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154 results for “Parameterization”
Global atmospheric simulation using the Super-Parameterized Community Atmosphere Model
<p>A 1-month subset from a global atmospheric simulation using the Super-Parameterized Community Atmosphere Model (SP-CAM), which implements the Multi-scale Modeling Framework (MMF) described in Khairoutdinov and Randall (2001) and Khairoutdinov et al. (2005). The version of the SP-CAM used here is described in Marchand et al. (2009) and Ovtchinnikov et al (2006), and was run at Pacific Northwest National Laboratory with DOE support. It is based on CAM 3.0 for the global atmospheric component, and uses the System for Atmospheric Modeling (SAM; Khairoutdinov and Randall 2003). This simulation is configured with CAM running the finite volume dynamical core on a 2x2.5 degree latitude-longitude grid with 26 vertical levels. The embedded CRM (SAM) is configured with 64 horizontal columns at 4 km grid spacing with 24 vertical levels (sharing the bottom 24 levels with the CAM grid), and single-moment microphysics. The simulation was initialized on 1 September 1997 and runs through June 2002, forced with observed monthly-mean sea surface temperatures. Only the month of July 2000 is uploaded here, which is what is required to reproduce the results in Hillman et al. (2018).</p>
A Fully-Parameterized Object-Side Light Field Dataset and Theory for Using Entrance and Exit Pupils as Natural Light Field Reference Planes for an Unfocused Plenoptic Camera
<p>We describe a dataset of light fields with full object-side parameterizations. The dataset contains PNG and ESLF files for all 32 images. 12 of them additionally contain depth maps and point clouds.</p>
UM experiments for "Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection"
<p>NetCDF4 files containing UM vn 11.1 data used in Lambert et al., 2020, Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection, submitted to Journal of Advances in Modeling Earth Systems.</p> <p>Key:</p> <p>"summaryday.nc" contain eleven months of data in each year, excluding either February or March.</p> <p>"summarydat2.nc" contain one month of data in each year, either February or March.</p> <p>"last5" indicates that for this simulation only the last five years of data are available.</p> <p>"llcs" are simulations with Lambert-Lewis.</p> <p>"gr" are simulations with Gregory-Rowntree.</p> <p>"llcsemu" are simulations with the Lambert-Lewis emulator.</p> <p>"gremu" are simulations with the Gregory-Rowntree emulator.</p> <p>"llcsemu_llcs" is the test simulation wherein the LLCS emulator is run equatorward of 30 degrees and the original LLCS convection scheme is run poleward of 30 degrees.</p> <p>"4xco2" have 4 x pre-industrial atmospheric carbon dioxide concentration. (Others have 1 x pre-industrial atmospheric carbon dioxide concentration.)</p> <p>"rh0.7" and "rh0.9" have LLCS RHCRIT set to 70% and 90% respectively.</p> <p>"30day" are one month simulations for July for which daily output are available. Other data are monthly mean only.</p> <p> </p>
Data and code for the paper "Macroscopic parameterization of positive streamer heads in air"
<p>This dataset contains the following:</p> <ul> <li><strong>ODE_solutions_no_photoi</strong>: the simulation results corresponding to figure 3 (see the README in the folder)</li> <li><strong>ODE_solutions_photoi</strong>: the simulation results corresponding to figure 6 (see the README in the folder)</li> <li><strong>input</strong>: the electron transport data used in the simulations (ionization coefficient, attachment coefficient, electron mobility) as a function of electric field strength</li> <li><strong>ODE_model.py</strong>: The version of the ODE model used in the paper</li> </ul> <p>The most recent version of the ODE model can be found at <a href="https://github.com/MD-CWI/streamer-head-ode">https://github.com/MD-CWI/streamer-head-ode</a></p>
Data for Publication "Sensitivity of precipitation in the highlands and lowlands of Peru to physics parameterization options in WRFV3.8.1"
<p>The data are made available as part of the paper "Sensitivity of precipitation in the highlands and lowlands of Peru to physics parameterization options in WRFV3.8.1", submitted to Geoscientific Model Development. This data set incorporates selected postprocessed files needed to reproduce the results presented in the paper. </p> <p>The files including the monthly means of precipitation for domain 2 (5 km) are named following the same structure:</p> <p> RR-D02-EXPERIMENTNAME-25km3Doms-YYYY_monthly.nc</p> <p>These are the options available in each case:</p> <ul> <li>EXPERIMENTNAME: Europe, SouthAmerica, Kenya, Micro13 or NoCumulus. These are the names included in Table 1 <br> from the paper.</li> <li>YYYY: 2008 or 2012. This is only applicable to precipitation.