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154 results for “parameterization”
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>
New parameterization scheme for modeling ozone-caused damage to vegetation in process-based models: data
<p>gmd-2024-6: Quantifying the role of ozone-caused damage to vegetation in the Earth system: A new parameterization scheme for photosynthetic and stomatal responses (Fang Li et al., 2024); https://gmd.copernicus.org/preprints/gmd-2024-6/. It includes three directory: input (O3 concentration), observations (collected PODY-An and PODY-gs), and simulations.</p> <p>It is co-supported by the National Natural Science Foundation of China (41875137) and Guangdong Major Project of Basic and Applied Basic Research (2021B0301030007).</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>
Run-time Performance Measurements to "Instanced Rendering of Parameterized 3D Glyphs with Adaptive Level-of-Detail using three.js"
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Exploration of the influence of environmental conditions on secondary organic aerosol formation and organic species properties using explicit simulations: development of the VBS-GECKO parameterization [Dataset]
<p>This dataset is composed of simulation outputs of the GECKO-A model that were used to optimize the VBS-GECKO SOA parameterization as described in the study :<br> <br> Lannuque, V., Camredon, M., Couvidat, F., Hodzic, A., Valorso, R., Madronich, S., Bessagnet, B., and Aumont, B.: Exploration of the influence of environmental conditions on secondary organic aerosol formation and organic species properties using explicit simulations: development of the VBS-GECKO parameterization, Atmos. Chem. Phys., https://doi.org/10.5194/acp-18-1-2018, 2018.<br> <br> <strong>Please cite the original ACP article when using these data in a publication.</strong></p> <p><br> The paper also contains more information about how these data were obtained.<br> <br> The "readme.txt" file gives quick explanations on how the dataset is formatted and how to read the different files that compose it.<br> <br> Victor Lannuque</p>
Improved cross-scale snow cover simulations by developing a scale-aware parameterization in the Noah-MP land surface model
<p>Noah-MP data used to support the analyses conducted by Abolafia-Rosenzweig et al.: <strong>Improved </strong><strong>cross-scale </strong><strong>snow cover simulations by developing a scale-aware parameterization in the Noah-MP land surface model</strong></p>
Data for "Online Learning of Entrainment Closures in a Hybrid Machine Learning Parameterization"
<p>Calibration data and visualization tools for "Online Learning of Entrainment Closures in a Hybrid Machine Learning Parameterization".</p>
Supplementary, raw data, and model for manuscript titled Coupling the TKE-ACM2 Planetary Boundary Layer Scheme with the Building Effect Parameterization Model
<p>This file contains 1. raw data simulated by WRF and PALM, and LiDAR and surface station measurements (<a href="https://zenodo.org/api/records/13959541/draft/files/Raw%20data%20PALM_WRF_Obs.zip/content" target="_blank" rel="noopener noreferrer">Raw data PALM_WRF_Obs.zip</a>); 2. supplementary drawing modeled and simulated U10, T2, and RH2 time series at surface stations (<a href="https://zenodo.org/api/records/13959541/draft/files/Supplementary.docx/content" target="_blank" rel="noopener noreferrer">Supplementary.docx</a>); 3. WRF version containing the TKE-ACM2+BEP (<a href="https://zenodo.org/api/records/13959541/draft/files/WRF433_TKE-ACM2+BEP.tar.gz/content" target="_blank" rel="noopener noreferrer">WRF433_TKE-ACM2+BEP.tar.gz</a>).</p>
Code and data for "Sensitivity of Northern Hemisphere climate to ice-ocean interface heat flux parameterizations"
