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248 results for “wrf”
Selected WRF output for Shimshal catchment, years 2011, 2012, 2013
<p>Includes selected variables for the Langtang catchment of the WRF run used in <em>Contrasting meteorological drivers of the glacier mass balance between the Karakoram and central Himalaya, </em>by PNJ Bonekamp, RJ de Kok, E Collier and WW Immerzeel. </p> <p>WRF version 3.8.1 is used for the simulations. The outer domain is forced with 6-hourly ERA-Interim data (0.75 o x0.75o), and grid analysis nudging is applied to the horizontal wind, water vapour mixing ratio and potential temperature fields in the highest 15 vertical levels. In all domains, the land cover dataset is updated using the climate change initiative dataset (CCI, Defourny et al. (2017)) of the European Space Agency (ESA), which has a spatial resolution of 300 m. The soil moisture, soil temperatures and skin temperatures is initialized with the GLDAS dataset (0.25x0.25, Rodell et al. 2004).</p> <p>Important parameter settings are:<br> - One way nesting<br> - Model top pressure<br> - Morrison microphysics<br> -YSU planetary boundary layer scheme wit topo_wind option=1<br> - Noah-MP scheme<br> -RRTGM radiation scheme<br> -MM5 Similarity Scheme<br> - Rayleigh damping at the top boundary<br> - Diffusion is calculated in physical space<br> <br> Included variables [unit']:<br> Time [hours from 1 December 2010], total simulation time is till 1 January 2014. All data are hourly variables. <br> ALBEDO [-]<br> GLW [Wm-2], longwave downward radiation<br> GRAUPELNC [m], non convective graupel<br> GRDFLX [Wm-2], ground heat flux <br> HFX [Wm-2], Sensible heat flux<br> HGT [m], altitude above sea level<br> LH [Wm-2], latent heat flux<br> LU_INDEX [-] #24 categories<br> LWDNB [Wm-2], longwave downward radiation<br> LWUPB [Wm-2], longwave upward radiation<br> RAINC [m], convective rainfall<br> RAINNC [m], non convective rainfall<br> SNOWC [-], snow cover<br> SNOWNC [-], non convective snowfall<br> SWDOWN [Wm-2], shortwave downwards radiations<br> T2 [K], 2-meter temperature<br> TSK [K], surface temperature<br> U10 [ms-1], wind in U-direction at 10-m<br> V10 [ms-1], wind in V direction at 10-m</p> <p>See for the full description of the variables Chapter 5 of the WRF user guide</p>
The WRF model output for selected foehn events at the northern foreland of the Moravian-Silesian Beskids, Czech Republic
<p>This dataset includes three output files from the Weather Research and Forecasting (WRF) model in the NetCDF format. The files are valid for three selected foehn events which affected the northern foreland of the Moravian-Silesian Beskids, Czech Republic and allows a deeper analysis of mechanism and impacts of these events.</p> <p>The dataset contains the model output for the following events:</p> <p>1) 14 January 2008, 10:00 UTC</p> <p>2) 31 October 2010, 06:00 UTC</p> <p>3) 30 October 2021, 06:00 UTC</p>
CORINE dataset for WRF-NoahMP model (v4.3, v4.2)
<p>This dataset is an interpolated and converted version of the CORINE 2012 (Version 2020_20u1) land cover raster database (https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012) to WRF geogrid compatible format. The original CORINE database has a 100 m resolution, but WRF requires a resolution defined in degrees, which is set to 0.00208333° in both directions (around 250 m at mid-latitudes). The WRF dataset covers the region: longitudes between -30° and 45.6°and latitudes between 26° and 72°. Interpolation is based on a simple most abundant category basis.</p> <p>Two versions are available:</p> <p>1) A conversion implying that the CORINE land cover types are used jointly with the USGS one, resulting an expansion of the number of land use codes. The original USGS codes span from 1 to 40, and CORINE from 51 to 98. (corine_2012v2020)</p> <p>2) The 44 CORINE categories are recategorized to fit the USGS categories based on Pineda et al. (2004) (corine2usgs_2012v2021)</p> <p>The resulting files are 1 bit binary files. There are two resolutions available, per tile one with 960x960 grid points (around 250 m resolution), and one with 480x480 grid points (around 500 m resolution), each tile covering a 2°x 2° region to make use easier. File names follow the convention of the WRF model. The WPS required index file can be found in the respective folders.</p> <p>Source code and table modification required for the application of the data, which can be found here:<br> https://github.com/BHajni/WRF-CORINE</p> <p> </p>
Data for publication of "Determining the sensitive parameters of WRF model for the prediction of tropical cyclones in the Bay of Bengal using Global Sensitivity Analysis and Machine Learning"
