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154 results for “parameterization”
Model simulated potential natural vegetation state in the western US under preindustrial, historic, and future (RCP8.5) atmospheric conditions using multiple parameterizations of the dynamic vegetation model TRIFFID.
<p>DATA DESCRIPTION<br> Author contact information:<br> Linnia R. Hawkins<br> Oregon State University<br> lhawkins@oregonstate.edu; linnia.hawkins@gmail.com<br> Data supporting 2019 Journal of Advances in Modeling Earth Systems publication</p> <p>Simulations of the equilibrium vegetation distribution in the western US performed with the climate model HadAM3p-HadRM3p-MOSES2-TRIFFID</p> <p>step1: Identify Influential Parameters<br> All files labeled step1.<br> EXPERIMENT DESCRIPTION: Data used in step 1: identify influential parameters <br> sensitivity experiment adjusting one parameter at a time 38 individual parameters were adjusted to 7 values, equally spaced<br> over a defined plausible range. For reference nine simulations with the default model parameterization are included, initiated with unique initial potential temperature perturbations. </p> <p>The data contains the vegetation state variables at the end of four-year simulations (January 2004 to December 2007) during which two equilibrium time steps with the dynamic vegetation model TRIFFID (Cox et al., 2001). The results are averaged over three simulations initiated with unique atmospheric potential temperature perturbations.</p> <p>FILE DESCRIPTION:<br> NETCDF: Each netcdf file contains the fractional coverage (field1391), leaf area index (field1392), and the canopy height (field1393) for 5 plant functional types (PFTs: broadleaf, needleleaf, c3 grass, c4 grass, shrub) simulated for November 29, 2007. </p> <p> File labeling scheme: <br> step1_parameter_settingindex_3ICave.nc</p> <p> parameter: the name of the only model parameter adjusted<br> setting index: the index of the parameter setting (1-7)<br> index of 1 references the lowest plausible parameter setting<br> index of 7 references the highest plausible parameter setting<br> index of 4 references the parameter setting half way between the lowest and highest plausible parameter settings. <br> 3ICave: references that the results have been averaged over 3 initial conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> </p> <p>step2: ParameterSensitivity<br> all files labeled step2<br> EXPERIMENT DESCRIPTION:<br> Data used in step 2: parameter sensitivity <br> Perturbed Parameter Experiment (PPE) simultaneously adjusting 18 parameters.<br> Latin hypercube sampling was employed to generate 250 unique parameterizations references with a SETID (ranging from 1-359)<br> The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds. <br> Data is averaged over 5 initial atmospheric conditions.</p> <p>FILE DESCRIPTION:<br> NETCDF: files contain either the fractional coverage (field1391) or the above ground biomass (field1512) for 5 plant functional types (PFTs) simulated for November 1907.</p> <p> File labeling scheme: <br> step2_variable_parametersetindex_5ICave.nc</p> <p> variable: the name of the variable contained in the file<br> setting index: the index of the parameter setting corresponding to the parameter set text files<br> 5ICave: references that the results have been averaged over 5 initial atmospheric conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]</p> <p>TXT: files contain a list of the model parameterizations (for each PFT and variable) and the corresponding to the parameter set index. <br> Parameters are labeled in row 1<br> Parameter set indices are shown in column 1</p> <p>RESTART: restart_region_TPPE_c374_1903-12-01.nc<br> The restart file contains the model state variables after spinup. This file was used to initiate all model simulations in step2.</p> <p>step3: ParameterSetSelection<br> All files labeled step3<br> EXPERIMENT DESCRIPTION:<br> Data used in step 3: parameter set selection<br> PPE simultaneously adjusting 10 parameters. <br> Latin hypercube sampling was employed to generate 140 unique model parameterizations, referenced with a SETID (ranging from 3-276).<br> The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds. <br> Data is averaged over 5 initial atmospheric conditions.