<p>This repository provides the source code and modeled data for three different ice-ocean heat flux<br> parameterizations of a 1-D idealized model, as well as 3-D climate models including CICE,<br> MPIOM and COSMOS, which are used in a GMD manuscript called "Sensitivity of Northern Hemisphere climate to ice-ocean interface heat flux parameterizations". The NCL-based scripts for plotting the figures are also provided.</p> <p><br> The file all.tar.gz consists of 4 folders as the following:</p> <p>1. 1-D<br> In the 1-D folder one can find the matlab source code for the 1-D idealized model with main.m being the main script and the others sub-scripts for calculating seasonal changes of different forcings, involving the surface albedo (albedo.m), shortwave fluxes (shortwave.m) and all other kinds of fluxes (otherfluxes.m).</p> <p>2. code<br> The code folder provides the source code of the three models used in our study: CICE, MPIOM and COSMOS.</p> <p>The most important code in terms of the ice-ocean heat flux in CICE can be found at code/cice/source/ice_therm_vertical.F90. The switch of the options of the three parameterizations can be achieved by changing the parameter "oceanic_heat" (1 for icebath, 2 for 2eq and 3 for 3eq) in the namelist when running the model.</p> <p>The mpiom folder contains the three different set of MPIOM source code for the three ice-ocean heat flux parameterizations respectively.</p> <p>In cosmos, one could find 4 sub-folders, with the folder echam5 containing the source code for the atmosphere module ECHAM5, and the other 3 folders containing the MPIOM source code incorporating with the three different ice-ocean heat flux parameterizations, similar as the mpiom folder.</p> <p>3. data<br> This folder provides the simulated output from the three models: CICE, MPIOM and COSMOS. Each model folder contains three sub-folders called 2eq, 3eq and icebath, representing the modelled data for the 2eq, 3eq and icebath parameterizations respectively. For CICE, we upload the modeled results of the last 10 years. For MPIOM and COSMOS, as the original data set are too large, here we upload the climatology of the data from the last 100 simulation years. Note that in cosmos, there are some additional variables which are listed seperately, namely the AMOC (amoc.nc), the sea surface pressure (slp.nc) as well as the surface temperature (tsurf.nc). reg.nc contains ocean temperature and salinity which have been interpolated onto a 1x1 regular grid.</p> <p>4. plot_figures<br> This folder gives the scripts based on NCL to plot the figures in the manuscript, with the *.ncl files being the plotting scripts and the *.eps being the figures. <br> All the code, data, scripts can be used by anyone who has interest.</p> <p> </p>
Improvement and Application of the Parameterization Scheme for Hydrothermal Properties of Rhizosphere Soil in Alpine Grassland
<p>Plant roots signifificantly altered soil hydrothermal properties but was not typically included in land-surface process models. This omission affected the simulation results of soil water content and temperature, particularly on the Qinghai-Tibet Plateau (QTP) where roots in the shallow soil were generally high. Global grassland above-ground biomass carbon data and root distribution model were used to build a geographically distributed, profifiled alpine grassland roots density dataset for the Community Land Model (CLM). The schemes for soil hydrothermal properties were modified to accommodate soil mineral, organic matter and roots. Compared with the original schemes, the new schemes presented an increasing or decreasing trend. The new schemes were verified by single-point, regional offline simulation, and the influence of alpine grassland roots on simulation results on the QTP were explored. The new schemes could effectively block the downward migration of 0-10 cm soil water, which were beneficial to water conservation. The heat storage capacity of 0-10 cm soil increased obviously, and the thermal conductivity showed a certain seasonal change. Therefore, the simulated dry deviation of 0-10 cm soil water content in the new schemes was significantly reduced. The simulated 0-10 cm soil temperature in the new schemes during soil non-freezing was generally lower than the observed, and the simulated cold deviation of this layer during soil freezing was also reduced.</p>
Replication Data for: ``Toward machine learning-augmented, bathymetry-aware parameterizations of mesoscale eddy buoyancy fluxes across upwelling slope fronts''
<p>This dataset contains the Python scripts for training the Artificial Neural Networks (ANNs), the trained ANNs, configuration files for the reference 2D MITgcm simulations, and model outputs used in the paper.</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>
Dataset for paper Pavel Perezhogin, Andrey Glazunov "Subgrid parameterizations of ocean mesoscale eddies based on Germano decomposition" submitted to JAMES.
