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195 results for “Coupled models”
Southern Hemisphere Circumpolar Wavenumber-4 Pattern Simulated in SINTEX-F2 Coupled Model
<p>These datasets are used to produce Figures in the paper by Senapati et al. (2024). </p> <p>Senapati, B., Morioka, Y., Behera, S. K., & Dash, M. K. (2024). Southern Hemisphere circumpolar wavenumber‐4 pattern simulated in SINTEX‐F2 coupled model. Journal of Geophysical Research: Oceans, 129, e2023JC020801. <a href="https://doi.org/10.1029/2023JC020801" rel="noopener">https://doi.org/10.1029/2023JC020801</a></p>
DYAMOND-II Model Outputs for "Boundary-Layer-Coupled and Decoupled Clouds in Global Storm-Resolving Models: Comparisons with the ARM Observations"
<p>This dataset contains outputs from the DYAMOND Phase-II simulations, focusing on Global Storm-Resolving Models (GSRMs) at various Atmospheric Radiation Measurement (ARM) sites. The dataset supports the analysis presented in the manuscript "Boundary-Layer-Coupled and Decoupled Clouds in Global Storm-Resolving Models: Comparisons with the ARM Observations."</p> <p>Included are high-resolution model outputs from nine GSRMs, detailing simulations of atmospheric processes at six ARM sites, including variables of clouds, temperature, humidity, wind, and surface fluxes. This dataset allows for a direct comparison between GSRM simulations and ARM field observations, facilitating the evaluation of PBL-coupled and decoupled clouds. For further inquiries or assistance regarding the dataset, please contact the corresponding author at su10@llnl.gov.</p>
Investigating solute transport and reaction using a mechanistically coupled geochemical and geophysical modeling approach
<p>This repository encompasses the acquired data for the five experiments of reactive percolation in a column with geoelectrical monitoring. The input and output files of the reactive transport simulations are also given. In addition, five movies of the reaction rate evolving with time are also part of this dataset. The files are:</p> <ul> <li><strong>physicochemical_data.xlsx</strong>: the ionic concentrations, alkalinity, pH, and electrical conductivity from the collected water samples, the pH monitored at two locations in the column, and the inlet and outlet water conductivity and temperature. Porosity and formation factor calculated from the measured calcium concentration are also given.</li> <li><strong>sp-data-exp1-exp2-exp3.xlsx</strong>: the self-potential (SP) method has been used to monitor the reactive percolation for experiments 1, 2, and 3.</li> <li><strong>sip-data-exp4.xlsx </strong>and <strong>sip-data-exp5.xlsx</strong>: the spectral induced polarization (SIP) method has been used to monitor the reactive percolation for experiments 4 and 5.</li> <li><strong>output_exp</strong><em><strong>X</strong></em><strong>.zip</strong>: compressed folders of all output files for the reactive transport simulations using Crunchflow software.</li> <li><strong>1DcalciteFT_ex<em>X</em>.in</strong>: input files for the reactive transport simulation using CrnchFlow software.</li> <li><strong>OldRifleDatabaseLiLi_nicole.dbs</strong>: database for the reactive transport simulation using CrnchFlow software.</li> <li><strong>video-rate-exp</strong><em><strong>X</strong></em><strong>.avi</strong>: movies of the reaction rate along the column and evolving with time for the complete duration of each experiment.</li> </ul>
flood modeling datas for Catastrophic outburst floods along the middle Yarlung Tsangpo River: responses to coupled fault and glacial activity on the southern Tibetan Plateau
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Model data and figure code for results and figures in the manuscript submitted to Geophysical Research Letters "Hysteresis of the Antarctic ice sheet with a coupled ice sheet climate model"
<p>This folder contains the model data and figure code for results and figures in the manuscript submitted to Geophysical Research Letters "Hysteresis of the Antarctic ice sheet with a coupled ice sheet climate model"</p> <p>The code for plotting the figures is the notebook Plot_figures.ipynb</p> <p>Fig1/simulation_output/ : Model output necessary for plotting the first figure </p> <p>The last timestep of each simulation is provided. There is one file for 1D variables (ice volume, ice volume above flotation), and one file for 2D variables (ice sheet thickness for instance).