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26 results for “mesoscale modelling”
Data used in "BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean"
<div> <p>This repository contains the data used to generate the figures for the submitted manuscript "BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean".</p> </div> <h3>Contents</h3> <div> <ul> <li> <p>Model input:</p> <ul> <li> <p>INPUTS: ocean model input/grid files</p> </li> <li> <p>PISCES_INPUTS: BGC input files</p> </li> <li> <p>OBC: open boundary forcing </p> </li> <li> <p>WEIGHTS: weight files for ERA interim forcing</p> </li> </ul> </li> </ul> </div> <div> <ul> <li> <p>Manuscript files:</p> <ul> <li> <p>data: files used to generate manuscript images</p> </li> <li> <p>config, src, notebooks: Python code and Jupyter notebooks used to generate images</p> </li> <li> <p>figures, supplementary: manuscript figures and supplementary figures</p> </li> </ul> </li> </ul> </div> <div> </div> <div><strong>Abstract: </strong>"We present BIOPERIANT12, a regional model configuration of the Southern Ocean (SO) at a mesoscale-resolving 1/12 degree. This is a stable, ocean–ice–biogeochemical configuration derived from the Nucleus for European Modelling of the Ocean (NEMO) modelling platform. It is specifically designed to investigate questions related to the mean state, seasonal cycle variability and mesoscale processes in the mixed layer and within the upper ocean (<1000 m). In particular, the focus is on understanding processes behind carbon and heat exchange, systematic errors in biogeochemistry and assumptions underlying the parameters chosen to represent these SO processes. The dynamics of the ocean model play a large role in driving ocean biogeochemistry and we show that over the chosen period of analysis 2000–2009 that the simulated dynamics in the upper ocean provide a stable mean state, as compared to observation-based datasets (themselves subject to biases such as sparsity of data, cloud cover, etc.), and through which the characteristics of variability can be described. Using ocean biomes to delineate the major regions of the SO, the model demonstrates a useful representation of ocean biogeochemistry and partial pressure of carbon dioxide (pCO2). In addition to a reasonable model mean state performance, through model–data metrics BIOPERIANT12 highlights several pathways for improving Southern Ocean model simulations such as the representation of temporal variability and the overestimation of biological biomass."</div>
Turbulent kinetic energy over large wind farms observed and simulated by the mesoscale model WRF (3.8.1)
<p>This repository contains the WRF configuration files necessary to reproduce the simulations <br> as described in Siedersleben et al. 2019 (https://doi.org/10.5194/gmd-2019-100)</p> <p>The file windturbines_GMD.txt contains the locations of <br> all windturbines implemented in the simulations. The corresponding attributes of each <br> wind turbine type is described in the wind-turbine-xx.tbl. Be aware that all windturbines use the same power and thrust coefficients only the different hub heights and rotor diameters are taken into account as described in Siedersleben et al. (2019).</p> <p>The namelist.input_nameOfSimulation files necessary to run the simulations are provided in this repository as well. You may notice that <br> there are less namelist files than simulations. The simulations not using a TKE source use the same namelists as the ones with a TKE a source. However, the WRF model needs to be recompiled using the manipolated module_wind_fitch.F (you find this file in this repository). The sensitivity studies investigating the impact of the uncertainties in the power and thrust coefficients use the namelist of the control simulation CNTRb, but with manipulated wind-turbine-x_modMin/Max.tbl wind turbine files.</p> <p>The two python files get_era5*.py can be used to retrieve the ERA5 data, driving the WRF model. <br> Note that the dates and pathes have to be adjusted in the python files. <br> After downloading the surface and model level data some postprocessing <br> is necessary as described nicely here: "http://valcap74.blogspot.com/2017/10/how-to-run-wrf-model-driven-by-era5-on.html". For this<br> purpose the simple script called postProcessERA5 (based on the blog entry mentioned above) can be used.</p>
