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248 results for “wrf”
Important contribution of N2O5 hydrolysis to the daytime nitrate in Xi'an, China during haze periods: isotopic analysis and WRF-Chem model simulation
<p>To quantify the contributions of major formation pathways to nitrate, a field observation, coupling with a WRF-Chem model simulation, was conducted in Xi’an, an inland megacity of China. Here is the dataset of ions, stable isotope compositions of nitrogen and oxygen of nitrate, T, RH and the results simulated by WRF-Chem model.</p>
Modeling the impact of a potential shale gas industry in Germany and the United Kingdom on ozone with WRF-Chem
<p>Germany and the United Kingdom have domestic shale gas reserves which they may exploit in the future to complement their national energy strategies. However gas production releases volatile organic compounds (VOC) and nitrogen oxides (NO<sub>x</sub>), which through photochemical reaction form ground-level ozone, an air pollutant that can trigger adverse health effects e.g. on the respiratory system. This study explores the range of impacts of a potential shale gas industry in these two countries on local and regional ambient ozone. To this end, comprehensive emission scenarios are used as the basis for input to an online-coupled regional chemistry transport model (WRF-Chem). Here we simulate shale gas scenarios over summer (June, July, August) 2011, exploring the effects of varying VOC emissions, gas speciation, and concentration of NO<sub>x</sub> emissions over space and time, on ozone formation. An evaluation of the model setup is performed, which exhibited the model's ability to predict surface meteorological and chemical variables well compared with observations, and consistent with other studies. When different shale gas scenarios were employed, the results show a peak increase in maximum daily 8-hour average ozone from 3.7 to 28.3 μg m<sup>-3</sup>. In addition, we find that shale gas emissions can force ozone exceedances at a considerable percentage of regulatory measurement stations locally (up to 21% in Germany and 35% in the United Kingdom) and in distant countries through long-range transport, and increase the cumulative health-related metric SOMO35 (maximum percent increase of ~28%) throughout the region. Findings indicate that VOC emissions are important for ozone enhancement, and to a lesser extent NO<sub>x</sub>, meaning that VOC regulation for a future European shale gas industry will be of especial importance to mitigate unfavorable health outcomes. Overall our findings demonstrate that shale gas production in Europe can worsen ozone air quality on both the local and regional scales.</p>
WRF GHI Mexico daily
<p>Typical mean year classification.</p> <p>GHI Post-process WRF output. </p> <ul> <li>Simulation period 365 days</li> <li>Model version V3.4</li> <li>Domains 2</li> <li>Horizontal resolution 27 and 9 km</li> <li>Input data ERA-Interim</li> <li>Time step 100 s</li> <li>Outputs frequency 120 (d01) and 10 minutes (d02)</li> <li>Nesting 1-way nesting</li> </ul> <p>Physics schemes  </p> <ul> <li> Microphysics WSM6 class</li> <li> Cumulus New Kain Fritsch</li> <li> Shortwave Dudhia</li> <li> Longwave Rapid Radiative Transfer Model</li> <li> LSM Noah</li> <li> PBL YSU</li> </ul> <p> </p>
WRF-Solar AOD550 & Clear-Sky Irradiance Forecasts & Verifying Observations over CONUS
<p>This dataset is fully described in and is a companion of Lee et al. (2022):</p> <p>Lee, J. A., P. A. Jiménez, J. Dudhia, and Y.-M. Saint-Drenan, 2023: Impacts of the aerosol representation in WRF-Solar clear-sky irradiance forecasts over CONUS. J. Appl. Meteor. Climatol., 62, 227–250, https://doi.org/10.1175/JAMC-D-22-0059.1.</p> <p>Aerosol optical depth (AOD) is a primary source of solar irradiance forecast error in clear-sky conditions. Improving the accuracy of AOD in NWP models like WRF will thus reduce error in both direct normal irradiance (DNI) and global horizontal irradiance (GHI), which should improve solar power forecast errors, at least in cloud-free conditions. In this study clear-sky GHI and DNI was analyzed from four configurations of the WRF-Solar model with different aerosol representations: 1) The default Tegen climatology; 2) Imposing AOD forecasts from the GEOS-5 model; 3) Imposing AOD forecasts from the Copernicus Atmosphere Monitoring Service (CAMS) model; and 4) The Thompson-Eidhammer aerosol-aware water/ice-friendly aerosol climatology. Over eight months of these 