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154 results for “Parameterization”
Simulations in Support of Parameterization of Unified Microphysics Across Scales version 1 (PUMASv1) Paper
<p>Simulations done in support of manuscript entitled: Importance of Ice Nucleation and Precipitation on Climate with the Parameterization of Unified Microphysics Across Scales version 1 (PUMASv1)</p>
Datasets used for developing sulfuric acid-water neutral and ion-induced particle formation parameterizations
<p>Supporting data for "New parameterizations for neutral and ion-induced sulfuric acid-water particle formation in nucleation and kinetic regimes" by Määttänen et al. (2017). A README file describing the datasets is included.</p> <p>Five datasets used for developing sulfuric acid-water neutral and ion-induced particle formation parameterizations, published in Määttänen et al. 2017, JGR Atmospheres (submitted) and at https://doi.org/10.5281/zenodo.1044366.</p> <p>Two datasets that have been used to produce Fig 8. of the manuscript: these datasets contain global model output of nucleation rates and total aerosol number concentrations.</p>
Data from: The PDI model system for parameterizing soil hydraulic properties
<p>The PDI ("Peters-Durner-Iden") model system represents a robust framework for parameterizing soil hydraulic properties, i.e. the water retention curve and the hydraulic conductivity curve, across the entire soil moisture spectrum. This model accounts for water retention and hydraulic conductivity in completely and partially-filled pores, including adsorption and film-flow. The model was developed in stages and a comprehensive overview of the model development and the model equations is provided in Peters et al. (2024). In this repository, we provide a Python file named "pdi.py" which can be used to compute the various submodels (PDI-VG, PDI-KOS, PDI-FX, ...) of the PDI model system. One MS Excel file is provided for easy access to one PDI model, the PDI-VG. The PYTHON functions contained in "pdi.py" can be used to calculate the water retention curve, the unsaturated hydraulic conductivity curve, the specific water capacity function, and the soil water diffusivity function. In addition, we provide five python scripts which illustrate how to call the various PDI functions in different contexts. Notably, "pdi.py" incorporates a utility function, 'export_hydrus_materin', which generates an ASCII file named "MATER.IN". This file serves as input for simulations with Hydrus-1D and Hydrus-2D3D, offering seamless integration with these simulation platforms. It's important to emphasize that the provided Python scripts and accompanying documentation are closely aligned with the research article by Peters et al. (2024). To streamline accessibility, the repository refrains from redundantly restating the theory or equations already detailed in the referenced publication.</p>
Secondary ice production parameterization output - COSMO model
<p>Secondary ice production via processes like rime splintering, frozen droplet shattering, and breakup upon ice hydrometeor collision have been proposed to explain discrepancies between in-cloud ice crystal and ice-nucleating particle numbers. To understand the impact of this kind of additional ice number generation on surface precipitation, we present one of the first studies to implement frozen droplet shattering and ice-ice collisional breakup parameterizations in a larger-scale model. We simulate a cold frontal rainband from the Aerosol Properties, PRocesses, And InfluenceS on the Earth's Climate campaign and investigate the impact of the new parameterizations on the simulated ice crystal number concentrations (ICNC) and precipitation. Near the convective regions of the rainband, contributions to ICNC can be as large from secondary production as from primary nucleation, but ICNCs greater than 50 L-1 remain underestimated by the model. Addition of the secondary production parameterizations also clearly intensifies the differences in both accumulated precipitation and precipitation rate between the convective towers and non-convective gap regions. We suggest, then, that secondary ice production parameterizations be included in large-scale models on the basis of large hydrometeor concentration and convective activity criteria.</p>
