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43 results for “Atmospheric waves”
Code and data for "Two-way coupled long-wave isentropic ocean-atmosphere dynamics"
<p>This repository contains the code and data needed to replicate the figures and simulations in the JFM Paper: "Two-way coupled long-wave isentropic ocean-atmosphere dynamics". Please see the readme.txt file for details.</p> <p>Edit:</p> <p>-v3 added a jfm_pysonly.zip which contains only the Python scripts to create the figures to allow for a faster separate download</p> <p>-v2 updated with JFM paper DOI https://doi.org/10.1017/jfm.2023.131 in file headers</p>
The simulated outputs analyzed in the article: "Understanding the influences of ocean waves on Arctic sea ice simulation: a modeling study with an atmosphere-ocean-wave-sea ice coupled model"
<p>In Ice-mass_[experiment] files, they include daily-averaged sea ice concentration and sea ice mass/area budgets.</p> <p>In Flux_[experiment] files, they include daily-averaged net ice surface flux, net shortwave/longwave radiation at the ice surface, latent/sensible heat flux at the ice surface, conductive heat flux at the top ice layer, and ice-ocean heat flux. </p>
Data and Code Supplement for "A Mountain-Induced Moist Baroclinic Wave Test Case for the Dynamical Cores of Atmospheric General Circulation Models"
<p>Code and Data Supplement for "A Mountain-Induced Moist Baroclinic Wave Test Case for the Dynamical Cores of Atmospheric General Circulation Models"<br> ===========================================================</p> <p>This directory contains the data and scripts used to create the plots from our publication as well as the source<br> code modifications necessary to run this test case within the CESM and MPAS models.</p> <p>Generating Plots<br> ---------------</p> <p>The `netcdf` directory contains the nominal half-degree runs necessary to generate nearly all of the plots from the paper. The one plot which is not reproducible from these data is the volume-integrated Eddy Kinetic Energy in the Spectral Element model. Storing high-resolution 4D wind fields requires a prohibitive amount of space. These data can be provided by the corresponding author, O.K. Hughes (owhughes@umich.edu). However, because this is several hundred GB of data I would strongly recommend generating these high-resolution runs yourself on your local system if you need them. Using 288 Intel Skylake cores (that is, 8 nodes each with two 18C processors) ran on the order of an hour.</p> <p><em>In order to generate the plots from the paper, you need only install NCL and then run</em> run.bash. Instructions for installing NCL<br> can be found in the `run.bash` script.</p> <p>Source Code Modifications<br> ----------------</p> <p><strong>CESM</strong><br> The `src` subdirectory contains the files `user_nl_cam` and `ic_baroclinic.F90`. Create a case using `--compset=FKESSLER` and `--run-unsupported` options when running `create_newcase`. If your case is located at `${CASE_DIR}`, then from within the directory containing this README, run `cp user_nl_cam ${CASE_DIR}/user_nl_cam`, and then run `cp ic_baroclinic.F90 ${CASE_DIR}/SourceMods/src.cam/`. Then build and run the model using the usual workflow.</p> <p><strong>MPAS</strong></p> <p>The MPAS code was run using a branch of the MPAS model provided by the model developers to the authors. While the source code modifications are provided in the `src` directory, I would strongly recommend contacting the corresponding author if you wish to run this test case in the MPAS codebase.</p>
Data from: Upper atmosphere heating from ocean-generated acoustic wave energy
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A survey of small-scale waves and wave-like phenomena in Jupiter's atmosphere detected by JunoCam
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Data and figures of JGR planet paper entitled "Pressure effects on the SEIS-InSight instrument, improvement of seismic records and characterization of long period atmospheric waves from ground displacements" by Raphael F. Garcia and co-authors
<p>Data and figures of the JGR Planet paper entitled "Pressure effects on the SEIS-<br> InSight instrument, improvement of seismic records and characterization of long<br> period atmospheric waves from ground displacements"<br> by<br> Raphael F. Garcia1, Balthasar Kenda2 , Taichi Kawamura2 , A. Spiga3,4, N.<br> Murdoch1 , P. Lognonné2, R. Widmer-Schnidrig5, N. Compaire1 , G.<br> Orhand-Mainsant1 , D. Banfield6, W. B. Banerdt7</p> <p>List of files and directories</p> <p>*extractAllDataFromFig.m : matlab code to extract the data in txt file from the<br> matlab figures listed in the following directories<br> Figure1<br> Figure10<br> Figure11<br> Figure2<br> Figure3<br> Figure4<br> Figure5<br> Figure6<br> Figure7<br> Figure8<br> Figure9</p> <p>*figures_combined : combined figures for JGR paper</p> <p>*INSIGHT_Data : data in miniseed format used in the paper<br> -- Deglitch_data : data after removing glitchs used in the paper<br> -- Data_figure11 : data used for the plots in figure 11 (plots done easily with<br> SeisGram software)</p> <p>*PREPRINT : preprint of the paper</p> <p> </p>
Numerical simulation "Numerical simulation of atmospheric Lamb waves generated by the 2022 Hunga-Tonga volcanic eruption"
<p>Numerical simulation results for the atmospheric Lamb waves generated by the Hunga-Tonga volcano explosion on January 15th 2022.</p>
Data for "Machine Learning Parameterization of Subgrid-Scale Orographic Gravity Wave Drag in a Middle-Atmosphere General Circulation Model" by Lu et al., submitted to JAMES, 2022.