</p> <p><br> FILE DESCRIPTION:<br> NETCDF: files contain either the biomass (field1512) fractional coverage (field1391) canopy height (field1393) for 5 plant functional types (PFTs) simulated for November 1907 or the net primary productivity (NPP; item3262_monthly_mean) for December 1903 through November 1907. </p> <p> File labeling scheme: <br> step3_variable_parametersetindex_5ICave.nc</p> <p> variable: the name of the variable(s) contained in the file<br> setting index: the index of the parameter setting corresponding to the parameter set text files<br> 5ICave: references that the results have been averaged over 5 initial atmospheric conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> item3262_monthly_mean – Net primary productivity – units: (kgC/m2/sec) – all PFTs</p> <p>TXT: files contain a list of the model parameterizations (for each PFT) and the corresponding to the parameter set index. <br> Parameters are labeled in row 1<br> Parameter set indices are shown in column 1</p> <p><br> production_runs<br> files labeled PI, historical, and future<br> EXPERIMENT DESCRIPTION:<br> Data simulated in the production runs. Model spinup was performed under preindustrial conditions with 10 unique model parameterizations (pset0-pset9). The resulting vegetation distribution for each parameterization after spinup are included and labeled PIrestarts. These restarts were used to initiate (or restart) the simulations under historic and future (RCP8.5) climate conditions. Files labeled historic contains the simulated vegetation state after 5 year simulations (2004-09-01 to 2009-08-30) with one TRIFFID equilibrium round occurring at the end. Files labeled future contain the simulated vegetation state after 5 year simulations (2054-09-01 to 2059-08-30) with one TRIFFID equilibrium round occurring at the end. </p> <p>FILE DESCRIPTION:<br> NETCDF: files contain the fractional coverage (field1391), leaf area index (field1392), canopy height (field1393), and biomass(field1512) for 5 plant functional types (in the order broadleaf, needleleaf, C3 grass, C4 grass, shrub).</p> <p><br> File labeling scheme:<br> pset* where * refers to the model parameterization 0-9<br> files were simulated with the model parameterization *, initiated with a unique initial condition (perturbation to the potential temperature field). </p> <p> Variables (PIrestart)<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf] <br> field1391_1 – fractional coverage of PFT – units: fraction – [needleleaf]<br> field1391_2 – fractional coverage of PFT – units: fraction – [c3grass]<br> field1391_3 – fractional coverage of PFT – units: fraction – [c4grass]<br> field1391_4 – fractional coverage of PFT – units: fraction – [shrub]<br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf]<br> field1392_1 – leaf area index of PFT – units: m2/m2 – [needleleaf]<br> field1392_2 – leaf area index of PFT – units: m2/m2 – [c3grass]<br> field1392_3 – leaf area index of PFT – units: m2/m2 – [c4grass]<br> field1392_4 – leaf area index of PFT – units: m2/m2 – [shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf]<br> field1393_1 – canopy height of PFT – units: meters – [needleleaf]<br> field1393_2 – canopy height of PFT – units: meters – [c3grass]<br> field1393_3 – canopy height of PFT – units: meters – [c4grass]<br> field1393_4 – canopy height of PFT – units: meters – [shrub]<br> </p> <p> Variables (historic/future)<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> <br> </p>
Dataset from the outputs of the article "Antarctic sensitivity to oceanic melting parameterizations"
<p>This dataset contains all the netCDF files from the simulations made for the article "Antarctic sensitivity to oceanic melting parameterizations". It also contains a general configuration file with the parameters for launching the simulations.</p> <p>The dataset is divided in three blocks: ISMIP6-ANT-16KM, ISMIP6-ANT-16KM-atmosphere (for the only-atmosphere simulations) and ISMIP6-ANT-16KM-ocean (for the only-ocean simulations). Each block contains three folders for the three different values of the basal melting parameter used in this study, 9620 m/yr, 14500 m/yr and 21000 m/yr and each folder a subset of six subfolders for the experiment projections: ctrlAE (for the control run), expAE02 (CCSM4), expAE03 (HadGEM2-ES), expAE04 (CESM2-WACCM), expAE05 (UKESM1-0-LL) and expAE10 (UKESM1-0-LL with low emissions). </p> <p>Reference: Juarez-Martinez, A., Blasco, J., Robinson, A., Montoya, M., and Alvarez-Solas, J.: Antarctic sensitivity to oceanic melting parameterizations, The Cryosphere, 2024.</p>
