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154 results for “parameterization”
Data for the parameterization of radiative transfer processes in urban climate models
<p><em>Radiative Transfer</em> <em>Model</em> (RTM) is a key component in microscale building resolving urban climate models (<em>UCM</em>), which are used to simulate the flow within urban area. We use different parameterizations of RTMs in the model system <a href="https://gmd.copernicus.org/articles/13/1335/2020/gmd-13-1335-2020.html">PALM</a> version 6.0 to show how much detail modellers should include in their simulation.</p> <p>We introduce the output PALM model results for two examples: (1) A simplified urban geometry consisting of an urban crossing (UC) and (2) a realistic urban domain located at the town square Ernst-Reuter-Platz in Charlottenburg in Berlin (ER). The netCDF files contain the radiative flux received by each surface in the domains, including the shortwave (direct and diffuse) radiation as well as the longwave radiation. Also, the data set includes the 3D flow variables (<em>u</em>, <em>v</em>, <em>w</em>) and the potential temperature. The model drivers (input data) for both examples are included as well.</p> <p>The data set consists of the following model input/output data:</p> <p>1) Simplified urban domain (UC):</p> <ul> <li>Input driver for the model PALM for UC (UC_model_driver.tar.gz)</li> <li>Radiation fluxes for UC when using RTM_01: radiation for horizontal surfaces (UC_RTM_01.nc)</li> <li>Radiation fluxes for UC when using RTM_02: sky view effect (building shadows) (UC_RTM_02.nc)</li> <li>Radiation fluxes for UC when using RTM_03: vegetation interaction with SW radiation (UC_RTM_03.nc)</li> <li>Radiation fluxes for UC when using RTM_04: receiving radiation from surface emission (UC_RTM_04.nc)</li> <li>Radiation fluxes for UC when using RTM_05: vegetation interaction with LW radiation (UC_RTM_05.nc)</li> <li>Radiation fluxes for UC when using RTM_06: single reflection (UC_RTM_06.nc)</li> <li>Radiation fluxes for UC when using RTM_07: vegetation interaction with reflected radiation (UC_RTM_07.nc)</li> <li>Radiation fluxes for UC when using RTM_08: multiple reflections (UC_RTM_08.nc)</li> <li>3D data for the UC reference case which includes u,v,w,theta</li> </ul> <p>2) Realistic urban domain (ER):</p> <ul> <li>Input driver for the model PALM for ER (ER_model_driver)</li> <li>Radiation fluxes for ER when using RTM_01: radiation for horizontal surfaces (ER_RTM_01.nc)</li> <li>Radiation fluxes for ER when using RTM_02: sky view effect (building shadows) (ER_RTM_02.nc)</li> <li>Radiation fluxes for ER when using RTM_03: vegetation interaction with SW radiation (ER_RTM_03.nc)</li> <li>Radiation fluxes for ER when using RTM_04: receiving radiation from surface emission (ER_RTM_04.nc)</li> <li>Radiation fluxes for ER when using RTM_05: vegetation interaction with LW radiation (ER_RTM_05.nc)</li> <li>Radiation fluxes for ER when using RTM_06: single reflection (ER_RTM_06.nc)</li> <li>Radiation fluxes for ER when using RTM_07: vegetation interaction with reflected radiation (ER_RTM_07.nc)</li> <li>Radiation fluxes for ER when using RTM_08: multiple reflections (ER_RTM_08.nc)</li> <li>3D data for the ER reference case which includes u,v,w,theta</li> </ul> <p>For more information and analysis, please check out the relevant publication in the international journal Geoscientific Model Development: Salim et. al, Importance of radiative transfer processes in urban climate models:A study based on the PALM model system 6.0, submitted to GMD.</p>
Calibration of a Parameterization for Near-Inertial Waves
<p>We use these data to calibrate a parameterization for near-inertial waves.</p>
Dataset to reproduce the figures in "Parameterizing the Impact of Unresolved Temperature Variability on the Large-Scale Density Field: Part 2. Modeling." in Journal of Advances in Modeling Earth Systems (JAMES)
Ocean circulation models have systematic errors in large-scale horizontal density gradients due to estimating the grid-cell-mean density by applying the nonlinear seawater equation of state to the grid-cell-mean water properties. In frontal regions where unresolved subgrid-scale (SGS) fluctuations are significant, dynamically relevant errors in the representation of current systems can result. A previous study developed a novel and computationally efficient parameterization of the unresolved SGS temperature variance andresulting density correction. This parameterization was empirically validated but not tested in an ocean model. In this study, we implement deterministic and stochastic variants of this parameterization in the pressure-gradient force term of a coupled ocean-sea ice configuration of CESM-MOM6 and perform a suite of hindcast sensitivity experiments to investigate the ocean response. The parameterization leads to coherent changes in the large-scale ocean circulation and hydrography, particularly in the Nordic Seas and Labrador Sea, which are attributable in large part to changes in the seasonally varying upper-ocean exchange through Denmark Strait. In addition, the separated Gulf Stream strengthens and shifts equatorward, reducing a common bias in coarse-resolution ocean models. The ocean response to the deterministic and stochastic variants of the parameterization is qualitatively, albeit not quantitatively, similar, yet qualitative differences are found in various regions.
