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248 results for “wrf”
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>
Arno River flooding 1966 | WRF-ENS forecasts
<p>Contains WRF-ENS forecasts for the Arno River flooding event occurred in Italy in November 1966. Files format is binary with auxiliary description file.</p>
North American watershed precipitation from WRF
<p>Precipitation estimates from the Weather Research and Forecasting (WRF) regional climate model. Each file contains the following variables for the stated watershed:</p> <p>lat: latitude of each grid point, in degrees north</p> <p>lon: longitude of each grid point, in degrees north</p> <p>time: day of simulation</p> <p>RAINC: accumulated total cumulus precipitation, in mm</p> <p>RAINNC: accumulated total grid scale precipitation, in mm</p> <p>SNOWNC: accumulated total grid scale snow and ice, in mm</p> <p>watershed mask: matrix where 1 indicates the grid point is inside the watershed, 0 is outside</p> <p>watershed mountain mask: matrix where 1 indicates the grid point is within the mountainous portion of the watershed, 0 is outside</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>
Data for Cloud resolving WRF simulations of precipitation and soil moisture over the central Tibetan Plateau: an assessment of various physics options
<p>This dataset accompanies the submitted paper in the Earth and Space Science:Cloud resolving WRF simulations of precipitation and soil moisture over the central Tibetan Plateau: an assessment of various physics options.</p>
WRF Data for ERL-118501.R2 by Navale and Karthikeyan (2024)
<p>Data from WRF model for Paper ERL-118501.R2</p> <p>Title: Understanding Tropical Cyclone Persistence over Land: A case study of Cyclone Gulab</p> <p>Authors: Ashish Navale and Karthikeyan L</p>
radar observations and WRF simulations of Typhoon Yagi (2024)
<p>This data set contains radar observations and WRF modal simulations (with Thompson and WDM6 microphysics schemes) of super Typhoon Yagi (2024).</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>
Comparing the Influence of Global Warming and Urban Anthropogenic Heat on Extreme Precipitation in Urbanized Pearl River Delta Area Based on WRF Dynamical Downscaling
<p>The simulation outputs from the Weather Research and Forecasting (WRF) v3.8.1 coupled with single layer urban canopy model from three experiments (HIST_AH300, HIST_AH0, and RCP85_AH300).</p> <p>Variables include hourly precipitation, wind, specific humidity, relative humidity, convective available potential energy, convective inhibition, temperature, model height, land use land cover, and topography.</p> <p> </p>
Storm-scale and fine-scale boundary layer structures of tropical cyclones simulated with the WRF-LES framework
<p>The numerical simulations were carried out on the Tianhe Supercomputer, China. The TCBL data used in this study are uploaded here. Due to the large number, the original simulation data are available on request (liuqy@cma.gov.cn or liguangwu@fudan.edu.cn).</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-informed calibration of WRF-Hydro data1
<p>This is the first part of a dataset used to produce the results in a data-informed calibration study of WRF-Hydro. </p> <p>1. It contains the simulated hourly soil moisture time series from 2016/10/1 to 2017/05/31 before and after calibration at seven in-situ stations. The 7 lines in the .txt files are for the 7 stations in the following order:</p> <p>stat1=['sld',36.461040, -121.381170]<br> stat2=['lgs', 37.261590, -122.132870]<br> stat3=['gry', 37.071800, -121.478800]<br> stat4=['lwd', 35.937080, -121.108030]<br> stat5=['blueoak',37.381667,-121.744722]<br> stat6=['hastings',36.386389,-121.550278]<br> stat7=['norris',35.534167,-121.083333]</p> <p>2. It contains the hourly simulated streamflow simulations from 2016/10/1 to 2017/05/31 before and after calibration at 9 USGS stream gages. The lines in the txt files are in the following order:</p> <p><br> gageID=["11169500","11169800","11159200","11148900","11160000","11157500","11149900","11152050","11152000"]</p> <p>3. It also contains the spatially-distributed soil parameters before and after calibration. The "smcmax", "dksat","bexp" parameters in the soil_properties.nc are for soil porosity, saturated hydraulic conductivity, and b coefficient, respectively. </p> <p> </p>
WRF-Noah/Alpine3D simulations for 2018-2021 snow seasons in Italian Central Apennines