</p> <ul> <li><span>melt_insoPI_output/ : melt branch, pre-industrial insolation. Results for different CO2 levels</span></li> <li><span>growth_insoPI_output/ : growth branch, pre-industrial insolation. Results for different CO2 levels</span></li> <li><span>melt_insoMAX_output/ : melt branch, maximum insolation. Results for different CO2 levels</span></li> <li><span>growth_insoMIN_output/ : growth branch, minimum insolation. Results for different CO2 levels</span></li> </ul> <p><span>compute_SLR_equivalent.py : code to compute the ice sheet volume in SLRe based on model output</span></p> <p><span>Fig1/SLR_files/ : contains the equilibrium ice sheet volume of the different simulations according to the CO2 level</span></p> <p> </p> <p>Fig2/simulation_output/ : Model output necessary for plotting the second figure </p> <p>The last timestep of each simulation is provided. </p> <ul> <li><span>melt_insoPI_enhancedmelt_albfb/ : melt branch, pre-industrial insolation, enhanced melt and albedo feedback. Results for different CO2 levels</span></li> <li><span>growth_insoPI_enhancedmelt_albfb/ : growth branch, pre-industrial insolation, enhanced melt and albedo feedback. Results for different CO2 levels</span></li> <li><span>melt_insoPI_enhancedmelt_fixedalb/ : melt branch, pre-industrial insolation, enhanced melt, no albedo feedback. Results for different CO2 levels</span></li> <li><span>growth_insoPI_enhancedmelt_fixedalb/: growth branch, pre-industrial insolation, enhanced melt, no albedo feedback. Results for different CO2 levels</span></li> </ul> <p><span>compute_SLR_equivalent.py : code to compute the ice sheet volume in SLRe based on model output</span></p> <p><span>Fig2/SLR_files/ : contains the equilibrium ice sheet volume of the different simulations according to the CO2 level</span></p> <p> </p> <p><span>Fig3/simulation_output/ : Model output necessary for plotting the third figure </span></p> <ul> <li><span>1xCO2_nocoupling/ : simulation with pre-industrial CO2 levels and insolation and no coupling to the ice sheet model</span></li> <li><span>8xCO2_nocoupling/ : simulation with 8xpiCO2 (pre-industrial CO2) levels, pre-industrial insolation and no coupling to the ice sheet model</span></li> <li><span>8xCO2_transient_albfb/ : quasi transient simulation, 8xpiCO2 levels, pre-industrial insolation, coupling with the ice sheet model </span></li> <li><span>8xCO2_transient_fixedalb/ : quasi transient simulation, 8xpiCO2 levels, pre-industrial insolation, coupling with the ice sheet model excluding the albedo-melt feedback</span></li> </ul> <p> </p>
Data Supporting Dissipation Scaled Internal Wave Drag in a Global Heterogeneously Coupled Internal/External Mode Total Water Level Model
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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>
Wood Brook catchment: A coupled phenology – surface energy balance model to understand stream – subsurface temperature dynamics
<p>This folder contains data, model input files, model executable and source code required to reproduce results in the paper:"Evaluating a coupled phenology – surface energy balance model to understand stream – subsurface temperature dynamics in a mixed-use farmland catchment" by Han Qiu, Phillip Blaen, Sophie Comer-Warner, David M. Hannah, Stefan Krause and Mantha S. Phanikumar (Water Resources Research).</p> <p>The catchment (referred to herein as Wood Brook, Mill Brook, Mill Haft, BIFOR) is a mixed-use farmland catchment in central England. The "Data" folder contains information needed to create model input files. The "Figures" folder contains MS excel files with observed data (streamflows, groundwater heads, stream, streambed and groundwater temperatures etc) and simulation results to recreate the figures in the WRR paper. The other three folders contain model inputs, outputs and source code.</p>
model output of Cumulus convection over land in cloud-resolving simulations with a coupled ray tracer
<p><strong>768_*</strong>: Output of cloud resolving simulations of a diurnal cycle (Augst 15th, 2016) with shallow cumulus clouds over Cabauw, the Netherlands. All simulations are performed with MicroHH, with radiative transfer computed either with a two-stream approximation or with ray tracing. </p> <ul> <li>2s_het: main simulation with two-stream solver</li> <li>2s_hom_sw: two-stream solver, horizontally averaged surface solar radiative fluxes</li> <li>2s_hom_hrsw: two-stream solver, horizontally averaged heating rates</li> <li>rt_het: main simulation with ray tracing, 256 samples per pixel </li> <li>rt_hom_sw: ray tracing, horizontally averaged surface solar radiative fluxes, 256 samples per pixel </li> <li>rt_hom_hrsw: ray tracing, horizontally averaged heating rates, 256 samples per pixel </li> <li>rt_het_128: ray tracing, 128 samples per pixel</li> <li>rt_het:_64 ray tracing, 64 samples per pixel</li> <li>rt_het:_32 ray tracing, 32 samples per pixel</li> </ul>
