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364 results for “Convection”
Simulation details for: Radar signatures and surface observations of elevated convection associated with damaging surface winds
<p>Identifying radar signatures indicative of damaging surface winds produced by convection remains a challenge for operational meteorologists, especially within environments characterized by strong low-level static stability and convection for which inflow is presumably entirely above the planetary boundary layer. Numerical model simulations suggest the most prevalent method through which elevated convection generates damaging surface winds is via "up-down" trajectories, where a near-surface stable layer is dynamically lifted and then dropped with little to no connection to momentum associated with the elevated convection itself. Recently, a number of unique convective episodes during which damaging surface winds were produced by apparently elevated convection coincident with mesoscale gravity waves were identified and cataloged for study. A novel radar signature indicative of damaging surface winds produced by elevated convection is introduced through six representative cases. One case is then explored further via a high-resolution model simulation and related to the conceptual model of "up-down" trajectories. Understanding the processes responsible for, and radar signature indicative of, damaging surface winds produced by gravity-wave coincident convection will help operational forecasters identify and ultimately warn for a previously underappreciated phenomenon that poses a threat to lives and property.</p>
Evaluation of Mesoscale Convective Systems in High Resolution E3SMv2
<p>This is the open data resource for the paper "Mesoscale Convective Systems Represented in High Resolution E3SMv2 and Impact of New Cloud and Convection Parameterizations" that submitted to the Journal of Geophysical Research: Atmospheres. </p>
HRRR Convective Objects for "Comparing Distributions of Overshooting Convection in HRRR Forecasts to Observations"
<p>Data files for all convective objects identified from High-Resolution Rapid Refresh (HRRR) model forecasts for the Contiguous United States initialized at 06 UTC every day of May and July 2021. Convection was defined in the following two ways, one corresponding to each file uploaded here:</p> <ul> <li>Meeting the criteria for convection defined by the Storm Labelling in Three-Dimensions (SL3D; Starzec et al. 2017) algorithm</li> <li>Satisfying two vertical velocity thresholds (2 m/s at 4 km altitude and 5 m/s at 8 km altitude)</li> </ul>
Data from: Physical mechanisms of deep convective boundary layer leading to dust emission in the Taklimakan desert
<p>Deserts play an important role in the climate system, which is closely associated with the emission and transport of dust aerosols. Based on the intensive observation experiment in the Taklimakan Desert, the potential physical processes between the deep convective boundary layer (CBL) and dust emission are revealed in this study. Deep CBL enables the formation of clouds in the late afternoon, leading to significant cooling of surface. Large-scale buoyant coherent structures thereby transform into the mechanical coherent structures confined near the surface. The responses promote the earlier occurrence of low-level jet (LLJ) than in cloudless conditions, which allows the downward transport of LLJ momentum and substantially increases surface wind. Therefore, dust emission is initiated by strong wind at dusk and lasts for several hours. The results are useful to predict dust emissions and improve our understanding of distinctive boundary-layer processes in desert regions.</p>
Data for Spaulding-Astudillo and Mitchell (2024a), "A simple model for the emergence of relaxation-oscillator convection"
<p>Data for Spaulding-Astudillo and Mitchell (2024a), "A simple model for the emergence of relaxation-oscillator convection"</p> <p>The main directories have a common nomenclature: e.g., minimal_1p1S_FSC_335K_N7200, where 1p1S indicates a solar constant 1.1 times the present day (1360), FSC indicates that it is a full-sky radiation run, 335 K is the surface temperature (fixed), and N7200 refers to the relaxation timescale of 7200 seconds for CAPE in the quasi-equilibrium closure to the convection scheme. </p> <p>In every directory, each .nc file contains 5 years of model output. There are 5 types of .nc files, which correspond to different output streams of ECHAM6. The main output stream is ..._echam.nc, which has daily-averaged output.</p> <p>The tendencies for gross deposition (gdep_ls) and gross condensation (gcnd_ls; both with units of kg/m2/s) from the large-scale scheme are in ..._g1am.nc. This is the mean-value stream, which records daily-averaged values. </p> <p>The tendencies for gross condensation in convective updrafts are stored in the variable "ddf13" in ..._debugs.nc (units of kg/m2/s). This is another output stream, which records hourly values usually for debugging purposes. The total pressure as a function of height (units of Pa) is stored in the variable "ddf8". Also, the implied surface heat source/sink in the surface energy budget that keeps the surface temperature fixed in time is tracked in the variable "zdf8" (units of W/m2). </p> <p>The experimental range of surface temperatures is 290-360 K, which remain fixed as a function of time in the simulations through the use of an artificial heat sink. The simulations are from the ECHAM6 climate model, which we ran in single-column mode. </p>