Data-driven Discovery of Snow Cover Parameterization
<p>All data were derived from SNOTEL, Version 1, and were preprocessed for training symbolic regression models in convenience. Train and test data are range from water years of 2001-2009, and of 2010-2018. All NaN value were removed from data and shape as [Sample, feat]. The dataset contains preprocessed dimensonless features:</p> <p>The name and unit of each feature were listed in sequence as follows:</p> <p> 1. Snow depth (mm)<br> 2. Snow water equivalent (mm)<br> 3. Standard deviation of sub-grid topography (m)<br> 4. Air temperature (K)<br> 5. Precipitation (mm/day)<br> 6. 1/snow density (mm/mm)<br> 7. 1/Standard deviation of sub-grid topography (m^-1)</p> <p>The name and unit of target were listed as follows:</p> <p> 1. Snow cover fraction [%]</p> <p>Some own defined constant:</p> <ol> <li>surface roughness (0.1 m)</li> <li>0 degree of temperature (273.16 K)</li> <li>own defined std threshold (200 m)</li> <li>mean SWE (122.3 mm)</li> </ol>
Efficient parameterization of transferable Atomic Cluster Expansion for water
<p>This collection contains files associated with Journal of Chemical Theory and Computation. "Efficient parameterization of transferable Atomic Cluster Expansion for water" (2024) paper:</p> <p>- ACE potentials for water.</p> <p>-Active set inverted (ASI) for the ACE potential</p> <p>- Training dataset that was used for fitting Atomic Cluster Expansion potential.</p>
daleihao/Topographic_Effects: Codes and data for GMD paper "A Parameterization of Sub-grid Topographical Effects on Solar Radiation in the E3SM Land Model (Version 1.0): Implementation and Evaluation Over the Tibetan Plateau"
<p>Codes and data to reproduce all results and plot all figures for GMD paper "A Parameterization of Sub-grid Topographical Effects on Solar Radiation in the E3SM Land Model (Version 1.0): Implementation and Evaluation Over the Tibetan Plateau"</p>
Data accompanying the paper titled: Evaluation of a forest parameterization to improve boundary layer flow simulations over complex terrain.
<p>The present repository contains the namelists, output data from the Weather Research and Forecasting (WRF) numerical simulations and observations described in the article "Evaluation of a forest parameterization to improve boundary layer flow simulations over complex terrain" submitted to the Geosciences Model Development journal.</p> <p>The numerical simulations were developed to evaluate the influence of a forest parametrization on the simulation of the boundary layer flow over moderate complex terrain in the context of the Perdigão 2017 field campaign. The numerical simulations used WRF large eddy simulation mode (WRF-LES). The short-term high resolution (40 m horizontal grid spacing) and long-term (200 m horizontal grid spacing) WRF-LES were run for an integration time of 12 hours and 1.5 months, respectively, with and without forest parameterization. The short-term simulations focus on low-level jet events over the valley, while the long-term simulations cover the whole intensive observation period (IOP) of the field campaign. The results are validated using lidar and meteorological tower observations.</p> <p>The files in the repository are organized as follows:</p> <p>Namelists used to run the WRF model are located in the 'namelists' sub-directory.</p> <p>Data slices of domains d03 and d04 covering the location of the three meteorological towers and LIDAR for the whole integration time are located in the 'reduced_domain' sub-directory.</p> <p>Direct WRF output for selected periods (representing the low-level-jet episodes observed) for domains d03 and d04 are located in the 'files_select_time' sub-directory.</p> <p>The observations used to validate the model are located in the 'observations' sub-directory. The complete dataset of observations from the field campaign can be found in the official campaign data repository: re3data.org: Perdigao Field Experiment; editing status 2020-03-20; re3data.org - Registry of Research Data Repositories. http://doi.org/10.17616/R31NJMN4 last accessed: 2021-10-13. The WRF model can be downloaded from https://www2.mmm.ucar.edu/wrf/users/download/get_sources_new.php.</p>
