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103 results for “WRF Simulations”
Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for October-December 2017
<p>Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for October-December 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 October-December 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_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for October-December 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for July-September 2017
<p>Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for July-September 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 July-September 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_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for July-September 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions in eastern China for January-March 2017
<p>Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions in eastern China for January-March 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-March 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_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for January-March 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for April-June 2017
<p>Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for April-June 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 April-June 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_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for April-June 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
WRF-Chem output- concentrations for the two simulations: Business as Usual and COVID for the two periods, March-April and May
<p>The WRF-Chem model was utilized to calculate the changes in O3 chemistry during the COVID lockdown period. We ran two simulations: (1) Business As Usual (BAU) and (2) COVID for the lockdown (30 March to 12 April 2020) and relaxation (18- 25 to 30 May 2020) periods. </p> <p>Here, we provide modelled concentrations for several chemical and physical species for 30 March to 12 April (only weekdays) and 18 to 30 May (only weekdays) for both simulations, BAU and COVID, over the AMB area during the morning (6-8 UTC), afternoon (13-15 UTC) and evening (19-21 UTC) hours. </p> <p>We also provide the WRF-Chem output used to create the air parcel trajectories and the ozone concentrations from the Metropolitan Area of Barcelona to the Pyrenees. </p>
Simulation performance of different planetary boundary layer schemes in WRF V4.3.1 on wind field over Sichuan Basin within "Gray zone" resolution
<p>Weather Research and Forecasting (WRF) model was <span>used</span> to <span>investigate the performance of different planetary </span>boundary layer <span>(PBL) </span>parameteri<span>z</span>ation schemes <span>on </span>simulat<span>ing</span> surface wind <span>fields over Sichuan Basin</span> at a spatial resolution of <span>0.33</span>km<span>.</span> <span>T</span>he <span>experiment</span> is based on <span>multi-</span>case stud<span>ies, so</span> 2<span>8 near-surface </span>wind events from 2021 to 2022 <span>were selected, and</span> <span>a</span> total of 112 <span>sensitivity </span>simulations were <span>carried out by</span> employing four common<span>ly used</span> <span>PBL </span>schemes: YSU, MYJ, MYNN2, and QNSE<span>, and</span> compared to observations.</p> <p>The mean 10 minutes observations of wind speed and direction during the study period from the Guanghan Airport are stored in one seperate txt file named OBS-28cases-every10min-10m-wind-Guanghan-Airoprt.txt.</p> <p>Wind field (U10, V10) at the central point from the WRF simulations of inner domain from all the PBL simulations are investigated, which are stored in txt format (.txt), and all the results are available from the first author(yuet@mail.iap.ac.cn).</p>
Realtime WRF Large-Eddy Simulation Data
<p>Sample data files from realtime micro-scale weather simulations that were performed to support UAV (Unmanned Aerial Vehicles) flights during ISARRA Lower Atmospheric Process Studies at Elevation – Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) field experiment. Two types of data are available: (1) ascii files that contain profiles and surface variables at select grid points that correspond with 3 key observing locations, (2) grib2 files contain 2D and 3D grids of variables described below and stored on levels AGL as described more below. The sample data includes all leadtimes from both domains. Domain 01 (1 km grid spacing) is initialized at 0400 UTC, lead times = 00h00m to 18h00m by 10m increments (108 files) and DO2 is initialized at 1000 UTC with leadtimes of 06h00m-18h00m by 10 min increments (72 files). Each grib2 file from the WRF-LES domain (D02) are around 100 MB per lead time. The full dataset is available here: <a href="https://doi.org/10.5065/83r2-0579">https://doi.org/10.5065/83r2-0579</a></p> <p>These simulations were performed by driving a nested grid configuration of the Weather Research and Forecasting model with its innermost mesh being run at 111 m grid spacing. The innermost grid was nested within a grid with 1 km grid spacing. The outermost grid being driven using operational forecast models data as described below. While the MYNN2 PBL scheme is used to parameterize turbulence in the 1 km grid, the PBL scheme is turned off within the 111 m grid, thus, allowing large-scale turbulent eddies to be resolved by WRF primitive equations. Subgrid-scale turbulence is diagnosed and stored within the TKE variable using Lilly (1966, 1967).