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25 results for “MPAs”
MPAS-Ocean Shallow Water Meshes
<p>The MPAS_Ocean_Shallow_Water_Meshes directory contains planar hexagonal mesh files in NetCDF format necessary for running a verification suite of shallow water test cases for the barotropic solver of ocean models using a mimetic finite volume spatial discretization based on the TRiSK scheme. It also contains mesh plots showing the cell centers, edge centers, vertices, and orientation of the normal vectors at the edges; plots of high resolution meshes superimposed on low resolution ones; plots of state variables interpolated from edges and vertices to cell centers along with the interpolation error; plots of state variables interpolated from a high resolution mesh to a low resolution one; and plots of various spatial operators of the TRiSK scheme applied to the state variables along with their error and convergence plots. The associated code can be cloned from the Github repository <a href="https://github.com/siddharthabishnu/Rotating_Shallow_Water_Verification_Suite.git">Rotating_Shallow_Water_Verification_Suite</a>. Please download the MPAS_Ocean_Shallow_Water_Meshes.zip file, unzip it, and place the resulting directory within the meshes directory of Rotating_Shallow_Water_Verification_Suite.</p>
Global 32-4 km variable-resolution mesh for the MPAS-Atmosphere model
<div> <div> <div>This mesh was created by a collaboration between the Department of Energy’s Water Cycle and Climate Extremes Modeling (WACCEM) project and the</div> <div>Mesoscale and Microscale Meteorology (MMM) Laboratory at the National Science Foundation National Center for Atmospheric Research (NSF/NCAR).</div> <div> </div> <div>The mesh contains 1,830,914 horizontal grid cells. The circular refinement region has a radius of approximately 20 degrees. The 4-32km grid-spacing range aims to achieve convection-permitting resolution in the high-resolution domain and resolution sufficient for the jet stream and mid-latitude wave activities (Lu et al., 2015) in the low-resolution domain.</div> <div> </div> <div>The netcdf file "x8.1830914.grid.nc" includes the variables defining the global unstructured grid for the MPAS model as described in the <a href="https://mpas-dev.github.io/files/documents/MPAS-MeshSpec.pdf">MPAS Mesh Specification</a>. Following the other MPAS mesh data, we provide graph.info.part.* files necessary for the Message Passing Interface (MPI) parallelism. For example, a simulation using 1024 MPI ranks will use graph.info.part.1024. For the number of MPI tasks not provided in this dataset, a user needs to create a new partitioning file using the METIS tool and the graph.info file as described in the MPAS user guide (available <a href="https://mpas-dev.github.io/atmosphere/atmosphere_download.html" target="_blank" rel="noopener">here</a>).</div> <div> </div> </div> </div> <div>This data will also be available from the <a href="https://mpas-dev.github.io/atmosphere/atmosphere_meshes.html">MPAS mesh website</a>.</div> <div> </div> <div>The mesh generation is supported by the U.S. Department of Energy Office of Science Biological and Environmental Research (BER) as part of the Regional and Global Model Analysis Program Area. We acknowledge the use of computational resources of the National Energy Research Scientific Computing Center (NERSC). The Pacific Northwest National Laboratory is operated for the Department of Energy by Battelle Memorial Institute under contract DE-AC05-76RL01830.</div>
Short-term Monitoring of Coral Reef Marine Protected Areas (MPAs) in the Municipality of Liloan, Central Visayas, Philippines
