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154 results for “parameterization”
VCR bay depth and seagrass shoot density data for parameterization of GEOMBEST++Seagrass
Data from the Virginia Coast Reserve relating bay fetch-depth and seagrass density-depth used to parameterize GEOMBEST++Seagrass for simulations investigating coupled barrier-marsh-bay evolution. DepthFetch data table: Length, depth, and type (seagrass or bare sediment) of all bathymetric transects used to construct the depth-fetch lookup table in the model. Transects were extracted from all VCR basins using the bathymetry from Richardson et al. (2014 doi:10.6073/pasta/63a22558d6650fae8232b6a8814a90d5), vary in length, and run with the dominant wind direction from basin margin to basin margin. ShootDensity tab: Shoot densities of restored Zostera marina plots in Hog Island Bay binned by depth for years 3-6 of seeding. Data originally from McGlathery (2013 doi:10.6073/pasta/5a6ea442cf59cabb3112bb634a968ae5). The maximum density of each bin is used as a representative shoot density based on depth.
Statistical model training data for "Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection"
<p>Gzipped CSV files containing convection scheme inputs and outputs used for training.</p> <p>Column format of each file:</p> <p>THETA_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,Q_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DTHETA_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DQ_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28</p> <p>where THETA_IN are input values of potential temperature [K], Q_IN are input values of specific humidity [kg/kg], DTHETA are changes in potential temperature due to convection [K], DQ are changes in specific humidity due to convection [kg/kg].</p> <p>Key:</p> <p>"llcs" are simulations with Lambert-Lewis.</p> <p>"gr" are simulations with Gregory-Rowntree.</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>All are 30 day simulations either for January "jan" or July "jul".</p> <p> </p>
Effects of different vegetation drag parameterizations on the tidal propagation in coastal marshlands - 2DLM simulation results
<p>This <strong>2DLM.zip</strong> file contains the results of in total 12 idealized tidal flooding simulations using MPAS-Ocean, with the vegetation drag resolved by the <strong>2 D</strong>imensional <strong>L</strong>andcover-type-determined <strong>M</strong>anning (<strong>2DLM</strong>) parameterization. The zip file has 3 folders and 4 files in each folder. Each file is named by a marsh type considered in a simulation, and each folder is named by a tidal amplitude (in meter) that drives the simulations.</p>
Effects of different vegetation drag parameterizations on the tidal propagation in coastal marshlands - 3DFV simulation results
<p>This <strong>3DFV.zip</strong> file contains the results of in total 15 idealized tidal flooding simulations using MPAS-Ocean, with the vegetation drag resolved by the <strong>3 D</strong>imensional <strong>F</strong>lexible <strong>V</strong>egetation (<strong>3DFV</strong>) parameterization. The zip file has 3 folders and 5 files in each folder. Each file is named by a vegetation aboveground biomass (in g m<sup>-2</sup>) considered in a simulation, and each folder is named by a tidal amplitude (in meter) that drives the simulations.</p>
Effects of different vegetation drag parameterizations on the tidal propagation in coastal marshlands - 3DSV simulation results
<p>This <strong>3DSV.zip</strong> file contains the results of in total 15 idealized tidal flooding simulations using MPAS-Ocean, with the vegetation drag resolved by the <strong>3 D</strong>imensional <strong>S</strong>tiff<strong> V</strong>egetation (<strong>3DSV</strong>) parameterization. The zip file has 3 folders and 5 files in each folder. Each file is named by a vegetation aboveground biomass (in g m<sup>-2</sup>) in a simulation, and each folder is named by a tidal amplitude (in meter) that drives the simulations.</p>
Effects of different vegetation drag parameterizations on the tidal propagation in coastal marshlands - 2DVM simulation results
<p>This <strong>2DVM.zip</strong> file contains the results of in total 15 idealized tidal flooding simulations using MPAS-Ocean, with the vegetation drag resolved by the <strong>2 D</strong>imensional <strong>V</strong>egetation-determined <strong>M</strong>anning (<strong>2DVM</strong>) parameterization. The zip file has 3 folders and 5 files in each folder. Each file is named by a vegetation aboveground biomass (in g m<sup>-2</sup>) considered in a simulation, and each folder is named by a tidal amplitude (in meter) that drives the simulations.</p>
Data from: Energy benefits and emergent space use patterns of an empirically parameterized model of memory-based patch selection
Many species frequently return to previously visited foraging sites. This bias towards familiar areas suggests that remembering information from past experience is beneficial. Such a memory-based foraging strategy has also been hypothesized to give rise to restricted space use (i.e. a home range). Nonetheless, the benefits of empirically derived memory-based foraging tactics and the extent to which they give rise to restricted space use patterns are still relatively unknown. Using a combination of stochastic agent-based simulations and deterministic integro-difference equations, we developed an adaptive link (based on energy gains as a foraging currency) between memory-based patch selection and its resulting spatial distribution. We used a memory-based foraging model developed and parameterized with patch selection data of free-ranging bison Bison bison in Prince Albert National Park, Canada. Relative to random use of food patches, simulated foragers using both spatial and attribute memory are more efficient, particularly in landscapes with clumped resources. However, a certain amount of random patch use is necessary to avoid frequent returns to relatively poor-quality patches, or avoid being caught in a relatively poor quality area of the landscape. Notably, in landscapes with clumped resources, simulated foragers that kept a reference point of the quality of recently visited patches, and returned to previously visited patches when local patch quality was poorer than the reference point, experienced higher energy gains compared to random patch use. Furthermore, the model of memory-based foraging resulted in restricted space use in simulated landscapes and replicated the restricted space use observed in free-ranging bison reasonably well. Our work demonstrates the adaptive value of spatial and attribute memory in heterogeneous landscapes, and how home ranges can be a byproduct of non-omniscient foragers using past experience to minimize temporal variation in energy gains.
