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248 results for “wrf”
Data from: WRF 1960-2014 winter season simulations of particulate matter in the Sahel: implications for air quality and respiratory health
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Input files for WRF simulations Across the the iCMV
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Input and output files for WRF urban simulations for the metropolitan area of Tel-Aviv
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Data archive for paper "WRF‐TEB: Implementation and Evaluation of the Coupled Weather Research and Forecasting (WRF) and Town Energy Balance (TEB) Model"
<p><strong>WRF-TEB data archive</strong></p> <p>This archive contains data and tools to reproduce results as included in <a href="https://doi.org/10.1029/2019ms001961">Meyer et al. (2020)</a>.</p> <p><strong>Prerequisites</strong></p> <ul> <li><a href="https://sylabs.io/">Singularity</a> version >= 3.</li> </ul> <p><strong>Usage</strong></p> <p>To run all models and plotting scripts included in integration test and meteorological evaluation, run the following command from your command-line interface.</p> <pre><code>NPROC=8 TYPE=evaluate tools/singularity/run.sh</code></pre> <p>where <code>NPROC=8</code> is the maximum number of processes to use. The output can be found in the <code>work/</code> folder.</p> <p><strong>HPC</strong></p> <p>If you want to use this in an HPC environment, use <code>tools/hpc</code> as a template. As an example, to run the evaluation on Imperial HPC using PBS (Portable Batch System), use:</p> <pre><code>qsub -v REPO_ROOT=$(pwd),TYPE=evaluate tools/hpc/job_imperial.sh</code></pre> <p><strong>Copyright and License</strong></p> <p>Copyright and licensing information are included at the top of source files or as separate files in folders.</p> <p><strong>References</strong></p> <p>Meyer, D., Schoetter, R., Riechert, M., Verrelle, A., Tewari, M., Dudhia, J., Masson, V., Reeuwijk, M., & Grimmond, S. (2020). WRF‐TEB: implementation and evaluation of the coupled Weather Research and Forecasting (WRF) and Town Energy Balance (TEB) model. Journal of Advances in Modeling Earth Systems. <a href="https://doi.org/10.1029/2019ms001961">https://doi.org/10.1029/2019ms001961</a></p>
Supporting information, WRF output, and processed WRF files used for the generation of the manuscript "Observational and modelling analysis of Canada's only F5/EF5 tornado"
<p>WRF simulation output and processed files used to generate the figures and calculations in the manuscript "Observational and modelling analysis of Canada's only F5/EF5 tornado". Two zipped folders are attached, one is from the original control simulation with the microphysics scheme on (MP), and the other is from the experimental simulation with the microphysics scheme turned off (NOMP).</p><p>Each simulation zipped folder includes sub-directories containing the processed observed and simulated surface station data, surface wet-bulb potential temperature, cross sections (the MP simulation only), and convective parameter fields at 2100 UTC 22 June 2007.</p><p>A supplemental material in the form of a movie showing the radar observation between 2000 UTC 22 June 2007 and 0000 UTC 23 June 2007 is also attached. See the manuscript's Figure 5 caption for more information on the data shown in the animation.</p>
WRF Output
<p>WRF domain is centered at (62°W, 40°N) with a horizontal resolution of 10 km. The horizontal grid dimensions are 399 × 299, covering the western subtropical North Atlantic.</p><p>Initial and lateral boundary conditions are extracted from ERA5 reanalysis data (0.25 X 0.25). The lateral boundary forcings are updated at 6 hourly intervals. The model was initialized at 00UTC on 15 Feb 2010, and the integration ended at 18 UTC on 17 March 2010. The 5-day average SST output from MITGCM for the same time period was is used to force the WRF model.</p>
Dataset for WRF-Chem
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The WRF-CMAQ-BCG model data
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Land use and land cover in China for WRF (Resolution: 500m), 2020
<p>WRF使用的中国2020年下垫面数据。</p>
Analysis of future heatwaves in the Pearl River Delta through CMIP6-WRF dynamical downscaling
<p>Heatwave datasets</p>
WRF model output used in an ideal model experiment demonstrating the applicability of a channel-synthesizing method for reducing uncertainties in satellite radiance transfer modeling
<p>This repository stores the WRF model output used in the paper "A novel channel-synthesizing method for reducing uncertainties in satellite radiance transfer modeling" submitted to Geophysical Research Letters.</p>
Scripts for Li 2024 JAMES WRF AH