<p>This is a sampling-event dataset of the short-term monitoring of Poblacion and Kadurong Reefs, two of the marine protected areas Municipality of Liloan, Cebu, Philippines. Water quality and ecological assessments were carried out to monitor the status and trends of biological and physical parameters associated with coral reefs using the standard protocols for surveying tropical marine resources. Specifically, the following measurements were conducted: (1) physico-chemical parameters, (2) phytoplankton and zooplankton occurrence and abundance, (3) fish occurrence and density, and (4) percent cover of benthic components of coral reef. The data can serve as the basis for the formulation and implementation of relevant measures for conservation and protection management of the Poblacion and Kadurong Reefs in Liloan, Cebu, Philippines.</p> <p>In this version, occurrence.csv was revised as described below:</p> <ul> <li>taxonID for <em>Abudefduf vaigiensis</em> (Quoy & Gaimard, 1825) and <em>Hemiaulus</em> P.A.C. Heiberg, 1863 were corrected.</li> <li>Author names with corrupted characters/symbols were corrected. </li> </ul>
MPAS-A v7.0 variable resolution simulations over China
<p>Dynamical downscaling is conducted by MPAS-A v7.0 with a global variable resolution (MPAS-VR) and truncated regional (MPAS-RCM) configurations.</p> <p>Two sets of MPAS experiments are carried out to evaluate the simulation performance of summer precipitation in China. One is a global variable resolution run (VR) using 4 times grid refinement over the domain center (32<sup>o</sup>N, 100<sup>o</sup>E), and the grid spacing smoothly varies from ~25km to ~92km outside the refinement. The other is a regional configuration experiment (RCM) with the grid truncated according to an ellipse in the same center.</p>
Model input data for the FACETS downscaling simulation with the CAM-MPAS model
<p>The archived file contains input data necessary to reproduce the set of simulations described in Sakaguchi et al., submitted to GWD, "Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM-MPAS variable-resolution model", using the experimental CAM-MPAS code further modified by Sakaguchi and Harrop (2022) for long-term AMIP-type simulations.</p>
MPAS-Albany Land Ice model simulations of Humboldt Glacier, North Greenland, from 2007–2100
<p>This dataset contains model input and output in netCDF format, model code, and analysis scripts for simulations of Humboldt Glacier, North Greenland, through the 21st century (Hillebrand et al., 2022) using the MPAS-Albany Land Ice model (Hoffman et al., 2018). We calibrate parameters controlling basal traction, iceberg calving, and submarine melt against observations from 2007–2017. We then explore the glacier’s sensitivity to climate forcing, iceberg calving, and basal conditions in an ensemble of 24 simulations from 2007–2100. We further explore its sensitivity to uncertainties in ice-shelf melt, bed topography, and calving rate limits in targeted sensitivity experiments. Input files include surface mass balance, ocean thermal forcing, and subglacial runoff forcings provided by ISMIP6 (Nowicki et al., 2020; Slater et al., 2020). Output includes basal traction optimization solutions for the year 2007; annual 2D ice speed, basal shear and driving stresses, and geometry; annual 3D temperature; and grounded, floating, and global mass budgets at every timestep.</p> <p>References:</p> <p>Hillebrand, T. R., Hoffman, M. J., Perego, M., Price, S. F., and Howat, I. M. (2022): The contribution of Humboldt Glacier, northern Greenland, to sea-level rise through 2100 constrained by recent observations of speedup and retreat, The Cryosphere, 16, 4679–4700, <a href="https://doi.org/10.5194/tc-16-4679-2022">https://doi.org/10.5194/tc-16-4679-2022</a>.</p> <p>Hoffman, M. J., Perego, M., Price, S. F., Lipscomb, W. H., Zhang, T., Jacobsen, D., et al. (2018). MPAS-Albany Land Ice (MALI): a variable-resolution ice sheet model for Earth system modeling using Voronoi grids. <em>Geoscientific Model Development</em>, <em>11</em>(9), 3747–3780.