Vibrato Analysis Dataset, Detection and Parameterization
<p>vibrato_analysis</p> <p>Vibrato Analysis Dataset,Detection and Parameterization</p> <p>Introduction</p> <p>This dataset was created as an outcome of a summer internship with Good-sounds project. Sound recordings were used for vibrato analysis. The dataset contains monophonic recordings of single notes and melodies for four instruments with annotations of four different recording sets regarding some vibrato parameters.</p> <p>It is organized according to the owner of the recordings. Sounds with vibrato and no vibrato are presented within the folders with their annotations in cvs format. All annotations except than the alto saxophone recordings include derivative analysis of pitch parameters.</p> <p>One of the recordings for violin was made in the Universitat Pompeu Fabra recording studio by a violist from MTG. Single notes of two octaves and four different melodies starting from different pitch are recorded in wav format sampled at 44.1 kHz. Each sound separated by semitones is recorded four times for no vibrato,slow, standard and fast rates of vibrato. Melodies were played for no vibrato and vibrato at a standard rate to have the attenuation at the end of the note.</p> <p>One other recording set was taken from Good-sounds project. The only brass instrument within the whole dataset is this one for alto saxophone. It was again recorded in the Universitat Pompeu Fabra / Phonos recording studio. This dataset for alto saxophone not only contains vibrato and non vibrato sounds but also some different tonal characteristics.</p> <p>Three other recordings are downloaded from a user named Carlos_Vaquero in <a href="https://freesound.org/">Freesound</a>. Violin, violoncello and transverse flute recordings were downloaded and used for non commercial purposes under creative commons license. Within the Carlos_Vaquero folder, audio files are separated according to the type of instruments.</p> <p>Dataset Description</p> <p>The database is meant for organizing the sounds in a handy way. It is organized according to the creator. In each three datasets, annotations and analysis parameters are available within the csv files and each has 11 field descriptor.</p> <ul> <li>Carlos_Vaquero <ul> <li>Transverse_flute</li> <li>Violin</li> <li>Violoncello</li> </ul> </li> <li>Good-sounds-Alto sax <ul> <li>Alto-sax</li> </ul> </li> <li>MTG - Violin <ul> <li>Violin</li> </ul> </li> </ul> <p>Except than alto-sax recordings, each contains following parameters:</p> <ul> <li><strong>Peak_Percentage:</strong> Percent wise proportion of peaks in first derivation of the pitch trajectory.</li> <li><strong>Mean_Difference:</strong> Mean value of the differences of two consecutive peaks.</li> <li><strong>Max_Difference:</strong> Maximum separation of side by side peaks.</li> <li><strong>Index_Max (first one):</strong> Index value of the maximum peak in the derivative array.</li> <li><strong>Location_Max (%):</strong> Location of the maximum peak in the array, percent wise.</li> <li><strong>Start_End_Time (seconds):</strong> Starting and ending time instants of vibrato in the recording.</li> <li><strong>Duration:</strong> Duration of the vibrato part of the recording.</li> </ul> <p>Documentation</p> <p>Related report can be found in the GitHub repository as "Vibrato Analysis Internship Report".</p> <p>License</p> <p>All the software is distributed with the Affero GPL v3 license.</p>
Dataset for "Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime" by Pahlavan et al. (2023)
Open the record for dataset details and reuse information.