No sustained mean velocity in the boundary region of plane thermal convection
<p>The dataset can be used to generate the figures from the publication titled <a href="https://doi.org/10.1017/jfm.2024.853">"<em>No sustained mean velocity in the boundary region of plane thermal convection</em>"</a>. Along with the data, the relevant Python scripts are also supplied for easy reproduction of the figures presented in this work.</p>
Thermal coupling mode in mantle-outer core convection predicted from an ultra-high-resolution numerical simulation of two-layer convection with a large viscosity contrast
<p>Movie of temperature and velocity fields in the highly viscous layer (HVL) and the low-viscosity layer (LVL) (left panels) and the close-up views focusing on the interior of the LVL (right panels). The viscosity contrast between the HVL and LVL is 10<sup>4</sup>.</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>
Dataset: Laboratory experiments of hydrothermal convection
<p>Data, figures, and source codes associated with laboratory experiments for the study of hydrothermal convection.</p>
Supplemental materials to "A quasi-2D model of convectively coupled vortices"
<p>math_derivation_note: A hand-written note of key mathematical steps, mostly about section 4 and Appendix C. </p> <p>quasi-2D model.zip: The package of the quasi-2D model code.</p> <p>postprocess_code_quasi2D.zip: The package of the postprocessing codes and intermediate files (.mat) related to the quasi-2D simulations.</p> <p>postprocess_code_CM1.zip: The package of the postprocessing codes and intermediate files (.mat) related to the CM1 simulation.</p> <p>Group_dh.avi: The Group-dh experiments with varying convective intermittency (dh/H). The first, second, and third column shows Group-dh-1, Ref, and Group-dh-2. Only the first member of each experimental ensemble is shown. The first row shows the raw vorticity normalized by f. The second shows the Gaussian-filtered vorticity (with a length scale of <em>l</em>=30 km) normalized by f. The black contour is the zero-value contour of the Gaussian-filtered vorticity.</p> <p>Group_dL.avi: The Group-<em>l</em> experiments with varying convective filter length <em>l.</em> The first, second, and third column shows Ref (<em>l</em>=30 km), Group-<em>l</em>-1 (<em>l</em>=45 km), and Group-<em>l</em>-2 (<em>l</em>=60 km). Only the first member of each experimental ensemble is shown. The first row shows the raw vorticity normalized by f. The second shows the Gaussian-filtered vorticity (with a length scale of <em>l)</em> normalized by f. The black contour is the zero-value contour of the Gaussian-filtered vorticity.</p> <p>Group_fE.avi: The Group-fE experiments with varying Coriolis parameter f and Ekman number E<em>.</em> They differ in the strength of the rotational flow. The first, second, and third column shows Group-fE-1 (f=1e-5 1/s), Ref (f=1e-4 1/s), and Group-fE-2 (f=2e-4 1/s). Only the first member of each experimental ensemble is shown. The first row shows the raw vorticity normalized by f. The second shows the Gaussian-filtered vorticity (with a length scale of <em>l=</em>30 km<em>)</em> normalized by f. The black contour is the zero-value contour of the Gaussian-filtered vorticity.</p> <p>Group_eta.avi: The Group-eta experiments with varying mesoscale feedback parameter eta<em>.</em> They differ in the strength of the mesoscale feedback. The first, second, and third column shows Group-eta-1 (eta=0), Group-eta-2 (eta=1.2), and Group-eta-3 (eta=1.4). Only the first member of each experimental ensemble is shown. The first row shows the raw vorticity normalized by f. The second shows the Gaussian-filtered vorticity (with a length scale of <em>l=</em>30 km<em>)</em> normalized by f. The black contour is the zero-value contour of the Gaussian-filtered vorticity.</p> <p>Please contact Dr. Hao Fu (haofu@uchicago.edu) if you have any questions!</p>
Enhanced Convective Microphysics Scheme and Its Impacts on Mean Climate Simulation in E3SM
<ol> <li>Simulation data of E3SM with an enhanced convective microphysics scheme.</li> <li>Source code of E3SM with an enhanced convective microphysics scheme.</li> </ol>
Code, Data, and Technical Note for SCREAM Beijing flood Convection-Permitting Regionally Refined Model 1.0 version
<p><a href="https://zenodo.org/api/records/15126670/draft/files/BeijingRRM-v0.1-SCREAM_push.tar.gz/content" target="_blank" rel="noopener noreferrer">BeijingRRM-v0.1-SCREAM_push.tar.gz</a> :</p> <p>The code used to generate all simulations for the paper entitled "Through the lens of a kilometer-scale climate model: 2023 Jing-Jin-Ji flood under climate change" submitted to Geophysical Research Letters. The SCREAM Beijing RRM source code is also available on GitHub at https://github.com/E3SM-Project/scream/tree/jzhang/RRM_tmp (last access: 28 Aug 2024) and a maint branch (BeijingRRM-v0.1; https://github.com/jsbamboo/scream/releases/tag/BeijingRRM-v0.1, last access: 28 Aug 2024). </p> <p><a href="https://zenodo.org/api/records/15126670/draft/files/files_scream-BeijingRRM-v1.0_storylines.zenodo.tar.gz/content" target="_blank" rel="noopener noreferrer">files_scream-BeijingRRM-v1.0_storylines.zenodo.tar.gz</a> : </p> <p>The runscripts, mapping files, masks used for analysis and figures in the paper. The simulation outputs and processed data are too large (3.3T) to upload to zenodo, and are available on the NERSC portal: https://portal.nersc.gov/archive/home/z/zhang73/www/files_scream-BeijingRRM-v1.0_storylines</p> <p><a href="https://zenodo.org/uploads/15126670" target="_blank" rel="noopener noreferrer">BeijingFlood_Doc.pdf</a> :</p> <p>The technical note documenting our practice in generating the SCREAM Beijing flood RRM configurations. Source page: https://acme-climate.atlassian.net/wiki/spaces/DOC/pages/4056318237/SCREAM+Beijing+Flood+RRM+Technical+Note</p>
