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79 results for “WRF model”
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>
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>
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>
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>
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 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>
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>
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>
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>
Data for publication of "Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes"
<p>Implementation of Gaussian process regression-based Bayesian optimisation (G-BO) using the emcee package (<a href="https://emcee.readthedocs.io/en/stable/" rel="nofollow">https://emcee.readthedocs.io/en/stable/</a>).</p> <p>For more information about the implementation of G-BO in optimising the Weather Research and Forecasting (WRF) model parameters, please refer to the paper - <a href="https://essopenarchive.org/doi/full/10.22541/essoar.171292045.52489731" rel="nofollow">Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes</a>.</p> <p><code>G-BO_script.ipynb</code> implements the GPR-based Bayesian optimisation using the Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler.</p> <ul> <li><strong>QMC_sobol_samples</strong>: This file contains the 128 parameter samples across the parameter space of three sensitive parameters utilizing the Quasi Monte-Carlo (QMC) Sobol sequence design.</li> <li><strong>nmae_all_128_ens_T_Rh</strong>: This file contains the normalised mean absolute error (NMAE) values of temperature (T) and relative humidity (Rh) of the 128 parameter sample WRF simulations. For more details, please refer to <a href="https://essopenarchive.org/doi/full/10.22541/essoar.171292045.52489731" rel="nofollow">this link</a>.</li> </ul>
WRF Model Output for the 3 km grid of the Hurricane Nature Run, days 08-07-2005 through 08-09-2005
<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 08-04-2005 to 08-06-2005
<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>
3-km high resolution model outputs using the WRF and WRF-Hydro model for HRB
<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 Heihe river basin (HRB). Model results are used for investigating the effect of lateral terrestrial water flow on regional climate modeling. The analysis results were published in a peer-reviewed journal at https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018JD030174</p> <p>Two experiments use the following model configuration: 3km horizontal resolution with 350*350 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 Leibniz Supercomputing Center (LRZ) SuperMUC. </p> <p>Model outputs are provided in daily step (originally derived from the hourly output). Filename with "wrfout_selvar_P_ET_R_DRA" provides P, ET, surface runoff, drainage, and filename with "wrfout_selvar_T_Q2_SM_SH2O_AWS_CONV" provides T, specific humidity, soil moisture, and liquid water, atmosphere water storage and convergence. Tagged precipitation from upper HRB is also provided.</p>
Dataset for the publication of "WRF model parameter calibration to improve the prediction of tropicalcyclones over the Bay of Bengal using Machine Learning-basedMultiobjective Optimization"
<p>The dataset consists of the modified WRF model software, that can be extracted and used in any Linux system with preinstalled required software.</p> <p>The namelists_file.zip consists of the namelist.input files that are used for the default and calibration simulations with different driving data namely, FNL files at 1deg with two nested domains, ERA files at 1deg with two nested domains, ERA files at 0.25deg with a single domain, and the ERA files at 0.25deg with two nested domains.</p>
North American Regional Reanalysis (NARR) data used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes"
<p>North American Regional Reanalysis (NARR) data from National Oceanic and Atmospheric Administration (NOAA) - 20 August 2013, 26 August 2013, 2 September 2013 - used as input information (initial and boundary condition) for WRF simulations described in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes" (Fathi et al., 2022 - egusphere-2022-1125). NARR data can be accessed and downloaded at the following web address "https://www.ncei.noaa.gov/products/weather-climate-models/north-american-regional/". </p>
Data used to create figures and tables in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"
<p>This dataset contains all simulation output and observational data of ground-based/satellite-retrieved meteorological and air quality for computing statistical metrics in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows:</p> <p>1. Simulation and observational results of meteorological and air quality including four folders:</p> <p> Day_PBLH: Daily PBLH data</p> <p> Hour_air: Hourly air quality data regarding PM2.5, O3, SO2, NO2 and CO</p> <p> Hour_met: Hourly meteorological data regarding T2, Q2, RH2, WS10 and precipitation</p> <p> Hour_radiation: Hourly surface radiation data</p> <p>2. Simulation and satellite-retrieved results of meteorological and air quality including nine folders:</p> <p> AOD: Yearly and seasonal AOD data</p> <p> CF: Yearly and seasonal CF data</p> <p> CO: Yearly and seasonal CO data</p> <p> LWP: Yearly and seasonal LWP data</p> <p> NO2: Yearly and seasonal NO2 data</p> <p> O3: Yearly and seasonal O3 data</p> <p> Precipitation: Yearly and seasonal precipitation data</p> <p> Radiation: Yearly and seasonal radiation data</p> <p> SO2: Yearly and seasonal SO2 data</p>
ScienceDex guides
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