</li> </ul> <p>The field means for the northeastern slopes follow this structure:</p> <p> VARIABLE-D02-ERA5-Peru-25-Present-EXPERIMENTNAME-2008.EastLow.fldmean.nc</p> <ul> <li>VARIABLE: CLOUDFRA, PW, RH2, RR, SMOIS or T2. This abbreviations represent the following variable from the model<br> respectively: cloud fraction, precipitable water, relative humidity at 2 meters, total precipitation, soil moisture<br> and temperature at 2 meters.</li> <li>EXPERIMENTNAME: Europe, SouthAmerica, Kenya, Micro13 or NoCumulus. These are the names included in Table 1<br> from the paper.</li> </ul> <p>These files contained hourly values so to obtain the monthly means or sums the user must perform the following command:</p> <p> cdo monmean/monsum in.nc out.nc</p> <p>Two .txt files including the information about the stations considered for the validation of the WRF experiments for year 2008 or 2012 are also included. The data is separated with white spaces, and the structure of the columns is the following one:</p> <p> STATION LATITUDE LONGITUDE ELEVATION COUNTRY PROVIDER REGION</p> <p>Finally, two .zip files are provided. Scripts.zip includes all the scripts used to read, process and plot the data from the model, and Namelist_files.zip includes all the namelist files used to run the WRF simulations.</p> <p> </p>
Sample ECCOH OH Parameterization
<p>A sample parameterization of OH for July created to be used with the ECCOH module of the GEOS Earth System Model. The methodology to generate the parameterization is described in detail in the GMD article "A Machine Learning Methodology for the Generation of a Parameterization of the Hydroxyl Radical". The parameterization is included as an example and should not be used for research purposes without consulting the publication and/or the authors. We have also included the dataset used to train the parameterization (OHParameterization_TrainingSet_1980_2019_M07.dat) as well as the training targets (OHParameterization_Targets_1980_2019_M07.dat). The training data are from the NASA MERRA2 GMI simulation (https://acd-ext.gsfc.nasa.gov/Projects/GEOSCCM/MERRA2GMI/). The scripts used to generate the training dataset and the parameterization can be found at https://zenodo.org/record/6046037. </p>
Dataset: Stochastic data-driven parameterization of unresolved eddy effects in a baroclinic quasi-geostrophic model
<p>This dataset is used to reproduce the figures in the manuscript "Stochastic data-driven parameterization of unresolved eddy effects in a baroclinic quasi-geostrophic model"</p> <p>The figures can be created with the following Python scripts:</p> <p>.</p> <p> </p>
Data for the "Technical Note: assessing predicted cirrus ice properties between two deterministic ice formation parameterizations" manuscript
<p>This repository contains the post-processed ECHAM-HAM data files for comparing KM21_GCM and ML20 of "Technical Note: assessing predicted cirrus ice properties between two deterministic ice formation parameterizations" study.</p> <p>The files are all netCDF4.</p>
Dataset: Intercomparison of flux, gradient, and variance-based optical turbulence ($C_n^2$) parameterizations
<p>This repository contains the dataset for the manuscript</p> <p>Pierzyna, M, et al. "Intercomparison of flux, gradient, and variance-based optical turbulence (Cn2) parameterizations." <em>Applied Optics</em>, 2024. <a href="https://doi.org/10.1364/AO.519942">https://doi.org/10.1364/AO.519942</a></p> <p>The data is organized in the following structure:</p> <ul> <li>`met_cn2_*_10m.nc`: netCDF files containing Cn2 estimated from meteorological data obtained at the CESAR site<br> using the flux-based and gradient-based methods at the 10 m level.</li> <li>`wrf_cn2_*.nc`: netCDF files containing Cn2 estimated from WRF model output using the variance-based method (80m)<br> and flux, gradient, and variance-based methods (10m).</li> <li>`wrf_meteo_*.nc`: netCDF files containing a cross-section of CESAR site extracted from WRF model output. This data<br> serves as input for `wrf_cn2_*.nc` files.</li> </ul>
Parameterized Tree Positions Dresden 2017
<p>The parameterized tree positions represent the location of all trees detected on the basis of a laser scan survey from 2017 within the City of Dresden (Germany). Geometric attributes such as tree height and crown diameter were derived for each tree. For this purpose, the urban forest was classified in the point cloud and then individual tree crowns were segmented. Based on these segments, geometric attributes were determined from the LiDAR point cloud.</p> <p>In total, this automated inventory contains ~ 3 million tree positions. This is an overestimation of the actual number of trees which is due to inaccuracies in the segmentation of individual trees, especially in dense tree stands. Despite this overestimation, the tree volumes detected in the point cloud are realistically reproduced. The methodology is described in detail in <a href="https://doi.org/10.1016/j.ufug.2022.127637">this article</a>.