returns as a numpy array</code></pre> <p>Below is a description of unique and common variables in each file. The psd variables have a shape [f,t], indicating the first dimension is frequency and the second is time, the f_mhz variable has shape [f], and all time series have shape [t]; here [f] and [t] denotes the number of elements in frequency and time arrays. </p> <p>------------------------------------</p> <p><strong>Magnetic field data set:</strong></p> <p><strong><em>Files</em></strong></p> <ul> <li>magnetic_field_psd.h5</li> <li>magnetic_field_psd.sav</li> </ul> <p><strong><em>Unique Data (variable in file)</em></strong></p> <ul> <li>psd <ul> <li>Power spectral density of the compressional magnetic field from THEMIS, Van Allen Probes, and GOES</li> <li>Units - nT<sup>2</sup>/mHz</li> <li>Shape - [f, t]</li> </ul> </li> </ul> <p>------------------------------------</p> <p><strong>Electric field data set:</strong></p> <p><strong><em>Files</em></strong></p> <ul> <li>electric_field_psd.h5</li> <li>electric_field_psd.sav</li> </ul> <p><strong><em>Unique Data (variable in file)</em></strong></p> <ul> <li>psd <ul> <li>Power spectral density of the azimuthal electric field from THEMIS, Van Allen Probes, and GOES</li> <li>Units - (mV/m)<sup>2</sup>/mHz</li> <li>Shape - [f, t]</li> </ul> </li> </ul> <p>------------------------------------</p> <p><strong>Common Data in the Magnetic and Electric Field Datasets (variable in file):</strong></p> <ul> <li>probe <ul> <li>Corresponding satellite of each time stamp</li> <li>Shape [t]</li> </ul> </li> <li>t <ul> <li>Time stamp of each time series; number of seconds since 1970 (UNIX time), [t]</li> <li>Units - s</li> <li>Shape [t]</li> </ul> </li> <li>f_mhz <ul> <li>Frequency of psd data, [f]</li> <li>Units - mHz</li> <li>Shape [f] - (19)</li> </ul> </li> <li>ae <ul> <li>OMNI AE index of each time stamp</li> <li>Units - nT'</li> <li>Shape [t]</li> </ul> </li> <li>al <ul> <li>OMNI AL index of each time stamp, units - nT</li> <li>Shape [t]</li> </ul> </li> <li>au <ul> <li>OMNI AU index of each time stamp </li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_t <ul> <li>OMNI IMF B of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_x <ul> <li>OMNI IMF Bx (GSM) of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_y <ul> <li>OMNI IMF By (GSM) of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_z <ul> <li>OMNI IMF Bz (GSM) of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>dst <ul> <li>OMNI Dst of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>kp - <ul> <li>OMNI Kp (Kp*10) of each time stamp</li> <li>units - NA</li> <li>Shape [t]</li> </ul> </li> <li>l_sh <ul> <li>L-shell of each time stamp</li> <li>Shape [t]</li> </ul> </li> <li>ls_t05 <ul> <li>L* from TS05 of each time stamp</li> <li>Shape [t]</li> </ul> </li> <li>mlt <ul> <li>Magnetic Local Time of each time stamp</li> <li>Unit - hour</li> <li>Shape [t]</li> </ul> </li> <li>n <ul> <li>OMNI Solar Wind Proton Density of each time stamp</li> <li>Units - n/cc</li> <li>Shape [t]</li> </ul> </li> <li>pdyn <ul> <li>OMNI Solar Wind Dynamic Pressure (flow pressure) of each time stamp</li> <li>Units - nPa</li> <li>Shape [t]</li> </ul> </li> <li>symh - b'OMNI Sym-H of each time stamp, units - nT', shape - (477205,)</li> <li>v_t <ul> <li>OMNI Solar wind V of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> <li>v_x <ul> <li>OMNI Solar wind Vx (GSE) of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> <li>v_y <ul> <li>OMNI Solar wind Vy (GSE) of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> <li>v_z <ul> <li>OMNI Solar wind Vz (GSE) of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> </ul> <p> </p> <p> </p> <p> </p>
Everest Model Output Statistics: improved wind speed forecasts
<p>Data and pre-trained random forest models necessary to correct GFS forecast data and produce improved Everest Forecasts.</p> <p>Please find the associated code at: github.com/MaxVWDV/Everest_wind_forecast</p>
ScienceDex guides
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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.