All data of the manuscript "A self-sustained charge neutrality intracloud lightning parameterization containing channel decay and reactivation" submitted to Geophysical Research Letters
<p>The data support the manuscript entitled "A self-sustained charge neutrality intracloud lightning parameterization containing channel decay and reactivation". Microsoft Notepad can open the *.txt files, they contain the channel information of two intracloud flashes (IC1 and IC2) and the channel elctrical parameters at the first fork of positive or negative leader channels. A normal video player software can open Movie_S1.avi, and it shows the entire development process of IC1 discharge.</p> <p>The data can be used freely for scientific purposes with the appropriate citation.</p>
Parameterization dataset openCOSMO-RS 24a
Open the record for dataset details and reuse information.
Data from: Evaluation of demographic history and neutral parameterization on the performance of Fst outlier tests
FST outlier tests are a potentially powerful way to detect genetic loci under spatially divergent selection. Unfortunately, the extent to which these tests are robust to non-equilibrium demographic histories has been under-studied. We developed a landscape-genetics simulator to test the effects of isolation by distance (IBD) and range expansion on FST outlier methods. We evaluated the two most commonly used methods for the identification of FST outliers (FDIST2 and BayeScan, which assume samples are evolutionarily independent) and two recent methods (FLK and Bayenv2, which estimate and account for evolutionary non-independence). Parameterization with a set of neutral loci ("neutral parameterization") always improved the performance of FLK and Bayenv2, while neutral parameterization caused FDIST2 to actually perform worse in the cases of IBD or range expansion. BayeScan was improved when the prior odds on neutrality was increased, regardless of the true odds in the data. On their best performance, however, the widely-used methods had high false-positive rates for IBD and range expansion and were outperformed by methods that accounted for evolutionary non-independence. In addition, default settings in FDIST2 and BayeScan resulted in many false positives under balancing selection. However, all methods did very well if a large set of neutral loci is available to create empirical p-values. We conclude that in species that exhibit IBD or have undergone range expansion, many of the published FST outliers based on FDIST2 and BayeScan are probably false positives, but FLK and Bayenv2 show great promise for accurately identifying loci under spatially-divergent selection.
