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Dataset results
238 results for “Atmosphere modeling”
Electron concentration profiles calculated using eight-component model of the ionospheric D-region and two different set of input atmospheric data
<p>The files contain electron concentration <i>Ne</i> profiles during solar X-ray flares that occurred on 9-11 June 2014. The altitude range is 50-90 km.</p><p>Values of electron concentration were calculated using eight-component model of the ionospheric D-region and two different set of input atmospheric data (MSIS neutral atmosphere model and Aura satellite measurements). Results are obtained for ten VLF paths: from European transmitters ICV, TBB, GQD, GBZ, and DHO to Mikhnevo geophysical observatory (55°N 38°E) and A118 SID station (43°N 1°E).</p><p>The data is presented as MATLAB files. Each .mat file contains data and variable "description" with data's structure information.</p>
Atmospheric Absorption Tables for AMSU-A Channels 4 through 9, RSS oxygen model
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Morphology of the excited hydroxyl in the Martian atmosphere: A model study. Where to search for airglow on Mars?
<p>The data for Remote Sensing article figures.</p>
Dataset for "Enhanced Regional Ocean Ensemble Data Assimilation Through Atmospheric Coupling in the SKRIPS Model"
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Dataset for the article titled ""An Updated Parameterization of the Unstable Atmospheric Surface Layer in WRF Modeling System"".
<p>The dataset is organized in to five ZIP folders as described below:</p> <p>1. Offline_Exp_Data: This contains data for stability parameter (z/L), transfer coefficient for momentum (CD), and heat (CH) simulated from different experiments using the bulk flux algorithm (offline mode) corresponding to different similarity functions over smooth (z0 = 0.01 m), transition (z0 = 0.1 m), and rough (z0 = 1 m) surfaces. This dataset corresponds to Figure 4.</p> <p>2. Similarity_Functions: This contains data for the similarity functions for momentum and heat in gradient (Phi_m and Phi_h) as well as integrated (Psi_m and Psi_h) forms with stability parameter (z/L) for considered functional forms of similarity functions (e.g., Businger et al., 1971 (BD71); Carl et al., 1973 (CL73); Kader and Yaglom, 1990 (KY90); and Fairall et al., 1996 (F96)) under unstable conditions. The dataset corresponds to Figures 2 and S1 (supporting information).</p> <p>3. WRF_Data1: This contains hourly averaged data for considered variables from WRF model simulations during the MAM (March–April–May) season. This dataset can be used to reproduce Figures 9, 10, and 11.</p> <p>4. WRF_Data2: This contains model output for considered variables extracted during highly convective hours (z/L<-10 over most of the domain) in the daytime. This dataset can be used to reproduce Figures 12, S3, S4, S5, and S6.</p> <p>5. WRF_Data3: This consists of data from different simulations (CTRL, Exp1-4) with the WRF model extracted at the location of the flux tower (23.412 N, 85.44 E (Ranchi), India). The dataset contains stability parameter (z/L), bulk Richardson number (RiB), transfer coefficients for momentum (CD) and heat (CH), sensible heat flux (HFX), 10-m wind speed (WS), u*2 (representative of momentum flux), and 2-m temperature (T2m). This dataset corresponds to Figures 5, 6, 7, 8, S2, S7, and S8.</p>
Code and Data for Atmospheric River Induced Precipitation in California as Simulated by the Regionally Refined Simple Convective Resolving E3SM Atmosphere Model Version 0
<p>Includes the code used for all simulations and grid configurations for the paper entitled "Atmospheric River Induced Precipitation in California as Simulated by the Regionally Refined Simple Convective Resolving E3SM Atmosphere Model Version 0" submitted to Geoscientific Model Development. Also included are the model output files for all cases and grid configurations used to generate the analysis and figures in the paper. </p>
Model data for: Analysis of the global atmospheric background sulfur budget in a multi-model framework
<p>The present dataset contains all model data used in the model intercomparison in ACP. All data is provided as monthly means. For more data, please contact the first author. V2 addresses inconsistencies in the time axes, vertical coordinates, and variable names between models.</p>
Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations: Data and Visualization Notebooks
<p>The data, jupyter notebooks, and saved model weights for the "Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations" Hu et al. (2025) arxiv preprint: <a href="https://arxiv.org/abs/2407.00124">arXiv:2407.00124</a>. This updated version contains more analysis notebooks together with related data/model.</p>
Dataset for Exploring Western North Pacific Tropical Cyclone Activity in the High-Resolution Community Atmosphere Model
<p>This dataset contains results from high-resolution, tropical cyclone-permitting experiments using Community Atmosphere Model version 5.</p>
Lidar ratio–depolarization ratio relations of atmospheric dust aerosols: the T-matrix modeling and high spectral resolution polarization lidar observations
<p><strong>Data for publication:</strong></p> <p><em><strong>Lidar ratio–depolarization ratio relations of atmospheric dust aerosols: the T-matrix modeling and high spectral resolution polarization lidar observations.</strong></em></p> <p>Mail:</p> <p>sato@riam.kyushu-u.ac.jp </p> <p><a href="mailto:bilei@zju.edu.cn">bilei@zju.edu.cn</a></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>
Model code, data, and plot scripts for the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".
