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154 results for “parameterization”
Data from: Simple parameterization of aerodynamic roughness lengths and the turbulent heat fluxes at the top of midlatitude August‐one glacier, Qilian Mountains, China
The fluxes of sensible heat (H) and latent heat (LE), which are generally the important parts of the energy and mass balances over glacier surfaces, are widely quantified by the bulk method. However, due to the difficulty of determining the aerodynamic roughness length z0m in this method, H and LE values may still have large uncertainties with significant inaccuracy. To acquire reliable varying and intrinsic z0m values, new simpler parameterizations for z0m values at different ranges of the friction velocity u*b were fitted in this study. The method was implemented using the related meteorological data and glacial sublimation/condensation measured at the top of the August‐one glacier (4817 m a.s.l.) in the Qilian Mountains from 1 July 2016 to 15 August 2017. The parameterization shows that z0m increased sharply when u*b exceeded 0.43 m/s (the approximate threshold value) and that the effect of snowdrift was slight for the hourly z0m values in the range 0.15 ≤ u*b ≤ 0.43 m/s, which could thus be used to calculate the daily z0m. During the wet period (1 July to 24 September 2016 and 5 May to 15 August 2017), the turbulent fluxes calculated by the bulk method showed that net radiation Rnet was the primary source of surface energy (60.7 w/m2), whereas during the dry period (25 September 2016 to 4 May 2017), the main heat sink was the positive H (28.5 w/m2) rather than the negative Rnet (–10.0 w/m2).
A scale-aware parameterization for estimating subgrid variability of downward solar radiation using high-resolution Digital Elevation Model data
<p>This is a statement on the shared data of the manuscript 'A scale-aware parameterization for estimating subgrid variability of downward solar radiation using high-resolution Digital Elevation Model data'</p> <p>Folder Codes includes scripts for plotting all figures of the manuscript.</p> <p>Folder grids includes all the data that are required by scripts in plotting.</p> <p><br> Contact information:<br> Siwei He<br> NOAA/ERSL/GSD<br> 325 Broadway<br> Boulder, CO 80305<br> hesiweide@gmail.com <br> </p>
Data and code for training and testing a ResMLP model with experience replay for machine-learning physics parameterization
<p>This directory contains the training data and code for training and testing a ResMLP with experience replay for creating a machine-learning physics parameterization for the Community Atmospheric Model. </p> <p>The directory is structured as follows:</p> <p>1. Download training and testing data: https://portal.nersc.gov/archive/home/z/zhangtao/www/hybird_GCM_ML</p> <p>2. Unzip nncam_training.zip</p> <p>nncam_training</p> <p> - models</p> <p> model definition of ResMLP and other models for comparison purposes</p> <p> - dataloader </p> <p> utility scripts to load data into pytorch dataset</p> <p> - training_scripts</p> <p> scripts to train ResMLP model with/without experience replay</p> <p> - offline_test</p> <p> scripts to perform offline test (Table 2, Figure 2)</p> <p>3. Unzip nncam_coupling.zip</p> <p>nncam_srcmods</p> <p> - SourceMods</p> <p> SourceMods to be used with CAM modules for coupling with neural network</p> <p> - otherfiles</p> <p> additional configuration files to setup and run SPCAM with neural network</p> <p> - pythonfiles</p> <p> python scripts to run neural network and couple with CAM</p> <p> - ClimAnalysis</p> <p> - paper_plots.ipynb</p> <p> scripts to produce online evaluation figures (Figure 1, Figure 3-10)</p> <p> </p>
Dataset for paper ''Observation and parameterization of bottom shear stress and sediment resuspension in a large shallow lake''
<p>The dataset contains Model data (2008, 2009) from AEM3D, processed bottom shear stress data, and raw current, turbidity, wind data collected in 2008 and 2009. </p>
Safety, Parameterization, and Mechanism of Transcranial Focused Ultrasound
ClinicalTrials.gov study NCT05792020. IPD Sharing: NO. Countries: 1. Publications: 0.
