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364 results for “Convection”
Characteristics and Representation of Subgrid Convective Flux in Tropical Cyclone Convection System at Convection-Permitting Resolution
<p><span>Data and analysis scripts for figures of Journal article (Characteristics and Representation of Subgrid Convective Flux in Tropical Cyclone Convection System at Convection-Permitting Resolution)</span></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>
Fortran/Python Interface in ARP-GEM1: Online Test of Neural Network Deep Convection
<p>Manuscript under review in AIES. Supporting Code and Dataset. </p> <p><strong>Abstract.</strong></p> <p>In this study, we present the integration of a neural network-based parameterization into the global atmospheric model ARP-GEM1, leveraging the Python interface of the OASIS coupler. This approach facilitates the exchange of fields between the Fortran-based ARP-GEM1 model and a Python component responsible for neural network inference. As a proof-of-concept experiment, we trained a neural network to emulate the deep convection parameterization of ARP-GEM1. Using the flexible Fortran/Python interface, we have successfully replaced ARP-GEM1's deep convection scheme with a neural network emulator. To assess the performance of the neural network deep convection scheme, we have run a 5-years ARP-GEM1 simulation where the neural network replaced ARP-GEM1's deep convection parameterization. The evaluation of averaged fields showed good agreement with output from an ARP-GEM1 simulation using the physics-based deep convection scheme. The Python component was deployed on a separate partition from the general circulation model, using GPUs to increase inference speed of the neural network.</p>
Primary data for "Temperature-dependence of the clear-sky feedback in radiative-convective equilibrium"
<p>Primary data for the manuscript "Temperature-dependence of the clear-sky feedback in radiative-convective equilibrium".</p> <p>The archive is described in more detail in the enclosed README file.</p>
Data for "On the resolution-dependence of cloud fraction in radiative-convective equilibrium"
<p>Cloud-resolving model output and analysis scripts for the paper "On the resolution-dependence of anvil cloud fraction and precipitation efficiency in radiative-convective equilibrium"</p>
C³ONTEXT: A Common Consensus on Convective OrgaNizaTion during the EUREC⁴A eXperimenT
<p>This dataset contains the manual cloud classifications of the EUREC4A field campaign time period as well as the post-processed data.</p> <p>The dataset is organised as following:</p> <ul> <li><strong>zooniverse_raw:</strong> Originial output data from the platform <a href="http://www.zooniverse.org">zooniverse.org</a> which has been used to create the classifications</li> <li><strong>processed_data:</strong> <ul> <li><strong>Level1: EUREC4A_ManualClassifications_l1.nc:</strong> Geographical and Cartesian coordinates are added to each label</li> <li> <p><strong>Level2: EUREC4A_ManualClassifications_l2.zarr</strong>: labels are converted to masks and combined for each classification</p> </li> <li> <p><strong>Level3: EUREC4A_ManualClassifications_l3_$WORKFLOW_$COMPOSITE.zarr</strong>: pixel-agreement among users/classifiers on each of the four meso-scale cloud patterns for daily composites and individual scenes.</p> </li> </ul> </li> <li> <p><strong>auxiliary_data:</strong></p> <ul> <li> <p><strong>EUREC4A_AuxiliaryData_NeuralNetworkClassifications.zip/GOES16_CH13_classifications_EUREC4A_30min.zarr:</strong> neural network classifications based on GOES-16 Advanced Baseline Imager (ABI) channel 13 (infrared) @ 30 minute intervals.</p> </li> <li> <p><strong>EUREC4A_AuxiliaryData_IorgSMetrics.zip/GOES16_IR_nc_Iorg_EUREC4A_10-20_-58--48.nc</strong>: Organisation index (Iorg) and mean cloud cluster size (S) derived from GOES-16 ABI infrared images of the domain 10-20N 58-48W.</p> </li> </ul> </li> </ul> <p>An example how to use the data can be found at <a href="https://github.com/observingClouds/EUREC4A_manualclassifications">github.com/observingClouds/EUREC4A_manualclassifications</a>.</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>
Effects of convective mergers on the evolution of microphysical and electrical activity in a severe squall line simulated by WRF coupled with explicit electrification scheme
<p>These are important data supporting the conclusion of the paper are available in the main text.The STORM973 dataset including BLNET data and radar data are provided here.Some of the E-WRF outputs are also provided here. The corresponding scripts are based on NCL (version 6.6.2.).</p>
Supporting data files for the manuscript titled "Wide versus narrow back-arc rifting: control of subduction velocity and convective back-arc thinning"
<p>This depository consists of the following numerical modelling output fields: materials (mat), viscosity (mu), temperature (t), coordinates (x, y), velocity (vx, vy) and time-step specific data required for visualization (loop)<br> The four sets of .mat (matlab-specific format) output files correspond to the four numerical modeling experiments presented in the manuscript titled Wide versus narrow back-arc rifting: control of subduction velocity and convective back-arc thinning by Zoltán Erdős, Ritske S. Huismans and Claudio Faccenna currently under revision at the journal Tectonics.<br> The modelling experiments were run with the 2D Arbitrary Lagrangean-Eulerean Finite Element geodynamic numerical code Fantom.<br> For ease-of-use we provide a simple matlab-script specifically designed to read-in and visualize the output data files.</p>
Data Supporting "Mesoscale Convective Clustering Enhances Tropical Precipitation"