<br> <br> The realtime simulations were produced twice per day in order to support mission planning and UAVs flight operations. A next-day simulation was run using forcing data from NCEP's Global Forecast System (GFS) while a day-of simulation was run using data from the High Resolution Rapid Refresh (HRRR). Both simulations were valid between 04:00 and 16:00 local time providing an opportunity to explore the impact of lateral boundary conditions on forecast skill. The dataset consists of a series of two sets of files: 3D grids and point profiles. The 3D grids consist of all relevant basic state parameters (p,T,U,RH) and diagnostics (e.g., sub-grid scale TKE, ceiling height, visibility) that have been interpolated to flight levels AGL using the Unified Post-Processor (UPP). The UPP was used to de-stagger the mass and wind fields (and compute wind in earth-relative coordinates), interpolate forecast data to flight levels AGL and to compute diagnostics such as visibility, ceiling height, and radar reflectivity.</p> <p>Profile and surface data are stored in ascii format for select grid points coincident with up to 3 fixed observation sites set up during LAPSE-RATE (i.e., Saguache, Moffat and Leach Airfield) with a time resolution of 0.666 sec. See README files for details. The 3D output files are stored in grib2 format which are available every 10 min. The grib2 data can be converted to netCDF using a variety of tools such as ncl_convert2nc command available on many linuxOS.</p> <p>---------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Details of ascii file names:</p> <p>XXX.dnn.VV.yyyymmddhh.gz</p> <p>where</p> <p>XXX is the location name (SAG - Saguache, MOF - Moffat, LEA - Leach)</p> <p>nn is the domain number (01,02,03)</p> <p>VV is the variable (see readme file for details)</p> <p>yyyymmddhh is the model initialization time (UTC)</p> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Details of grib2 data files:</p> <p>Data are stored as follows: one file per forecast lead time using the following file naming convention:</p> <p>WRFPRS_YYYYMMDDhhmm_dnn.lh_lm</p> <p>where</p> <p>hhmm is the hour and minute of the day the model run was initialize</p> <p>nn = domain number</p> <p>lh = forecast outlook hour</p> <p>lm = forecast outlook min</p> <p>valid_time = hhmm + lhlm</p> <p>zlevels = (30,80,150,300,500,750,1000,1250,1500,1750,2000,2500,3000,3500,4000,4500,5000 m AGL)<br> <br> DatasetTemporalCoverage: 14 - 19 July 2018<br> Forecasts were issued twice per-day and valid between 04:00 and 16:00 LT (10:00 and 22:00 UTC)</p> <p>Gridded data are stored at 10 min intervals</p> <p><br> Interpolated 3D Variables: temperature,pressure,u,v,w,RH,sub-gridscale turbulence kinetic energy, energy dissipation, etc at selected heights AGL<br> <br> 2D Diagnosed variables:<br> Ceiling height, visibility, precip rate, accumulated precip, precipitable water, vertically- integrated condensed water, downwelling shortwave and longwave radiation at surface, sensible and latent heat flux, 10 m U and V, 2 m T and specific humidity<br> </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>
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>
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>
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>
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>
WRF Output for 1 km Simulation of Spiral Gravity Waves with 25 Levels
<p>WRF Output for 1 km Simulation of Spiral Gravity Waves with 25 Levels</p>
WRF Output for 1.5 km Simulation of Spiral Gravity Waves
<p>WRF Output for 1.5 km Simulation of Spiral Gravity Waves</p>
WRF Output for 1 km Simulation of Spiral Gravity Waves
<p>WRF Output for 1 km Simulation of Spiral Gravity Waves, gzipped.</p>
Code for GMD publication - Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in WRF (v4.1.3) model during ICE-POP 2018
<p>In this repository, we include the source codes for WRF microphysics parameterization used in the GMD publication "Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in WRF (v4.1.3) model during ICE-POP 2018."</p> <p>The four 2-moment bulk microphysical parameterization codes, WDM6, WDM7, Thompson, and Morrison, are divided into 3 WDM, Thompson, and Morrison codes, and each code has been modified so that detailed microphysical processes can be checked in wrfout.</p> <p>The WDM6 and WDM7 schemes include numerical errors for ice microphysical parameterization (Kim and lim, 2021) and for cloud evaporation and melting processes (Lei et al., 2020).</p> <p>Namelist files shows the namelist.input for each case.</p> <p> </p> <p> </p>
WRF simulation data
<p>We have investigated the impact of the (non)scale-aware cumulus parameterization scheme on the simulation for heavy rainfall events in South Korea. <br> These files contian the WRF simulation data of main variables, which is used in this paper. The spatial domain is 4x4 km (D03) and 1.33x1.33km (D04), with a time domain that spans the 10 hours period beginning at 1800 UTC 15 July 2017 and with 1-hour time steps. Format is NetCDF containing detailed metadata. Three-dimensional meteorological field variables include the 2 wind components (i.e., x- and y- directions), water vapor mixing ratio, and temperature. Two-dimensional variables include convective precipitation and non-convective precipitation.</p>
WRF Simulation Files
<p>All WRF and WPS files (compiled fortran files and namelists) for a convection-permitting simulation performed on a tropical cyclone-milatitude waveguide interaction involving Irma in 2017. </p>
Effects of convective mergers on the evolution of microphysical and electrical activity in a severe squall line simulated by WRF coupled with explicit electrification scheme
<p>These are important data supporting the conclusion of the paper are available in the main text.The STORM973 dataset including BLNET data and radar data are provided here.Some of the E-WRF outputs are also provided here. The corresponding scripts are based on NCL (version 6.6.2.).</p>
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