Data and Codes for Evaluating and Improving Scale-Awareness of a Convective Parameterization Closure Using Cloud-Resolving Model Simulations of Convection
<p>Provide necessary fields averaged over different subdomain sizes from 64 km to 4 km (see 64 to 4 .7z files) processed from the output of CRM simulation of MC3E case (for TWP-ICE case, please get the processed data and associated codes from http://doi.org/10.5281/zenodo.4542461). Also, associated codes for calculation of important fields (like dCAPEls, dCAPEe, Msa and so on) are also provided in code.7z. Please see all "note.txt" files in code.7z to know how to use these codes.</p>
Modeling organic aerosol over Europe in summer conditions with the VBS-GECKO parameterization: sensitivity to secondary organic compound properties and IVOC emissions [Dataset]
<p>This dataset is composed of simulation outputs of the CHIMERE model that were used to evaluate the VBS-GECKO SOA parameterization as described in the study :</p> <p>Lannuque, V., Couvidat, F., Camredon, M., Aumont, B., and Bessagnet, B. : Modelling organic aerosol over Europe in summer conditions with the VBS-GECKO parameterization: sensitivity to secondary organic compound properties and IVOC emissions, Atmos. Chem. Phys., <em>to be submitted soon</em>.<br> <br> <strong>Please cite the original ACP article when using these data in a publication.</strong></p> <p><br> The paper also contains more information about how these data were obtained.</p> <p>The dataset is composed of 34 netcdf files. The 17 "daily" files gather the daily averages and the 17 "profile" files present the average day profiles.</p> <p>Victor Lannuque</p>
Data from: Model parameterization of four species distribution models
<p>Species Distribution Models (SDMs) are practical tools to assess the habitat suitability of species with numerous applications in environmental management and conservation planning. The manipulation of the input data to deal with their spatial bias is one of the advantageous methods to enhance the performance of SDMs. However, the development of a model parameterization approach covering different SDMs to achieve well-performing models has rarely been implemented. We integrated input data manipulation and model tuning for four commonly-used SDMs: generalized linear model (GLM), gradient boosted model (GBM), random forest (RF), and maximum entropy (MaxEnt), and compared their predictive performance to model geographically imbalanced biased data of a rare species complex of mountain vipers. Models were tuned up based on a range of model-specific parameters considering two background selection methods: random and background weighting schemes. The performance of the fine-tuned models was assessed based on a recently identified localities of the species. The results indicated that although the fine-tuned version of all models shows great performance in predicting training data (AUC > 0.9 and TSS > 0.5), they produce different results in classifying out-of-bag data. The GBM and RF with higher sensitivity of training data showed more different performances. The GLM, despite having high predictive performance for test data, showed lower specificity. It was only the MaxEnt model that showed high predictive performance and comparable results for identifying test data in both random and background weighting procedures. Our results highlight that while GBM and RF are prone to overfitting training data and GLM over-predict non-sampled areas MaxEnt is capable of producing results that are both predictable (extrapolative) and complex (interpolative). We discuss the assumptions of each model and conclude that MaxEnt could be considered as a practical method to cope with imbalanced-biased data in species distribution modeling approaches.</p>
Results of the CCM SOCOLv3 runs with and without orographic gravity waves (OGW) parameterizations
<p>1. Model run without any OGW parameterization:</p> <p>T_no_OGW.nc – Temperature (K)</p> <p>U_no_OGW.nc – Eastward zonal wind (m/s)</p> <p> </p> <p>2. Model run with the OGW parameterization of Lott and Miller:</p> <p>T_Lott_Miller.nc – Temperature (K)</p> <p>U_Lott_Miller.nc – Eastward zonal wind (m/s)</p> <p> </p> <p>3. Model run with the OGW parameterization of the Saint-Petersburg University (SPBU): </p> <p>T_SPBU.nc – Temperature (K)</p> <p>U_SPBU.nc – Eastward zonal wind (m/s)</p> <p>-------------------------------------------------------------------------------------------------</p> <p> (monthly and zonal mean values averaged over 2009-2018 time period) </p>
Supplementary material: Is over-parameterization a problem for profile mixture models?
