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37 results for “idealized modeling”
Model data and namelists for Sterzinger et al. (2022) - "Do arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations"
<p>Model data and namelists for "<a href="https://acp.copernicus.org/preprints/acp-2022-36/">Do arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations</a>"</p> <p>Horizontally averaged data is provided in NetCDF4 files (oliktok.nc, ascos.nc, summit.nc) for all output variables. Horizontally averaged vertical momentum flux is provided in a separate file for each simulation (*_vert_momentum_flux.nc files).</p> <p>Info on variables is provided by the RAMS model variable guide PDF <a href="https://vandenheever.atmos.colostate.edu/vdhpage/rams/docs/RAMS-VariableList.pdf">available here</a>.</p> <p>Model namelists are provided for each simulation (*_RAMSIN files). ASCOS initialization sounding info is provided within the ASCOS_RAMSIN file - initialization soundings are provided in SOUND_IN files.</p>
Realistic and idealized model output of surface currents and tracers used in frontal maintenance analysis.
<h1>Realistic model</h1> <p><em>./surface_child.zarr</em> directory is a zarr dataset containing a realistic model model based on the Regional Ocean Modeling System \citep[ROMS][]{haidvogel.ea:08} configured with a high-resolution ($\sim$300~m), two-way nested child domain within a coarser ($\sim$1.5~km in the region of nesting) model of the Texas-Louisiana continental shelf, described by \citet{schlichting.ea:23}. The coarser parent model includes the entire coastlines of Louisiana and Texas, and has been run over multiple years of simulated time to examine, for example, interannual variability in harmful algal blooms \citep{thyng.ea:13} along the Texas coast, and the extent of the Mississippi-Atchafalaya plume \citep{zhang.ea:12}. Both models have 30 vertical layers, with resolution focused near the surface. The mean water depth in the nested region is about 50~m. </p> <h1>Idealized model</h1> <p><em>./reduced_channel_files</em> contain six resolution cases spanning 200 m to 10 km horizontal resolution. The idealized ROMS model domain for all of them is 100~km by 300~km, with no-slip walls along the short (north/south) boundaries, and re-entrant along the long (east/west) boundaries. Horizontal resolution is varied from 200~m to 10~km. The initial condition is designed to roughly mirror conditions in the Mississippi-Atchafalaya river plume in the northern Gulf of Mexico, which is a location of strong buoyancy forcing, energetic eddy activity, and intense frontal processes \citep[see][]{hetland:17}. The channel has a constant depth of 50~m, with a constant rotation throughout the domain, $f=10^{-4}$~s$^{-1}$. Initial tracer distributions of temperature, $T$, and salinity, $S$, are defined by constant values specifying the vertical and horizontal density gradients, $N^2 = -g \rho_z \rho_0^{-1} = 10^{-4}~\mathrm{s}^{-2}$ and $M^2 = g \rho_y \rho_0^{-1} = 10^{-6}~\mathrm{s}^{-2}$:<br>\begin{align}<br> T &= T_0 + \frac{N^2}{g \rho_0 \alpha} z\\<br> S &= S_0 + \frac{M^2}{g \rho_0 \beta} y \quad ,<br>\end{align}<br>where $\alpha$=0.17~$^\circ$C$^{-1}$ and $\beta$=0.76~g$^{-1}$~kg are the linear thermal and haline expansion coefficients in a linearized equation of state, <br>\begin{equation}<br> \rho = \rho_0 (1 - \alpha T + \beta S) \quad ,<br>\end{equation}<br>where $\rho_0$ is a reference density.<br>A small perturbation to the initial $u$ velocity (in the periodic direction) with a period of 100~km in both the $x$- and $y$-directions and an amplitude of 0.01~m~s$^{-1}$ is applied to supply perturbations on which instabilities can form and grow. The simulations include a weak near inertial (0.9$f$) wind stress in the $x$-direction,<br>\begin{equation}<br> \label{eqn:roms_wind}<br> \tau^x = \tau^x_0 \mathrm{sin}(0.9 f\,t) \quad ,<br>\end{equation}<br>to mimic the diurnal winds observed in the northern Gulf of Mexico.</p>
Dataset for paper Pavel Perezhogin, Laure Zanna, Carlos Fernandez-Granda "Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model" submitted to JAMES.
