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37 results for “idealized modeling”
Files from barotropic and baroclinic idealized model runs of the Southern Indian Ocean
<p>These data files correspond to two idealized model runs of the Southern Indian Ocean using the Regional Ocean Modelling System (ROMS) as a framework. Both simulations are forced with monthly mean QuikSCAT winds and are run at a 1/3 degree resolution. </p> <p>The barotropic model is single layer with realistic ETOPO2 bathymetry, a two arc minute ocean-floor elevation data-set smoothed to a resolution of 55.2 km. The file corresponding to this simulation is named: roms_avg_barotropic.</p> <p>The baroclinic model is a 1 and a half layer model where the value of the pycnocline depth and the reduced gravity parameter is set at the initialization stage. Two simulations are presented, the first where 'relaistic' initialization parameters of H=800m and g'= 0.0134 m/s(^2), and the second where the density gradient between the active and passive layers is reduced to a g' of 0.0076 m/s(^2). The two data sets corresponding to these simulations are titled: roms_avg_800_0134 and roms_avg_800_0076</p> <p>Below find a list of variable names and descriptions:</p> <p>zeta=anomaly in thickness of active layer<br> ubar= mean zonal velocity of active layer<br> vbar= mean meridional velocity of active layerh=depth of bathymetry in barotropic model; pycnocline depth in baroclinic model<br> coast=coastline<br> lon_rho=longitude corresponding the density coordinates<br> lat_rho=latitude corresponding the density coordinates<br> lon_u=longitude corresponding the zonal velocities<br> lat_u=latitude corresponding the zonal velocities<br> lon_v=longitude corresponding the density velocities<br> lat_v=longitude corresponding the meridional velocities<br> time=days since model simualtion started</p>
Bern3D model output data from idealized co2 increase-decrease simulations to investigate reversibility in the Earth system
<p>The data described below is output from the Bern3D intermediate complexity model and idealized CO2 increase-decrease simulations to investigate reversibilty and hysteresis for different maximum co2 forcings.</p> <p><br> The data are provided as .csv and .nc files<br> The first row in the .csv files contains the header, which describes the variable. The naming convention is as follows:</p> <p>c#k#_VARIABLE</p> <p>c# indicates the maximum co2 as times pre-industrial (c2 to c5)<br> k# indicates the equilibrium climate sensitivity of the respective simulation in degrees C (k2 to k5)<br> and VARIABLE indicates the value of the respective variable, which are:<br> co2: change in atmospheric co2 concentration in [ppm]<br> amoc: change in maximum of the Atlantic meridional overturning circulation in [Sv]<br> ohc: change in ocean heat content in [10^24 J]<br> seaice: sea-ice area remaining as fraction of the pre-industrial cover<br> Om_arag: fraction of water with Omega_arag > 3 in the upper 175 m<br> o2_thermo: change in thermocline (200-600 m) oxygen concentration in [mmol m^-3]<br> for each variable a separate file exists where the variable and co2 are provided.</p> <p><br> Spatial data to create the maps of hysteresis on a grid-cell basis are provided for the two scenarios as .nc files. The naming is as follows:</p> <p>c#k#_hyst_o2thermo.nc</p> <p>where c# corresponds again to maximum co2 as times pre-industrial and k# to the equilibrium climate sensitivity. The .nc files contain the coordinate (latitude, longitude) centers (lat_t, lon_t) and edges (lat_u, lon_u) as well as the hysteresis area (hystA_o2thermo) in [mmol m^-3].</p> <p><br> The files can be readily importet in python, for example, by:<br> import pandas as pd<br> import xarray as xr<br> <br> # for the .csv files<br> df = pd.read_csv('path+filename', sep=',', header=0, index_col=None)<br> <br> # for the .nc files<br> ds = xr.open_dataset('path+filename')</p> <p><br> For additional information or in case of questions please contact:<br> Aurich Jeltsch-Thömmes<br> aurich.jeltsch-thoemmes@unibe.ch</p>
The Instabilities of the Antarctic Slope Current in an Idealized Model
<p>This dataset includes the model configuration and long-term mean data used in the paper entitled 'The Instabilities of the Antarctic Slope Current in an Idealized Model'.</p>
Interaction of waves with idealized high-relief bottom roughness, model parameters and code
Interactions between waves and large bottom roughness were investigated using Large Eddy Simulations of oscillatory flow over infinite hemisphere arrays. Simulations were made using the open source computational fluid dynamics code OpenFOAM. Wave amplitude, period, and hemisphere spacing were varied to investigate the dependence of kinematics and dynamics on dimensionless parameters, including the Keulegan-Carpenter number (KC), the ratio of wave orbital excursion to roughness element size. This archive includes the input files and user written routines to reproduce the simulations in the paper. Yu, X., J. H. Rosman, and J. L. Hench, 2018. Interaction of waves with idealized high-relief bottom roughness, Journal of Geophysical Research (Oceans), doi:10.1002/2017JC013515.
