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5 results for “data assimilation, coupled model”
MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region. Article data
<p>Regional coupled and Earth System models are fundamental numerical tools for climate investigations, downscaling of predictions and projections, process-oriented understanding of regional extreme events, and many more applications. Here we introduce a newly developed coupled regional modeling framework for the Mediterranean region, called MESMAR (Mediterranean Earth System model at ISMAR) version 1, which is composed of the WRF atmospheric model, the NEMO oceanic 15 model, and the HD hydrological discharge model, coupled via the OASIS coupler. The model is implemented at moderate resolution (about 1/12° for the ocean and river routing, while twice coarser for the atmosphere) for long-term investigations.</p> <p>The gzipped tarball contains data files contained in the manuscript associated with the MESMARv1 description and submitted to Geoscientific Model Developments:</p> <p>MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region</p> <p>by Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino,<br> Rosalia Santoleri, Chunxue Yang</p>
Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: Example of AWI-CM - output files and plot scripts
<p>This archive outputs_plotting.zip contains the raw output files (STDOUT) from the scaling runs performed for the paper "Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: Example of AWI-CM" submitted to GMD (gmd-2019-167). Further the scripts to extract timing information from the raw output files and plot scripts are included.</p> <p>The archive SST-DA_plotting.zip contains the scripts to compute RMS errors for the free ensemble run (output file in gmd_N46_free.zip) and the SST assimilation run (gmd_N46_sst.zip) and to plot these. The two output files contain each a Netcdf file with the ensemble mean state information and the stdout file from the model run.</p>
The prediction data analyzed in "Seasonal Arctic sea ice prediction using a newly developed fully coupled regional model with the assimilation of satellite sea ice observations"
<p>The outputs of seasonal predictions with the new modeling system analyzed in the article including:</p> <p>Sea ice concentration (SIC)</p> <p>Sea ice thickness (SIT)</p> <p>Sea surface temperature (SST)</p> <p>Near surface air temperature (T2) </p>
Dataset for "Enhanced Regional Ocean Ensemble Data Assimilation Through Atmospheric Coupling in the SKRIPS Model"
Open the record for dataset details and reuse information.
Data for the article entitled "Strongly Coupled Data Assimilation of Ocean Observations into an Ocean-Atmosphere Model" by Tang et al. 2021, GRL
<p>We stored the output data for the free run and the data assimilation experiments. All the data is stored in netCDF format and named by XX1_ensmean_XX2_monmean.nc. The prefix XX1 indicates the simulation scenarios, where 'free_run' refers to the free run, 'wcda' the weakly coupled assimilation run, 'scda' the strongly coupled data assimilation run without vertical localization for atmosphere, and 'scda_vert' the strongly coupled assimilation run with vertical localization for atmosphere. The XX2 represents variables from the simulations, where 'temp2' refers to 2 meter temperature, 'u10' 10 metre U wind component, 'v10' 10 metre V wind component, 'st_p' temperature at pressure levels, 'uv_p' U and V component of wind at pressure levels, and 'q_p' specific humidity at pressure levels.</p>
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