<a href="https://doi.org/10.5194/gmd-11-3747-2018"> https://doi.org/10.5194/gmd-11-3747-2018</a></p> <p>Nowicki, S., Goelzer, H., Seroussi, H., Payne, A. J., Lipscomb, W. H., Abe-Ouchi, A., et al. (2020). Experimental protocol for sea level projections from ISMIP6 stand-alone ice sheet models. <em>The Cryosphere</em>, <em>14</em>(7), 2331–2368.<a href="https://doi.org/10.5194/tc-14-2331-2020"> https://doi.org/10.5194/tc-14-2331-2020</a></p> <p>Slater, D. A., Felikson, D., Straneo, F., Goelzer, H., Little, C. M., Morlighem, M., et al. (2020). Twenty-first century ocean forcing of the Greenland ice sheet for modelling of sea level contribution. <em>The Cryosphere</em>, <em>14</em>(3), 985–1008.<a href="https://doi.org/10.5194/tc-14-985-2020"> https://doi.org/10.5194/tc-14-985-2020</a></p>
Representation of submesoscale baroclinic instabilities in ROMS and MPAS-O
<h2>Idealized Model Outputs</h2> <p>This dataset archive contains a subset of the model outputs that are necessary for reproducing the analyses presented in the accompanying manuscript. The datasets correspond to either the MPAS-O or ROMS ocean models at resolutions ranging from 10 km to 100 m. Velocity and corresponding invariant (vorticity/divergence) variables and tracer variables are saved here with only their surface values. Additionally, all variables except those required to compute kinetic energy time series over the simulation period are only saved for a time window that corresponds to the shaded region in Figure 4 of the accompanying manuscript. Some grid metric variables necessary for MPAS-O calculations are also included within "MPAS-O_Initial_*.nc" files. There are two zipped tar directories (mpaso_winds.tar.gz and roms_winds.tar.gz) containing the outputs of all wind stress sensitivity tests needed to recreate the results shown in Figure 2. All datasets are saved in NetCDF format.</p>
JEDI-MPAS Test and Application Files
JEDI-MPAS is the interface between the generic components of the Joint Effort for Data assimilation Integration (JEDI) system and the atmospheric core of the Model for Prediction Across Scales (MPAS). The JEDI software package is a unified, innovative data assimilation system for Earth System Prediction. Developed and distributed by UCP's Joint Center for Satellite Data Assimilation (JCSDA), JEDI is versatile and sophisticated enough for a variety of applications, from operational weather forecasting and atmospheric research on High Performance Computing (HPC), to learning the fundamentals of data assimilation by running idealized toy models on your laptop. The atmospheric component of MPAS is a non-hydrostatic model using an unstructured centroidal Voronoi mesh with smoothly varying resolution for global or regional domains. The primary development partners for MPAS are Los Alamos National Laboratory and the National Center for Atmospheric Research. This data set provides example observation files, model backgrounds, and other input files needed to run a variety of JEDI-MPAS applications, including the comprehensive suite of unit tests and example activities that are described in online tutorials. This data set accompanies JEDI-MPAS, version 1.0.0, released 24 September 20.
MPAS_CMAQ-paper-dataset
<p>It contains all the data to generate Figure 4 - 9 in the paper titled "Development of the MPAS-CMAQ Coupled System for Multiscale Global Air Quality Modeling"</p> <p> </p>
High-resolution climate simulations using the Model for Prediction Across Scales - Atmosphere (MPAS-A; version 5.1)
