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248 results for “wrf”
WRF-Chem Volcano output - Etna Christmas 2018 (run L2)
<p>Output from a model run of a modified version of WRF-Chem, which we refer to as WRF-Chem Volcano (WCV). At the time of submission of this dataset, these data were the basis of the modelling part of a paper in preparation, working title: "WRF-Chem modelling and TROPOMI observations of halogen chemistry within the plume of Etna's Christmas 2018 eruption".</p> <p>The code of WCV is maintained <a href="https://github.com/LukeSurl/WCV">here</a>. Development of this code will continue after the submission of this repository, <a href="https://github.com/LukeSurl/WCV/tree/20200521">This branch</a> is a permanent record of the code as used to create this dataset.<br> This is a simulation of the chemistry of the plume of the 2018 eruption of Mount Etna. This simulation includes bromine, chlorine and mercury chemistry. The time period covered is the 24-27 December, however please note that the author considers the first 24 hours of simulation to be "spin up", and that the modelled volcanic fluxes were assigned based on observations at midday on the 25th, and were not calibrated specifically for the 26th or 27th.</p> <p>"Run L2" refers the internal serial number assigned to this model run.</p> <p><strong>Overview of files</strong></p> <p><strong><em>Output files: wrfout_d0*_YYYY-MM-DD_HH:MM:SS</em></strong></p> <p>Output data is contained in NetCDF files. Each file contains hourly snapshots of the 3D simulation with fields for various parameters. Most of the 4D (three spatial dimensions and time) variables record modelled mixing ratios of various species (in units ppm). Many other variables are standard WRF-Chem output variables; please see <a href="https://ruc.noaa.gov/wrf/wrf-chem/">WRF-Chem's documentation</a>. If the meaning of a variable is unclear, please <a href="mailto:luke.surl@latmos.ipsl.fr?subject=WCV%20run%20L2%20dataset">contact the author</a>.</p> <p>Files are separated by model domain and by time. The "d0x" value specifies the model domain. Domain 1 is the full extent of the modelled area and has grid cells of 30 km × 30 km. Domains 2, 3, 4 each model a successively smaller area subset at a 3x greater resolution than their parent domain. Please see the ACPD paper for further details on this.<br> For operational reasons, the number of hours in different output files is slightly irregular:</p> <ul> <li>Files <em>wrfout_d0x_2018-12-24_00:00:00</em> contain 24 hourly snapshots from 2018-12-24_00:00:00 -- 2018-12-24_23:00:00</li> <li>Files <em>wrfout_d0x_2018-12-25_00:00:00</em> contain 24 hourly snapshots from 2018-12-25_00:00:00 -- 2018-12-25_23:00:00</li> <li>Files <em>wrfout_d0x_2018-12-26_00:00:00</em> contain 1 snapshot, 2018-12-26_00:00:00 only</li> <li>Files <em>wrfout_d0x_2018-12-26_01:00:00</em> contain 24 hourly snapshots from 2018-12-26_01:00:00 -- 2018-12-27_00:00:00</li> <li>Files <em>wrfout_d0x_2018-12-27_01:00:00</em> contain 24 hourly snapshots from 2018-12-27_01:00:00 -- 2018-12-28_00:00:00</li> </ul> <p><strong><em>Input files</em></strong></p> <p>This repository also contains various input files for the WRF-Chem simulation.</p> <ul> <li><em>wrfinput_d0x</em> (netcdf format) defines the initial conditions assigned for the beginning of the simulation (2018-12-24 00:00:00) for each of the model domains.</li> <li><em>wrfbdy_d01</em> (netcdf format) defines the boundary conditions for the simulation.</li> <li><em>wrfchemi_d0x_YYYY-MM-DD_HH:MM:SS</em> (netcdf format) defines, for each day and domain of the simulation, anthropogenic emissions.</li> <li><em>wrfchemv_d0x</em> (netcdf format) defines the volcanic emissions. A file is specified for each domain.</li> <li><em>namelist.input</em> (text file) defines various wrf-chem runtime options </li> </ul>
Summary wind statistics from NEWA WRF mesoscale ensemble
<p>These files contain summary statistics from the NEWA WRF mesoscale ensemble for a Northern European domain.</p> <p>Each netCDF file includes:<br> - Mean wind speed (50, 75, 100 and 150 m AGL)<br> - Surface mean air density<br> - Surface static fields (latitude, longitude, surface elevation and surface roughness)<br> - Wind speed and direction frequency distribution (100 m AGL)</p> <p>The WRF simulation setup and parameterizations correspond to those in the README.ensemble file.</p>
Data set - Idealized ridge simulations with ICAR and WRF