15-min output forecasts are compared against high-quality irradiance observations at NOAA SURFRAD and Solar Radiation (SOLRAD) stations located across CONUS. In general, WRF-Solar with GEOS-5 AOD had the lowest errors in clear-sky DNI, while WRF-Solar with CAMS AOD had the highest errors, higher even than the two aerosol climatologies, which is consistent with validation of the four AOD550 datasets against AERONET stations. For clear-sky GHI, the statistics differed little between the four models, as expected due to the lesser sensitivity of GHI to aerosol loading. Hourly-average clear-sky DNI and GHI was also analyzed, and additionally compared with CAMS model output directly. CAMS irradiance performed competitively with the best WRF-Solar configuration (with GEOS-5 AOD). The markedly different performance of CAMS versus WRF-Solar with CAMS AOD indicates that CAMS is apparently less sensitive to AOD550 than WRF-Solar is.</p> <p>wrf_aeronet_aod550_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 317 MB</p> <p>WRF-Solar AOD550 spatially interpolated to AERONET stations and AERONET observed AOD550. Organized in arrays sorted by model cycle time (once daily at 09 UTC from 20191119 to 20200730) and model lead time (every 15 min to 45 h). The four WRF-Solar experiments correspond to the four from Lee et al. (2022).</p> <p>wrf_surfrad_solrad_inst_dni_ghi_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 102 MB</p> <p>WRF-Solar clear-sky DNI and clear-sky GHI interpolated to SURFRAD & SOLRAD stations, and SURFRAD & SOLRAD observed clear-sky DNI and clear-sky GHI. Organized in arrays sorted by model cycle time (once daily at 09 UTC from 20191119 to 20200730) and model lead time (every 15 min to 45 h). The four WRF-Solar experiments correspond to the four from Lee et al. (2022).</p> <p>wrf_surfrad_solrad_hrly_dni_ghi_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 26 MB</p> <p>WRF-Solar clear-sky DNI and clear-sky GHI interpolated to SURFRAD & SOLRAD stations, and SURFRAD & SOLRAD observed clear-sky DNI and clear-sky GHI. All values are averaged to time-ending 1-hourly averages. Organized in arrays sorted by model cycle time (once daily at 09 UTC from 20191119 to 20200730) and model lead time (every 1 h to 45 h). The four WRF-Solar experiments correspond to the four from Lee et al. (2022).</p> <p>cams_surfrad_solrad_hrly_dni_ghi_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 26 MB</p> <p>CAMS clear-sky DNI and clear-sky GHI interpolated to SURFRAD & SOLRAD stations, and SURFRAD & SOLRAD observed clear-sky DNI and clear-sky GHI. All values are averaged to time-ending 1-hourly averages. Organized in arrays sorted by model cycle time (once daily at 00 UTC from 20191119 to 20200730) and model lead time (every 1 h to 54 h).</p>
Project Pythia: Output from WRF V3.6 model
Project Pythia is a community resource for learning how to analyze geosciences data using the Scientific Python Ecosystem. This data set is used primarily on the WRF-Python webpage to illustrate how WRF-Python can be used in conjunction with the Python packages Cartopy, Matplotlib, and Basemap. The data set is also used in a vertical interpolation example that is also found on the WRF-Python webpage. The central latitude for the data is 38.5N and the central longitude is 97.5W.
WRF-Chem output- concentrations for the 4 simulations: reference, urbanization, extension of agricultural areas and more urban parks
<p>We use the WRF-Chem air-quality model with an urban canopy scheme to investigate how land use (urban, agriculture, parks) affects the urban climate and chemical processes (dry deposition, biogenic emissions), resulting in changes in air quality for scenarios proposed by the Urban Master Plan of the Metropolitan Area of Barcelona. We selected a heatwave period, 4th-7th July 2015, with maximum temperatures reaching 40 °C in the interior of the AMB and 35 °C on the coast. Here we provide averaged model output for the heatwave period for the different pollutants, surface temperature, wind speed, PBLH, deposition, and biogenic emissions under different urban scenarios: reference (REF), urbanization (URB), extension of agriculture (AGR), and increase in urban parks (PARK).<br><br>The results for a period before the heatwave (1-3 July 2015), with temperatures 3-4 degrees lower than during the heatwave, are also provided. <br><br>Land-use and irrigation is also provided for each scenaio. </p>
WRF model output
Open the record for dataset details and reuse information.