<p>The data is updated due to the response to review in the third round.</p> <p>The directory structure is :</p> <pre><code>├── barotropic │ ├── data │ ├── figures │ └── code ├── pyqg │ ├── data │ ├── figures │ └── code ├── NEMO │ ├── data │ ├── figures │ └── code </code></pre> <p>Where <strong>barotropic</strong> - experiments in barotropic fluid, <strong>pyqg</strong> - experiments in QG model, <strong>NEMO</strong> - experiments in primitive equation model.</p> <p>Subfolder <strong>data</strong> contains numerical simulations, <strong>figures</strong> contains plotters of Figures for Publication, and <strong>code</strong> is source code with parameterized barotropic/QG/primitive equation ocean models. </p>
Dataset and code for the manuscript 'Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks'
<p>This repository contains the code and data used in the manuscript 'Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks'. <br> Manuscript authors: Dr. Aakash Sane, Dr. Brandon G. Reichl, Dr. Alistair Adcroft, Dr. Laure Zanna<br> Manuscript preprint link: https://doi.org/10.48550/arXiv.2306.09045</p>
Data for the figures of Sasaki et al. entitled "Turbulence across the Antarctic Circumpolar Current in the Indian Southern Ocean: Micro-Temperature Measurements and Finescale Parameterizations"
<p>1) Microstructure.nc includes profiles of epsilon and diapycnal diffusivity obtained from FP07 measurements.</p> <p>2) Fine_parameters.nc includes profiles of buoyancy frequency (N), shear-strain ratio, normalized internal wave energy, and polarization ratio of the counterclockwise (CCW) versus the clockwise (CW) component of the shear.</p> <p>3) epsilon_FineParam.nc includes profiles of epsilon calculated by finescale parameterizations. The subscript of each variable (eps_Fine_X) in this file corresponds to the row letter (a-l) of Table. 1.</p> <p>4) Fine_spectral_shape.nc includes profiles of Hstrain and spectral slope (s). </p>
Effect of Inguinal Hernia Repair on Uroflowmetric Parameteres
ClinicalTrials.gov study NCT03450811. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Data from: The effect of cost surface parameterization on landscape resistance estimates
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Data from: Disentangling the formation of contrasting tree-line physiognomies combining model selection and Bayesian parameterization for simulation models
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Data from: Energy benefits and emergent space use patterns of an empirically parameterized model of memory-based patch selection
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Codes and datasets associated with the paper "Parameterizing the Energy Dissipation Rate in Stably Stratified Flows"
<p>Here, you will find some of the codes and datasets utilized in the article: "Parameterizing the Energy Dissipation Rate in Stably Stratified Flows". (<a href="https://arxiv.org/abs/2001.02255">https://arxiv.org/abs/2001.02255</a>)</p> <p>The direct numerical simulations were performed using the HERCULES code (<a href="https://github.com/friedenhe/HERCULES">https://github.com/friedenhe/HERCULES</a>). For all the simulations, the bulk Reynolds number is kept at 20,000. The bulk Richardson number is varied from 0.1 to 0.5. The zip files Re20000RiXX.zip contains the various turbulence statistics. </p> <p>Re20000Ri10: Ri_b = 0.1</p> <p>Re20000Ri20: Ri_b = 0.2</p> <p>Re20000Ri30: Ri_b = 0.3</p> <p>Re20000Ri40: Ri_b = 0.4</p> <p>Re20000Ri50: Ri_b = 0.5</p> <p>Every dat file (within the zip archives) is in ascii format and has 10 rows and 286 columns. Each row corresponds to normalized time (ranging from 1 to 10). Each column represents vertical model levels. </p> <p> </p>
ScienceDex guides
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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.