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96 results for “Surface winds”
Surface dust coverages on rock targets in Gale crater: Influence of seasonal wind variability, elevation and proximity to aeolian sand fields.
<p>The following dataset accompanies the paper submission to AGU - JGR: Planets for the paper titled: "</p> <p><span>Surface dust coverages on rock targets in Gale crater: Influence of seasonal wind variability, elevation and proximity to aeolian sand fields."</span></p>
Data for "Near Surface Maximum Winds during the Landfall of Hurricane Harvey"
<p>This archive corresponds to the data described in Alford et al. (2018) to be published in <em>Geophysical Research Letters</em>. Please see the included readme.txt file for details about each data file.</p> <p>Citation for paper: Alford, A. A., M. I. Biggerstaff, G. D. Carrie, J. L. Schroeder, B. D. Hirth, and S. M. Saugh, 2018: Near-surface maximum winds during the landfall of Hurricane Harvey. <em>Geophysical Research Letters</em>, <strong>46</strong>. https://doi.org/10.1029/2018GL080013.</p> <p> </p>
Dust formation and mass loss around intermediate-mass AGB stars with initial metallicity Zini ≤ 10-4 in the early Universe - I. Effect of surface opacity on stellar evolution and the dust-driven wind
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017MNRAS.466.1709T">Dust formation and mass loss around intermediate-mass AGB stars with initial metallicity Zini ≤ 10-4 in the early Universe - I. Effect of surface opacity on stellar evolution and the dust-driven wind</a></p>
water surface at about 32 m/s wind speed
<p>Filmed at University of Miami's SUSTAIN wind wave tank (http://sustain.rsmas.miami.edu/) with the imaging slope gauge (ISG) developed by the Air-Sea Interaction group of Heidelberg University (http://www.iup.uni-heidelberg.de/institut/forschung/groups/gw). More information about the ISG can be found at https://doi.org/10.2971/jeos.2014.14015.</p> <p>filmed at: 10000fps</p> <p>playback at: 60fps</p> <p>image size: approx, 28cmx24cm</p> <p>top down view</p>
water surface at about 39 m/s wind speed
<p>Filmed at University of Miami's SUSTAIN wind wave tank (http://sustain.rsmas.miami.edu/) with the imaging slope gauge (ISG) developed by the Air-Sea Interaction group of Heidelberg University (http://www.iup.uni-heidelberg.de/institut/forschung/groups/gw). More information about the ISG can be found at https://doi.org/10.2971/jeos.2014.14015.</p> <p>filmed at: 10000fps<br> playback at: 60fps<br> image size: approx, 28cmx24cm</p>
water surface at about 80 m/s wind speed
<p>Filmed at University of Miami's SUSTAIN wind wave tank (http://sustain.rsmas.miami.edu/) with the imaging slope gauge (ISG) developed by the Air-Sea Interaction group of Heidelberg University (http://www.iup.uni-heidelberg.de/institut/forschung/groups/gw). More information about the ISG can be found at https://doi.org/10.2971/jeos.2014.14015.<br> filmed at: 10000fps<br> playback at: 60fps<br> image size: approx, 28cmx24cm</p>
Simulation datasets for: Intense surface winds from gravity wave breaking in simulations of a destructive macroburst
<p>Shortly after 0600 UTC (midnight local time) 9 June 2020, a convective line produced severe winds across parts of northeast Colorado that caused extensive damage, especially in the town of Akron. High-resolution observations showed gusts exceeding 50 m s<sup>−1</sup>, accompanied by extremely large pressure fluctuations, including a 5-hPa pressure surge in 19 s immediately following the strongest winds and a 15-hPa pressure drop in the following 3 min. Numerical simulations of this event (using the WRF Model) and with horizontally homogeneous initial conditions (using Cloud Model 1) reveal that the severe winds in this event were associated with gravity wave dynamics. In a very stable postfrontal environment, elevated convection initiated and led to a long-lived gravity wave. Strong low-level vertical wind shear supported the amplification and eventual breaking of this wave, resulting in at least two sequential strong downbursts. This wave-breaking mechanism is different from the usual downburst mechanism associated with negative buoyancy resulting from latent cooling. The model output reproduces key features of the high-resolution observations, including similar convective structures, large temperature and pressure fluctuations, and intense near-surface wind speeds. The findings of this study reveal a series of previously unexplored mesoscale and storm-scale processes that can result in destructive winds.