<p>This dataset contains the results of two numerical simulations over Italian Central Apennines at 3 km resolution for the three snow seasons 2018/19, 2019/20 and 2020/21. The file wrf-noah_2018-2021.nc contains the WRF-Noah model output, while the file wrf-alpine3d_2018-2021.nc contains the WRF-Alpine3D model output.</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>
Data from: Prediction of three years of annual rain attenuation statistics at Ka-band in French Guiana using the Numerical Weather Prediction model WRF
<p><span>This study highlights the interest in using an Atmospheric Numerical Simulator (ANS) relying on a high-resolution weather forecast model coupled with an ElectroMagnetic Module (EMM) to compute Ka-band rain attenuation statistics in an equatorial region. An optimization of the parametrisation of the Weather Research and Forecasting meteorological model (WRF) is carried out using measurements collected from a propagation experiment carried out by CNES and ONERA near Kourou in French Guiana. Both simulated and experimental annual Complementary Cumulative Distribution Functions (CCDF) of rain attenuation are presented in this dataset.</span></p> <p>More specifically, this dataset includes the statistical distribution from both the WRF-EMM model and from the propagation experiment for the years 2017, 2018, 2020 and the whole three-year period.</p>
Scenario Configurations for Simulating Organic Aerosol in Delhi using WRF-Chem and a VBS Approach
<p>Parameter configuration files for a study into the sensitivity of model predictions (in this case WRF-Chem) of organic aerosol mass loadings, and composition, to organic aerosol production processes.</p> <p>The production processes for both anthropogenic (ANTH) and biomass burning (BB) generated organic aerosols are investigated. 5 production processes are perturbed for each, making a total of 10 parameters for the whole study.</p> <p>The production processes are:</p> <ol> <li>VBS aging rate (VBS_AGERATE): the reaction rate of VBS compounds with OH. Expressed as a reaction rate in cm<sup>3</sup> molec.<sup>-1</sup> s<sup>-1</sup>.</li> <li>SVOC volatility distribution (SVOC_VOLDIST): expressed in terms of an equivalent age (dimensionless between 0-1). This is translated using a simple aging model into a volatility distribution for the emitted VBS compounds.</li> <li>SVOC oxidation rate (SVOC_OXRATE): the degree of oxidation that occurs with, or is induced by, each reaction with an OH molecule. Range is 0.075 (one extra oxygen atom) to 0.45 (six extra oxygen atoms).</li> <li>IVOC scaling (IVOC_SC): scaling factor for emissions of IVOC's alongside the SVOC's. Initial IVOC emitted amount is taken to be x1.5 the non-volatile OA mass in the emission inventory. This scaling factor, ranging from 0 to 3, modifies that initial emitted amount, to give the final IVOC fraction to add.</li> <li>SVOC scaling (SVOC_SC): scaling factor for emissions of SVOCs. This applied to the volatility distribution generated from the SVOC volatility distribution. For anthropogenic emissions this ranges from 0.1 to 4. For biomass burning emissions this ranges from 0.5 to 4.</li> </ol> <p>SVOC_VOLDIST, IVOC_SC, and SVOC_SC combine to give the VBS_FRAC_[X] fractional volatility distributions. These volatility bins start at Ci*=-2 , and increase decadally to Ci*=6.</p> <p>The template namelist into which these parameters are inserted is included too. This is for a modified version of WRF-Chem 3.8.1 - it will not work with the standard WRF-Chem model.</p> <p> </p>
Data used to simulations 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 input data of simulations by WRF-CMAQ, WRF-Chem and WRF-CHIMERE 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. WRF-CMAQ input data including emission, ICs and lateral BCs of meteorology and air quality:</p> <p>YYYYMM.zip represents the input data for each month for simulations. Due to the large size of the compressed file containing input data each month, there may be interruptions when uploading it to Zenodo. Therefore, we will split each compressed file into 50MB. If users want to browse the file, they can download the segmented files, and then merge them into the YYYYMM.zip file using the Linux command line "unzip 'YYYYMM.zip.*' -d combined"</p>
Emission input data for WRF-CMAQ
<p>Emission input data for WRF-CHIMERE in eastern China during 2017.</p>
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