A High-Resolution Dataset of Global Urban Fraction for Mesoscale Urban Modelling
<p>Coupled urban-atmospheric models are extensively used to understand the urban environment and its impact on atmospheric processes. A common requirement of these models is information about the “urban fraction” (fraction of model grid covered by impervious surface area (ISA)). The European Space Agency (ESA) WorldCover product provides a global land cover map for the base year of 2020 and 2021 at a spatial resolution of 10 m. The dataset is based on Sentinel-1 and Sentinel-2 data with an overall accuracy of 74.4% (2020) and 76.7% (2021). In this study we process the WorldCover dataset and provide a ready-to-use “urban fraction” that can be incorporated in urban modelling systems. The dataset contains GeoTIFF and Weather Research and Forecasting Pre-processing System (WRF-WPS) format files for 1, 0.5, 0.25, 0.009 (~1 km), 0.0027 (~300 m), and 0.0009 (~100 m) degree spatial resolutions. The GeoTIFF files can be converted to other urban mesoscale modelling systems. Please check the README.txt for more information on using the dataset.</p> <p>Note: version 2.0.0 uses WorldCover 2021 v200 dataset for processing of urban fractions, while version 1.0.0 uses WorldCover 2020 v100 dataset.</p> <p>For more information please see here: <a href="https://1drv.ms/w/s!Ai5IcIuv5U4DioElr0E8CEq0DE3CSw?e=Wdp9i4">https://1drv.ms/w/s!Ai5IcIuv5U4DioElr0E8CEq0DE3CSw?e=Wdp9i4</a></p>
Scripts and datas for "A unified energy-constrained mesoscale parameterisation for ocean climate models".
<p>Scripts and datasets used for creating the results of a submitted work :</p> <p><strong>R. Torres, R. Waldman, G. Madec, C. de Lavergne, R. Séférian and J. Mak</strong>: <em>A unified energy-constrained mesoscale parameterisation for ocean climate models. </em>(submitted in JAMES).<em><br></em></p> <p>Datas include eORCA1 mesh files (directory "mesh") and simulations output (direcotories "runs/*/output"). However, to avoid heavy archive, only 2D simulations output are provided. The post-processed 3D variables are first pre-processed for each simulations (directories "runs/*/post/post/post_averag_1995-2017").</p> <p>The reference EKE of <a href="https://doi.org/10.1029/2023gl104688">Torres et al. (2023)</a> is provided (directory "obs/postprocessed_kinetic_energy") while other observational reference datasets have to be download by the user (e.g. <a href="https://www.ncei.noaa.gov/archive/accession/NCEI-WOA18">World Ocean Atlas 2018</a>, <a href="https://gmd.copernicus.org/articles/13/3643/2020/">Tsujino et al. (2020)</a> and <a href="https://www.bodc.ac.uk/data/published_data_library/catalogue/10.5285/04c79ece-3186-349a-e063-6c86abc0158c/">RAPID</a>)</p> <p>IPython notebooks for computing and plotting metrics are provided :</p> <ul> <li><em>james-eke-heat_budget.ipynb</em> : plots for heat transport and global heat storage (section 4.1)</li> <li><em>james-eke-southern_ocean.ipynb</em> : plots for Southern Ocean (section 4.2) analysis</li> <li><em>james-eke-north_atlantic.ipynb</em> : plots for North Atlantic and Labrador Sea (section 4.3) analysis</li> <li><em>james-eke-timeseries.ipynb</em> : plot 0D metric timeseries for simulations (including spin-up)</li> </ul> <p>Note however that these scripts use the author python library XOCE availbale on GitHub: https://github.com/torresr-cnrm/xoce. All the scripts have been runned using the version 0.2 of XOCE. Feel free to contact (romain.torres@meteo.fr) for any help in installing and using this library.</p>
Shock physics mesoscale modeling of shock stage 5 and 6 in ordinary and enstatite chondrites: modeling data
<p>These data are related to:</p> <p>Moreau, J., Kohout, T., Wünnemann K., Halodova, P., Haloda, J., 2019.<br> Shock physics mesoscale modeling of shock stage 5 and 6 in ordinary and enstatite chondrites.<br> Icarus, 332, 50-65. <a href="https://doi.org/10.1016/j.icarus.2019.06.004">https://doi.org/10.1016/j.icarus.2019.06.004</a></p> <p>Any use of these files, scripts (partial or complete) in research papers, please reference the paper above + Moreau et al. (2017, 2018) (references compiled in the above-mentioned paper).</p> <p>To use these files, you will need:<br> - authorized access to the iSALE shock physics code (iSALE-Dellen version) re-compiled with our modifications, with reference<br> to the manual in your work<br> - access to the pySALEPlot tool for iSALE users made by T. Davison acknowledged in your work<br> - running the iSALE models to generate the different jdata.dat files (average size of a jdata.file is 7 Go)<br> - python<br> - Ubuntu or macOS</p> <p> </p> <p>(more info in README.txt file)</p>