Processed model output used in 'The impact of winds on AMOC in a fully-coupled climate model'
<p>Processed model output from wind-nudging experiments used to investigate the Atlantic Meridional Overturning Circulation. </p> <p> </p> <p>For further details, see </p> <p>Roach, L. A, Blanchard-Wrigglesworth E. Ragen, S., Cheng, W., Armour, K. and Bitz, C. M.. (2022). The impact of winds on AMOC in a fully-coupled climate model. In review at Geophysical Research Letters</p>
Pore-scale modeling and investigation on thermal-hydro-mechanical-chemical coupled rock dissolution and fracturing process
<p>Attached files include the executable file of the pore-scale multi-field coupled LBM-DEM program written by C language, post-processing programs to record the reactive surface area and reactive temperature written by MATLAB, and the simulation results of rock acid fracturing with 20MPa at the injection hole.</p>
LiftWEC Deliverable D3.4 - Dataset from coupled hydrodynamic model - SPAR configuration
<p>This dataset contains the OrcaFlex model (.dat files) and the time series of interest (.out files) from the simulations of the LiftWEC coupled hydrodynamic model for the SPAR configuration. Details about the numerical model and results are presented in the corresponding deliverable LiftWEC D3.4 - Implementation of a Coupled Hydrodynamic Model.</p> <p>The simulations are sorted by DLC (1.1 & 6.1). The DLC 6.1 "new mooring" corresponds to simulations of a different mooring system presented in Section 8.1.4 of deliverable D3.4.</p> <p>To understand how the time series are extracted and calculated, the python scripts and input files are provided in the folder PostProc_inputs.</p> <p>For each DLC, an Excel file is present that summarizes the variables main statistics for each simulation (sheet ALL_STATS) and statistics on the whole DLC (sheet DLC_STATS).</p>
A tightly coupled river-ocean model for simulating combined flood due to storm surge and river flow in coastal-urban areas
<p>Coastal flooding, resulting from storm surges or extreme river flows, causes significant causalities and damage to properties in low-lying areas. The simultaneous occurrence of river flows and storm surges, termed combined/compound events, exacerbates the flood risk compared to independent occurrences. Combined flood events are simulated with the help of hydraulic and hydrodynamic models using a loosely or tightly coupled approach. In the loosely coupled approach, a hydrodynamic model simulates storm surges first, and a hydraulic model then simulates inland flood due to river overflow considering surge as the boundary condition at the river mouth/estuary. Conversely, the tightly coupled approach involves simultaneous simulation of both river flow and storm surge by coding the mathematical representation of river and ocean flow dynamics in the same numerical model. This allows the interaction between river and ocean flows to be simulated anywhere in the combined river-ocean computational domain, making it highly relevant for simulating combined floods. However, existing models based on this approach encounter numerical instability, especially in inland regions where topography variation is steep and highly uneven. Also, such combined models are highly limited for large scale applications. Therefore, this research focuses on developing a tightly coupled 2D finite volume river-ocean model called IROMS-C2D. The developed model intends to address the limitations of the previous models and provide a stable solution framework for the simulation of combined flooding resulting from the interaction of storm surges and river flows in coastal urban areas. Further, it enhances our understanding of flood risks in coastal areas, particularly in urban settings, and facilitates the formulation of effective measures for flood control and adaptation of coastal infrastructure.</p>
A tightly coupled river-ocean model for simulating combined flood due to storm surge and river flow in coastal-urban areas