<p>The NetCDF data file involving the decision tree strucutre attributes of the random forest emulator.</p> <p>gcm_regressors/<br> The data file involving the decision tree strucutre attributes (in NetCDF format)</p>
Gravity Wave Morphology During the 2018 Sudden Stratospheric Warming Simulated by a Whole Neutral Atmosphere General Circulation Model
<p>This dataset includes a complete set of raw data, metadata and saved session data which is necessary for re-producing figures in a paper entitled "Gravity Wave Morphology During the 2018 Sudden Stratospheric Warming Simulated by a Whole Neutral Atmosphere General Circulation Model" submitted to the Journal of Geophysical Research - Atmosphere.</p>
Parameterization of Wave-Induced Stress in Large-Eddy Simulations of the Marine Atmospheric Boundary Layer
<p>This dataset contains the PALM simulation data for all groups and the reference data from Jiang et al.</p>
Large-eddy simulation of an atmospheric bore and associated gravity wave effects on wind farm performance in the Southern Great Plains
<p>Animations from weather radars (observations) and simulations (modeling) of an atmospheric bore and associated gravity waves during the AWAKEN field campaign from a case study on 06 June 2023. The first animation is reflectivity from the NEXRAD WSR-88D system at the Oklahoma City radar site (KTLX) operated by the National Weather Service. The second animation is wind speed at 95, 145, and 270 m agl from the Texas Tech X-Band radars at the AWAKEN site. The third and fourth animations are for simulation results using the Weather Research and Forecasting model (WRF) with two domains. The third animation is vertical velocity at 1 km agl and potential temperature at 200 m agl on domain d01, which has a horizontal grid spacing of 300 m. The fourth animation is hub-height wind speed and perturbation pressure along with simulated power output for 3 wind turbines in each of the four rows on domain d01, which has a horizontal grid spacing of 20 m. The wind turbines are NREL 2.8 MW turbines parameterized using a generalized actuator disk. </p> <p>These animations are included as supplementary material for the manuscript "Large-eddy simulation of an atmospheric bore and associated gravity wave effects on wind farm performance in the Southern Great Plains" submitted to <em>Wind Energy Science</em>. </p>
Influence of thermospheric impacts of solar activity on the general circulation and long-period planetary waves in the middle atmosphere
<p>This dataset contains model output files and examples of scripts for depicting figures related to the article "Influence of thermospheric impacts of solar activity on the general circulation and long-period planetary waves in the middle atmosphere " by A.V. Koval, N. M. Gavrilov, A. I. Pogoreltsev, N. O. Shevchuk.</p> <p><br> Contents:<br> Output data from model simulations averaged over 16 pairs of model runs with high and low solar activity:</p> <p><br> gh_m1_hsa5.dx, gh_m1_lsa5.dx – amplitudes of the geopotential height variations in g.p.m. at high and low solar activity caused by long-period PW modes with zonal wavenumber 1 averaged over 80 time subintervals selected from pairs of the MUAM runs (structure described in w1_tp1_hsa.ctl, w1_tp1_lsa.ctl).<br> gh_m2_hsa5.dx, gh_m2_lsa5.dx, gh_m3_hsa5.dx, gh_m3_lsa5.dx, gh_m4_hsa5.dx, gh_m4_lsa5.dx – the same but for zonal wavenumbers 2,3,4.<br> gh_m1_disp_dif5.dx – dispersion of differences in corresponding wave amplitudes between high and low solar activitiy (w1_tp1_disp.ctl).<br> gh_m2_disp_dif5.dx, gh_m3_disp_dif5.dx, gh_m4_disp_dif5.dx – the same but for zonal wavenumbers 2,3,4.<br> index_refr1_1_5.dx, index_refr1_2_5.dx, index_refr1_3_5.dx, index_refr1_4_5.dx – the zonal-mean quasi-geostrophic complex refractivity index squared (RI2) for PW modes with zonal wavenumbers 1-4 (ind_refr1_1.ctl).<br> uq_vwres1_pv_Jan_1_5.dx, uq_vwres1_pv_Jan_2_5.dx, uq_vwres1_pv_Jan_3_5.dx, uq_vwres1_pv_Jan_4_5.dx – the Eliassen-Palm flux for PW modes with zonal wavenumbers 1-4 (uqep1_vwres_1.ctl).<br> zw_a0_grads5_hsa.dx, tp_a0_grads5_hsa.dx – zonal-mean zonal wind in m/s and temperature in K for December – February averaged over 16-member ensemble of model runs for high solar activity (m0_zw5_hsa.ctl, m0_tp5_hsa.ctl).<br> zw_a0_grads5_lsa.dx, tp_a0_grads5_lsa.dx – the same but for the low solar activity<br> disp_U_dif5_.dx, disp_T_dif5_.dx – dispersion of differences in zonal wind and temperature between high and low solar activitiy for December – February averaged over 16-member ensemble of model runs (tp1_disp.ctl).<br> Additional data including outputs from separate model runs are available from the authors upon request.</p>