Model intercomparison of medicane Ianos using ten mesoscale numerical frameworks
<p>The dataset provides numerical simulations of the high-impact medicane Ianos of September 2020. It is based on a collective effort with five mesoscale models to look for a robust response among ten numerical frameworks used in the community involved in the networking activity of the EU COST Action "MedCyclones" <a href="https://medcyclones.eu/">https://medcyclones.eu/</a></p> <p>The five mesoscale models are:</p> <ul> <li>The BOLAM hydrostatic model and the MOLOCH non-hydrostatic, fully compressible model developed at CNR-ISAC available upon request to <a href="mailto:dinamica@isac.cnr.it">dinamica@isac.cnr.it</a></li> <li>The Met Office Unified Model (MetUM) available for use under a closed licence agreement, further information at <a href="http://www.metoffice.gov.uk/research/modelling-systems/unified-model">http://www.metoffice.gov.uk/research/modelling-systems/unified-model</a></li> <li>The Meso-NH mesoscale non-hydrostatic model of the French research community freely available under CeCILL-C license agreement on <a href="http://mesonh.aero.obs-mip.fr">http://mesonh.aero.obs-mip.fr</a> with two variants included: <ul> <li>one run at Centre National de Recherches Météorologiques (MESONH-CNRM)</li> <li>one run at Laboratoire d’Aérologie (MESONH-LAERO)</li> </ul> </li> <li>The WRF (Weather Research and Forecasting) non-hydrostatic, fully compressible model freely available at <a href="https://github.com/wrf-model/WRF/releases">https://github.com/wrf-model/WRF/releases</a> with five variants included: <ul> <li>one run at the Aristotle University of Thessaloniki (WRF-AUTH)</li> <li>two run at CNR-ISAC (WRF-ISAC and WRF-ISAC-2)</li> <li>one run at the National Observatory of Athens (WRF-NOA)</li> <li>one run at the University of the Balearic Islands (WRF-UIB)</li> </ul> </li> </ul> <p>Four sets of simulations are provided:</p> <ul> <li>Control simulations obtained by initialising the models at 00 UTC on 15 September 2020 and using 6-h operational analyses from the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) as initial and lateral boundary conditions. The horizontal grid spacing is set to 10 km, which approximately matches the resolution of IFS analyses and requires parameterization of deep convection.</li> <li>A first sensitivity test obtained by initialising the models 12 h earlier at 12 UTC on 14 September 2020.</li> <li>A second sensitivity test obtained by using ECMWF Reanalysis v5 (ERA5), which provides higher frequency (hourly) but lower spatial resolution (about 30 km), as initial and lateral boundary conditions.</li> <li>A third sensitivity test obtained by setting the horizontal grid spacing to 2 km, which allows explicit representation of deep convection.</li> </ul> <p>The model output is stored every 3 h until 00 UTC 20 September 2020 and interpolated onto the same regular 0.1°×0.1° horizontal grid and pressure levels. The data files are formatted in Network Common Data Form (NetCDF) and named <strong>runs_ILBC_DDHH_RES.nc</strong> where</p> <ul> <li><strong>ILBC</strong> describes the initial and lateral boundary conditions (IFS or ERA5) </li> <li><strong>DDHH</strong> describes the initial day and hour (1500 or 1412) </li> <li><strong>RES</strong> describes the horizontal grid spacing (10 or 2 km)</li> <li>simulated infrared brightness temperatures are provided in extra files with <strong>RTTOV</strong> suffix for five of the models and variants</li> </ul>
Evaluating mesoscale model predictions of diurnal speedup events in the Altamont Pass Wind Resource Area of California