Early Diagnosis of Patients With Mild Cognitive Impairment Through the Parameterization of Functional Tests.
ClinicalTrials.gov study NCT03851198. IPD Sharing: YES. Countries: 1. Publications: 0.
Muscles Strength and Gait Parameteres After TKA
ClinicalTrials.gov study NCT06061367. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Simple parameterization of aerodynamic roughness lengths and the turbulent heat fluxes at the top of midlatitude August‐one glacier, Qilian Mountains, China
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Impact of Convective Parameterizations on Atmospheric Mesoscale Kinetic Energy Spectra in Global High-resolution Simulations
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Data for "Data Imbalance, Uncertainty Quantification, and Generalization via Transfer Learning in Data-driven Parameterizations: Lessons from the Emulation of Gravity Wave Momentum Transport in WACCM"
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Parameterization of grass grid pavers for urban microclimate modelling to simulate the heat mitigation potential of unsealing measures in cities
<p><strong>README: </strong></p> <p>The exact version (V 5.1.2 Compatibility Release) of the ENVI-met model used for this paper, the parameterizations and the input data to run the ENVI-met model and to reproduce the model outputs of all the performed scenario simulations shown in this paper, as well as all the scripts for post-processing of the model output data needed to derive the presented results of this manuscript are provided in this permanent archive. The supplementary material (data and code) includes the following files: </p> <p>Appendix_1: ENVI-met model code of the version used to reproduce the outputs of this paper (V 5.1.2 Compatibility Release): <br> Appendix_1.exe: ENVImet model Version 5.1.2 setup </p> <p>Appendix_2: Model input data: for GGP scenarios including the model database parameterizations: <br> Appendix_2.1.zip: Input data for extreme scenario <br> Appendix_2.2.zip: Input data for realistic scenario <br>Eeach zip file contains: model driver file for LBCs, project file, project database, model domain file, and simulation task file. </p> <p>Appendix_3: Python scripts for postprocessing and extraction of model output data: <br> Appendix_3.1.enpy: Python script for exporting surface data <br> Appendix_3.2.enpy: Python script for exporting atmosphere data and soil data</p> <p>Appendix_4: R script for data analyses of model results: <br> Appendix_4.rmd: R script for data analyses of extracted simulation results and plotting </p> <p> </p>
Dataset for "Mesoscale Eddy-Induced Sharpening of Oceanic Tracer Front and its Parameterization"
<p>Data of the tracer experiments and the associated diagnostics in the shallow water model for the ocean front study.</p><ul><li><strong>eforc.tar.gz</strong>: diagnosed eddy forcing fields for different tracers;</li><li><strong>exps_trs.tar.gz</strong>: solutions in offline tracer experiments on the coarse grid;</li><li><strong>forc_uvh.tar.gz</strong>: mass fluxes and layer thicknesses used to advect tracers;</li><li><strong>params.tar.gz</strong>: parameters (eddy-induced velocities, diffusivity) used for tracer experiments</li></ul><p>Please contact Yueyang Lu via <strong>yueyang.lu@miami.edu</strong> if there are any questions.</p>
Video Supplement to Technical Note: assessing predicted cirrus ice properties between two deterministic ice formation parameterizations
<p>This video first describes the motivation behind the work presented in "Technical Note: assessing predicted cirrus ice properties between two deterministic ice formation parameterizations", which is to compare two approaches for simulating deterministic ice nucleation. It then details each approach in the context of how in-situ cirrus ice nucleation is simulated in the ECHAM-HAM GCM.</p>
Parameterization strategies data
<p>This dataset accompanies the submitted paper in the Water Resources Research: Parameterization strategies for a nonlinear soil water stress reduction function in a macroscopic root water uptake model. With this dataset, we improved and evaluate two different data-driven approaches to parameterize a nonlinear soil water stress reduction function, through additionally considering some processes affecting root-water-uptake patterns.</p>
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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.