<p>The model codes, data, and plot scripts used in the paper, "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".</p> <ul> <li>7_experiments.zip contains modified model code and output data of each experiment in this study.</li> <li>off-line test.zip contains off-line test code and output data.</li> <li>plot_scripts.zip are the NCL scripts used for figures in the paper.</li> </ul>
Model code, data, and plot scripts for the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".
<p>The model codes, data, and plot scripts used in the paper, "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".</p> <ul> <li>Figs&Table are the NCL scripts used for figures and table in the paper.</li> <li>Model_Results contains output data of each experiment in this study.</li> <li>Mods_Scripts contains modified model code.</li> <li>Offline_Code contains off-line test code.</li> </ul>
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>
Surrogate modelling for the forecast of Seveso-type atmospheric pollutant dispersion
<p>Online resource 1 - Test-data response for GIM model.</p> <p>Online resource 2 - Test-data response for RGI model.</p>
On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model: Article Data
<p>NetCDF datatset of presented results from the publication titled "On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model" in the Journal of Geophysical Research - Atmospheres, Paper #2021JD036214R.</p>
Selected data analysed in the JGR Atmosphere manuscript " An application of the maximum entropy production method in the WRF Noah land surface model"
<p>The control experiment (hereafter WRF-CTL) and the MEP experiment (hereafter WRF-MEP) simulations results interpolated to the observation stations. The simulation period was 1 June to 31 August 2015 with 30 hours from 12:00 UTC (20:00 Beijing time (BJT)) each day, and the latest 24-hour outputs are provided.</p>
Model output dataset used in "Sensitivity of Heavy Convective Precipitation Simulations to Changes in Land-atmosphere Exchange Processes over China"
<p>This dataset accompanies the paper by Zhang et al. "Sensitivity of Heavy Convective Precipitation Simulations to Changes in Land-atmosphere Exchange Processes over China".</p> <p>Three heavy precipitation events were modeled using the WRF v3.9 model:</p> <p>(1) The_21_July_Beijing_Rainstorm_Simulation<br> (2) The_30_July_Ningxia_rainstorm_Simulation<br> (3) The_19_June_Jiangxi_rainstorm_Simulation</p> <p>Furthermore, three cases were designed for each heavy precipitation event: (1) control experiment (DEFAULT), using the default M-O option (<em>C<sub>zil</sub></em> ~ 0); (2) constant <em>C<sub>zil</sub></em> (CZIL0.01, CZIL0.05, CZIL0.1, CZIL0.3, CZIL0.5 and CZIL0.8), with <em>C<sub>zil</sub></em> values of 0.01, 0.05, 0.1, 0.3, 0.5, and 0.8; (3) a dynamic canopy-height dependent <em>C<sub>zil</sub></em> (NEWCZIL).</p> <p>Plain Language Summary for this paper:<br> Over recent decades, the frequent occurrence of heavy precipitation events has caused devastating ecological and socioeconomic impacts, such as agriculture losses, infrastructure damage, and casualties. High-resolution atmospheric modeling at a convection-permitting grid spacing (≤4 km) provides valuable applications for predicting heavy precipitation. Precipitation can be strongly affected by the energy and moisture exchanges between land surface and atmosphere. However, the representation of land-atmosphere interactions in atmospheric models and the responses of precipitation to land-atmosphere exchange efficiency remain great uncertainties. This study performed 3-km high-resolution atmospheric modeling with a dynamic vegetation-type-dependent land-atmosphere exchange scheme for three typical heavy precipitation events that occurred over areas with different dominant land-cover types. The results showed that land-atmosphere exchange efficiency mainly affected the precipitation intensity as well as the onset and peak time of precipitation. The dynamic exchange scheme modifies the efficiency of land-atmosphere exchanges to match local land cover conditions and could reproduce well the field observations, especially the intensity and location of the heaviest rainfall which usually serve as the most concerned variable in a major rainstorm event. Our findings show that the dynamical scheme could help achieve more accurate precipitation simulations.</p>
Dataset for "Multidecadal regime shifts in North Pacific subtropical mode water formation in a coupled atmosphere-ocean-sea ice model" by Kim et al., 2022 in Geophysical Research Letters
<p>Kiel Climate Model pre-industrial simulation data used in the Geophysical Research Letters publication titled “Multidecadal regime shifts in North Pacific subtropical mode water formation in a coupled atmosphere-ocean-sea ice model” by Kim et al., 2022</p>
Model code, data, and plot scripts for the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".
<p>The code, scripts, and data used in the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".</p> <ul> <li>All Figures&Table and their corresponding NCL scripts are under the directory of Figs&Table. </li> <li>The modified model code, corresponding original model code, and model run scripts are under the directory of Mods_Scripts.</li> <li>The postprocessing NCL scripts, which select useful variables from simulation results, are under the directory of PostProcessing.</li> <li>The zonal mean data from model results used for making figures and corresponding data processing scripts are under the directory of Model_Results.</li> <li>The FORTRAN code used for offline tests is under the directory of Offline_Code.</li> <li>The code, data, and NCL scripts used for the figures and table in the Appendix are under the directory of Appendix.</li> </ul>
ScienceDex guides
Understand access before you commit
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.