Urbanization numerical experiment in Shanghai: WRF model outpts
<div>A numerical experiment was conducted using the WRF model to explore the impacts of urbanization and urban growth on convective rainfall in the subtropical city of Shanghai (China). Details on the experiment and results are summarized in the paper by Qi et al. (2024) entitled: "Contradictory influences of urbanization on intense convective rainfall in a subtropical city".</div> <div> </div> <div>The data is stored in NetCDF format and contains rainfall, moisture, temperature, and omega variables for the three scenarios explored in the paper.</div>
Lightning Prediction in the Tehran Region Using the WRF Model with Multiple Physical Parameterizations and an Ensemble Approach
<p><span>The Grid Analysis and Display System (</span>GrADS)<span> </span><span>and</span><span> </span><span>Python</span><span> </span><span>scripts and the output data from simulations that we used in this study.</span></p>
Output of WRF BEP+BEM model and Pseudo Global Warming composites
<p>Output for the submitted article "Assessing the intensity of heatwaves in a warming climate at the urban scale: A case study of the Metropolitan Area of Barcelona"</p> <p>Sergi Ventura1, JR Miro2, Ricard Segura-Barrero1, Fei Chen3, Alberto Martilli4, Changhai Liu5, Kyoko Ikeda5, Gara Villalba1,6,*</p> <p><br>1 Sostenipra Research Group (SGR 01412), Institute of Environmental Sciences and Technology (MDM-2015-0552), Z Building, Universitat Autònoma de Barcelona (UAB), Campus UAB, 08193 Bellaterra, Barcelona, Spain<br>2 Department of Territory and Sustainability, Meteorological Service of Catalonia, Generalitat de Catalunya, Barcelona, Spain<br>3 Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China<br>4 Research Center for Energy, Environment and Technology, CIEMAT, Madrid, Spain<br>5 National Center for Atmospheric Research, Boulder, CO 80301, USA<br>6 Department of Chemical, Biological and Environmental Engineering, Universitat Autònoma de Barcelona (UAB), Campus UAB, 08193 Bellaterra, Barcelona, Spain</p> <p><br>* Corresponding author: gara.villalba@uab.cat</p> <p>All rights lie with the authors.</p> <p> </p> <p>We use WRF BEP+BEM to investigate the sensitivity of multiple heat wave periods to climate change under the SSP370 scenario. The simulations show mean temperature increases of 2.5 ºC by the mid-21st-century and 4.2 ºC by the end of the century in the Metropolitan Area of Barcelona, a Mediterranean region in northeastern Spain.</p> <p>This Zenodo repository contains the following:</p> <ul> <li> <p><strong>met_em initial and boundary conditions for the Metropolitan Area of Barcelona</strong>:<br><code>met_em</code> files including the initial and boundary conditions used for modeling a historical heatwave (2020), as well as projected PGW-MID (2070) and PGW-END (2100) periods.</p> </li> <li> <p><strong>wrfout_d03, model output</strong>:<br>WRF output at 1 km resolution for the Metropolitan Area of Barcelona on <strong>August 1st, 2020</strong> (a historical heatwave day), representing the historical, mid-century (2070), and end-of-century (2100) periods.</p> </li> <li> <p><strong>PGW composites</strong>:<br>NetCDF-format plots of <strong>relative humidity (RH)</strong> and <strong>temperature (T)</strong> maximum and minimum values. These are provided for the historical control period (CTL), mid-century (MID), and end-of-century (END), across the four analyzed synoptic weather patterns (SS, SA, DA, and DAU).</p> </li> </ul> <p> </p> <p> </p>
Simulations of FLEXPART-WRF used for testing TRansport Of water VApor (TROVA) software (Part II)
<p>Necessary data for carrying out the tests using TRansport Of water VApor (TROVA) software for backward in time. The TROVA software, was developed in Python and Fortran for the study of moisture sources and sinks. <span><span><span>In addition, the Python code is provided for the representation of the results.</span></span></span></p>
WEATHER AND CLIMATE PILOT: WRF OUTPUT EXAMPLE
<p>WRF OUTPUT EXAMPLE (14 AUGUST 2021 00UTC RUN)</p>
CONTINUUM HYDROLOGICAL MODEL SAMPLE DATA FOR WRF 24 OCTOBER 2021 (APOLLO MEDICANE CASE)
<p>CONTINUUM HYDROLOGICAL MODEL SAMPLE DATA FOR WRF 24 OCTOBER 2021 (APOLLO MEDICANE CASE). Data are ready for publication on MyDewetra platform</p>
Processed ground observation and WRF-CAMQ data for Greater Bay Area, 2015-2021
<p>The pre-processed training and testing dataset for the paper <em>Development of an LSTM-Broadcasting deep-learning framework for regional air pollution forecast improvement. </em>The format of data is compatible with <a href="https://github.com/jvhs0706/regional-forecast-new">the official implementation</a>.</p>