<p>This dataset contains input files for the Weather Research and Forecasting (WRF) model related to the manuscript "Evaluating mesoscale model predictions of diurnal speedup events in the Altamont Pass Wind Resource Area of California," to be submitted to the <em>Journal of Applied Meteorology and Climatology</em> by Arthur, et al. Included are:</p> <ul> <li><strong>namelist.wps</strong>: used by the WRF preprocessing system (WPS) to configure the model domain and initial/boundary conditions</li> <li><strong>Vtable.HRRR</strong>: used by WPS to process data from the High-Resolution Rapid Refresh (HRRR) model for WRF initial/boundary conditions</li> <li><strong>namelist.input.mynn</strong>: used to run the MYNN PBL simulation</li> <li><strong>namelist.input.3dpbl</strong>: used to run the 3D PBL simulation</li> <li><strong>windturbines.txt</strong>: used to define the location and type of wind turbines included in the simulations</li> <li><strong>wind-turbine-*.tbl</strong>: used to define the parameters of each turbine type (see Table 1 in Arthur et al.) <ul> <li><strong>1</strong>: NREL 1.7MW, H=80m, D=103m</li> <li><strong>2</strong>: NREL 2.3MW, H=80m, D=107m</li> <li><strong>3</strong>: NREL 2.3MW, H=80m, D=116m</li> <li><strong>4</strong>: Vestas V47 0.66MW, H=60m, D=47m</li> <li><strong>5</strong>: Bonus B54 1.0MW, H=55m, D=54m</li> </ul> </li> </ul> <p>This work was prepared by LLNL under Contract DE-AC52-07NA27344.</p>
The Mixed Layer Depth in the Ocean Model Intercomparison Project (OMIP): Impact of Resolving Mesoscale Eddies: supporting data
<p>This file contains a jupyter notebook (python language) used to produce the figures of a manuscript submitted to the journal Geoscientific Model Development, and the data necessary to reproduce the figures.</p> <p>Abstract of the manuscript:</p> <p>The ocean mixed layer is the interface between the ocean interior and the atmosphere or sea ice, and plays a key role in climate variability. It is thus critical that numerical models used in climate studies are capable of a good representation of the mixed layer, especially its depth. Here we evaluate the mixed layer depth (MLD) in six pairs of non-eddying (1° resolution) and eddy-rich (up to 1/16°) models from the Ocean Model Intercomparison Project (OMIP), forced by a common atmospheric state. For model validation, we use an updated MLD dataset computed from observations using the OMIP protocol (a constant density threshold). In winter, low resolution models exhibit large biases in the deep water formation regions. These biases are reduced in eddy-rich models but not uniformly across models and regions. The improvement is most noticeable in the mode water formation regions of the northern hemisphere. Results in the Southern Ocean are more contrasted, with biases of either sign remaining at high resolution. In eddy-rich models, mesoscale eddies control the spatial variability of MLD in winter. Contrary to a hypothesis that the deepening of the mixed layer in anticyclones would make the MLD larger globally, eddy-rich models tend to have a shallower mixed layer at most latitudes than coarser models do. In addition, our study highlights the sensitivity of the MLD computation to the choice of a reference level and the spatio-temporal sampling, which motivates new recommendations for MLD computation in future model intercomparison projects.</p>
Impact of Convection-permitting and Model Resolution on the Simulation of Mesoscale Convective System Properties over East Asia: companion dataset
<p>This folder includes the intermediate data for the following manuscript:</p><p>Ding et al., Impact of Convection-permitting and Model Resolution on the Simulation of Mesoscale Convective System Properties over East Asia</p><p>The simulations were done using ICON-NWP (ICON Numerical Weather Prediction) model, version 2.6.1, over Asian monsoon region (62E–150E, 5.5N–54.5N) for 2020 summer. At the moment, we upload the intermediate data for MCS tracking. For more data, please contact the authors.</p>
The simulated dataset associated with the paper "Mesoscale modelling of optical turbulence in the atmosphere: The need for ultrahigh vertical grid resolution"
<p>The WRF model-generated meteorological profiles are available in netcdf format. More information will be provided shortly. </p>
The Heat and Carbon Characteristics of Modelled Mesoscale Eddies in the South Atlantic Ocean
<p>Datasets in this repository are generated from BIOPERIANT12-CNCLNG01 model and are part of the manuscript entitled: "The Heat and Carbon Characteristics of Modelled Mesoscale Eddies in the South Atlantic Ocean". </p>
Dataset for: Anniés et al., "Accessing structural, electronic, transport and mesoscale properties of Li-GICs via a complete DFTB-model with machine-learned repulsion potential"