Supplementary data to Schmiester et al. *Efficient parameterization of large-scale dynamic models based on relative measurements*
<p>This archive contains Supplementary data to the manuscript <em>Efficient parameterization of large-scale dynamic models based on relative measurements</em> by Leonard Schmiester, Yannik Schälte, Fabian Fröhlich, Jan Hasenauer and Daniel Weindl.</p>
Non-equilibrium fractionation during ice cloud formation in iCAM5: evaluating the common parameterization of supersaturation as a linear function of temperature
<p>This archive includes data and python scripts to create the figures in</p> <p>Duetsch, M., Blossey, P. N., Steig, E. J., and Nusbaumer, J. M. (2019). Non-equilibrium fractionation during ice cloud formation in iCAM5: evaluating the common parameterization of supersaturation as a linear function of temperature. Submitted to J. Adv. Model. Earth Sy.</p> <p><strong>Model</strong></p> <p>Model output data are saved in model.tar.gz</p> <p>For all simulations, monthly averages of the following variables are saved in h0 files (used for Figures 3, 6, 8, 9):<br> T: temperature (Si_real simulations only)<br> PS: surface pressure (Si_real simulations only)<br> U: zonal wind (Si_real simulations only)<br> V: meridional wind (Si_real simulations only)<br> LANDFRAC: fraction of surface area covered by land<br> PRECT_H2O: total precipitation rate for H2O<br> PRECT_HDO: total precipitation rate for HDO<br> PRECT_H218O: total precipitation rate for H218O<br> H2OV: H2O mixing ratio for vapor<br> HDOV: HDO mixing ratio for vapor<br> H218OV: H218O mixing ratio for vapor<br> H2OI: H2O mixing ratio for cloud ice<br> HDOI: HDO mixing ratio for cloud ice<br> H218OI: H218O mixing ratio for cloud ice<br> H2OL: H2O mixing ratio for cloud liquid<br> HDOL: HDO mixing ratio for cloud liquid<br> H218OL: H218O mixing ratio for cloud liquid</p> <p>For the control and Si_real microphysical sensitivity simulations, 6-hourly averages of the following variables are saved in h1 files (used for Figures 4, 5, 7):<br> PRECL_H2O: Large-scale precipitation rate for H2O (Si_real simulations only)<br> PRECL_HDO: Large-scale precipitation rate for HDO<br> PRECL_H218O: Large-scale precipitation rate for H218O<br> PRECL_SAT: Large-scale precipitation rate for Si tracer (Si_real simulations only)<br> PRECL_TMP: Large-scale precipitation rate for T tracer (Si_real simulations only)<br> PRECL_RVD: Large-scale precipitation rate for R^D tracer<br> PRECL_RVO: Large-scale precipitation rate for R^18O tracer</p> <p>To limit the size of the data set, only the first 5 years of the simulations are saved.</p> <p><strong>Measurements</strong></p> <p>Ice core measurements for present-day climate and last glacial maximum are saved in icecores_PD.txt and icecores_LGM.txt, respectively.</p> <p>LGM values are averaged from 19000 BCE to 16000 BCE, PD values are averaged from 1000 BCE to 2000 CE.</p> <p>Antarctic surface snow measurements from Masson-Delmotte et al. (2008) are available at https://doi.org/10.1594/PANGAEA.681697</p> <p><strong>Python scripts</strong></p> <p>Scripts run with python 3.6</p> <p>Required packages:<br> - numpy<br> - matplotlib<br> - netCDF4<br> - datetime<br> - Basemap<br> - copy<br> - intergrid<br> - cmocean</p>
Data in Support of Rainbow Parameterization