Supplemental materials to "A Laboratory Analogy for Mixing by Shallow Cumulus Convection"
<p>These are the supplemental materials to the paper "A Laboratory Analogy for Mixing by Shallow Cumulus Convection" published in Journal of Fluid Mechanics. DOI: <span>10.1017/jfm.2025.173</span> </p> <ul> <li>code_raw_data_video_temperature.zip - The MATLAB postprocessing code, raw video, and raw temperature profile data.</li> <li>reference_exp_S3_evolution_show.mp4 - An introductory movie of the reference experiment (S3) with an illustration. </li> <li>change_heating_voltage_F1(speed_x_5.76)_F5.mp4 - A movie about experiments that change the heating power. Experiment F1 (left panel) is accelerated with a factor of 5.76.</li> <li>change_initial_thickness_T2_T4_T5_(all_speed_x_16).mp4 - A movie about experiments that change the initial thickness of the syrup layer. All experiments are accelerated with a factor of 16.</li> <li>change_syrup_concentration_S1_S3_S7_(all_speed_x_16).mp4 - A movie about experiments that change the initial syrup concentration. All experiments are accelerated with a factor of 16. </li> <li>derivation_note.pdf - A math derivation note for some equations in the manuscript.</li> </ul> <p>If you have any questions, please contact Dr. Hao Fu: haofu736@gmail.com; haofu@uchicago.edu </p>
Object-based evaluation of precipitation systems in convection-permitting regional climate simulation over eastern China
<p>Data used in the manuscript "<strong>Object-based evaluation of precipitation systems in convection-permitting regional climate simulation over eastern China</strong>" which was submitted to Journal of Geophysical Research: Atmospheres. </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>
Dynamics in a stellar convective layer and at its boundary: Comparison of five 3D hydrodynamics codes
<p>Supplementary materials for the paper "Dynamics in a stellar convective layer and at its boundary: Comparison of five 3D hydrodynamics codes". The data contained in the .tar.gz archives can be read and visualised using the Jupyter notebooks available on the CoCoPy repository (<a href="https://github.com/robert-andrassy/CoCoPy">https://github.com/robert-andrassy/CoCoPy</a>) and on the CoCo Hub (<a href="https://www.ppmstar.org/coco">https://www.ppmstar.org/coco</a>). The four parts of the archive 2D-slices.tar.gz need to be concatenated using the standard Unix tool "cat" before decompression.</p> <p>Two minor bugs affecting the 1D profiles are corrected in this version. Everything else is the same as in Version 1.</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>
Global Impact of Heat from Wildfire on Convection and Aerosols
<p>Dataset of model output in the work 'Global Impact of Heat from Wildfire on Convection and Aerosols'.</p> <p>Dataset File List</p> <p>File headed with 'CTL' : model output in the CTL run with aerosols but without heat from fires</p> <p>1. CTL_Aerosols.nc : output of aerosol variables in the CTL run </p> <p>2. CTL_Convection_Part1.nc : part1 output of convection variables in the CTL run</p> <p>3. CTL_Convection_Part2.nc : part1 output of convection variables in the CTL run</p> <p>File headed with 'EXP' : model output in the EXP run with both aerosols and heat from fires</p> <p>1. EXP_Aerosols.nc : output of aerosol variables in the EXP run </p> <p>2. EXP_Convection_Part1.nc : part1 output of convection variables in the EXP run</p> <p>3. EXP_Convection_Part2.nc : part1 output of convection variables in the EXP run</p>
SMART Radar and WSR-88D Data Associated with "Mobile Radar Observations of Hurricanes at Landfall. Part II: Convectively Coupled Vortex Rossby Waves"
<p>The data contained in this archive are associated with "Mobile Radar Observations of Hurricanes at Landfall. Part II: Convectively Coupled Vortex Rossby Waves" in review in the <em>Journal of the Atmospheric Sciences</em>. Two sets of data associated with Hurricanes Isabel (2003) and Matthew (2016) are contained. Each subset of data contains the raw radar files that contribute to the manuscript in cfradial netCDF format.</p> <p>A readme file in included that describes the variables and format of the radar volume files. Questions about the dataset may be directed to addisonalford@ou.edu, drdoppler@ou.edu, or gordon.carrie-1@ou.edu.</p>
extreme-rainfall convective feature dataset from Guangzhou SPOL
<p>Extreme-rainfall convective feature dataset built from Guangzhou SPOL, which used for publication: https://doi.org/10.1007/s00376-022-1319-8.</p>
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