Oservational data for sfdda nudging analysis in WRF model over China during 2017
<p>Oservational data for sfdda nudging analysis in WRF model over China during 2017.</p>
Calibrated Western USA Noah-MP Parameter Set Forced by ERA5-WRF
<p>These three files include the default (original) Noah-MP parameter set for the Western USA, and parameter sets based on calibration to daily streamflow data and monthly streamflow data. </p>
WRF simulation data
<p>We have investigated the impact of varying physics parameterization schemes on the real-time short-term forecasts for the recent typhoon cases that affected South Korea. <br> These files contain the main variables in WRF output data for 3-day forecasts of typhoons Trami (init:2018092700UTC) and Faxai (init:2019090600UTC).<br> The domain spatial resolutions are 12X12km (D01) and 1.33X1.33km (D03). The data format is NetCDF containing detailed metadata. <br> Three-dimensional meteorological field variables include the 10m wind components, accumulated rain, sea-level pressure, latitude, and longitude.<br> Four-dimensional meteorological field variables include the U, V, T, Qc, Qr, Qi, Qs, and Qg.<br> </p>
WRF-DART/RTTOV input and (processed) output files for GMD-2022-30
<p>The <em><strong>Exp-01.tar.gz ~ Exp-14.tar.gz (including true_processed.tar.gz and ctrl_processed.tar.gz) </strong></em>store the processed output files. Untarrring each of them will generate a list of subdirectories named after UTC time, txt files and netcdf files. (1) The subdirectories named after the UTC time. For example, 202008180200 denotes 02:00 UTC 18 August 2020. The subdirectory 202008180200 includes preassim_mean.nc and postassim_mean.nc files, which denote the prior and posterior estimates of cloud and non-cloud variables (and diagnosed variable). Both preassim_mean.nc and postassim_mean.nc include seven variables: CWP denotes the cloud water path (kg m<sup>-2</sup>). CWC denotes the mixing ratio (kg kg<sup>-1</sup>) of cloud hydrometers, including cloud droplet, rain, snow, ice, and graupel. P denotes the pressure (hPa), Re_CLOUD denotes the effective radius of liquid water clouds, QVAPOR denotes the water vapor mixing ratio, T denotes the perturbation potential temperature, U denotes the u-wind component, and V denotes the v-wind component. (2)Txt files which are the quantitative metrics of cloud and precipitation simulation errors. FSS_pre.txt and FSS_pos.txt store the fraction skill score for the prior and posterior estimates of cloud coverage. MAE_pre.txt and MAE_pos.txt store the Mean Absolute Error for the prior and posterior estimates of CWP. rain_TS_pre.txt and rain_TS_pos.txt store the TS scores for the prior and posterior estimates of precipitation. rain_MAE_pre.txt and rain_MAE_pos.txt store the MAE for the prior and posterior estimates of rain rate. (3) Netcdf files including mpe_mpi.nc, rain_pre.nc, and rain_pos.nc. mpe_mpi.nc includes the Mean Profile Error (MPE) for the prior and posterior estimates of non-cloud variables (Q, T, U, and V). rain_pre.nc and rain_pos.nc store the results for the prior and posterior estimates of rain rate (mm h<sup>-1</sup>).</p> <p>The <em><strong>nature_run.tar.gz</strong></em> file contains the WRF output files for the nature run.</p> <p>The <em><strong>model_codes.tar.gz</strong></em> file includes the source code of WRF-ARW (v4.1.1), WPS (v4.1), RTTOV (v12.3), and DART (Manhattan release v9.8.0).</p> <p>The <em><strong>input_file.tar.gz</strong></em> file includes the input namelist files for the WRF/WPS/DART models (tool) for the nature run, control run, and fourteen cycled data assimilation experiments. In addition, the input observation sequence files for the DART tool at each analysis time are provided.</p> <p>The <em><strong>Figure_script.tar.gz</strong></em> file includes the visualization scripts of Figure 1~Figure 16 in the manuscript.</p>
WRF initial condition files
<p>This dataset contains initial conditions files, namelist file and a piece of source code for running the WRF idealised baroclinic wave simulation. With these files you can reproduce the results which will be described in the upcoming paper by Rantanen et al.</p>
Dataset for model input of WRF model for the paper:Modulation of Extratropical Cyclones by Previous Cyclones via the Sea Surface Temperature Anomaly over the Sea of Japan in Winter
<p>This is the dataset and code for generating the lower boundary condition which used in our study submitted to the JGR-Atmospheres. The meteorological data for the initial condition are available on NCEP-FNL ftp database.</p>
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