<p>This Archive contains data in support of the publication "Predicting Future Rainbows: A tutorial in Parameterization"</p> <p>The files are as follows:</p> <p>Monthly mean data sets from 4 year simulations:</p> <ul> <li>cc_cam6357fv_rainbow10.nc </li> <li>cc_cam6357fv_rainbow10_SST4K_2xCO2.nc </li> <li>cc_cam6357fv_rainbow10_pi.nc</li> </ul> <p>Instantaneous data used to build the parameterization and analyze the diurnal cycle: </p> <ul> <li>cchr_Jan_cam6357fv_rainbow10_hr.nc </li> <li>cchr_Jul_cam6357fv_rainbow10_hr.nc</li> </ul> <p>Model code: micro_pumas_cam.F90</p> <p>Analysis Code (Jupyter Notebooks):</p> <ul> <li>rainbow_monthly_mac.ipynb. (for monthly)</li> <li>rainbows10sens_mac.ipynb (for single month instanatnaeous)</li> </ul> <p> </p>
A 1D coupled physical-biogeochemical model for the North Atlantic for studying vertical carbon flux parameterizations
<p>This repository provides the model output and code for analysis in the following article:</p> <p>Wang, B., & Fennel, K. (2023). An assessment of vertical carbon flux parameterizations using backscatter data from BGC Argo. <em>Geophysical Research Letters</em>, 50, e2022GL101220. <a href="https://doi.org/10.1029/2022GL101220">https://doi.org/10.1029/2022GL101220</a></p> <p> </p>
Dataset to Report on skill of CMIP6 models to simulate alkalinity and improved parameterizations for large scale alkalinity distribution
<p>Six sensitivity experiments were conducted with the ocean-sea ice-biochemistry model FESOM2.1-REcoM3 to investigate the sensitivity of total alkalinity to changes in parametrizations. The results are reported in part two of the report:</p> <p>Hinrichs, C., & Hauck, J. (2022). OceanNETS Task 4.4 – Report on skill of CMIP6 models to simulate alkalinity and improved parameterizations for large scale alkalinity distribution. <a href="https://oceanrep.geomar.de/id/eprint/56871/1/Deliverable_4.4_vfinal.pdf">https://oceanrep.geomar.de/id/eprint/56871/1/Deliverable_4.4_vfinal.pdf</a></p> <p>This item contains the data and plotting routine to reproduce Figure 14 in the report. The model data has been post-processed into global mean depth profiles.</p>
Model output from "The chance of freezing – a conceptional study to parameterize temperature-dependent freezing by including randomness of ice-nucleating particle concentrations"
<p>Model output from "The chance of freezing – a conceptional study to parameterize temperature-dependent freezing by including randomness of ice-nucleating particle concentrations", accepted for publication in Atmospheric Chemistry and Physics, 2023, same authors.<br> The simulations were done using MIMICA version4 (Savre at el., 2014) and the data includes all model output presented in the publication.</p>
Data set: Statistically parameterizing and evaluating a positive degree-day model to estimate surface melt in Antarctica from 1979 to 2022
<p><strong>Version 2:</strong></p> <p><strong>Updates from version 1: Monthly, daily, and hourly dist-PDD and uni-PDD outputs have been added.</strong></p> <p><strong>https://doi.org/10.5194/tc-17-3667-2023</strong></p> <p> </p> <p>Version 1:</p> <p>This dataset accompanies Zheng et al. (2023): Statistically parameterizing and evaluating a positive degree-day<br> model to estimate surface melt in Antarctica from 1979 to 2022, The Cryosphere.</p> <p>This dataset contains annual PDD model output.</p>
Data for the publication "Significant Increase in Graupel and Lightning Occurrence in a Warmer Climate Simulated by Prognostic Graupel Parameterization"
<p>This dataset includes a set of 11yr simulations using the MIROC6 global aerosol-climate model under the pre-industrial (PI, aerosol emission at the year 1850), present-day (PD, aerosol emission at the year 2000), and future warming (SST+4K, a uniform 4 K increase in sea surface temperature) conditions.</p> <p>The data are used in the manuscript entitled "Significant Increase in Graupel and Lightning Occurrence in a Warmer Climate Simulated by Prognostic Graupel Parameterization".</p>
Post-fire flood hazard model (PF2HazMo) version 1.0.0: Model scripts and parameterization and validation data
Open the record for dataset details and reuse information.
Data from: The PDI model system for parameterizing soil hydraulic properties
Open the record for dataset details and reuse information.