<p>GPrep training data, GPrep jupyter notebook, .skf files.</p> <p>The GPrep code is available at https://doi.org/10.5281/zenodo.3697913</p>
Data for "Properties of the lateral mesoscale eddy-induced transport in a high-resolution ocean model: Beyond the flux-gradient relation" (Lu et al. JPO)
<p>Preprocessed data to reproduce results and figures in "Properties of the lateral mesoscale eddy-induced transport in a high-resolution ocean model: Beyond the flux-gradient relation" (Lu et al., In Review of <em>Journal of Physical Oceanography</em>). </p> <p>Feel free to contact Yueyang Lu via <strong>yxl1496@miami.edu</strong> if you have any questions.</p>
Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"
<p>Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"</p>
Data and Code for "Mesoscale modeling of deformations and defects in thin crystalline sheets"
<p>Research data and code supporting the paper ""Mesoscale modeling of deformations and defects in thin crystalline sheets"".</p> <p> </p> <p><strong>Code (apfc-python-surf.zip)</strong></p> <p>The implementation of the APFC model is performed in python by exploiting the pseudo-spectral Fourier method. Library pyfftw is adopted. However, standard fft libraries can be used as well by changing the corresponding module/functions. The code supports equations of the APFC model both coupling with the evolution of the surface considered in this work and on a simple flat domain. Updates can be found in the GitLab repository linked below.</p> <p> </p> <p><strong>GitLab repository for the code</strong></p> <p><a href="https://gitlab.com/3ms-group/apfc_python/">https://gitlab.com/3ms-group/apfc_python/</a></p> <p> </p> <p><strong>Data</strong></p> <p>The data.zip files contain the simulation results and auxiliary scripts used to produce the results illustrated in the paper's figures. The folder numbering refers to the one used for the figures in the final version.</p> <p> </p> <p>For further information please contact the authors.</p>
Post-processed SAM (System for Atmospheric Modeling) simulation output for "Tipping to an Aggregated State by Mesoscale Convective Systems"
<p>Statistics output files for all variables, for a select number of SAM (System for Atmospheric Modeling v. 6.11) simulation runs used in the study "Tipping to an Aggregated State by Mesoscale Convective Systems". The following simulations are included: DIU, OCEAN, DIU2OCEAN branch A1, DIU2OCEAN branch A2.</p>
CFD Modeling Results and Related Data and Codes for Plotting of "A Mesoscale-to-LES Modeling of Tornado-like Vortex and Associated Local Strong Winds in Urban Area"
<p>The CFD modeling outputs, derived maximum wind fields in the analysis area, the topography data, the Python codes used to produce the figures, as we as the namelist of WRF simulation are available. The CFD modeling outputs are in binary format. The ctl. files of corresponding binary data (or dataset if ordered chronologically) are available in each directory (named after each experiment in our study).</p>
Evaluation of Precipitation Forecast by the Operational China Meteorological Administration Mesoscale Model during the 2020 Meiyu Period
Open the record for dataset details and reuse information.
Active Regions of Secondary Ice Production in a Modeled Tropical Mesoscale Convective System
<p>The animations of the modeled tropical mesoscale convective system (MCS). The red regions indicate the cloud regions with the secondary ice production rate dNice/dt >10<sup>5</sup> m<sup>-3</sup> s<sup>-1</sup>. The numerical simulation of the MCS was performed with the help of the Environment and Climate Change Canada's (ECCC) Global Environmental Multiscale (GEM) model.</p> <p>For details see Korolev, A., Z. Qu, J. Milbrandt, I. Heckman, M. Cholette, M. Wolde, C. Nguyen, G. M. McFarquhar, P. Lawson, and A. M. Fridlind: High ice water content in tropical mesoscale convective systems (a conceptual model). Atmos. Chem. Phys., 2024.</p>
data shown in manuscript "WRF-Comfort: Simulating micro-scale variability of outdoor heat stress at the city scale with a mesoscale model"
<blockquote> <p>data shown in manuscript "WRF-Comfort: Simulating micro-scale variability of outdoor heat stress at the city scale with a mesoscale model"</p> </blockquote>
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