The ECHAM/MESSy idealized (EMIL) model set-up: data of reference simulations
<p>This data set contains the data from simulations performed with the ECHAM/MESSy IdeaLized (EMIL) dry dynamical core model, as presented in the GMD(D) publication by Garny et al., available under doi https://doi.org/10.5194/gmd-2019-330. For details, please refer to the enclosed data description file.</p>
Data, Sensitivity of 21st-century projected ocean new production changes to idealized biogeochemical model structure
<p>Data for reproducing figures in journal article submitted to Biogeosciences in December 2020.</p> <p>Data generated from global 1-degree simulations of the CESM in an ocean-ice configuration.</p> <p>NP model by Brett. See 10.5281/zenodo.4361705 for code for NP model and to use this dataset to recreate paper figures.</p>
Source Code—Motorized chain models of the ideal chromosome
<p>Code and data for reproducing the findings in "Motorized chain models of the ideal chromosome" by Zhiyu Cao & Peter G Wolynes</p> <p>We carried out hybrid Brownian-Gillespie simulations of the polymer dynamics for the swimming motors. The programs can run in parallel on Cuda software. We use periodic boundary conditions without considering the self-avoiding and excluded volume effect. In addition, we solve the self-consistent equation by an iterative method.</p>
Model run with METROMS to evaluate open boundary conditions in CICE [idealized wind]
<p>Support for time-varying open boundary conditions (OBC) have been developed for sea ice in the Los Alamos Sea Ice Model (CICE) by Pedro Duarte (NPI, Norway). This dataset is a result of using the coupled ocean (ROMS) and sea ice (CICE) modelling framework METROMS (<a href="https://github.com/metno/metroms">https://github.com/metno/metroms</a>) in order to test the effects of the above mentioned boundary conditions. The specific application of METROMS that was used was MET Norway's main forecasting system for the Barents Sea; the Barents-2.5km model; details about the model can be found at <a href="https://ocean.met.no/models">https://ocean.met.no/models</a>.</p> <p>The model was initialized from the TOPAZ4 model (Sakov et al., 2012)<strong> </strong>and was run for the period 2019.09.01 - 2019.09.20, one time without OBC and one time with OBC enabled using input data from TOPAZ4<strong> </strong>at the boundaries. In both runs the model was set up realistically, but with the exception of idealized wind forcing. More specifically, the wind was blowing 10 m/s in the positive xi-direction until 2019-09.07 and then 10/m/s in the negative xi-direction for the rest of the simulation. The reasoning for this was to clearly demonstrate the effects of the OBC. Initially, the wind is blowing the ice away from the boundary and without using OBC for ice, it leaves open water in its path due to no information coming in through the boundary. With OBC enabled however, the simulation appear a lot more sensible with sea ice from TOPAZ4 coming in through the boundaries. When the wind switches to the opposite direction after 2019.09.07, on the case without OBC results in ice piling up at the boundary after some time. However, with OBC enabled, the ice exists the model domain.</p> <p> </p>
Model data used for the paper: "Heat extremes driven by amplification of phase-locked circumglobal waves forced by topography in an idealized atmospheric model"
<p>Atmospheric model output to reproduce the results of the study: "<strong>Heat Extremes Driven by Amplification of Phase-Locked Circumglobal Waves Forced by Topography in an Idealized Atmospheric Model" </strong>published in Geophysical Research letters.<br> <br> Authors: B. Jiménez-Esteve. K. Kornhuber and D. I.V. Domeisen <br> <br> DOI: <a href="https://doi.org/10.1029/2021GL096337">https://doi.org/10.1029/2021GL096337</a><br> <br> For more information about the model setup and the design of the experiments please refer to the above publication.</p>
