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248 results for “WRF”
Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF
<p>This repository contains namelists needed to reproduce the WRF(V4.4) simulations analyzed in "Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF"</p>
1-km high resolution model outputs using the WRF and WRF-Hydro model Raw data from the manuscipt "Process-based Atmosphere-Hydrology-Malaria Modeling: Performance for Spatio-temporal Malaria Transmission Dynamics in Sub-Saharan Africa "
<p>Here we provide the model outputs from the numerical climate model WRF (Weather Research and Forecasting) and its hydrological coupled model WRF-Hydro for the Health and Demographic Surveillance Systems (HDSS) site regions of Nouna in Burkina Faso. Model results are used for investigating the influence of surface hydrology representation, environmental and climate-sensitive driver factors on malaria incidence.<br>The experiments use the following model configuration: 1km horizontal resolution with 200*200 grid points, WSM6 microphysics, ACM2 PBL, and RRTM & Dudhia radiation scheme. WRF uses the Noah LSM, and WRF-Hydro uses the Noah LSM with enhanced lateral hydrological description (https://ral.ucar.edu/projects/wrf_hydro/overview). These simulations were conducted in the Karlsruhe Steinbuch Centre for Computing (SCC) Horeka.</p> <p>Model outputs are provided in daily step (originally derived from the hourly output). Filename with "wrf-hydro_pr_2000-2020_d02-1km.nc" provides Precipitation,<br>n mm/day"wrf-hydro_tas_2000-2020_d02-1km.nc" provides mean temperature in Celsius, "wrf-hydro_tasmax_2000-2020_d02-1km.nc" provides maximum temperature in Celsius, "wrf-hydro_tasmin_2000-2020_d02-1km.nc" provides minmum temperature in Celsius, "wrf-hydro_dtr_2000-2020_d02-1km.nc" provides diurnal temperature ranges in Celius, "wrf-hydro_rh_2000-2020_d02-1km.nc" provides relative humudity in % and "wrf-hydro_sw_2000-2020_d02-1km.nc" provides the surface hydrology.</p>
Evaluation of high-resolution WRF simulation in urban areas - Effect of different physics schemes on simulation performance in the Rhine-Main-Neckar area
<p>This dataset contains data sampled from a WRF sensitivity study saved in NetCDF format. The study was run over 4 months of the year 2020. The folders contain the following data:</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Datasets</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>wrf_met_sample_full.nc</td> <td>all</td> <td>WRF meteorology sampled at the 19 weather stations in the simulation domain</td> </tr> <tr> <td>wrf_met_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>wrf_met_sample_full_ucmheights.nc</td> <td>only 2020_12</td> <td>Same as wrf_met_sample_full.nc but only for the run using the vertical layer distribution of UCM</td> </tr> <tr> <td>wrf_met_sample_full_and_quant_ucmheights.nc</td> <td>only 2020_12</td> <td>Same as wrf_met_sample_full_and_quant.nc but only for the run using the vertical layer distribution of UCM</td> </tr> <tr> <td>wrf_pblh_sample_full.nc</td> <td>all</td> <td>WRF PBLH (and custom PBLH_RIB) sampled at the 2 radio sonde stations in the simulation domain</td> </tr> <tr> <td>wrf_pblh_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>era5_met_sample_full.nc</td> <td>all</td> <td>ERA5 meteorology sampled at the 19 weather stations in the simulation domain</td> </tr> <tr> <td>era5_met_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>era5_pblh_sample_full_and_quant.nc</td> <td>all</td> <td>WRF PBLH (and custom PBLH_RIB) sampled at the 2 radio sonde stations in the simulation domain, resampled onto the measured meteorology and with summary statistics</td> </tr> </tbody> </table> <p>Each of these files contains the samples and statistics as NetCDF Variables. These Variables have multiple dimensions, which describe the individual datapoints. For the WRF samples, these dimensions are:</p> <table> <tbody> <tr> <td><strong>Dimension</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Time</td> <td>time