STIM model output for "Aurorally-driven Supersonic Gravity Waves in Saturn's Atmosphere"
<p>Saturn Thermosphere-Ionosphere Model (STIM) output corresponding to the figures shown in the paper "Aurorally-driven Supersonic Gravity Waves in Saturn's Atmosphere".</p>
Comparison of atmospheric refractivity estimation methods and their influence on radar wave propagation predictions
<p>Environmental predictions in the marine atmospheric surface layer (MASL) are imperative to optimize X-band radar system performance in marine environments. Evaporation ducts (ED) lead to anomalous propagation where characterization of EDs in the MASL occurs primarily through two methods: <i>in-situ </i>measurements and numerical modeling. This study investigates differences in co-located and synchronous refractivity estimations from the CASPER-East campaign. Propagation predictions are generated for refractive profiles from <i>in-situ </i>measurements, Monin-Obukov boundary layer similarity theory, and numerical weather prediction forecasts. Variations in evaporation duct height (EDH) are found to be a primary driver of differences in propagation between the estimated refractivity profiles, where location of the EDH relative to the transmitter changes the sensitivity of propagation predictions to EDH estimates. Differences in propagation are large when EDH estimates span the transmitter height and the lowest EDH across the methods is small, regardless of how much variation there is in EDH estimates. When the lowest EDH is small and EDH estimates span the transmitter height there are differences in physical regimes causing large propagation discrepancies – e.g., leakage into versus trapping within the duct. Variation in EDH between the methods is greatest in stable environments. M-deficit and curvature of the refractive profiles also influence propagation specifically in scenarios when EDH spans the transmitter. When all EDHs are below the transmitter, EDH variance is the primary contributor to propagation variance, but M-deficit and profile curvature variance play a secondary role. M-deficits and curvature between the methods agree most often during periods of atmospheric stability.</p>
Comparison of atmospheric refractivity estimation methods and their influence on radar wave propagation predictions
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Supplementary Information Table for J. Geophys. Res. article: A Survey of Small-Scale Waves and Wave-Like Phenomena in Jupiter's Atmosphere Detected by JunoCam
<p>Text version of the Table in the Supplementary Information file associated with the J. Geophys. Res. article by Orton et al. (2020) A Survey of Small-Scale Waves and Wave-Like Phenomena in Jupiter’s Atmosphere Detected by JunoCam. The columns provide values for various sizes and orientations associated with waves detected by the Juno spacecraft's JunoCam visible imaging instrument. The values include the perijove, PJ (orbit close approach), image number, number of wave crests detected, the mean longitude and latitude, the length and width of the wave packet, the wavelength (mean distance between wave crests), and the tilt (angle of the wave crest with respect to the direction of the wave).</p>
Wind profile in the wave boundary layer and its application in a coupled atmosphere-wave model
<p>The simulation data for the study</p>
Martian Dust Storms and Gravity Waves: Disentangling Water Transport to the Upper Atmosphere
<p>The data for JGR:Planets article figures.</p>
The data for "Climatology of the Residual Mean Circulation of the Martian Atmosphere and Contributions of Resolved and Unresolved Waves Based on a Reanalysis Dataset" by A. Asumi, K. Sato, M. Kohma, and Y. Hayashi in JGR, Planet
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Dataset of "Gravity waves – Toward connecting space weather and atmosphere-ionosphere coupling"
<p>The first file is the Ap file, describing the variation of the geomagnetic index used in the model simulations. The data files starting with sw are the model simulation files for the different model experiments. Each file is on a 2x15 (lat x lon) horizontal grid and on 66 pressure levels. There are 24 longitude and 91 latitudes. The files describe the simulations from 7th to 9th August 2018 in two hour time steps. The output variables in each file are zonal wind, geopotential height, and zonal gravity wave drag, in this given order. The files are formatted in the standard binary format for GraDS. </p>
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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.