<p>The dataset consists of the directory tree of .zarr archives. See <a href="https://github.com/m2lines/pyqg_generative/blob/master/Google-Colab/dataset.ipynb">Github repository</a> for the description of the dataset.</p> <p>The directory tree is:</p> <pre><code>├── eddy │ ├── 48 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 64 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 96 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ └── hires ├── jet │ ├── 48 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 64 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 96 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ └── hires</code></pre> <ul> <li>Every individual dataset is a <code>.zarr</code> <a href="https://zarr.readthedocs.io/en/stable/">archive</a></li> <li><code>eddy/jet</code> - configuration of the pyqg; eddy is default; See <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022MS003258">Ross2022</a> for description</li> <li><code>hires.zarr</code> - high-resolution simulation at 256x256 grid</li> <li><code>48/64/96</code> - resolution of the coarse models</li> <li><code>lores.zarr</code> - low-resolution simulation</li> <li><code>gauss.zarr</code>, <code>sharp.zarr</code> - training datasets for prediction of subgrid forcing obtained with Gaussian or Sharp filters</li> <li><code>hires-gauss.zarr</code>, <code>hires-sharp.zarr</code> - high-resolution simulation projected onto coarse grid with Gaussian or Sharp filters</li> </ul> <p>The directory tree is split into small tar.gz files each representing a separate .zarr archive. Download any required parts of the dataset and unpack with:</p> <p><strong>tar -xf *.tar.gz </strong></p> <p><strong>The directory tree will be restored automatically!</strong></p>
Model output publication 'Mechanisms of salt overspill at estuarine network junctions explained with an idealized model'
<p>The model output for the publication in JGR: Oceans titled 'Mechanisms of salt overspill at estuarine network junctions explained with an idealized model'</p>
The Potential Influence of Maritime Continent Deforestation on El Niño-Southern Oscillation: Insights from Idealized Modeling Experiments
<p>Codes for the Figures and Tables shown in the paper "The Potential Influence of Maritime Continent Deforestation on El Niño-Southern Oscillation: Insights from Idealized Modeling Experiments"</p> <p>Also, the restart and initial data are used to drive CESM1.</p>
IDEAL Study: Blinded RCT for the Impact of AI Model for Cerebral Aneurysms Detection on Patients' Diagnosis and Outcomes
ClinicalTrials.gov study NCT06118840. IPD Sharing: NO. Countries: 1. Publications: 1.
Idealized estuary model data
<p>Idealized estuaries with different channel dimensions and forcing conditions</p>
Model data for "Factors Modulating Variability of Eddy Kinetic Energy in the Southern Ocean from Idealized Simulations" "
<p>This dataset contains the all the idealized simulations with different topographic features.</p>
Impact of Estuarine Dams on the Estuarine Parameter Space and Sediment Flux Decomposition: Idealized Numerical Modeling Study.
<p>Input data to run the model; output data and scripts used to generate figures.</p>
WRF model output used in an ideal model experiment demonstrating the applicability of a channel-synthesizing method for reducing uncertainties in satellite radiance transfer modeling
<p>This repository stores the WRF model output used in the paper "A novel channel-synthesizing method for reducing uncertainties in satellite radiance transfer modeling" submitted to Geophysical Research Letters.</p>
DATA for Evaluating Scale-Aware Boundary Layer Similarity Functions and Their Mechanisms in Tropical Cyclone Modeling Using Idealized Large-Eddy Simulations
<p>data.xlsx has the data to make Figs.1-4</p> <p> </p>
Data for "Supercells and Tornado-like Vortices in an Idealized Global Atmosphere Model"
<p>Data used in a manuscript on supercells and tornado-like vortices, for submission to ESS.</p>
The role of idealized storms in sand wave formation: A numerical modeling study
<p>The typical Delft3D cases for "The role of idealized storms in sand wave formation: A numerical modeling study".</p>
Model data for Sterzinger and Igel (2023) "Simulated Idealized Arctic Cloud Sensitivity to Above Cloud CCN Concentrations"
<p>Data for Sterzinger and Igel (2023) "Simulated Idealized Arctic Cloud Sensitivity to Above Cloud CCN Concentrations"</p> <p> </p> <p>all_data.tar.gz contains the processed horizontally-averaged data used to plot the figures in the paper.</p> <p> </p> <p>data_processing.tar.gz contains the scripts to process the 4-D data output from the model and namelists in https://doi.org/10.5281/zenodo.7991355</p>
A new predictive group-contribution ideal-heat-capacity model, and its influence on second-derivative properties calculated using a free-energy equation of state
<p>Calculated data for figures 4-8 presented in publication.</p>
Impact of Estuarine Dams on the Estuarine Parameter Space and Sediment Flux Decomposition: Idealized Numerical Modeling Study.
<p>Input data to run the model; output data and scripts used to generate figures.</p>
Transcriptome Profiling Provides Insights into Mongolian Gerbil as an Ideal NAFLD Model : Earlier Onset of Inflammation and Fibrosis as Well as High Expression of HCC-Associated Genes
GEO Series GSE157169. Meriones unguiculatus. 18 samples. Type: Expression profiling by high throughput sequencing.
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