<p> All the materials are presented in the order they are referred to in the manuscript. At the end of this document, we include the section entitled Reproducibility, which explains some of the simulation procedures, as well as some sample command lines like used in this work to simulate and fit the data. Throughout this material, blank spaces are left on purpose. </p> <p> </p>
Data supporting "Modeling and evaluating the effects of irrigation on land-atmosphere interaction in southwestern Europe with the regional climate model REMO2020-iMOVE using a newly developed parameterization"
<p>This data supports the analysis of the manuscript Asmus et al. 2023 "Modeling and evaluating the effects of irrigation on land-atmosphere interaction in southwestern Europe with the regional climate model REMO2020-iMOVE using a newly developed parameterization".</p><p><strong>Simulation data</strong></p><p>The simulation data is created with REMO2020-iMOVE using the new irrigation parameterization. The results are saved as NetCDF files with monthly mean values and/or time series (hourly) of single variables for the analysis period. A list of the simulations can be found below.</p><p><strong>Observation data </strong></p><p>The observation data is published with the kind permission of ISPRA which hosts the SCIA database (www.scia.isprambiente.it). If you use this data, please make sure to include the following data source:<br>SCIA by ISPRA - Area Climatologia operativa - Via V. Brancati 48 00144 Roma. <br>We downloaded monthly mean values for the variables T2Max, T2Min and T2Mean from <a href="http://193.206.192.214/servertsutm/serietemporali100.php">http://193.206.192.214/servertsutm/serietemporali100.php </a>(last accessed on 14/10/2022) to verify the model results with and without irrigation parameterization. </p><p>By untarring the tarballs, the data structure is created that is necessary to execute the analysis scripts.</p><p>For more information or additional data please contact the author.</p><p> </p><p><strong>tarball | exp_number | description </strong></p><p>067015.tar.gz | 067015 | not irrigated</p><p>067016.tar.gz | 067016 | irrigated with "adaptive water application scheme"</p><p>067017.tar.gz | 067017 | irrigated with "adaptive water application scheme"</p><p>067019.tar.gz | 067019 | irrigated with "flexible time water application scheme"</p><p>067020.tar.gz | 067020 | irrigated with "prescribed water application scheme"</p><p>observation_scia.tar | - | observation data from SCIA </p><p> </p><p><strong>Data structure for simulation data</strong></p><p>\<exp_number><br> \monthly<br> \hourly<br> \var_series<br> \<variable><br><br>Note:<br>\067015 includes static variables<br> \irrifrac (irrigated fraction)<br> \bla (land-sea-mask)</p>
Data from: Model parameterization of four species distribution models
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Supplementary material: Is over-parameterization a problem for profile mixture models?
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Data from: A clinically parameterized mathematical model of Shigella immunity to inform vaccine design
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Data from: Evaluation of demographic history and neutral parameterization on the performance of Fst outlier tests
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Peptide design by optimization on a data-parameterized protein interaction landscape
GEO Series GSE118147. Saccharomyces cerevisiae. 3 samples. Type: Other.
KPP code for MITgcm v62r with added parameterization for Langmuir Circulation
<p>This dataset contains all the routines in the KPP package for version 62r of MITgcm with added parameterizations for Langmuir Circulation, used in a hindcast study of the West Antarctic Peninsula (WAP). </p>
Model code and data for "Mitigation of the double ITCZ syndrome in BCC-CSM2-MR through improving parameterizations of boundary-layer turbulence and shallow convection" by Lu et al., submitted to Geoscientific Model Development, https://doi.org/10.5194/gmd-2020-40, in review, 2020.
<p>Description of the files:</p> <p>“BCC_CSM2_MR.code.tar” contains the codes and run scripts for the medium-resolution Beijing Climate Center Climate System Model version 2 (BCC-CSM2-MR). Detailed description of the model refers to the paper “The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6” by Wu et al., Geosci. Model Dev., 12, 1573–1600, https://doi.org/10.5194/gmd-12-1573-2019, 2019.</p> <p>“BCC_CSM2_MR.inputdata.tar” contains the input data needed to run the model.</p> <p>“REF_amip.rar” contains the output data from the REF_amip experiment.</p> <p>“NEW_amip.rar” contains the output data from the NEW_amip experiment.</p> <p>“REF_cmip.rar” contains the output data from the REF_cmip experiment.</p> <p>“NEW_cmip.rar” contains the output data from the NEW_cmip experiment.</p> <p>“UWMT_amip.rar” contains the output data from the UWMT_amip experiment.</p> <p>“mHack_amip.rar” contains the output data from the mHack_amip experiment.</p>
Quantifying Transition Zone Radiative Effects in Longwave Radiation Parameterizations
<p>The files uploaded here consist of the band averaged aerosol optical properties used in our study and the data behind the figures presented in the manuscript.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.