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10 results for “Convection Parameterization”
UM experiments for "Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection"
<p>NetCDF4 files containing UM vn 11.1 data used in Lambert et al., 2020, Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection, submitted to Journal of Advances in Modeling Earth Systems.</p> <p>Key:</p> <p>"summaryday.nc" contain eleven months of data in each year, excluding either February or March.</p> <p>"summarydat2.nc" contain one month of data in each year, either February or March.</p> <p>"last5" indicates that for this simulation only the last five years of data are available.</p> <p>"llcs" are simulations with Lambert-Lewis.</p> <p>"gr" are simulations with Gregory-Rowntree.</p> <p>"llcsemu" are simulations with the Lambert-Lewis emulator.</p> <p>"gremu" are simulations with the Gregory-Rowntree emulator.</p> <p>"llcsemu_llcs" is the test simulation wherein the LLCS emulator is run equatorward of 30 degrees and the original LLCS convection scheme is run poleward of 30 degrees.</p> <p>"4xco2" have 4 x pre-industrial atmospheric carbon dioxide concentration. (Others have 1 x pre-industrial atmospheric carbon dioxide concentration.)</p> <p>"rh0.7" and "rh0.9" have LLCS RHCRIT set to 70% and 90% respectively.</p> <p>"30day" are one month simulations for July for which daily output are available. Other data are monthly mean only.</p> <p> </p>
Simulations for convection during the intensive observation period of the TWP-ICE field campaign: results from cloud-resolving model and convective parameterization schemes
<p>Simulated convections from cloud-resolving model and convective parameterization schemes during the intensive observation period of the TWP-ICE field campaign, which are used to investigate the scale-awareness problem of convective parameterization schemes. The corresponded observations are also included in this dataset.</p>
Model simulations using a parameterization of convective organization effects
<p>We propose a parameterization scheme of convective organization effects based on a moisture-distribution approach. We implement it into a regional climate model and evaluate its performance against a convection-permitting model simulation. The related model simulations are included in this dataset.</p>
Data and analysis scripts for "Impact of grid spacing, convective parameterization and cloud microphysics in ICON simulations of a warm conveyor belt"
<p>The data relates to the analysis of "Impact of grid spacing, convective parameterization and cloud microphysics in ICON simulations of a warm conveyor belt" and is in support and reference to the work titled as the same for a research article authored by Choudhary and Voigt. The file 'data.zip' contains different directories named as per the computations related to particular analysis as mentioned below:</p> <table> <tbody> <tr> <td> <p>Directory names and content inside 'data.zip'</p> </td> </tr> <tr> <td> <p>Name of directory</p> </td> <td> <p>Content</p> </td> </tr> <tr> <td> <p>trajectories </p> </td> <td> <p>Output from run of LAGRANTO tool on regridded ICON simulations<strong><sup>*</sup></strong> (the input data is not provided here). The output contains computations of 'warm conveyor belt (WCB)' trajectories and different variables traced along them which are stored in different files named as outtrace1.nc and so on. The subdirectories are named as per the resolution, convection and cloud microphysics parametrization used in model setup, for e.g. '0.025convon/1m' corresponds to 2.5 km resolution with parametrized convection and 1-moment cloud microphysics. The file with name ending with 'box' corresponds to different subclasses of trajectory as described in the work. </p> </td> </tr> <tr> <td> <p>dhr</p> </td> <td> <p>diabatic heating rate, traced along WCB and binned in pressure levels (for 1-moment cloud microphysics)</p> </td> </tr> <tr> <td> <p>dynamical variables</p> </td> <td> <p>av- absolute vorticity, pv- potential vorticity and w- vertical wind traced along WCB and binned in pressure levels (for 1-moment cloud microphysics)</p> </td> </tr> <tr> <td> <p>statistics</p> </td> <td> <p>statistics (different statistical computation of WCB parcels ascent for 1- and 2- moment cloud microphysics, details described in work)</p> </td> </tr> <tr> <td> <p>theta_e</p> </td> <td> <p>equivalent potential temperature for 2.5 km explicit convection simulation (.pkl file) for 2016.09.22 12UTC, 2016.09.23 12UTC and 2016.09.24 12UTC in files numbered 1, 2 and 3 respectively.