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10 results for “subgrid-scale”
Subgrid-scale effects in cloud-like atmospheric flows: Colliding thermals - volume 2
<p>Datasets to perform surrogate modeling of subgrid-scale effects in cloud-like atmospheric flows. Please see README.md file for detailed description and metadata.</p>
Dataset: Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges
<p>Direct numerical simulation (DNS) data for 2D forced homogeneous isotropic turbulence (FHIT) and 2D Rayleigh-Benard Convection (RBC). This data is used to discover subgrid-scale (SGS) closures for momentum and heat fluxes.</p> <p>Paper: "<strong>Learning Closed-Form Equations for Subgrid-Scale Closures From High-Fidelity Data: Promises and Challenges</strong>" <br> <a href="https://doi.org/10.1029/2023MS003874">https://doi.org/10.1029/2023MS003874</a></p>
Supporting data for "A convolution method to assess subgrid-scale interactions between flow and patchy vegetation in biogeomorphic models"
<p>Dataset necessary to reproduce the results and analyses presented in the paper:</p> <p>Gourgue, O., van Belzen, J., Schwarz, C., Bouma, T.J., van de Koppel, J. & Temmerman, S. (2020) A convolution method to assess subgrid-scale interactions between flow and patchy vegetation in biogeomorphic models, Journal of Advances in Modeling Earth Systems, submitted.</p> <p>The dataset contains:</p> <ul> <li>Process-based model simulations, including their input files and the Python scripts to generate them (pre-processing), as well as the output files and Python scripts to post-process them.</li> <li>Flume experiment data processed for the model calibration.</li> <li>Python scripts to generate the figures and tables of the manuscript.</li> </ul>
Supplementary material for "A Constrained Spectral Approximation of Subgrid-Scale Orography on Unstructured Grids"
<p>Supplementary material for:</p> <ul> <li>Chew, R.; Dolaptchiev, S.; Wedel, M.-S.; Achatz, U. <br>A Constrained Spectral Approximation of Subgrid-Scale Orography on Unstructured Grids</li> </ul> <p>The <em>results_datasets.tar.gz</em> archive contains the simulation datasets for the results presented in Fig. 4-9, 11-16, and B1.</p> <p>The <em>Fig_10-wind_direction_study.tar.gz</em> archive contains the simulation datasets for the results presented in Fig. 10.</p> <p>Input parameters necessary to reproduce these simulation runs are included as metadata (attributes) to the datasets. Otherwise, the input parameter scripts can be found in the <code>inputs</code> subpackage of the source code.</p> <p>Furthermore, the following results can be generated by the corresponding scripts:</p> <table> <tbody> <tr> <td><strong>Figures</strong></td> <td><strong>Scripts</strong></td> </tr> <tr> <td>Fig. 1-3</td> <td><code>runs.idealised_isosceles</code></td> </tr> <tr> <td>Fig. D1</td> <td><code>runs.taper_test</code></td> </tr> <tr> <td>Fig. E1</td> <td><code>runs.idealised_delaunay</code></td> </tr> </tbody> </table> <p> </p> <p>Refer to the software repository URL for details on downloading the source code.</p> <p> </p>
Variations of Subgrid-scale Turbulent Fluxes in the Convective Boundary Layer at Gray Zone Resolutions
<p>This dataset contains the data used in the submitted manuscript of Liu and Zhou 2022 JAS. Please refer to the manuscript for the detailed description of the dataset.</p>
Data-driven subgrid-scale modeling of forced Burgers turbulence using deep learning with generalization to higher Reynolds numbers via transfer learning
<p>These are the data files for use with the codes in https://github.com/envfluids/Burgers_DDP_and_TL.</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>
COSP output for the TWPICE and KWAJEX case with varying cloud subgrid-scale parameters
<p>The control experiment is named SCG_LIN, in which the domain-averaged cloud fraction and condensate from CRM_LIN are used, along with median values of <em>L</em><sub>cf</sub>, <em>L</em><sub>cw</sub> and <em>v</em>. Two sensitivity experiments were carried out for each parameter using either lower or upper quartiles, while keeping the other two parameters unchanged. </p> <table> <tbody> <tr> <td> <p>CRM_LIN</p> </td> <td> <p>CRM output with LIN microphysics</p> </td> </tr> <tr> <td> <p>CRM_SAM</p> </td> <td> <p>CRM output with SAM1MOM microphysics</p> </td> </tr> <tr> <td> <p>CRM_M2005</p> </td> <td> <p>CRM output with M2005 microphysics</p> </td> </tr> <tr> <td> <p>SCG_LIN</p> </td> <td> <p>SCG output based on domain-averaged clouds from CRM_LIN and median subgrid-scale parameters (<em>L</em><sub>cf </sub>= 1.6, <em>L</em><sub>cw </sub>= 0.9, <em>v </em>= 2)</p> </td> </tr> <tr> <td> <p>SCG_SAM</p> </td> <td> <p>Same as SCG_LIN but based on clouds from CRM_SAM</p> </td> </tr> <tr> <td> <p>SCG_M2005</p> </td> <td> <p>Same as SCG_LIN but based on clouds from CRM_M2005</p> </td> </tr> <tr> <td> <p>SCG_Exact</p> </td> <td> <p>SCG output using exact parameters derived from CRM_LIN</p> </td> </tr> <tr> <td> <p>SCG_<em>L</em><sub>cf</sub>(3.2)</p> </td> <td> <p>Same as SCG_LIN but for <em>L</em><sub>cf </sub>= 3.2 in cloud overlap</p> </td> </tr> <tr> <td> <p>SCG_<em>L</em><sub>cf</sub>(0.9)</p> </td> <td> <p>Same as SCG_<em> L</em><sub>cf</sub>(3.2) but for <em>L</em><sub>cf </sub>= 0.9</p> </td> </tr> <tr> <td> <p>SCG_<em>L</em><sub>cw</sub>(1.5)</p> </td> <td> <p>Same as SCG_LIN but for <em>L</em><sub>cw </sub>= 1.5 in condensate overlap</p> </td> </tr> <tr> <td> <p>SCG_<em>L</em><sub>cw</sub>(0.5)</p> </td> <td> <table> <tbody> <tr> <td> <p>Same as SCG_<em> L</em><sub>cw</sub>(1.5) but for <em>L</em><sub>cw </sub>= 0.5</p> </td> </tr> </tbody> </table> <p> </p> </td> </tr> <tr> <td> <p>SCG_<em>v</em>(0.8)</p> </td> <td> <p>Same as SCG_LIN but for <em>v </em>= 0.8</p> </td> </tr> <tr> <td> <p>SCG_<em>v</em>(5.9)</p> </td> <td> <p>Same as SCG_<em>v</em>(0.8) but for <em>v </em>= 5.9</p> </td> </tr> </tbody> </table>
Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow
<p>Data for the six test cases of 2D turbulence explored in the paper, Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow.</p>
Subgrid-scale effects in cloud-like atmospheric flows: Colliding thermals - volume 1
<p>Datasets to perform surrogate modeling of subgrid-scale effects in cloud-like atmospheric flows. Please see README.md file for detailed description and metadata.</p>
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International Brain Laboratory public data
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OpenNeuro
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