since start of simulation</td> </tr> <tr> <td>station_id</td> <td>the ID of the station where the sample was taken (meteo - length 19, PBLH - length 2)</td> </tr> <tr> <td>pbl</td> <td>Planetary Boundary Layer scheme (Bou-Lac / MYJ / YSU)</td> </tr> <tr> <td>lsm</td> <td>Land Surface Model scheme (N / NMP)</td> </tr> <tr> <td>slm</td> <td>Surface Layer Model scheme (MM5 / MO)</td> </tr> <tr> <td>urb</td> <td>Urban Parametrization scheme (SLUCM / BEP)</td> </tr> </tbody> </table> <p>Not all combinations between different simulation schemes exist, so some values in the NetCDF Variables are NaNs.</p>
Model output for "Numerically consistent budgets of potential temperature, momentum, and moisture in Cartesian coordinates: application to the WRF model"
<p>These data were produced with WRFlux v1.2.1 (https://github.com/matzegoebel/WRFlux/) from a numerical simulation with the community model WRF. Simulations represent the evolution of a convective boundary layer in the atmosphere over an idealized 2D mountain ridge. The data are published in connection with the article "Numerically consistent budgets of potential temperature, momentum and moisture in Cartesian coordinates: Application to the WRF model" in "Geoscientific Model Development" (https://doi.org/10.5194/gmd-15-669-2022).</p> <p>Three-dimensional (x, z, t) fields of five prognostic variables are provided: Potential temperature (T), water vapor mixing ratio (Q), cross-mountain (U), along-mountain (V), and vertical windspeed (W). All fields are averaged in time (30 min averaging interval) and in the along-mountain direction y.</p> <p>The repository contains the following files:</p> <p>grid.nc : variables related to the WRF numerical grid, air density<br> [U,W,T,Q]_flux.nc : resolved and subgrid-scale fluxes<br> [U,W,T,Q]_tendency.nc : resolved and subgrid-scale tendency components<br> UVWT_MEAN.nc : averaged values of the variables themselves<br> plotting.py : python script to approximately reproduce the figures of the paper. Requires the python packages matplotlib, xarray, and netcdf4.</p> <p>Figure 6 in the paper cannot be accurately reproduced with these data since the original figure uses 4D (x, y, z, t) output.</p> <p>For details on the simulation, refer to the article.</p>
Files with RUNE experiment measurements and WRF modelling results
<p>This file contains all data used for the figures in the paper, including horizontal transects, vertical profiles and WRF model output for the different locations. Sample namelist for the 1st of January 2016 are supplied for all model runs that are discussed in the paper. For more details, see the corresponding paper "Evaluating mesoscale simulations of the coastal flow using lidar measurements".</p> <p> </p>
Training and Testing Data, Associated Code, and WRF Code for ML-based nonhydrostatic alternative scheme in dynamical core of atmosphere
<p>Data and codes for a nonhydrostatic alternative scheme (NAS) in dynamical core of atmosphere based on machine learning.</p> <p>In this new version, the randomly sampled training data samples testing data samples from nonhydrostatic simulations in WRF baraclinic wave test are provided. They are processed into a new data structure, which can be directly utilized in training and testing. </p> <p>Follow the instructions in README.txt and download the training and testing data, and the associated codes.</p> <p>Here we provide 3 parts of data and codes:</p> <p>1, Training and testing data from WRF;</p> <p>2, Training and testing codes for two machine learning emulators: machine learning and neural network</p> <p>3, WRF application.</p>
Lightning Assimilation in the Weather Research and Forecasting (WRF) Model: Technique Updates and Assessment of the Applications from Regional to Hemispheric Scales