<p>The data are made available as part of the paper "Determining the sensitive parameters of WRF model for the prediction of tropical cyclones in the Bay of Bengal using Global Sensitivity Analysis and Machine Learning", submitted to Geoscientific Model Development. This data set incorporates selected post-processed files needed to reproduce the results presented in the paper.</p> <p>The data contains six zip files, that are:</p> <ul> <li>Namelist.input files for the WRF model simulations of ten tropical cyclones</li> <li>WRF model simulation outputs using the default parameter values</li> <li>WRF model simulation outputs using the optimal parameter values (which give minimum RMSE value)</li> <li>IMDAA surface observations and IMERG precipitation data</li> <li>IMD observed tracks of ten tropical cyclones</li> <li>Ipython notebooks of sensitivity analysis and machine learning codes</li> </ul> <p>The remaining files are the ncl scripts that were used to obtain the figures. The ncl scripts used the data in the zip files.</p>
WRF simulations of 2017 Europe Heatwave
<p>The WRF model (version 4.1.4) domain was configured with a 9 km grid spacing (547x462 grid cells) with 51 vertical levels covering Europe. National Centers provide the initial and boundary conditions for Environmental Prediction Final Operational Model Global Tropospheric Analyses (NCEP FNL) data, which is available at a 6-hourly and 1-degree temporal and spatial resolution, respectively. The simulations started on 2017-06-08 at 00:00 UTC and ended on 2017-06-25 at 00:00 UTC and with 3-hour steps. The following parameterization schemes were used: the Rapid Radiative Transfer model scheme for longwave radiation, Goddard shortwave scheme, Bougeault-Lacarrère for the planetary boundary layer, WRF single moment 6-class scheme as microphysics, Kain-Fritsch as cumulus scheme, and multi-layer Building Energy Parameterization (BEP) as the urban scheme. The simulations are performed using two LSMs, i.e., Noah and Noah-MP. For the LULC, the Moderate Resolution Imaging Spectroradiometer (MODIS) data is used in two ways: i) the default MODIS LULC which has one urban class `Urban and Built-up Lands'; and ii) the MODIS LULC product in which its original urban land cover is replaced by the 10 urban LCZ classes, available from the European LCZ map at 100 m spatial resolution (hereafter denoted as WUDAPT LULC).</p> <p>The data format is NetCDF containing detailed metadata. Two-dimensional variables are included, horizontal wind components at 10 m AGL, atmospheric pressure at the surface, water vapor mixing ratio at 2 m AGL, the temperature at 2 m AGL, land use/ land cover, sensible heat flux, latent heat flux, and ground heat flux, the height of the terrain, and land mask.</p> <p>These WRF simulations are used to analyze the results of the "Modelling Large-Scale Heatwave by Incorporating Enhanced Urban Representation" manuscript submitted to JGR-Atmospheres.</p>
Wood Buffalo Environmental Association (WBEA) Historical Monitoring Data used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes"
<p>Wood Buffalo Environmental Association (WBEA) Historical Monitoring Data from two monitoring stations Bertha Ganter – Fort McKay and Barge Landing for 20 August 2013 to 2 September 2013. This data was used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes" (Fathi et al., 2022 - egusphere-2022-1125) for model output and observational data comparisons. The same data can be accessed and downloaded from "<a href="https://wbea.org/historical-monitoring-data/">https://wbea.org/historical-monitoring-data/</a>".</p>
A High Resolution (3km) Reanalysis Database for Mediterranean Coastal Winds Downscaled from ERA5, using the WRF Model
<p>A high resolution (3km) reanalysis database of Mediterranean coastal winds was constructed to support a research on potential sailing mobility in Antiquity. The database was created by downscaling the ERA5 reanalysis database using the WRF numerical prediction model.</p> <p>A detailed description of the reanalysis database is provided in the attached PDF file. The database format is GRIB version 2 and the total volume of the data files is 435GB. The GRIB files are hosted at <a href="https://coastalwinds.haifa.ac.il">https://coastalwinds.haifa.ac.il</a> as their total volume exceeds the volume that could be provided by Zenodo. Required files can therefore be downloaded from this location.</p> <p><strong>Link to the GRIB data files and index map:</strong></p> <p><strong><a href="https://coastalwinds.haifa.ac.il">https://coastalwinds.haifa.ac.il</a></strong></p> <p><strong>Acknowledgements:</strong></p> <p>The Data Science Research Center (DSRC) at Haifa University kindly provided funding towards the creation of this data set.</p>
WRF Geographic Files for Cordillera Mountain Range Precipitation Experiments