<p>The verification of models in general is a non-trivial task and can, due to epistemological and practical reasons, never be considered as complete. As a consequence, a model may yield correct results for the wrong reasons, i.e. by a different chain of processes than found in observations. While in the atmospheric sciences guidelines and strategies exist to maximize the chances that models are correct for the right reasons, these are mostly applicable to full-physics models, such as numerical weather prediction models. The Intermediate Complexity Atmospheric Research (ICAR) model is an atmospheric model employing linear mountain wave theory to represent the wind field. In this wind field atmospheric quantities, such as temperature and moisture are advected and a microphysics scheme is applied to represent the formation of clouds and precipitation. This study conducts an in-depth process-based evaluation of ICAR, employing idealized simulations to increase the understanding of the model and develop recommendations to maximize the probability that its results are correct for the right reasons. To contrast the obtained results from the linear-theory-based ICAR model to a full-physics model, idealized simulations with the Weather Research and Forecasting (WRF) model are conducted. The impact of the developed recommendations is then demonstrated with a case study for the South Island of New Zealand. The results of this investigation suggest three modifications to improve different aspects of ICAR simulations. The representation of the wind field within the domain improves when the dry and the moist Brunt-Väisälä frequencies are calculated in accordance to linear mountain wave theory from the unperturbed base state rather than from the time-dependent perturbed atmosphere. Imposing boundary conditions at the upper boundary different to the standard zero gradient boundary condition is shown to reduce errors in the potential temperature and water vapor fields. Furthermore, the results show that there is a lowest possible model top elevation that should not be undercut to avoid influences of the model top on cloud and precipitation processes within the domain. The method to determine the lowest model top elevation is applied to both the idealized simulations as well as the real terrain case study. Notable differences between the ICAR and WRF simulations are observed across all investigated quantities such as the wind field, water vapor and hydrometeor distributions, and the distribution of precipitation. The case study indicates a large shift in the precipitation maximum for the ICAR simulation employing the developed recommendations in contrast to an unmodified version of ICAR. The cause for the shift is found in influences of the model top on cloud formation and precipitation processes in the ICAR simulations. Furthermore, the results show that when model skill is evaluated from statistical metrics based on comparisons to surface observations only, such analysis may not reflect the skill of the model in capturing atmospheric processes such as gravity waves and cloud formation.</p>
WRF data for downscaling, used in Learned multi-resolution dynamical downscaling for precipitation
<p>This study uses regional climate model (RCM) simulated precipitation at low and high spatial resolution, to develop convolution neural network (CNN) based approaches, that can emulate high resolution modeled data using low resolution modeled data with cheaper computational resource than running dynamical downscaling at the high spatial resolution. Specifically, we define two types of CNNs, one that stacks variables directly and one that encodes each variable before stacking, and train each CNN type both with a conventional loss function, such as Mean Square Error (MSE), and with a conditional generative adversarial network (CGAN), for a total of four CNN variants. We compare the four new CNN-derived high resolution precipitation with precipitation generated from a bi-linear interpolater and the state-of-the-art CNN-based super-resolution (SR) technique, using the original high resolution precipitation from the RCM as ground truth. We find that SR technique produces similar results to the interpolator with smoother spatial and temporal distributions and smaller data variabilities and extremes than ground truth shows. While the new CNNs trained by MSE generate better results over some regions than the interpolator and SR technique, their predictions are still not as close as ground truth. The CNNs trained by CGAN generate more realistic and physically reasonable results. This advanced technique improves not only the data variability in time and space, and but also the extremes, such as intense and long-lasting events, based on event-feature tracking algorithm.</p>