<p>WRF namelists and python scripts to reproduce the figures in the manuscript </p>
WRF Modified File-The Role of WISHE in the Rapid Intensification of Super Typhoon Hinnamnor (2022)
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WRF met-data for generative diffusion model post-processing precipitation map
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Part 1 of simulation output for WRF-Chem with enabling ARI and ACI
<p>Part 1 of simulation output for WRF-Chem with enabling aerosol-radiation interactions (ARI) and aerosol-cloud interactions (ACI) in eastern China during 2017.</p>
WRF-STILT Particle Trajectories for Boston, MA, USA, 2013-2014
This dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data and footprint products for two receptors located in Boston, Massachusetts, USA, for July 2013 - December 2014. Meteorological fields from version 3.6.1 of the Weather Research and Forecasting model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the "receptor" location), to create the adjoint of the transport model in the form of a "footprint" field. The footprint, with units of mixing ratio (ppm) per surface flux (umol m-2 s-1), quantifies the influence of upwind surface fluxes on CO2 and CH4 concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. Footprints are provided for the two receptors at two temporal and spatial scales: three days of surface influence over the whole North American coverage area at 1-degree resolution and 24 hours of surface influence within a smaller region close to the measurement locations ('near field') at 0.1-degree resolution.
CARVE: L4 Gridded Particle Trajectories for WRF-STILT model, 2012-2016
This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) model inputs for particle receptors located at positions along Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) flight paths (2012 - 2015) and various meteorological stations in Alaska and the Canadian Arctic. Each product consists of multiple NetCDF files packaged as a TAR/GZIP file. These data correspond to WRF-STILT model footprint data also generated by the CARVE science team.
Pre-ABoVE: Gridded Footprints from WRF-STILT Model, Barrow, Alaska, 1982-2011
This dataset provides Stochastic Time-Inverted Lagrangian Transport model outputs for receptors located at the NOAA Barrow Alaska Observatory for 12 selected years (15 August to 15 October) across the 30-year, 1982 to 2011, study timeframe. Meteorological fields from version 3.5.1 of the Weather Research and Forecasting model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the "receptor" location), to create the adjoint of the transport model in the form of a "footprint" field. The footprint, with units of mixing ratio (ppm --- CO2; ppb --- CH4) per (umol m-2 s-1 --- CO2; nmol m-2 s-1 --- CH4), quantifies the influence of upwind surface fluxes on concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. The simulation results included in this dataset are crucial for understanding changes in Arctic carbon cycling and are part of a retrospective analysis to link changes in atmospheric composition at Arctic receptor sites with shifts in ecosystem structure and function. Each file provides the surface influence-function footprints on a lat/lon/time grid from WRF-STILT simulations for the receptor location.
ABoVE: Level-4 WRF-STILT Footprint Files for Circumpolar Receptors, 2016-2019
This dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) Footprint data products for receptors (observations) located at positions along flight paths and at various fixed observing sites at circumpolar locations at northern latitudes during 2016-2019. Each aircraft and station position is treated as an independent receptor in the WRF-STILT model in order to simulate the land surface influence on observed atmospheric constituents. The footprints are independent of chemical species and can be applied to different flux models and incorporated into formal inversion frameworks. The particle trajectories that determine the footprint field are constrained only by the outer edges of the WRF modeling domain. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by the thawing of Arctic permafrost.
CMS: Hourly Carbon Dioxide Estimated Using the WRF Model, North America, 2010
This data set contains estimated hourly CO2 atmospheric mole fractions and meteorological observations over North America for the year 2010 at a horizontal grid resolution of 27 km and vertical resolution from the surface to 50 hPa. The data are output from the Penn State WRF-Chem version of the Weather Research and Forecasting (WRF) model using lateral boundary conditions and surface fluxes from the CMS-Flux Inversion system.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.