</p> </td> </tr> <tr> <td> <p>ipv</p> </td> <td> <p>same as above but for isentropic potential vorticity</p> </td> </tr> <tr> <td> <p>mslp</p> </td> <td> <p>mslp.nc- same as above but for mean sea level pressure field </p> </td> </tr> <tr> <td> <p>cyclone_track</p> </td> <td> <p>File name with convention 'nawdexnwp-5km-mis-0001_vladiana_cyclonepressure.nc' corresponds to file containing central mean sea level pressure of cyclone Vladiana during the simulation period for 5 km resolution with parametrized convection and 1-moment cloud microphysics. </p> <p>coord_1m.npy- the coordinate/ location of cyclone Vladiana based on central mean sea level pressure </p> </td> </tr> <tr> <td> <p>pte</p> </td> <td> <p>Pressure tendency equation (PTE) analysis. Subdirectories are named after different resolutions of simulation with 1-moment cloud microphysics and parametrized convection (except for 10, 5 and 2.5 km where convection is explicit).</p> <p>Further, the .npy files are named as per the different vertically integrated tendency terms of PTE equation they represent, as mentioned below:</p> <p>dp_dt: surface pressure</p> <p>dfi_dt: geopotential (upper boundary of integral)</p> <p>i_diab_res: diabatic heating (including residual)</p> <p>i_itt: virtual temperature</p> <p>i_tadv: temperature advection</p> <p>i_vmt: vertical motions</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>*</strong>Note: The original ICON simulations which are not part of this dataset were carried out by Prof. Aiko Voigt (University of Vienna) at the Mistral High Performance Computing system of the German Climate Computing Center (DKRZ) in Hamburg, Germany. The primary data of the ICON simulations (run scripts, namelists, scripts for lateral boundary data) are published at KITopen of Karlsruhe Institute of Technology, https://doi.org/10.5445/IR/1000123695. Note that the KITopen dataset includes all simulations of \cite{svchd20}, from which a subset is analyzed here.</p> <p> </p> <p>The file 'scripts.zip' contains two directories which is related with analysis presented in the work:</p> <p>'plotting_scripts' includes script for creating different plots/ figures showcased in the work </p> <p>'processing_scripts' includes computations for producing different quantities/ data that are named as described above in the table for 'data.zip'</p> <p> </p>
Statistical model training data for "Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection"
<p>Gzipped CSV files containing convection scheme inputs and outputs used for training.</p> <p>Column format of each file:</p> <p>THETA_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,Q_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DTHETA_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DQ_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28</p> <p>where THETA_IN are input values of potential temperature [K], Q_IN are input values of specific humidity [kg/kg], DTHETA are changes in potential temperature due to convection [K], DQ are changes in specific humidity due to convection [kg/kg].</p> <p>Key:</p> <p>"llcs" are simulations with Lambert-Lewis.</p> <p>"gr" are simulations with Gregory-Rowntree.</p> <p>"4xco2" have 4 x pre-industrial atmospheric carbon dioxide concentration. (Others have 1 x pre-industrial atmospheric carbon dioxide concentration.)</p> <p>"rh0.7" and "rh0.9" have LLCS RHCRIT set to 70% and 90% respectively.</p> <p>All are 30 day simulations either for January "jan" or July "jul".</p> <p> </p>
Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations: Data and Visualization Notebooks
<p>The data, jupyter notebooks, and saved model weights for the "Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations" Hu et al. (2025) arxiv preprint: <a href="https://arxiv.org/abs/2407.00124">arXiv:2407.00124</a>. This updated version contains more analysis notebooks together with related data/model.</p>
Simulation data for "A new convective parameterization applied to Jupiter: implications for water abundance near the 24deg N region"
<p>Simulation outputs and initialization for the journal article titled "A new convective parameterization applied to Jupiter: implications for water abundance near the 24º N region" by Sankar & Palotai.</p>
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>
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>
Impact of Convective Parameterizations on Atmospheric Mesoscale Kinetic Energy Spectra in Global High-resolution Simulations
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