</p> <p>Each tree has the following attributes:</p> <ul> <li>"treeID_utm33_tiles" - ID</li> <li>"H_Tree" – tree height (m) </li> <li>"H_Crown" – crown height (m)</li> <li>"H_Trunk" – trunk height (m)</li> <li>"D_Crown" – crown diameter (m)</li> <li>"X", "Y","Z" – position</li> </ul> <p>The dataset is available either as a Shapefile or GeoJSON in the coordinate system ETRS89/UTM zone 33 (EPSG: 25833).<br> The dataset was divided into tiles. The tile number results from the coordinate of the lower left corner in the coordinate reference system.</p> <p>The source data used was made freely available by the “Landesamt für Geobasisinformation Sachsen” (GeoSN) under the license "Data license Germany - attribution - Version 2.0" and can be downloaded under the following links:<br> LiDAR: <a href="https://www.geodaten.sachsen.de/downloadbereich-digitale-hoehenmodelle-4851.html">https://www.geodaten.sachsen.de/downloadbereich-digitale-hoehenmodelle-4851.html</a><br> 3D Building Model: <a href="https://www.geodaten.sachsen.de/downloadbereich-digitale-3d-stadtmodelle-4875.html">https://www.geodaten.sachsen.de/downloadbereich-digitale-3d-stadtmodelle-4875.html</a><br> Aerial Imagery: <a href="https://www.geodaten.sachsen.de/downloadbereich-dop-4826.html">https://www.geodaten.sachsen.de/downloadbereich-dop-4826.html</a></p>
Dataset for the "a parameterization for cloud organization and propagation by evaporation-driven cold pools edges"
<p>When the negatively buoyant air in the cloud downdrafts reaches the surface, it spreads out horizontally, producing cold pools. A cold pool can trigger new convective cells. However, when combined with the ambient vertical wind shear, it can also connect and upscale them into large mesoscale convective systems (MCS). Given the broad spectrum of scales of the atmospheric phenomenon involving the interaction between cold pools and the MCS, a parameterization was designed here. Then, it is coupled with a classical convection parameterization to be applied in an atmospheric model with an insufficient spatial resolution to explicitly resolve convection and the sub-cloud layer. A new scalar quantity related to the deficit of moist static energy detrained by the downdrafts mass flux is proposed. This quantity is subject to grid-scale advection, mixing, and a sink term representing dissipation processes. The model is then applied to simulate moist convection development over a large portion of tropical land in the Amazon Basin in a wet and dry-to-wet 10-days period. Our results show that the cold pool edge parameterization improves the organization, longevity, propagation, and severity of simulated MCS over the Amazon and other different continental areas.</p><p> </p>
'Wind theft' from onshore wind turbine arrays: Sensitivity to wind farm parameterization and resolution
<p>Data and namelists from Pryor S.C., Shepherd T.J., Volker P., Hahmann A.N. and Barthelmie R.J.: ‘Wind theft’ from onshore wind turbine arrays: Sensitivity to wind farm parameterization and resolution. <em>Journal of Applied Meteorology and Climatology (doi:10.1175/JAMC-D-19-0235.1)</em></p>
Supplementary underlying data for "Evaluating parameterization protocols for hydration free energy calculations with the AMOEBA polarizable force field"
<p>This dataset includes additional underlying data for the publication "Evaluating parameterization protocols for hydration free energy calculations with the AMOEBA polarizable force field"</p> <p>Contents:</p> <p>Tukey Honest Significant Difference (HSD) results for solutes 1-47 across all seven parameter sets, as *.txt. These are pairwise comparisons of results between all possible parameter sets. Significant differences are treated as p < 0.05.</p>
Benchmarks for POPL'24 Paper "Commutativity Simplifies Proofs of Parameterized Programs"
<p>This archive contains the benchmark programs used in the POPL'24 paper <i>"Commutativity Simplifies Proofs of Parameterized Programs"</i> by A. Farzan, D. Klumpp and A. Podelski. <a href="https://doi.org/10.1145/3632925">https://doi.org/10.1145/3632925</a></p><p>A preprint of the paper can be found at <a href="https://arxiv.org/abs/2311.02673">https://arxiv.org/abs/2311.02673</a>.</p>
Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF
<p>This repository contains namelists needed to reproduce the WRF(V4.4) simulations analyzed in "Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF"</p>
A Correlated-k Parameterization for O2 Photolysis in the Schumann-Runge Bands