Idealized large eddy model simulations
<p>This dataset contains matlab and grads output of four large eddy model simulations of the atmospheric boundary layer in Matlab data format. They are used to create figures presented in a paper titled "Large eddy simulations of diurnal entrainment/detrainment of the shallow cumulus clouds in subtropical Western Pacific Ocean". The matlabscripts and figures are in the les_scripts_figs.tar.gz file. Install the *.mat files and dropsonde netcdf files in the following "data" directory tree structure, then the figures can be recreated by running the matlabscripts. The *.ctl and *.dat files are the grads output that contains a 10 min interval statistics of model variables, i.e. stats_sw.dat for 3D variables and stats_sfc.dat for 2D surface variables of these LES experiments. The name of the variables are listed in their corresponding *.ctl files. </p> <p> >ls data<br> CAMP2EX_AVAPS_RD41_v1_20190927_212721.nc L2<br> CAMP2EX_AVAPS_RD41_v1_20190927_222459.nc L2sst<br> CAMP2EX_AVAPS_RD41_v1_20190928_004304.nc les_grads_L1sst.tar.gz<br> cld_iso.mat les_grads_L1.tar.gz<br> cld_mat.tar.gz les_grads_L2sst.tar.gz<br> cldtb_ent_det.mat les_grads_L2.tar.gz<br> L1 mse_cld_entr.mat<br> L1sst tke_cld_all.mat</p> <p>The L1, L1sst, L2, and L2sst are the subdirectories under data. Install the following files in their corresponding experiment subdirectory. </p> <p>>ls data/L1<br> L1_2721.mat L1_qc_w_tke.mat stats_sm.ctl stats_sw.dat<br> L1_cld_depth.mat stats_sfc.ctl stats_sm.dat<br> L1_mse_cld_entr.mat stats_sfc.dat stats_sw.ctl</p> <p>>ls data/L1sst<br> L1sst_2721.mat stats_sfc.ctl stats_sm.dat<br> L1sst_cld_depth.mat stats_sfc.dat stats_sw.ctl<br> L1sst_mse_cld_entr.mat stats_sm.ctl stats_sw.dat</p> <p>>ls data/L2<br> L2_2723.mat stats_sfc.ctl stats_sm.dat<br> L2_cld_depth.mat stats_sfc.dat stats_sw.ctl<br> L2_mse_cld_entr.mat stats_sm.ctl stats_sw.dat</p> <p>>ls data/L2sst<br> L2sst_2723.mat stats_sfc.ctl stats_sm.dat<br> L2sst_cld_depth.mat stats_sfc.dat stats_sw.ctl<br> L2sst_mse_cld_entr.mat stats_sm.ctl stats_sw.dat<br> </p>
Validation of an Idealized Aorta Model Analysed through Fluid-Structure Interaction Simulation with Robin-Neumann Partitioned Approach
<p>The aorta is multiphysics system where hemodynamics and wall structural mechanic are mutually influenced. A fluid-structure interaction approach is appropriate to describe the mechanical alterations suffered by the aortic wall in response to altered hemodynamic patterns. This work demonstrates the validation of the simulated idealized aorta model with a fluid-structure interaction (FSI) model through modified PIMPLE solver to use Robin-Neumann partitioned approach for the strongly-coupled algorithm using solids4foam v2. The validation involves the comparison of streamlines, pressure, and displacements with in vivo measurements. The geometry is reconstructed from the healthy aorta presented in 10.5281/zenodo.5801938. Our analysis shows that the streamlines and pressure pattern are comparable with the literature data acquired using rich medical imaging data. The maximum diameter deformation at the level of abdominal aorta is comparable with measured data and the diameter deformation profile along the cardiac cycle correctly follow the velocity profile. According to this results, our work shows a high-performance simulation suitable for several future works.</p>
Data presented in "Fitting cumulus cloud size distributions from idealized cloud resolving model simulations"