</p> <p><strong>Significance Statement </strong></p> <p>Downbursts of intense wind can produce significant damage, as was the case on 9 June 2020 in Akron, Colorado. Past research on downbursts has shown that they occur when raindrops, graupel, and hail in thunderstorms evaporate and melt, cooling the air and causing it to sink rapidly. In this research, we used numerical models of the atmosphere, along with high-resolution observations, to show that the Akron downburst was different. Unlike typical lines of thunderstorms, those responsible for the Akron macroburst produced a wave in the atmosphere, which broke, resulting in rapidly sinking air and severe surface winds.</p>
Data from: Influence of topography and the underlying surface of the Bohai Sea on wind and gust forecasts
<p class="MsoNormal"><span>Accurate gust forecasts can reduce potential threats to people's lives and properties, but we need more reliable forecasting methods and models. A recent development is the meteorologically stratified gust factor (MSGF) model, which is more accurate in forecasting gusts than the previous gust factor model. The regional terrain and underlying surface both have crucial effects on the gust factor. We therefore combined observations from the China Meteorological Administration over the ocean surface and along the coast with the MSGF model to explore the influence of topography and the underlying surface on wind and gust forecasts. The regional terrain and underlying surface affected the peak gust climatologies, the mean wind speed, the mean prevailing wind direction and the gust factors. The topography and the underlying surface had different impacts in different ranges of the mean wind speed. The strong turbulence that causes changes in the gust factor under light winds is not initiated over rough underlying surfaces. When the mean wind speed is >2.5 m s<sup>−1</sup>, the underlying surface influences both the wind speed and the gust factor. A rough underlying surface stimulates stronger turbulence and increases the gust speed and gust factor, whereas a smooth underlying surface directly increases the mean wind speed and the gust speed by different magnitudes to reduce the difference between them, thus decreasing the gust factor. We evaluated the ability of the MSGF model to forecast gusts and verified a method combining the products of a numerical model and the MSGF model in gust forecasts.</span></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>
Simulation datasets for: Intense surface winds from gravity wave breaking in simulations of a destructive macroburst
Open the record for dataset details and reuse information.
Data from: Influence of topography and the underlying surface of the Bohai Sea on wind and gust forecasts
Open the record for dataset details and reuse information.
Simulation details for: Radar signatures and surface observations of elevated convection associated with damaging surface winds
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Evaluation and Projection of Surface Wind Speed over China Based on CMIP6 GCMs
<p>This file is for the upload of CN05.1 data for 2020JD033611RR.</p>
Data for Paper: Wind-wave momentum flux in steep, strongly forced, surface gravity wave conditions
<p>Laboratory measurements of wind, waves, and airside static pressure under low to moderate wind forcing (U10 ~ 6 -16 m/s) collected in Oct 2022 in the SUSTAIN wind-wave facility at the University of Miami.</p> <p>This dataset includes 11 runs, all of which contain monochromatic waves generated by the wave paddles with various wind forcing exerted above. All data is in ".mat" formate readable via MATLAB.</p> <p>Experiment set up and positions of instruments are documented in more details in the manuscript Tan et al (2024): Wind-wave momentum flux in steep, strongly forced, surface gravity wave conditions.</p> <p> Fig_3: time series static pressure p sampled at 100 Hz and horizontal/vertical wind speed (u/w) sampled at 1000 Hz</p> <p>Fig_4: Frictional velocity u_star_Rn* obtained at differenet heights (h) using frictional velocity</p> <p>Fig_5: a folder that containes the phase-averaged, spline-interpolated static pressure (p2_total), X-coordinate (long-wave phase), Y coordinate (heights above the stationary water) and the u/w at respective heights to generate airflow streamlines</p> <p>Fig_6 and 7: NSS-based phase-averaged, spline-interpolated pressure (delta_P_new).