<p>We present multi-seasonal simulations representative of present-day and future environments using the global Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1 with high resolution (15 km) throughout the Northern Hemisphere. We select 10 simulation years with varying phases of El Niño–Southern Oscillation (ENSO) and integrate each for 14.5 months. We use analyzed sea surface temperature (SST) patterns for present-day simulations. For the future climate simulations, we alter present-day SSTs by applying monthly-averaged temperature changes derived from a 20-member ensemble of Coupled Model Intercomparison Project phase 5 (CMIP5) general circulation models (GCMs) following the Representative Concentration Pathway (RCP) 8.5 emissions scenario. Daily sea ice fields, obtained from the monthly-averaged CMIP5 ensemble mean sea ice, are used for present-day and future simulations.</p> <p>Due to storage limitations, the full dataset is much too large to be published (~50TB). Instead, a subset consisting of 6-hourly warm season (May-September) 2-meter temperature, precipitation, and 500hPa height is presented. If you wish to access the full dataset (as presented in Michaelis et al. 2019), please contact one of the authors.</p>
diagnostic file output from MPAS-A(tmospheric) mode
<p>diagnostic file output from MPAS-A(tmospheric) component which is in an unstructured grid </p>
MPAS-Albany Land Ice v6.0 Test Cases
<p>This archive includes test case for the MPAS-Albany Land Ice v6.0 model.</p> <p>See http://mpas-dev.github.io/ for links to model source code and User's Guide.</p>
Semi-implicit barotropic mode solver using ForTrilinos in MPAS-O and its test data
<p>To run the ForTrilinos-enabled MPAS-O,</p> <ol> <li>Download all files</li> <li>Install Trilinos <ul> <li>Unzip: tar -xzvf Trilinos.tar.gz</li> <li>cd Trilinos ; mkdir build ; cd build ; cp ../do-configure_gnu ./</li> <li>Check install directories and options in 'do-configure_gnu'</li> <li>Run 'do-configure_gnu'</li> <li>Trilinos information & installation refer to <a href="https://trilinos.github.io/">https://trilinos.github.io/</a></li> </ul> </li> <li>Install ForTrilinos (inside Trilinos) <ul> <li>cd Trilinos/ForTrilinos ; mkdir build ; cd build ; cp ../do-configure_gnu ./</li> <li>Check Trilinos and ForTrilinos install directories and options in 'do-configure_gnu'</li> <li>Run 'do-configure_gnu'</li> <li>ForTrilinos information & installation refer to <a href="https://fortrilinos.readthedocs.io/en/latest/">https://fortrilinos.readthedocs.io/en/latest/</a></li> </ul> </li> <li>Install ForTrilinos-enabled MPAS-O <ul> <li>Unzip: tar xzvf MPAS-Model_fortrilinos.tar.gz</li> <li>cd MPAS-Model_fortrilinos</li> <li>Check ForTrilinos directories at line 478 (FORTRILINOS_ROOT) in 'Makefile' </li> <li>Install PIO (refer to <a href="https://ncar.github.io/ParallelIO/">https://ncar.github.io/ParallelIO/</a>)</li> <li>MAPS-O information & installation refer to <a href="https://mpas-dev.github.io/ocean/ocean.html">https://mpas-dev.github.io/ocean/ocean.html</a></li> <li>For GNU compiler: make gnu-nersc USE_PIO2=true FORTRILINOS=true</li> </ul> </li> <li>Run test cases <ul> <li>Example <ul> <li>cd MPAS-O_Initial_data/baroclinicEddies/strong_scaling</li> </ul> </li> <li>Link a MPAS-O compiled executable to a test case directory: <ul> <li>ln -fs MPAS-Model_fortrilinos/ocean_model MPAS-O_Initial_data/baroclinicEddies/strong_scaling/</li> </ul> </li> <li>Link a XML deck (solver configurations) for Trilinos to a test case directory <ul> <li>ln -fs xml_decks/no_precond/stratimikos.xml_SCG MPAS-O_Initial_data/baroclinicEddies/strong_scaling/stratimikos.xml</li> </ul> </li> <li>Run <ul> <li>mpirun -n $N ocean_model</li> <li> <p>If the simulation was successful, you will see:</p> <pre><code>tail -n 1 log.ocean.0000.out Logging complete. Closing file at ...</code></pre> <p> </p> </li> </ul> </li> <li>For the global test case, please download here: <a href="https://doi.org/10.5281/zenodo.1252425">MPAS-O_V6.0_RRS30to10.tar</a>. Please see <a href="http://mpas-dev.github.io/">http://mpas-dev.github.io</a> for User's Guide, github release page, description of each test case, and more.</li> </ul> </li> </ol>
High-resolution climate simulations using the Model for Prediction Across Scales - Atmosphere (MPAS-A; version 5.1)
Open the record for dataset details and reuse information.