<p>Updated repository containing the data produced and analzed in the manuscript entitled "Fitting cumulus cloud size distributions from idealized cloud resolving model simulations" by J. Savre and G. Craig, submitted to the Journal of Advances in Modeling Earth Systems.</p> <p>New uploads: time series file (T_S), vertical profiles (profiles_tot.nc) and horizontal slices at 2km (slice_z_2000_bis.nc). Uploaded files concern the LBA case only.</p>
Particle tracking data in idealized and realistic estuary models
<p>Particle tracking data in the realistic North River estuary model, Delaware estuary model, and idealized estuary models with different channel dimensions.</p>
Inputs to an idealized numerical model based on MITgcm and model results
<p>This dataset contains input files for model runs used in Leng et al. 2021, "Temporal Evolution of a Geostrophic Current under Sea Ice: Analytical and Numerical Solutions" (to be submitted to JPO). The model used in this study is an idealized channel configuration based on the Massachusetts Institute of Technology general circulation model (MITgcm). Original source code is the MITgcm_c66m. Some representative output is also included.</p> <p>code: contains code configuration.</p> <p>input: contains parameters, initial conditions, forcing, etc.</p> <p>output: contains model output.</p>
DATASET_JGR_field investigation & idealized model results
<p><strong>This dataset supplements the article: Li, H., Yang, Q., Mo, S., Huang, J., Wang, S., Xie, R., Luo, X., Liu, F., Formation of turbidity maximum in the Modaomen Estuary of the Pearl River, China: the roles of mouth bar, submitted to the Journal of Geophysical Research: Oceans (2022JC018766)</strong></p> <p>Contact information: Dr. Liu, F., School of Ocean Engineering and Technology, Sun Yat-sen University, Guangzhou, 510275, China.</p> <p>Email: liuf53@mail.sysu.edu.cn (Liu, Feng)</p> <p><strong><em>Brief view of Dataset</em></strong></p> <p><strong>1. Field investigation</strong></p> <p>Hydrodynamics, suspended sediment concentration, and sediment particle size distribution were measured via four cruises shipboard in the mouth bar zone of the ME during the dry season of 2020. Eighteen gauging stations covering the mouth bar zone of the ME were observed during both the neap tide (26 October 2020) and spring tide (1 November 2020). The data from gauging stations #16, #17, and #18 during ebb and ebb-flood periods during the neap tide were the same. This directory contains the following datasets.</p> <p><strong>-data_current_velocity.xlsx:</strong> station number, time, longitudinal, latitude, x, y, depth observed from ADCP, depth of the last characteristic layer in the water column (depth_last_cell, m), depth of the first characteristic layer in the water column (depth_first_cell, m), and current velocity (cm/s) and direction (°) of six characteristic layers during spring and neap tides.</p> <p><strong>-data_salinity_turbidity_SSC.xlsx:</strong> station number, time, longitudinal, latitude, x, y, depth observed from ADCP, depth observed from OBS (depth_OBS_H), turbidity (ntu), salinity (psu), and suspended sediment concentration (SSC, mg/L) during spring and neap tides.</p> <p><strong>-data_particle_size_distribution.xlsx:</strong> station number, mean size (μm), suspended sediment size distribution of three characteristic layers, and bed sediment size distribution during spring and neap tides.</p> <p><strong>-data_river_discharge&water_level.xlsx:</strong> Hydrological conditions during investigation period during both neap and spring tides, i.e., river discharge at Makou station and tidal level at Sanzao station.