The dataset of the manuscript: A Sub-Grid Parameterization Scheme for Topographic Vertical Motion in CAM5-SE
<p><a href="https://zenodo.org/api/files/1f1181b5-2418-40af-b0ee-d05da12eed37/figure_code.zip?versionId=83ff16db-ce42-4c35-80cc-f8f8cee2c7d5">figure_code.zip</a>: code of drawing figures.</p> <p><a href="https://zenodo.org/api/files/1f1181b5-2418-40af-b0ee-d05da12eed37/modification_code.zip?versionId=6f6e777a-8c5c-4134-b4e2-34985ff6c595">modification_code.zip</a>: add the code of topographic vertical montion and sub-grid topographic parameterization scheme in CAM5-SE.</p> <p><a href="https://zenodo.org/api/files/1f1181b5-2418-40af-b0ee-d05da12eed37/output_data.rar">output_data.rar</a>: the results of sensitivity experiments for CAM5-SE.</p> <p><a href="https://zenodo.org/api/files/1f1181b5-2418-40af-b0ee-d05da12eed37/draw_data.rar">draw_data.rar</a>: interpolated data according to output results and inputdata of drawing figures.</p> <p> </p>
Accelerate the Parameterization of Unified Microphysics Across Scales (PUMAS) on the graphics processing unit (GPU) with directive-based methods
<p>Code used to produce results of paper titled "Accelerate the Parameterization of Unified Microphysics Across Scales (PUMAS) on the graphics processing unit (GPU) with directive-based methods" by Sun et al.</p> <p>Includes:</p> <p>- Source code of CAM to perform a CPU or GPU simulation</p> <p>- Source code of PUMAS stand-alone kernel for GPU porting (OpenACC and OpenMP offload), an example batch script for build/run on Casper (NCAR's cluster) and the input dataset</p> <p>- Dataset to reproduce the figures in the paper</p> <p> </p> <p>Contact details: Jian Sun (sunjian@ucar.edu)</p>
Code and extensive data for training neural networks for radiation, used in "Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0""
<p>Data and code used in a paper submitted to JAMES titled :<em> Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System</em></p> <p>1) The files <strong>ml_training_*.7z</strong> contain extensive datasets (in NetCDF format) for training neural network versions of the RRTMGP gas optics scheme as described in the paper. The datasets are read by <a href="https://github.com/peterukk/rte-rrtmgp-nn/blob/main/examples/rrtmgp-nn-training/ml_train.py">ml_train.py.</a></p> <p>2) The ML datasets were in turn generated using the input profiles (in NetCDF format) inside <strong>inputs_to_RRTMGP.zip </strong>by running the Fortran programs <code>rrtmgp_sw_gendata_rfmipstyle.F90 and rrtmgp_lw_gendata_rfmipstyle.F90 </code>in <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em>, which call the RRTMGP gas optics scheme, The input profiles contain <strong>millions of columns, hundreds of perturbation experiments (including hypercube-sampled gas concentrations), are derived from several different data sources (including CAMS reanalysis, GCM, and CKDMIP-MMM), and span present-day, preindustrial, and future atmospheric conditions.</strong> They could be used to generate training data for developing emulators of the full RTE+RRTMGP radiation scheme, not just gas optics (see nn_dev on the <a href="https://github.com/peterukk/rte-rrtmgp-nn">RTE+RRTMGP-NN repository on Github</a>, used in a previous paper where different emulation methods were compared)</p> <p>3) The Fortran and Python code used for data generation and NN training are found in<a href="https://github.com/peterukk/rte-rrtmgp-nn/tree/main/examples/rrtmgp-nn-training"> <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em> </a>on the main branch on Github; <strong>an archived version is also included here </strong>(<strong>rte-rrtmgp-nn-2.0.zip</strong>). See the readme in the above sub-directory for further information.</p> <p> </p>
Machine learning based lightning parameterizations for CONUS
<p>The dataset used for training Machine Learning models for lightning parameterization for CONUS, as well as the code to reproduce the analysis. The trained models with network weights are also available.</p>