<p>This dataset includes the correlated-k table (both 4- and 12- terms, named as 'k4_O2SR.dat' and 'k12_O2SR.dat', respectively) used in our 1-D photochemical model for parameterizing O2 absorption cross sections in the O2 Schumann-Runge (SR) bands (175–205 nm). Transmission data are saved in files 'Transmission_.csv'. Photolysis frequencies and rates for gases calculated from the 1-D photochemical model can be accessed in files 'PHOTO_.csv'. Matlab is used to plot figures, and the code is in 'OLD_CORRK.m'. '.zip' files should be decompressed for using 1-D output data. Those data are comparisons of different O2 photolysis parameterizations (AF82 band model, Old exponential sum, WACCM, Correlated-k) at the SR bands with/without scattering in the 1-D photochemical model. Specifically, in '1PAL.zip' and '0001PAL.zip', '00' refers to WACCM without scattering, '01' refers to WACCM with scattering, '10' refers to correlated-k without scattering, '11' refers to correlated-k with scattering. 'OLD_1PAL.zip' refers to the 1-D model with old exponential sum fits. 'AF_1PAL.zip' refers to the 1-D model with AF82 band model. All the files can be open with MATLAB code 'OLD_CORRK.m' and be used to plotting all the figures in the paper 'A Correlated-k Parameterization for O2 Photolysis in the Schumann-Runge Bands'.</p>
Post-fire flood hazard model (PF2HazMo) version 1.0.0: Model scripts and parameterization and validation data
<p>Human development at the foot of the mountains faces sediment-laden flood hazards characterized by high-velocity, erosive flows carrying mud and debris, and when flood control infrastructure that protects communities fills with sediment, it loses capacity. The estimation and management of sediment-laden floods have proven challenging because cycles of wildfire, precipitation, and infrastructure sedimentation are still poorly understood. Efforts to model compound hazards such as post-fire floods are relatively new, and existing models do not consider the role of flood control infrastructure, such as debris retention basins and flood channels, in the development of post-fire floods. Here we present data sources and calibration methods to estimate sediment-laden flood hazards downstream of infrastructure on a catchment-by-catchment basis using the Post-Fire Flood Hazard Model (PF2HazMo), a stochastic modeling approach that utilizes continuous simulation to resolve the effects of antecedent conditions and system memory. Data sources provide parameter ranges needed for stochastic modeling, and several performance measures are considered for model calibration. With application to three catchments in Southern California, we show that PF2HazMo predicts the median of the simulated distribution of peak bulked flows within the 95% confidence interval of observed flows, with an order of magnitude range in bulked flow estimates depending on the performance measure used for calibration. Using infrastructure overtopping data from a post-fire wet season, we show that PF2HazMo accurately predicts the number of flood channel exceedances. Model applications to individual watersheds reveal whether existing infrastructure is undersized to contain present-day and future overtopping hazards based on current design standards.</p>
A Parameterized Model for Differential Galaxy Counts at Any Wavelength
<p>Collected literature values of Schechter functions, resulting parameter values of fits to those data as a function of wavelength, and Python scripts to compute simulated differential galaxy counts based on those parameterisations.</p> <p> </p> <p>Future revisions of galaxy_counts.py can be found at https://github.com/Onoddil/macauff/blob/main/macauff/ galaxy counts.py, and future revisions of literature and parameterisations can be found at https://onoddil.github.io/galaxy evolution/galaxy counts.html.</p>
Dataset for "Application of dissipative particle dynamics to interfacial systems: Parameterization and scaling"
<p>Dataset for figures of the upcoming article "Application of dissipative particle dynamics to interfacial systems: Parameterization and scaling" submitted to "AIP Advances".</p>
Data used for developing a parameterization for spatial distribution of solar irradiance over rugged terrain
<p>This dataset includes data and results produced while developing a parameterization for the spatial distribution of solar radiation over mountainous terrain. The method is designed for applications in Earth System Models.</p> <p>The four items included in this dataset are the following:</p> <p><strong>GFDL_preproc_dems</strong> includes digital elevations maps derived from the Shuttle Radar Topography Mission (SRTM) for three sample domains.</p> <p><strong>single-time-step </strong>Includes atmospheric optical properties used as input for Monte Carlo simulations.</p> <p><strong>gmd_2021_grids_light_*</strong> Include maps of terrain parameters derived from SRTM elevation data, and the partition of the domains in homogeneous tiles (sub-grid units) for different number and types of of land units.</p> <p><strong>rmc_simul_res</strong> results of the Monte Carlo simulations, consisting of maps of simulated solar radiation for different domains and solar angles.</p>
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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.