</p> <p><strong>2. Idealized model results</strong></p> <p>Model settings and results of the idealized estuaries. The <strong>'Grid_idealized_estuary.grd'</strong> and <strong>'Grid_Enc_idealized_estuary.enc'</strong> are essential grid files for Delft3D-FLOW model. The scenarios with mouth bar and without mouth bar share the same grid file. 32 sites are set up in the central longitudinal section of the models to monitor the aggregate parameters in the longitudinal section near the outlet. The results of aggregate parameters are as follows.</p> <p>(1) Idealized estuary with a mouth bar: The formats of the result files are<strong> 'mouthbar_XX_MDM(Site No.).mat'</strong>. The 'XX' consists of bed level, salinity, bed shear stress, sedimentcoh1 (i.e., cohesive suspended sediment concentration), and vertical and horizontal velocities.</p> <p>(2) Idealized estuary without mouth bar: The formats of the result files are <strong>'nobar_XX_MDM(Site No.).mat'</strong>. The 'XX' consists of bed level, salinity, bed shear stress, sedimentcoh1 (i.e., cohesive suspended sediment concentration), and horizontal and vertical velocities.</p>
Studying the scale selection of mixed Rossby-gravity waves: Idealized simulations with the TIGAR model
<p>Mixed Rossby-gravity waves peak in two atmospheric regions in reanalysis: the upper troposphere and the upper stratosphere. The scales of MRG waves are different in these two regions, which can be seen e.g. on real-time MRG wave vertical profiles (https://modes.cen.uni-hamburg.de/products#MRG). In order to understand the MRG wave scale selection in these regions, we run idealized simulations with the TIGAR model (Vasylkevych and Zagar, 2021) with a symmetric initial height perturbation with respect to the equator and zonal wind profiles derived from ERA5 reanalysis (Hersbach et al, 2020). In addition, we also run TIGAR simulations with symmetric initial height perturbation and idealized zonal jets centered at various latitudes.</p>
Datasets used for the paper "Enhanced Blocking Frequencies in Very-high Resolution Idealized Climate Model Simulations"
<p>Atmospheric model and processed data for reproducing the results of "Enhanced Blocking Frequencies in Very-high Resolution Idealized Climate Model Simulations" currently submitted to Geophysical Research Letters.</p>
Potential map generated by the RF spatial model to define ideal zones for the occurrence of high density of giant trees in the Amazon
<p>The provided image is a theoretical map of giant tree density in the Amazon, generated from a spatial model based on the **Random Forest** algorithm. The map displays the spatial distribution of tree density, representing the number of trees taller than 60 meters per square kilometer (trees/km²). The model was developed using climatic, topographic, and soil variables to predict areas with higher concentrations of these giant trees.</p> <p>The areas are color-coded according to different density ranges, where:<br>- Lighter shades indicate lower tree density (≤ 5 trees/km²),<br>- Darker shades indicate higher density (up to 141 trees/km²).</p> <p>Biogeographic provinces within the Amazon biome, such as the **Guiana Shield**, **Xingu-Tapajós**, and **Roraima**, are highlighted, showing distinct density patterns across the Amazon region. This map is a valuable tool for understanding the spatial distribution of giant trees in the Amazon and plays a crucial role in conservation efforts and ecological monitoring in the region.</p>
Croco Idealized Model Output for paper: https://doi.org/10.1029/2024JC021204
<p>This is the output of croco idealized model for the JGR-Oceans paper</p>
Modeling the thermodynamic properties of saturated lactones in non-ideal mixtures with the SAFT-γ Mie approach. JCED 2023
<p>All computational data in the publication.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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