Input and output files for WRF urban simulations for the metropolitan area of Tel-Aviv
<p><span><span>We implement and verify for the first time four Weather Research and Forecasting model urban configurations, </span></span><span><span>focused on the coastal metropolitan area of Tel-Aviv (MTA) using updated land use and morphological maps. We analyze the mesoscale summertime flow and the urban canopy (UC) role in the occurrence of different hodograph dynamics observed within MTA at night. These events may be significant in air quality research. The four configurations – bulk (MM), single-layer (SLUCM), multi-layer (BEP), and BEP coupled with the building energy model – reproduce the observed diurnal temperature and wind cycles, with similar 10m wind direction bias and RMSE (15<sup>o</sup> and ~30<sup>o</sup>, respectively), with preference for MM and SLUCM at night. However, the SLUCM shows the lowest skill for the 10m wind speed (WS) (bias and RMSE 1ms<sup>-1</sup>), and the BEP shows the largest underestimation of the 2m temperature, ~-2.5<sup>o</sup>C. In the SLUCM, the WS increases over an UC and with increasing building heights. These results call for a re-examination of the SLUCM WS parameterization. The simulations show that at night, a convergence line (CL) builds up with the urban heat island, downstream of the NW flow. West of the CL, the wind continues flowing from the sea, and rotates anti-clockwise to form a non-elliptical sea-breeze hodograph. Removing MTA UC restores an elliptical hodograph. East of the CL, the UC supports an elliptical hodograph with a clockwise rotation through the NE sector, previously reported as dynamically unstable. We expect such wind hodograph dynamics within similar coastal metropolitan areas.</span></span></p>
COSIPY-WRF Daily SMB output 2014-2018
<p>Daily average values of </p>
Simulation data for WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions
<p>This repository provides the test simulation data for "WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions" published in Geoscientific Model Development. The configurations for sensitivity experiments are described in this paper. Please contact the corresponding author Tzung-May Fu (fuzm@sustech.edu.cn) for more details.</p>
CWEX13 WRF WFP (20170321)
<p>The user input required to run the Wind Farm Parameterization (WFP) of the Weather Research and Forecasting (WRF) model. </p>
CWEX13 WRF WFP
<p>The user input required to run the Wind Farm Parameterization (WFP) of the Weather Research and Forecasting (WRF) model. </p> <p>namelist.input: a WRF namelist example of WFP simulation with 12-m vertical grid, on 24 Aug 2013</p> <p>wind-turbine-1.tbl: the turbine power curve and other parameters required for the WFP</p> <p>Please see further details at https://www.geosci-model-dev-discuss.net/gmd-2017-128/</p>
Global horizontal irradiance from WRF-Solar and NSRDB over CONUS
<p>This dataset has been used in the publication: Jimenez, P.A., J. Yang, J.-H. Kim, M. Sengupta, and J. Dudhia: Assessing the WRF-Solar model performance using satellite-derived irradiance from the National Solar Radiation Database. J. Applied Met. & Climatol., 61, 129-142.</p> <p>The article presents the reference WRF-Solar configuration and describes the value of shortwave irradiance retrievals from the National Solar Radiation Database (NSRDB) to improve the WRF-Solar performance. The dataset consist of global horizontal irradiance from several WRF-Solar simulations over the contiguous U.S. spanning the year of 2018, and collocated satellite retrievals for the same year.</p> <p>The dataset will help to:</p> <ol> <li>Ensure reproducibility of results in the article above</li> <li>Provide a reference WRF-Solar simulation and adequate observations over an extended period to continue improving the WRF-Solar performance</li> </ol>
WRF Model namelist
<p>This is WRF model's namelist for the article "Characteristics and variability of precipitation across different sectors of an extra-tropical cyclone – a case study over the high-latitudes of the Southern Ocean" accepted for publication in Journal of Geophysical Research: Atmospheres (11/2023) by SCH Truong, ST Siems, P May, Y Huang, E Vignon, A Gevorgyan</p>
Dataset for the article titled ""An Updated Parameterization of the Unstable Atmospheric Surface Layer in WRF Modeling System"".