SCM files for "Representing Effects of Surface Heterogeneity in a Multi-Plume Eddy Diffusivity Mass Flux Boundary Layer Parameterization"
<p>Supporting files for manuscript "Representing Effects of Surface Heterogeneity in a Multi-Plume Eddy Diffusivity Mass Flux Boundary Layer Parameterization" by N.P. Arnold.</p> <p>The data consists of source code modifications relative to v11.2.0 of the GEOS GCM, modified boundary conditions for running single column model experiments, and netcdf model output from those experiments.</p>
Dataset for the article titled ""An Updated Parameterization of the Unstable Atmospheric Surface Layer in WRF Modeling System"".
<p>The dataset is organized in to five ZIP folders as described below:</p> <p>1. Offline_Exp_Data: This contains data for stability parameter (z/L), transfer coefficient for momentum (CD), and heat (CH) simulated from different experiments using the bulk flux algorithm (offline mode) corresponding to different similarity functions over smooth (z0 = 0.01 m), transition (z0 = 0.1 m), and rough (z0 = 1 m) surfaces. This dataset corresponds to Figure 4.</p> <p>2. Similarity_Functions: This contains data for the similarity functions for momentum and heat in gradient (Phi_m and Phi_h) as well as integrated (Psi_m and Psi_h) forms with stability parameter (z/L) for considered functional forms of similarity functions (e.g., Businger et al., 1971 (BD71); Carl et al., 1973 (CL73); Kader and Yaglom, 1990 (KY90); and Fairall et al., 1996 (F96)) under unstable conditions. The dataset corresponds to Figures 2 and S1 (supporting information).</p> <p>3. WRF_Data1: This contains hourly averaged data for considered variables from WRF model simulations during the MAM (March–April–May) season. This dataset can be used to reproduce Figures 9, 10, and 11.</p> <p>4. WRF_Data2: This contains model output for considered variables extracted during highly convective hours (z/L<-10 over most of the domain) in the daytime. This dataset can be used to reproduce Figures 12, S3, S4, S5, and S6.</p> <p>5. WRF_Data3: This consists of data from different simulations (CTRL, Exp1-4) with the WRF model extracted at the location of the flux tower (23.412 N, 85.44 E (Ranchi), India). The dataset contains stability parameter (z/L), bulk Richardson number (RiB), transfer coefficients for momentum (CD) and heat (CH), sensible heat flux (HFX), 10-m wind speed (WS), u*2 (representative of momentum flux), and 2-m temperature (T2m). This dataset corresponds to Figures 5, 6, 7, 8, S2, S7, and S8.</p>
Dataset for "Contributions of Parameterized Gravity Waves and Resolved Equatorial Waves to the QBO Period in a Future Climate of CESM2" by Lee et al. (2024)
Open the record for dataset details and reuse information.
Data for "Improved simulation of Madden–Julian Oscillation with the modified moist physical parameterizations for a global climate model"
<p>Model output data for the manuscript "Improved simulation of Madden–Julian Oscillation with the modified moist physical parameterizations for a global climate model", including convective precipitation (PRECC), large-scale precipitation (PRECL), mean SST and U850, <span>column-integrated MSE tendency anomalies, and boundary layer moisture convergence. </span></p>
Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations: Data and Visualization Notebooks
<p>The data, jupyter notebooks, and saved model weights for the "Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations" Hu et al. (2025) arxiv preprint: <a href="https://arxiv.org/abs/2407.00124">arXiv:2407.00124</a>. This updated version contains more analysis notebooks together with related data/model.</p>
Data for "Stable climate simulations using a realistic GCM with neural network parameterizations for atmospheric moist physics and radiation processes"
<p>This is sampling data of "Stable climate simulations using a realistic GCM with neural network parameterizations for atmospheric moist physics and radiation processes".</p> <p>'qv_nn_in' for the large scale specific humidity, [kg/kg]<br> 'T_nn_in' for the large scale temperature, [K]<br> 'dqvls_nn_in' for the large scale moisture advection, [kg/kg/s]<br> 'dTls_nn_in' for the large scale moisture advection, [K/s]<br> 'qtend_check' for the moistening rate by CRM, [kg/kg/s]<br> 'stend_check' for the heating rate by CRM, [K/s]<br> 'SOLS' for direct shorwave solar radiation down to surface, [W/m2]<br> 'SOLSD' for diffusive shortwave solar radiation down to surface, [W/m2]<br> 'SOLL' for direct near infrared solar radiation down to surface, [W/m2]<br> 'SOLLD' for diffusive near infrared solar radiation down to surface, [W/m2]<br> 'SOLIN' for insolation at model top, [W/m2]<br> 'FSNS' for net shortwave radiation at model surface, [W/m2]<br> 'FSNT' for net shortwave radiation at model top, [W/m2]<br> 'FLNS' for net longwave radiation at model surface, [W/m2]<br> 'FLNT' for net longwave radiation at model top, [W/m2]<br> 'SPPS' for surface pressure, [Pa]</p> <p>To download the full dataset of the SPCAM simulation in 1998. Please click the dropbox link: https://www.dropbox.com/s/p841v1tw00rokdy/SPCAM_VAR_1998.tar.gz?dl=0</p>