Global variable-resolution simulations of extreme precipitation over Henan, China in 2021 with MPAS-Atmosphere v7.3
<p>This repository encompasses data and software to "Global variable-resolution simulations of the 2021 extreme precipitation event in Henan, China with MPAS-Atmosphere v7.3". The data and model are integral to our study, which focuses on simulating a significant rainstorm event in Henan, China, in July 2021.</p> <p>Contained within this archive are the following components:</p> <p>MPAS-Atmosphere v7.3: This is the core model employed in our study. MPAS v7.3 is a versatile global variable-resolution model, adept at simulating extreme weather events at varying scales.</p> <p>MPAS mesh data: The global meshes generated for the experiments.</p> <p>CMA Observation Data: Ground-based observational data from the China Meteorological Administration, crucial for the validation of our simulation results.</p> <p>ERA5 Reanalysis Data: These datasets are used to further validate the outcomes of our simulations, providing a comprehensive set of atmospheric parameters.</p> <p>GFS Data: The Global Forecast System (GFS) data serve as the input fields for the MPAS model, providing essential initial conditions for our simulations.</p> <p>This collection is aimed at offering researchers a holistic package for studying, replicating, or extending our findings on extreme weather phenomena. The data and model provided here are not only crucial for reproducing the results presented in our study but also offer a valuable resource for further research in atmospheric sciences and climate modeling.</p>
Data for 'Storm Surge Modeling as an Application of Local Time-stepping in MPAS-Ocean'
<p>Data for 'Storm Surge Modeling as an Application of Local Time-stepping in MPAS-Ocean', submitted to the Journal for Advances in Modeling Earth Systems (JAMES).</p> <p>The repository consists of three main parts:</p> <ol> <li>The `run` directory contains a pre-built run directory as an example.</li> <li>The `scripts` directory contains the scripts that were used to generate important plots.</li> <li>The `data` directory contains model output from performance and accuracy experiments.</li> </ol>
MPAS-Ocean Barotropic Tidal Simulation Run Directories
<p>This file contains the initial files needed to run the barotropic tidal simulations in MPAS-Ocean.</p> <p>Download the resolution you want (icos7-10 or vr45to5). Inside the resolution directory is "inline", "scalar", and "shared". "Inline" is the run directory for inline SAL. "Scalar" is the run directory for scalar SAL. "Shared" contains data needed to run either case.</p> <p> The steps are:</p> <ol> <li>download</li> <li>unzip: tar xvfz filename.tar.gz</li> <li>navigate to run directory (resolution/inline OR resolution/scalar)</li> <li>add a link to shared files (e.g., ln -s ../shared/* )</li> <li>add a link to your compiled executable of MPAS-Ocean</li> <li>run, e.g. mpirun -n $N ocean_model</li> </ol> <p>If the simulation was successful, you will see:</p> <pre><code>tail -n 1 log.ocean.0000.out Logging complete. Closing file at ...</code></pre> <p>See Github page: https://github.com/E3SM-Project/E3SM for Quick Start Guide and more.</p>
MPAS-Seaice Testing Dataset V1
<p>Grid and forcing data needed to test the MPAS-Seaice model.</p>
MPAS-Ocean V6 Run Directories
<p>Each file includes everything needed to run a simulation with MPAS-Ocean V6.0. The steps are:</p> <ol> <li>download</li> <li>unzip: tar xvfz filename.tar.gz</li> <li>add a link to your compiled executable of MPAS-Ocean V6</li> <li>run, e.g. mpirun -n $N ocean_model</li> </ol> <p>If the simulation was successful, you will see:</p> <pre><code>tail -n 1 log.ocean.0000.out Logging complete. Closing file at ...</code></pre> <p>See <a href="http://mpas-dev.github.io">http://mpas-dev.github.io</a> for User's Guide, github release page, description of each test case, and more.</p>
Moving from MPAs to area-based management measures in the South China Sea
<p>Dataset and R script for analysis of species composition in the South China Sea</p>
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