<p>Coastal flooding, resulting from storm surges or extreme river flows, causes significant causalities and damage to properties in low-lying areas. The simultaneous occurrence of river flows and storm surges, termed combined/compound events, exacerbates the flood risk compared to independent occurrences. Combined flood events are simulated with the help of hydraulic and hydrodynamic models using a loosely or tightly coupled approach. In the loosely coupled approach, a hydrodynamic model simulates storm surges first, and a hydraulic model then simulates inland flood due to river overflow considering surge as the boundary condition at the river mouth/estuary. Conversely, the tightly coupled approach involves simultaneous simulation of both river flow and storm surge by coding the mathematical representation of river and ocean flow dynamics in the same numerical model. This allows the interaction between river and ocean flows to be simulated anywhere in the combined river-ocean computational domain, making it highly relevant for simulating combined floods. However, existing models based on this approach encounter numerical instability, especially in inland regions where topography variation is steep and highly uneven. Also, such combined models are highly limited for large scale applications. Therefore, this research focuses on developing a tightly coupled 2D finite volume river-ocean model called IROMS-C2D. The developed model intends to address the limitations of the previous models and provide a stable solution framework for the simulation of combined flooding resulting from the interaction of storm surges and river flows in coastal urban areas. Further, it enhances our understanding of flood risks in coastal areas, particularly in urban settings, and facilitates the formulation of effective measures for flood control and adaptation of coastal infrastructure.</p>
Atmospheric and Coupled Model inter-comparison Study
<p>This repository contains software tools, model output datasets and plotting scripts that used for evaluations and figures presented in the study.</p>
Supporting data for manuscript: "Global-scale evaluation of coastal ocean alkalinity enhancement in a fully-coupled Earth system model"
<p>Supporting data for manuscript: "Global-scale evaluation of coastal<br> ocean alkalinity enhancement in a fully-coupled Earth system model"</p> <p>Authors: Julien Palmieri and Andrew Yool</p> <p>Institute: National Oceanography Centre, European Way, Southampton<br> SO14 3ZH, UK</p> <p>This repository consists of four main sets of files:</p> <p>1. Matlab scripts used for analysis, figure plotting and table<br> preparation</p> <p> Filenames of the format: Figure_??.m</p> <p>2. Raw netCDF output files from UKESM1 for six model experiments</p> <p> Filenames of the format: medusa_c*.nc</p> <p>3. BGCVal processed timeseries shelve files</p> <p> Filenames of the format: u-c*.shelve.txt</p> <p>4. CMM2 processed timeseries netCDF files</p> <p> Filenames of the format: c*_global.nc</p> <p>File sets 2-4 are read and processed by script files in file set 1<br> </p>
Data set supporting publication "Hydrological Coupling and Decoupling of Hydric Hemi-boreal Forest Sites Inferred from Soil Water Models and Tree-Ring Chronology" by Kalvāns A. and Dauškane I. accepted for publication in the scientific journal Forests
<p>These files are supporting information for the article:</p> <p>Kalvāns, Daukškane (<em>accepted</em>). Hydrological Coupling and Decoupling of Hydric Hemi-boreal Forest Sites Inferred from Soil Water Models and Tree-Ring Chronology. <em>Forests</em></p> <p>The data set comprises following elements:</p> <ol> <li>Hydrus-1D soil water model setup files and calculation results ([1_Hydrus_1D_hydric_forest_soil_water_model_instances.zip]) for two study plots and 12 model instances, with following naming convention: [{Site Identifier}__{proportion of active leaf area index}_kLAI__{forced groundwater exfiltration rate cm/day}_SeepIn_const]. That is model instance named [P1__0.4_kLAI__0.05_SeepIn_const.h1d], considers the study site Plot_1, the active leafe area proportion is 0.4 and it has applied constant rate of groundwater exfiltration at the base of the soil column of 0.05 cm/day. The models are forced by E-OBS v26.0e data set for the period from 1980-01-01 to 2022-06-30.</li> <li>Black alder <em>Alnus glutinosa</em> tree ring chronologies for the two study plots ([2_tree_ring_data.zip])</li> <li>Soil and ground-water observation time series for the two study plots ([3_soil_ground_water_observations.zip])</li> </ol>
Sexual Counseling According to the PLISSIT Model in Primary Infertile Couples
ClinicalTrials.gov study NCT04791683. IPD Sharing: NO. Countries: 1. Publications: 6.
Data from: Phylogeography in continuous space: coupling species distribution models and circuit theory to assess the effect of contiguous migration at different climatic periods on genetic differentiation in Busseola fusca (Lepidoptera: Noctuidae)
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Data from: A coupled soil water balance model for simulating depression-focused groundwater recharge
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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.