Brief introduction to parameterization of downward longwave radiation based on long-term baseline surface radiation measurements in China
<p>This short vidio is used to briefly introduce the contents of the article "Parameterization of downward longwave radiation based on long-term baseline surface radiation measurements in China".</p>
Data set of simulated rimed aggregates for "A riming-dependent parameterization of scattering by snowflakes using the self-similar Rayleigh-Gans approximation"
<p><strong>Simulated rimed aggregates</strong> generated with https://github.com/jleinonen/aggregation in setting "aggregation followed by riming".</p> <p>Aggregates were built from between 10 to 700 monomer crystals of <strong>columns, dendrites, needles, plates or rosettes</strong> with mean sizes of 100 or 200 micrometer. Then they were exposed to ELWP = 2.0 kg m⁻². Monomer crystals are composed of cubical elements with resolution 20 micrometer. Frozen rime droplets are also represented by 20 micrometer cubes.</p> <p>The data set contains folders with <strong>evolution (evol) and shape files for each monomer crystal type</strong>. For each particle one evolution and one corresponding shape file exists. The evolution (evol) file contains particle mass, rime mass, area, size, fall speed (Heymsfield&Westbrook, 2010), fall speed (Khvorostyanov&Curry, 2005) for each step during the aggregation and riming process. The corresponding shape file contains the x,y,z positions of the cubical elements that compose the particle for each step. <strong>For further documentation see readme.</strong></p>
Parameterization of beam—beam-induced optical distortions of van der Meer scans at the LHC
<p>These two data sets provide the coefficients of the parameterisation of the (L/L<sub>0</sub>)<sub>Opt</sub> luminosity-bias factor as a function of the normalized nominal separation, for horizontal and vertical vdM scans, respectively. The data sets are part of a paper with title "Impact of Beam-Beam Effects on Absolute Luminosity Calibrations at the CERN Large Hadron Collider" and are referred thereby as Table 10 and Table 11.</p>
Global Datasets of Hourly Carbon and Water Fluxes Simulated Using a Satellite-based Process Model with Dynamic Parameterizations
<p>This new global hourly dataset serves as a 'handshake' among process-based models, remote sensing, and the eddy covariance flux network, providing a reliable long-term estimate of global gross primary productivity (GPP) and evapotranspiration (ET) with diurnal patterns and facilitating studies related to ecosystem functional properties, global carbon, and water cycles.</p> <p>The dataset include the GPP and ET of sunlit and shaded leaf components at an hourly timescale and a spatial resolution of 0.25-degree from 2001 to 2020.</p>
Dataset used in the paper "Verifying the parameterization of vertical eddy viscosity and diffusivity in the bottom boundary layer"
<p>This dataset is created from the data obtained over the continental shelf of the East China Sea in July 2020 during a cruise of the training ship Nagasaki-maru of Nagasaki University (NN055), and used in the manuscript entitled "Verifying the parameterization of vertical eddy viscosity and diffusivity in the bottom boundary layer" by Takahiro Endoh, Takuya Hirooka, and Yoshinobu Wakata, which will be submitted to Journal of Physical Oceanography.</p>
Data from: Limitations of separate cloud and rain categories in parameterizing collision-coalescence for bulk microphysics schemes
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Responses of Pine Island and Thwaites glaciers to melt and sliding parameterizations
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Coarsened fine-grid model data for: A machine learning parameterization of clouds in a coarse-resolution climate model for unbiased radiation
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Data from: Parameterizing the robust design in the BUGS language: lifetime carry‐over effects of environmental conditions during growth on a long‐lived bird
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Model output of: Excitation of an MJO event in response to a transient sea surface warming in Super-Parameterized CAM
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