Parameterizing Subgrid Variations of Land Surface Heat Fluxes to the Atmosphere Improves Land Precipitation Simulation with the NCAR CESM1.2
<p>The dataset is the output of CESM that is used in the paper "Parameterizing Subgrid Variations of Land Surface Heat Fluxes to the Atmosphere Improves Land Precipitation Simulation with the NCAR CESM1.2" submitted to <em>Geophysical Research Letters</em>.</p>
Experimental data for "Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models"
<p>This dataset is for the validation data in Chen et al. (2020). It contains data for three different LG M50 cells undergoing an experiment in which the cells are charged in a constant-current/constant-voltage fashion and discharge at a constant current for different C-rates (C/10, C/2, 1C and 1.5C). Apart from the current and voltage, the temperatures of the cell surface and the thermal chamber in which they are cycled is recorded too.</p> <p><strong>References:</strong></p> <p>Chang-Hui Chen <em>et al</em> 2020 <em>J. Electrochem. Soc.</em> <strong>167</strong> 080534 (<a href="https://doi.org/10.1149/1945-7111/ab9050">https://doi.org/10.1149/1945-7111/ab9050</a>)</p>
Empirical shapes for the publication "Modifications of the K-Profile parameterization with nondiffusive fluxes for wave effects"
<p>This dataset contains data for the reproduction of the empirical shape functions G_l and g_s introduced in the publication "Modifications of the K-Profile parameterization with nondiffusive fluxes for wave effects".</p>
Link-operator parameterized boolean models from scale-free topologies
<p>See analysis on this dataset here: <a href="https://druglogics.github.io/brf-bias/random-scale-free-networks-analysis.html">https://druglogics.github.io/brf-bias/random-scale-free-networks-analysis.html</a></p>
Data from: Parameterizing the robust design in the BUGS language: lifetime carry‐over effects of environmental conditions during growth on a long‐lived bird
1. Since the initial development of the robust design, this capture‐recapture model structure has been modified to estimate temporary emigration, and expanded to include auxiliary information such as band recovery and live resight data using maximum likelihood approaches. These developments have allowed investigators to separately assess individual and group effects on true survival, site fidelity, and temporary emigration. Additionally, recent advances in the BUGS language have allowed researchers to develop increasingly complex, user‐specified models in Bayesian frameworks. 2. The robust design has rarely been implemented in the BUGS language, and previous attempts to parameterize the robust design in BUGS exhibited strong bias in estimates of temporary emigration rates. Given the limitations of current parameterizations of the robust design in Bayesian frameworks, and our research objectives, we have developed a parameterization of the robust design in the BUGS language that produces unbiased estimates of all model parameters. 3. We use this novel model structure to examine lifetime carry‐over effects of environmental conditions during early life on annual breeding probabilities of Pacific black brent (Branta bernicla nigricans) breeding on the Yukon‐Kuskokwim River Delta in western Alaska. We found that individuals that were more structurally developed as goslings bred at increased rates as adults (β = 0.14, f = 0.94), with no effect on adult survival (β = 0.01, f = 0.62). Additionally, we provide evidence for long‐term declines in apparent survival of breeding adult females at the population level (β = ‐0.01, f = 0.90). 4. This novel model structure can be easily expanded (Gibson et al., in review), and has important implications for population modelling at broad scales, where we apply it to a declining population of Pacific black brent. Given long‐term declines in gosling growth on the Yukon‐Kuskokwim Delta, we predict future declines in population trajectories as a result of lifetime carry‐over effects of environmental conditions during growth on adult fecundity, and long‐term declines in adult survival.
Data and Codes for "Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime" by Pahlavan et al. (2023)
<p>This is part of the code and data related to the paper entitled Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime, available at https://arxiv.org/abs/2309.09024.</p><p>The original sources of the codes are the v1.0.0 version of open source software EnsembleKalmanProcesses.jl for EKI analysis, accessible at zenodo.org/records/7806813, and the \emph{qbo1d} code for the 1D-QBO model simulations, accessible at github.com/DataWaveProject/qbo1d.git.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.