</p> <p>Fig_8: phase-averaged form stress based on measurements and NSS for all 11 runs</p> <p>Fig_9: NSS-based form stress deviation from measured form stress (NSS miscal) against wind-steepness and wave age;</p> <p>Fig_10 and 11: wave growth rate (gamma) against wave age (Cp/ustar) and two other parameterization from Fig.10</p> <p>(The revised version contains the projection of Donelan (1999) and Yang et al. (2013)'s data to the U10/Cp parameterization in panel (b) per reviewer's suggestion);</p> <p>Fig_12: form stress values (tau_form) and form stress to total stress (tau_tot) ratio.</p> <p>(The revised version contains U10 per reviewer's suggesion).</p> <p>This project was Funded in part by Office of Naval Research/Naval Research Laboratory base program unit 73-1Y91.</p> <p>Please cite our JGR: Oceans paper "Wind-wave momentum flux in steep, strongly forced,1 surface gravity wave conditions" if you were to use our dataset.</p> <p>Contact: Peisen Tan <pxt254@miami.edu> for different levels of raw data collected in this experiment.</p> <p>We kindly ask the readers who use our dataset to cite our paper:</p> <p><span>Tan, P.</span><span>, </span><span>Savelyev, I.</span><span>, </span><span>Laxague, N. J. M.</span><span>, </span><span>Haus, B. K.</span><span>, </span><span>Curcic, M.</span><span>, </span><span>Matt, S.</span><span>, et al. (</span><span>2025</span><span>). </span><span>Wind-wave momentum flux in steep, strongly forced, surface gravity wave conditions</span><span>. </span><em>Journal of Geophysical Research: Oceans</em><span>, </span><span>130</span><span>, e2024JC021616. </span><a href="https://doi.org/10.1029/2024JC021616">https://doi.org/10.1029/2024JC021616</a></p> <p>We would also appreciate if you can send us a copy of your manuscript if you have used our data. Thank you!</p>
Code for Atmospheric Research publication - Height correction method based on the Monin–Obukhov similarity theory for better prediction of near-surface wind fields
<p>In this repository, we include the source codes for WRF namelist, figures, and height correction used in the Atmospheric Research publication "Height correction method based on the Monin–Obukhov similarity theory for better prediction of near-surface wind fields"</p> <p>The namelist.wps and namelist.input in WRF namelist are using for making input and running simulation, and Fig scripts in Figure scripts are using for plotting the figures in the paper.</p> <p>hgt_corr in Height correction method is a code to correct the disparity of the 10-m height definition between the model and observation by applying the developed the height correction algorithm based on the Monin-Obukhov similarity theory.</p>
Dataset used in Sea surface wind structure observed by wave gliders during tropical cyclones
<p>Sea surface wind vector observed by three wave gliders deployed in the Western Pacific Ocean. The observation level is 1.2 meter.</p>
Wave field in a wind and paddle tank: effect of a thin surface layer of fish oil
<p>Video showing the surface elevation field in a wind- and paddle-wave tank in clean water condition (tap water) and in water covered with a thin layer of fish oil. Reference wind speed 8 m/s and JONSWAP-like paddle spectrum (Hs = 0.062 cm, Tp =1.0 s) Experiments carried out in the flume of the First Institute of Oceanography (Qingdao, P.R. China).</p>
Wave field in a wind tank: effect of a thin surface layer of fish oil.
<p>Video showing the surface elevation field in a wind-wave tank in clean water condition (tap water) and in water covered with a thin layer of fish oil. Reference wind speed 6 m/s. Experiments carried out in the flume of the First Institute of Oceanography (Qingdao, P.R. China).</p>
Roles of Wind-Driven Currents and Surface Waves in Sediment Resuspension and Transport During a Tropical Storm
<p>Roles of Wind-Driven Currents and Surface Waves in Sediment Resuspension and Transport During a Tropical Storm</p>
raw 3D wind data at 30cm above the nebkha surface
<p>The raw wind data over a nebkha collected 2018. The other dataset the results of the quadrant analysis.</p>
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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)
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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.