<p>These WRF geographic files (in netcdf format) were used as input for experiments to evaluate the sensitivity of Tropical Cyclone precipitation to the height of the Cordillera Mountain Range in Luzon, Philippines. A total of six files are included, for two domains (d01 and d02) and three different orographic experiments (orig, redu, enha).</p> <p>These are provided to allow reproduction of the said sensitivity experiments.</p>
WRF output for the Geophysical Research Letters publication "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific"
<p>This dataset is the Weather Research and Forecasting (WRF) model output for the <em>Geophysical Research Letters</em> publication entitled "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific". The dataset includes the azimuthal-averaged parameters with 0.2°resolution, three-day forecast, and two experiments for all cases analyzed in the publication. Due to the data size, only the variables used in the figures are uploaded (i.e., relative humidity, relative vorticity, temperature, and water vapor mixing ratio). Detailed information, composite calculation, and model settings can be found in the publication.</p>
Datasets for Quantifying the impact of land use and land cover change on moisture recycling with convection-permitting WRF-tagging modeling in the agro-pastoral ecotone of northern China
<p>These datasets are the processed and refined data that support and lead to the described results and allow other readers to assess the conclusions in the paper, entitled “<strong>Quantifying the impact of land use and land cover change on moisture recycling with convection-permitting WRF-tagging modeling in the agro-pastoral ecotone of northern China </strong> ”.</p>
Supplementary data for the manuscript entitled "Evolution of the convective boundary layer in a WRF simulation nested down to 100 m resolution during a cloud-free case of LAFE 2017 and comparison to observations" (JGR Atmospheres)
<p>This dataset contains additional material to reproduce the simulation and some of the figures of the manuscipt entitled "Evolution of the convective boundary layer in a WRF simulation nested down to 100 m resolution during a cloud-free case of LAFE 2017 and comparison to observations" in the Journal of Geophysical Reasseach - Atmospheres.</p>
Emission input data for WRF-CHMIERE
<p>Emission input data for WRF-CHIMERE in eastern China during 2017.</p>
Simulation results from WRF-CMAQ with enabling aerosol-radiation interactions in eastern China for January-April 2017
<p>Simulation results from WRF-CMAQ with enabling aerosol-radiation interactions in eastern China for January-April 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for January-April 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_CCTM_ACONC.zip:</p><p>Air quality file including CCTM_ACONC_v3852_YYYYMMDD.nc on each day for January-April 2017, which contains these variables:</p><p>ASO4I, ANO3I, ANH4I, ANAI, ACLI, AECI, ALVPO1I, ASVPO1I, ASVPO2I, ALVOO1I, ALVOO2I, ASVOO1I, ASVOO2I, AOTHRI, ASO4J, ANO3J, ANH4J, ANAJ, ACLJ, AECJ, AOTHRJ, AFEJ, ASIJ, ATIJ, ACAJ, AMGJ, AMNJ, AALJ, AKJ, ALVPO1J, ASVPO1J, ASVPO2J, AXYL1J, AXYL2J, AXYL3J, ATOL1J, ATOL2J, AXYL2J, AXYL3J, ATOL1J, ABNZ3J, AISO1J, AISO2J, AISO3J, ATRP1J, ATRP2J, ASQTJ, AALK1J, AALK2J, APAH1J, APAH2J, APAH3J, AORGCJ, AOLGBJ, AOLGAJ, ALVOO1J, ALVOO2J, ASVOO1J, ASVOO2J, ASVOO3J, APCSOJ, ASOIL, ACORS, ACLK, ASO4K, ANO3K, ANH4K, O3, SO2, NO2, CO, and NH3.</p><p>YYYYMM_CCTM_APMDIAG.zip:</p><p>Air quality file including CCTM_APMDIAG_v3852_YYYYMMDD.nc on each day for January-April 2017, which contains these variables:</p><p>PM25AT, PM25AC and PM25CO.</p><p>YYYYMM_CCTM_PHOTDIAG1.zip:</p><p>Air quality file including CCTM_PHOTDIAG1_v3852_YYYYMMDD.nc on each day for January-April 2017, which contains these variables:</p><p>OZONE_COLUMN, NO2_COLUMN, CO_COLUMN, SO2_COLUMN, HCHO_COLUMN, TROPO_O3_COLUMN and AOD_W550_ANGST.</p>
WRF 6-km simulation of western US 1981-2010 daily hydrometeorological dataset - part 1
<p>This repo includes the raw input data and scripts (of plots in the main text) for the following manuscript:</p> <p>Chen et al., Antecedent Hydrometeorological Conditions of Wildfire Occurrence in the Western U.S. in a Changing Climate</p> <p>Details and companion scripts will be uploaded after the manuscript is accepted.</p>
Irrigation-induced potential evapotranspiration decrease in the Heihe River Basin, Northwest China, as simulated by the WRF model
<p>This dataset is for the plots in the article titled "Irrigation-induced potential evapotranspiration decrease in the Heihe River Basin, Northwest China, as simulated by the WRF model", which was published by Journal of Geophysical Research: Atmospheres. There are totally nine files in the ".mat" format for Matlab. The nine data files are corresponding to nine Figures in the article.</p>