<p>Figure 1. The data is proprietary, but it can be purchased from Vaisala Inc. (https:// <a href="http://www.vaisala.com/en/products/systems/lightning-detection">www.vaisala.com/en/products/systems/lightning-detection</a>), and the WWLLN raw data are also available for purchase at <a href="http://wwlln.net">http://wwlln.net</a>.</p> <p>Figure 2. Maps, data is not applicable.</p> <p>Figure 3. Data file: NLDN_WWLLN_Prism_Rainfall_Analysis.xlsx</p> <p>Figure 4. Data file: NLDN_WWLLN_METVARS_T2_Jul_2016.xlsx</p> <p>Figure 5. Data file: CONUSall_METOBS_q_Jul_2016.xlsx</p> <p>Figure 6. Data file: CONUSall_METOBS_ws_Jul_2016.xlsx</p> <p>Figure 7. Created using the R script: Hemi_Rain_ModelOnlyWGPM.R based on the R object files: AnnualRainFall_CFC_WRF_Hemi_BASE_*.rds, AnnualRainFall_CFC_WRF_Hemi_LTA_*.rds, and GPM_WRF_Paired_rain2Hemispheric_July2016.rds.</p> <p>Figure 8. Created using the R script: Hemi_Rain_Aanlysis.R based on the R object files: AnnualRainFall_CFC_WRF_Hemi_BASE_*.rds and AnnualRainFall_CFC_WRF_Hemi_LTA_*.rds.</p> <p>Figure 9. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx</p> <p>Figure 10. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx</p> <p>Figure 11. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx</p> <p>Figure 12. Created using the R script: CreateCPCdataforUSdomain_vs_Prism.R based on the R oject files: Prism_CFC_WRF*.rds</p> <p>Figure 13. Data file: Hemi_lta_METOBS_T2_Jul_2016.xlsx</p> <p>Figure 14. Data file: Hemi_lta_METOBS_q_Jul_2016.xlsx</p> <p> </p>
WRF dataset for soil moisture initialization experiments
<p>Modeling results of "Role of Land–Atmosphere Interaction in the 2016 Northeast Asia Heat Wave: Impact of Soil Moisture Initialization", by Yoon et al. (submitted). Data contains the modeling outputs (500GPH, SAT and soil moisture) from CTL and LIS experiments. Detailed description can be founded in the research paper.</p>
Ancillary data for the wrf_to_tell processing chain
<p>This data supports the sequence of processing scripts that convert the meteorology from IM3's climate simulations using the Weather Research and Forecasting (WRF) model into input files ready for use in the Total ELectricity Load (TELL) model. More details about the processing chain and the associated scripts can be found in the IM3 components library: https://github.com/IMMM-SFA/im3components/tree/main/im3components/wrf_to_tell.</p>
WRF 3.5km North Atlantic August - October 2020 (TC tracks and atmospheric composites data)
<p>This zip file contains simulated TC tracks and some atmospheric composites for August to October 2020.</p> <p>- TC tracks information: timing, intensity, and location</p> <p>- Composites: vertical wind shear (200 - 850 hPa), potential intensity, 850 hPa absolute vorticity, and 700 hPa specific humidity.</p>
Archive of NASA-Unified WRF model daily forecasting simulations for DOE TRACER IOP
<pre># Copyright 2022 NASA GSFC All rights reserved. # Creative commons attribution 4.0 international license NASA-Unified WRF model daily simulations for DOE TRACER IOP Document updated: 22 June 2022 Point of contact: Takamichi Iguchi (ESSIC UMD, Code612 NASA GSFC), takamichi.iguchi@nasa.gov Toshi Matsui (ESSIC UMD, Code612 NASA GSFC), toshihisa.matsui-1@nasa.gov Contents: ./READMEtracer.txt # this file ./namelist.wps.tracer_iop_31.template # namelist.wps file to configure WRF Pre-Processing System (WPS) ./namelist.input.real.tracer_iop_31.template # namelist.input file for NU-WRF model real.exe ./namelist.input.wrf.tracer_iop_31.template # namelist.input file for NU-WRF model wrf.exe ./${YYYY}${MM}${DD} # these directories contain files produced from 48-hours NU-WRF forecasting from 00UTC on ${YYYY}${MM}${DD}: pyplot_${YYYY}-${MM}-${DD}_${HH}${MN}${SC}.png # Plot for Composite radar reflectivity (dBZ) # PBL height (m) + 10-m horizontal wind (850hPa-level wind in plots before 06/02/2022), # OLR TOA (W m-2), and 5-mins-accumulated IC+CG lighting flash extent density (flash km-2) # Note that this composite dBZ is calculated from NSSL 2-moment microphysics for S-band, # not from POLARRIS radar simulator pyplot.gif # Gif annimation file combining the png plot files for 1~48 hours in the forecasting accprecip_${YYYY}-${MM}-${DD}_${HH}${MN}${SC}.png # Plot