The bulk parameterizations of turbulent air-sea fluxes in NEMO4: the origin of Sea Surface Temperature differences in a global model study
<p>This repository contains the code and the data used to produce the results of "The bulk parameterizations of turbulent air-sea fluxes in NEMO4: the origin of Sea Surface Temperature differences in a global model study" a discussion paper by G. Bonino, D. Iovino, L. Brodeau, S. Masina submitted to Geoscientific Model Development.</p> <p>- DATA.tar contains the 5 days model outputs to produce the figures in the manuscript.</p> <p>- CODE.tar contains the code and the namelists to run the experiments. The namelists and the modified code for run each experiments are available in the subfolder CODE/cfgs/. </p>
Simulation data for "A new convective parameterization applied to Jupiter: implications for water abundance near the 24deg N region"
<p>Simulation outputs and initialization for the journal article titled "A new convective parameterization applied to Jupiter: implications for water abundance near the 24º N region" by Sankar & Palotai.</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>
Macromolecule distributions input file for the OCEANFILMS parameterization
<p>This dataset provides the inputs required for the OCEANFILMS parameterization of sea spray organic matter emissions, as it has been implemented in the Energy Exascale Earth System Model version 1 (E3SMv1; Burrows et al., 2014; 2022). The file was generated as described in Burrows et al. (2014). Briefly, we used the Parallel Ocean Program (POP; Maltrud et al., 1998) to simulate the general circulation and its biogeochemical elemental cycling (BEC) routines (Moore et al., 2004) to simulate marine biogeochemistry. Both are components of the Community Earth System Model (CESM; http://www2.cesm.ucar.edu, Hurrell et al., 2013). Calculations were performed using the CESM 1.0 beta release 11.</p> <p>The macromolecular distributions contained in these files were calculated based on various fields from these CESM simulations.</p> <p>1. <strong>Lipids</strong> were calculated on the basis of the POP-simulated phytoplankton and zooplankton concentrations (Burrows et al., 2014; Eq. 11).</p> <p>2. <strong>Polysaccharides</strong> were assumed to have a concentration identical to POP-simulated semilabile dissolved organic carbon (SDOC; Burrows et al., 2014; Eq. 12).</p> <p>3. <strong>Proteins</strong> were assumed to have a concentration equal to one-third of POP-simulated SDOC (Burrows et al., 2014; Eq. 13).</p> <p>4. A <strong>processed</strong> DOC group was estimated as a residual term from these surface pools (Burrows et al., 2014; Eq. 14, and surrounding text). This field has negligible impacts on the results of the OCEANFILMS parameterization, but has been retained here for clarity.</p> <p>5. A <strong>humic</strong> compound group was estimated by assuming a fixed concentration of humic substances below the mixed layer, and estimating the effects of mixing this humic matter into the ocean mixed layer (Burrows et al., 2014; Eq. 17). As with the processed DOC group, this field's impact on the resulting predictions of the OCEANFILMS parameterization is negligible, but it has been retained here for clarity.</p> <p>All fields have the units µM C.</p> <p>The dataset is also available form the E3SM inputdata repository (last access 17 March 2022): https://web.lcrc.anl.gov/public/e3sm/inputdata/atm/cam/chem/trop_mam/marine_BGC/monthly_macromolecules_0.1deg_bilinear_latlon_year01_merge_date.nc </p>
Data for the publication: A parameterization of cirrus cloud formation: Revisiting competing ice nucleation
<p>The files contain the data sets shown in the publication "A parameterization of cirrus cloud formation: Revisiting competing ice nucleation" to appear in J. Geophys. Res. Atmos. in 2022 (revised manuscript submitted) The files include a power point slide containing the schematic and xmgrace plot files containing the research data (ASCII) shown in all other figures that appear in the paper and its appendix.</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.