High resolution WRF simulation of Hurricane Sandy (2012) wind during landfall
<p>A 96-hour high-resolution simulation of Hurricane Sandy was conducted using the Advanced Research version 3.3.1 of the Weather Research and Forecasting (WRF) model initialized at 1200 UTC 26 October 2012 (Johnsen et al., 2013) and using a horizontal resolution of 500 m. Domain size was 2,660x2,500 km with 150 vertical layers. The Yonsei University planetary boundary layer (PBL) scheme, the Noah land model, and the MM5 Monin-Obukhov similarity theory surface layer model were used. Cloud physics was modeled using WSM6 6-class microphysics with graupel. Convection parameterization was not used for this high-spatial-resolution simulation. National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analyses were used for initialization and boundary conditions, with the latter updated every 6 hours.</p> <p>Data herein is a landfall-regime subset (Schiavone et al., 2016) of the full simulation. The spatial domain is 500x500 km containing the 50 lowest layers, with a time domain that spans the 12-hour period beginning at1200 UTC 29 October 2012 and with 3-hour time steps. Format is NetCDF containing detailed metadata. Three-dimensional meteorological field variables include the 3 wind components, potential temperature, water vapor mixing ratio, geopotential, and atmospheric pressure. Two-dimensional variables include horizontal wind components at 10 m AGL, potential temperature and water vapor mixing ratio at 2 m AGL, atmospheric pressure at the surface, terrain height, and the Coriolis sine latitude term, which is required to calculate potential vorticity.</p> <p>These WRF simulation data were used, along with WSR-88D Doppler radar data and surface wind observations, to demonstrate that roll vortices occurred in the atmospheric boundary layer over the landfall region during Hurricane Sandy's landfall period (Schiavone et al., 2020).</p>
WRF v3.9 evaluation statistics, April and October 2015 Ireland
<p>Dataset related to Chapter 4 of PhD thesis "Modelling of accidental radioactive releases for Ireland" By Cillian Joy.<br> This folder contains a file for each statistic used. Time series data is aggregated for each month of the WRF v3.9 evaluation, April and October 2015.<br> Data is for each station (28), meteorology variable (wind speed, wind direction, and precipitation) for each statistic (7 for wind speed and precipitation, and 3 for wind direction.<br> For each location, the value provided is for the month. For example, Fractional Bias for one location for the month is an average of all time steps (1464).</p> <p>The seven statistic are:</p> <ol> <li>Factor of data within a factor (FAC 1.5)</li> <li>Factor of data within a factor (FAC 2.0)</li> <li>Fractional Bias (FB)</li> <li>Normalised Mean Square Error (NMSE)</li> <li>Correlation (R)</li> <li>Geometric Mean Bias (GMB)</li> <li>Geometric Mean Variance (GMV)</li> </ol> <p>21 May 2020, Cillian Joy, cillian.joy@nuigalway.ie</p>
WRF-Hydro simulated hydrographs for Jan-1989 and Nov-1994 extreme events (Cyprus)
<p>WRF-Hydro simulation results for two extreme events occurred in Cyprus in January 1989 and November 1994. For specific information on simulation setup and study area, please refer to:</p> <p>Camera, C., Bruggeman A., Zittis, G., Sofokleous, I., Arnault, J., 2020. Simulation of extreme rainfall and streamflow events in small Mediterranean watersheds with a one-way coupled atmospheric-hydrologic modelling system. NHESSD, https://doi.org/10.5194/nhess-2020-43.</p>
Wind farm power short-term prediction using WRF model and Kalman filtering
<p>This repository contains the data used and generated in the paper:</p> <p>Mamani, R., & Hendrick, P. (2019). Wind farm power short-term prediction using WRF model and Kalman filtering. ECOS 2019</p>
Compound extremes WRF simulations
<p>All the data are available in the daily timestep. The extension with irrigation-on and irrigation-off represent two different simulations.<br> All variable details;<br> SLP- Sea Level Pressure<br> SKINTEMP- Land Surface Temperature<br> T2- Air Temperature<br> LHF - Latent Heat Flux<br> SHF - Sensible Heat Flux<br> LHFSHF - Latent Heat Flux + Sensible Heat Flux<br> SH - Specific Humidity<br> PBLH - Planetary Boundary Layer Height<br> SMOIS - Soil Moisture (0-60 cm)<br> VPD - Vapour Pressure Deficit<br> vq_Integrated uq_Integrated - u and v Component of Vertically Integrated Moisture</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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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
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