for 1, 3, 6-hours, and total accumulated surface precipitation (mm) accprecip.gif # Gif annimation file combining the png plot files for 1~48 hours in the forecasting polarris_zh_zdr_rh_vr_${YYYY}_${MM}${DD}_${HH}${MN}${SC}.png # Plot from POLARRIS radar simulator in NU-WRF for QCed Reflectivity (dBZ), # differential reflectivity (dB), cross-polar correlation (-), and # radial velocity (m s-1) at 0.5 degree elevation angle polarris_zh_zdr_rh_vr.gif # Gif annimation file combining the png plot files roughly every hour # for 1~48 hours in the forecasting polarris_zh_4sweeps_${YYYY}_${MM}${DD}_${HH}${MN}${SC}.png # Plot from POLARRIS radar simulator in NU-WRF for QCed Reflectivity (dBZ) # at 0.5, 1.8, 4.0 and 8.0 degree elevation angles polarris_zh_4sweeps.gif # Gif annimation file combining the png plot files roughly every hour # for 1~48 hours in the forecasting # following files are produced 3 days late # day1 represent the first 0-24hr forecast, day2 represents the 24-48hr forecast. CFAD_con_day?.png # Convective part of Contoured Frequency of Altitude Diagrams CFAD_str_day?.png # Stratiform part of Contoured Frequency of Altitude Diagrams QVP_con_day?.png # Convective part of QVP-like domain-mean radar profiles QVP_str_day?.png # Stratiform part of QVP-like domain-mean radar profiles RadarFrac_day?.png # Composite Radar Horizontal Fraction (0-1) by different minimum reflectivity thresholds</pre>
WRF-Chem output supporting manuscript "Sources of Black Carbon Deposition to the Himalayan Glaciers in Current and Future Climates"
<p>Selected output from WRF-Chem v3.6.1 on black carbon deposition, rainfall, and snowfall over Southeast Asia. These files were used to prepare the figures and tables in the manuscript "Sources of Black Carbon Deposition to the Himalayan Glaciers in Current and Future Climates" by these authors. Files are in NetCDF format and contain metadata describing their contents. The naming convention is:</p> <p>YYYY_MM_EXT_daily_12km_dustfix.nc</p> <p>where YYYY_MM is the simulated year and month and the extensions are:</p> <p>NFC - No Further Control emission scenario</p> <p>MIT - Mitigation emission scenario</p> <p>EN - Simulated El Nino year</p> <p>LN - Simulated La Nina year</p> <p> </p>
Atmospheric CO2 simulations over Indian sites using STILT driven by WRF meteorology.
<p>This data contains atmospheric CO2 simulations at a temporal resolution of 3 hours over 15 Indian sites using a Lagrangian transport model, STILT driven by WRF meteorology during May 2017.</p> <p>Note: You are encouraged to contact the creator before using this data in any presentation or publication. </p> <p>Reference:</p> <p>Jithin Sukumaran, Dhanyalekshmi Pillai, Vishnu Thilakan, Saradambal Lekshmi, Gokul Udayakumar, Thara Anna Mathew, Aparnna Ravi, Manoj M G. How critical is the accuracy of the atmospheric transport modelling to improve the urban CO2 emission in India? - A Lagrangian-based approach. (2024), JGR Atmospheres. [Under review]</p> <p> </p>
WRF Model Output for the 3 km grid of the Hurricane Nature Run, days 08-01-2005 to 08-03-2005
<p>This is WRF model output for the Hurricane Nature Run presented in Nolan et al. (2013). The tar file contains gzipped netcdf files each with 6 model outputs at 30 minute intervals.</p>
WRF Model Output for the 3 km grid of the Hurricane Nature Run, days 08-01-2005 to 08-03-2005
<p>This is WRF model output for the Hurricane Nature Run presented in Nolan et al. (2013). The tar file contains gzipped netcdf files each with 6 model outputs at 30 minute intervals.</p>
Multi-scale modeling - WRF-CIM coupling
<p>In this dataset you can find WRF and CIM simulated data produced and used in the paper (<a href="https://www.sciencedirect.com/science/article/pii/S2212095518301688">Multi-scale modeling of the urban meteorology: Integration of a new canopy model in the WRF model</a>).</p> <p>More details on the datasets can be found in the Python Notebook.</p> <p>Additional data (namelists for ex.) can be obtained directly by contacting the authors.</p>
