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15 results for “parameter calibration”
North Temperate Lakes LTER General Lake Model Parameter Set for Lake Mendota, Summer 2016 Calibration
The General Lake Model (GLM), an open source, one-dimensional hydrodynamic model, was used to simulate various physical, chemical, and biological variables on Lake Mendota between 15 April 2016 and 11 November 2016. GLM (v.2.1.8) was coupled to the Aquatic EcoDynamics (AED) module library via the Framework for Aquatic Biogeochemical Modeling (FABM). GLM-AED requires four major “scripts†to run the model. First, the glm2.nml file configures lake metadata, meteorological driver data, stream inflow and outflow driver data, and physical response variables. Second, the aed2.nml file configures various biogeochemical modules for the simulation of oxygen, carbon, phosphorus, and nitrogen, among others. Third, aed2_phyto_pars.nml configures all parameters pertaining to phytoplankton dynamics. And fourth, aed2_zoop_pars.nml configures all parameters pertaining to zooplankton dynamics. This dataset contains parameter descriptions and values as they were used to simulate organic carbon and greenhouse gas production on Lake Mendota in summer 2016. Meteorological data and stream files used in this calibration are also included in this dataset. Additional methods and model descriptions can be found in J.A. hart’s Masters Thesis, University of Wisconsin-Madison Center for Limnology, May 2017. Readers are referred to the GLM (Hipsey et al. 2014) and AED (Hipsey et al. 2013) science manuals for further details on model configuration.
A Novel Framework to Harmonise Satellite Data Series for Climate Applications: Matchups, Calibration Parameters and Residuals
<p>The datasets included with this archive supplement the journal article:</p> <p>Giering, R.; Quast, R.; Mittaz, J.P.D.; Hunt, S.E.; Harris, P.M.; Woolliams, E.R.; Merchant, C.J. A Novel Framework to Harmonise Satellite Data Series for Climate Applications. <em>Remote Sens. 2019</em>, <strong>11</strong>, 1002. doi:<a href="https://doi.org/10.3390/rs11091002">10.3390/rs11091002</a>.</p> <p>The archive includes a README file with further explanations.</p>
Dataset for: In-operando microwave scattering-parameter calibrated measurement of a Josephson travelling wave parametric amplifier
<p>Dataset for manuscript "In-operando microwave scattering-parameter calibrated measurement of a Josephson travelling wave parametric amplifier", <a href="https://arxiv.org/abs/2406.03063">arXiv:2406.03063</a></p> <p>Containing the uncalibrated raw measurement data and the calibrated dataset after applying the 8-term error model.</p>
FaIR v1.6.2 calibrated and constrained parameter set
<p>These .json files provides the 2237 ensemble members that are used to run the FaIR simple climate model in the IPCC's Sixth Assessment Report contributions to Working Group 1 and Working Group 3.</p> <p>v1.1 of this dataset includes two versions of the files:</p> <ul> <li>the original full parameter set `fair-1.6.2-wg3-params.json` as in v1.0</li> <li>reduced file size versions `fair-1.6.2-wg3-params-slim.json` and `fair-1.6.2-wg3-params-common.json` where only the parameters that vary between ensemble members are included in the former and everything else is in the latter. This is optimised for running the Working Group 3 climate assessment.</li> </ul> <p>Drawn from an initial prior ensemble of 1 million, the following constraints are placed upon the results:</p> <ul> <li>representation of observed warming from 1850-2019, within observational uncertainty</li> <li>representation of observed ocean heat content change 1971-2018, within observational uncertainty</li> <li>reproduction of near-present day atmospheric concentrations of CO2 from the carbon cycle</li> <li>airborne fraction of CO2 with a distribution similar to the assessed range of Chapter 5, IPCC Working Group 1 AR6</li> <li>equilibrium climate sensitivity with a similar distribution to Chapter 7, IPCC Working Group 1, AR6</li> <li>transient climate response with a similar distribution to Chapter 7, IPCC Working Group 1, AR6</li> <li>projected future warming that is of a similar distribution to the SSP scenarios of Chapter 4, IPCC Working Group 1, AR6, when run with prescribed concentrations</li> </ul> <p><strong>Changelog</strong></p> <ul> <li>v1.1: inclusion of lightweight parameter sets.</li> <li>v1.0: original upload full parameter sets.</li> </ul>
All simulation results, figures and code regarding the manuscript: Calibrating models of cancer invasion: parameter estimation using Approximate Bayesian Computation and gradient matching
<p>We present two different methods to estimate parameters within a partial differential equation (PDE) model of cancer invasion. The model describes the spatio-temporal evolution of three variables -- tumour cell density, extracellular matrix density and matrix degrading enzyme concentration -- in a one-dimensional tissue domain. The first method is a likelihood-free approach associated with Approximate Bayesian Computation (ABC); the second is a two-stage gradient matching method based on smoothing the data with a Generalized Additive Model (GAM) and matching gradients from the GAM to those from the model. Both methods performed well on simulated data. To increase realism, additionally we tested the gradient matching scheme with simulated measurement error and found that the ability to estimate some model parameters deteriorated rapidly as measurement error increased.</p>
Calibrated Western USA Noah-MP Parameter Set Forced by ERA5-WRF
<p>These three files include the default (original) Noah-MP parameter set for the Western USA, and parameter sets based on calibration to daily streamflow data and monthly streamflow data. </p>
Modelling Kepler red giants in eclipsing binaries: calibrating the mixing-length parameter with asteroseismology
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018MNRAS.475..981L/abstract">Li et al. (2018)</a>. MESA version 8118.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/stx3079">10.1093/mnras/stx3079</a></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>
All simulation results, figures and code regarding the manuscript: Calibrating models of cancer invasion: parameter estimation using Approximate Bayesian Computation and gradient matching
Open the record for dataset details and reuse information.