<p>The dataset is organized in to five ZIP folders as described below:</p> <p>1. Offline_Exp_Data: This contains data for stability parameter (z/L), transfer coefficient for momentum (CD), and heat (CH) simulated from different experiments using the bulk flux algorithm (offline mode) corresponding to different similarity functions over smooth (z0 = 0.01 m), transition (z0 = 0.1 m), and rough (z0 = 1 m) surfaces. This dataset corresponds to Figure 4.</p> <p>2. Similarity_Functions: This contains data for the similarity functions for momentum and heat in gradient (Phi_m and Phi_h) as well as integrated (Psi_m and Psi_h) forms with stability parameter (z/L) for considered functional forms of similarity functions (e.g., Businger et al., 1971 (BD71); Carl et al., 1973 (CL73); Kader and Yaglom, 1990 (KY90); and Fairall et al., 1996 (F96)) under unstable conditions. The dataset corresponds to Figures 2 and S1 (supporting information).</p> <p>3. WRF_Data1: This contains hourly averaged data for considered variables from WRF model simulations during the MAM (March–April–May) season. This dataset can be used to reproduce Figures 9, 10, and 11.</p> <p>4. WRF_Data2: This contains model output for considered variables extracted during highly convective hours (z/L<-10 over most of the domain) in the daytime. This dataset can be used to reproduce Figures 12, S3, S4, S5, and S6.</p> <p>5. WRF_Data3: This consists of data from different simulations (CTRL, Exp1-4) with the WRF model extracted at the location of the flux tower (23.412 N, 85.44 E (Ranchi), India). The dataset contains stability parameter (z/L), bulk Richardson number (RiB), transfer coefficients for momentum (CD) and heat (CH), sensible heat flux (HFX), 10-m wind speed (WS), u*2 (representative of momentum flux), and 2-m temperature (T2m). This dataset corresponds to Figures 5, 6, 7, 8, S2, S7, and S8.</p>
WRF-human code and dataset
<p>This repository contains human thermal stress data presented in the WRF-human manuscript, as well as codes used to plot figures in the manuscript.</p>
WRF namelist files for Tarouilly et al., 2024
<p>This repository contains the namelist files to reproduce WRF simulations used in "Did historical storms used in probable maximum precipitation (PMP) estimation reach maximum effiiciency? A large model ensemble approach". </p>
Simulated wind field outputs from WRF and significant wave height results from SWAN simulations.
<p>Supplementary materials for the article titled <em>“Super Typhoons Simulation: A Comparison of WRF and Empirical Parameterized Models for High Wind Speeds.”</em> The data include simulated wind field outputs and significant wave height results from SWAN simulations.</p>
input files for CROCO-WRF-OASIS3-MCT coupled model with a nest in CROCO
<p><span>This tar file contains all the input files needed to run the coupled model CROCO-WRF with OASIS3-MCT coupler with one 2-way nested domain in CROCO. This example configuration is based on the BENGUELA region at low resolution (this is the example of regional configuration of the CROCO model: see CROCO documentationa nd tutorials for more details</span>)</p>
input files for CROCO-WW3-WRF-OASIS3-MCT coupled model
<div>This tar file contains all the input files needed to run the coupled model CROCO-WW3-WRF with OASIS3-MCT coupler over an example configuration of the BENGUELA region at low resolution (this is the example of regional configuration of the CROCO model: see CROCO documentation and tutorials for more details)</div>
ECMWF operational analysis data for driven the WRF model in HRB
<p>All the ECMWF operational analysis data forcing data (2008-2012) related to the thesis (<a href="https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/deliver/index/docId/81907/file/PhD_Zhang.pdf">https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/deliver/index/docId/81907/file/PhD_Zhang.pdf</a>)</p> <p> </p> <p> </p>
WRF Output for 2 km Simulation of Spiral Gravity Waves
<p>WRF output file for the 2km Spiral Gravity Wave simulation of Nolan and Onderlinde (2022).</p>
WRF Output for 0.5 km Simulation of Spiral Gravity Waves
<p>WRF Output for 0.5 km Simulation of Spiral Gravity Waves</p>
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