WRF output. Resolution: 12 km. Domain: Eastern North America
<p>Derived products from WRF simulations at 12 km resolution. LBC: ERA-Interim. Output every 10-min. Output level: 3rd model level </p> <p>Details given in: Pryor S.C. et al. (2018): Inter-annual variability of wind climates and wind turbine annual energy production. Manuscript published in Wind Energy Science.</p> <p>5 netcdf files;</p> <p>Pryoretal_WES2018_part1.nc contains information shown in Figure 3. Included variables; lat, lng, medianWSHH, CI(medianWSHH), vectors of data in WT grid cells and in grid cells without WT.</p> <p>Pryoretal_WES2018_part2.nc contains information shown in Figure 4. Included variables; Weibull distribution fits by wind speed bin and year, and FFT of output from TX, IA, and NY.</p> <p>Pryoretal_WES2018_part3.nc contains information shown in Figure 5. Included variables; lat, lng, 50th and 90th percentile WI, AEP and difference, vectors of data in WT grid cells and in grid cells without WT.</p> <p>Pryoretal_WES2018_part4.nc contains information shown in Figure 6.Included variables; lat, lng, 50th and 90th percentile AEP and difference, vectors of data in WT grid cells and in grid cells without WT.</p> <p>Pryoretal_WES2018_part5.nc contains information shown in Figure 7. Included variables; lat, lng, time of minimum, time of maximum.</p> <p> </p>
Mechanisms for a record-breaking rainfall in the coastal metropolitan city of Guangzhou, China: observation analysis and nested very-large-eddy simulation with the WRF Model
<p>A video shows the processes of a record-breaking rainfall in the coastal metropolitan city of Guangzhou, China simulated by WRF nested very-large-eddy simulation.</p>
WRF-Chem output. Resolution: 24 km. Domain: Eastern North America
<p>Derived products from WRF-Chem simulations at 24 km resolution.</p> <p>Details given in: Crippa P. et al. (2019): Sensitivity of simulated aerosol properties over eastern North America to WRF-Chem parameterizations. Manuscript published in the Journal of Geophysical Research.</p> <p>14 netcdf files:</p> <p>Crippaetal_JGR2019_AOD.nc contains monthly mean AOD cloud screened for each ensemble member.</p> <p>Crippaetal_JGR2019_net_radiation.nc contains monthly mean surface net radiation for each ensemble member.</p> <p>Crippaetal_JGR2019_NO3_fine.nc contains monthly mean NO3 in the fine aerosol mode for each ensemble member.</p> <p>Crippaetal_JGR2019_SO4_fine.nc contains monthly mean SO4 in the fine aerosol mode for each ensemble member.</p> <p>Crippaetal_JGR2019_PM25_daily.nc contains daily PM<sub>2.5 </sub>concentrations for each ensemble member.</p> <p>Crippaetal_JGR2019_O3_daily.nc contains daily mean surface ozone concentrations in the fine aerosol mode for each ensemble member.</p> <p>Crippaetal_JGR2019_PBLH.nc contains monthly mean planetary boundary layer height for each ensemble member regridded on the MERRA-2 resolution</p> <p>Crippaetal_JGR2019_T2.nc contains monthly mean surface temperature at 2m for each ensemble member regridded on the MERRA-2 resolution</p> <p>Crippaetal_JGR2019_QPBL.nc contains monthly mean specific humidity in the PBL for each ensemble member regridded on the MERRA-2 resolution</p> <p>Crippaetal_JGR2019_PPT.nc contains monthly total precipitation for each ensemble member regridded on the MERRA-2 resolution</p> <p>Crippaetal_JGR2019_lat_WRF-Chem.nc contains the latitude coordinates of the WRF-Chem grid</p> <p>Crippaetal_JGR2019_lon_WRF-Chem.nc contains the longitude coordinates of the WRF-Chem grid</p> <p>Crippaetal_JGR2019_lat_MERRA2.nc contains the latitude coordinates of the MERRA2 grid</p> <p>Crippaetal_JGR2019_on_MERRA2.nc contains the longitude coordinates of the MERRA2 grid</p>
Selected WRF output for Langtang 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>
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International Brain Laboratory public data
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OpenNeuro
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