MD simulation of POPC bilayer using a force field calibrated to NMR order parameters
<p>Force field parameters and simulation trajectory for 34-lipid bilayer simulation where the force field has been calibrated to reproduce the NMR C-H 13C order parameters. Temperature is 300K, and simulation was conducted in NPT ensemble with asymmetric pressure coupling. The trajectory is 1 microsecond long. The simulation was started from an equilibrated conformation and few tens of nanoseconds of pre-equilibration was run before obtaining the 1 microsecond production run. The trajectory has been centered to the simulation box. The simulations where conducted on GROMACS 2020 (GPU). </p>
CLM-FATES parameter estimation using the 'calibrate, emulate, sample' approach - experimental results
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Calibration of Nonlinear Rheology Parameters in the Salt Lake Basin from the March 18, M5.7 Magna, Utah, Earthquake Sequence Records
<p>Simulated time series and scripts to extract intensity measures described in the U.S. Geological Survey (USGS) technical report (under USGS award number G22AP00033).</p> <p>The simulation results are located "<a href="../api/records/10892430/draft/files/time-series-all.zip/content" target="_blank" rel="noopener noreferrer">time-series-all.zip</a>" with folder names defined as in the table-of-contents shown in the additional descriptions, all waveform data are in ASCII format, where the first column is time in seconds from the initial time of the event, the second column is velocity in m/s. The python scripts used to calculate intensity measurements based on the time series are located in "<a href="../api/records/10892430/draft/files/itensity-measures-calculation.zip/content" target="_blank" rel="noopener noreferrer">itensity-measures-calculation.zip</a>", with package requirements listed within each script.</p>
Estimating labquake source parameters: spectral inversion from a calibrated acoustic system
<p>Laboratory acoustic emissions (AEs) serve as small-scale analogues to earthquakes, offering fundamental insights into seismic processes. To ensure accurate physical interpretations of AEs, rigorous calibration of the acoustic system is essential. In this paper, we present an empirical calibration technique that quantifies sensor responses, instrumentation effects, and path characteristics into a single entity termed instrument apparatus response.</p> <p>Using a controlled seismic source with different steel balls, we retrieve the instrument apparatus response in the frequency domain under typical experimental conditions for various piezoelectric sensors (PZTs) arranged to simulate a three-component seismic station. Removing these responses from the raw AEs spectra allows us to obtain calibrated AE source spectra which are then effectively used to constrain the AEs seismic source parameters.</p> <p>We apply this calibration method to acoustic emissions (AEs) generated during unstable stick-slip behavior of quartz gouge in double-direct shear experiments. The calibrated AEs range in magnitude from -7.1 to -6.4 and exhibit stress drops between 0.075 MPa and 4.29 MPa, consistent with earthquake scaling relation. </p> <p>This result highlights the strong similarities between AEs generated from frictional gouge experiments and natural earthquakes. Through this acoustic emission calibration we gain physical insights into the seismic sources of laboratory AEs, enhancing our understanding of seismic rupture processes in fault gouge experiments.</p>
S1 - SWMM parameters prior and posterior range during calibration
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Camera calibration parameters
<p>Camera calibration parameters (intrinsics,extrinsics and distortion) for the RGBD camera Astra and the RGBD camera Intel Realsense F200</p> <p>Format: OpencvStorage/xml</p> <p>Generated using Opencv calibration functions</p>
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