<p>These data are in support of "Mesoscale Convective Clustering Enhances Tropical Precipitation" by P. Angulo-Umana and D. Kim. </p> <p>In the tropics, extreme precipitation events are often caused by mesoscale systems of organized, spatially clustered deep cumulonimbi, posing a substantial risk to life and property. While the clustering of convective clouds has been thought to strengthen precipitation intensity, no quantitative estimates of this hypothesized enhancement exist. In this study, after isolating the effects of mesoscale convective clustering on precipitation, we find that strongly clustered convection precipitates more intensely than weakly clustered convection. We further show that this enhancement is primarily attributable to an increase in convective precipitation intensity when the environment is less than 70% saturated, with increases in stratiform cloud cover being of equal or greater importance when the environment is closer to saturation. Our results suggest that a correct representation of mesoscale organized convective systems in numerical weather and climate models is needed for accurate predictions of extreme precipitation events.</p>
Type IIP Supernovae IV. Shock Breakout from Progenitor Stars Modeled with Convective Overshoot and Mass Loss [Dataset]
<p>Input and output files of MESA and STELLA associated with the published journal article Balaji et al. (2022; doi:10.3847/1538-4357/ac7528). We used MESA version r-10398 and MESA SDK version 20180822.</p> <p>We employ two sets of mass loss schemes: a standard “Dutch” scheme and an “enhanced” episodic late mass loss scheme to construct circumstellar matter. Evolution of the enhanced mass loss models in MESA from the pre-main sequence stage through nuclear burning stages until just before core-collapse is contained in “pre-ccsn_enhanced_mass_loss.tar.xz”. The “MESA_inputs” folder contains all the inlist files and “run_star_extras.f”. “run_star_extras.f” is the same for all the enhanced mass loss models. The inlists for each of the different models can be easily obtained by editing the parameters: “initial_mass” in “inlist_cluster”, overshoot factors in “inlist_control” and the saved model names in “inlist_convert” and “inlist_star”. Each of the “#M_z0.0060_f_#_f0_0.005.tar.xz” directories, indicative of a model of particular ZAMS mass and overshoot parameter (f), contains “*.mod” files and a “LOGS” directory which in turn contains “history.data” files and the “profile#.data” file at core-collapse.</p> <p>Similar data files for the Dutch mass loss models are given in the Zenodo publication: <a href="https://doi.org/10.5281/zenodo.3783492">https://doi.org/10.5281/zenodo.3783492</a></p> <p>The “ccsn.tar.xz” folder contains input and output files from the core-collapse step executed in MESA, separately for the Dutch mass loss and enhanced mass loss models. The “MESA_inputs” folder contains all the inlist files where “run_star_extras.f” is the same for all the Dutch mass loss models and for all the enhanced mass loss models separately. The inlists for each of the different models can be obtained by editing “initial_mass” and “inject_until_reach_model_with_total_energy” in “inlist_edep”. Each of the “#M_z0.0060_f_#_f0_0.005_edep_#.tar.xz” directories, now, also indicative of the EDEP of explosion contains the “*.mod” files after every step, the “mesa.abn” and “mesa.hyd” files which initialise STELLA, and a LOGS directory which contains the “history.data” files after every step. </p> <p> The “STELLA.tar.xz” folder contains the output files and “stella_extras” from STELLA for each of the Dutch mass loss and Enhanced mass models separately in a “res” directory. For the Dutch mass loss, 12 solar mass, f = 0.025, EDEP = 1.0 FOE model which was studied extensively in our work, the “mesa.dat” file inside "/strad/run" of STELLA was modified to display profiles of radiation hydrodynamic variables (in “mesa.swd”) at earlier time instants than what is reported by STELLA, by default.</p> <p>The python code: "csm-profile-maker.py" constructs profiles of circumstellar matter from mass loss data from MESA which was then appended to the models before initialisation of STELLA.</p> <p>For more details on the methods of simulations, see section 4 of the published article. </p>
On the role of rheological memory for convection-driven plate reorganizations
<p>% ==================================================================== %<br> % Data directory for 3D spherical, thermal convection models presented %<br> % in Fuchs and Becker (2022) with a strain-dependent weakening and % <br> % hardening rheology following the formulation of %<br> % Fuchs and Becker (2019). %<br> % %<br> % The directory contains an input_files, MATLAB_Scripts, and each % <br> % model directory with a certain name. %<br> % input_file Contains all the input files for CitcomS % <br> % MATLAB_Scripts Contains all the MATLAB scripts required to %<br> % reproduce the figures in the manuscript %<br> % $ModelName Contains a MATLAB directory for individual %<br> % data from each model, %<br> % a TPR directory for toroidal-poloidal data for %<br> % each degree, and, %<br> % a txt_data for data picked from CitcomS models %<br> % which is then visualized in MATLAB. %<br> % For the models discussed in the paper, surface %<br> % maps plots and surface grd-files are available %<br> % within those directories as well. % <br> % ==================================================================== %</p> <p>MATLAB_Scripts directory: <br> -------------------------<br> To visualize certain data for each model you can run the script <br> Analyze_Citcom_Models in the MALTAB_Scripts directory, e.g., <br> <br> Analyze_Citcom_Models(Name,PlotParam,PlotParam2,S)</p> <p>where, <br> Name is the model name as a string variable,<br> PlotParam a switch to save (1) or not save (0) the figures,<br> PlotParam2 a switch to activate (1) or deactivate (0) plotting, <br> S a switch to define the scaling of the model parameter,<br> no scaling (0), scaling with the diffusion time scale (1), <br> or scaling with the overturn time OT (2).</p> <p>With this script one can visualize all the time-dependent data picked<br> from the CitcomS models. The CitcomS models can be reproduce with the <br> input-files given in the input_files directory. </p> <p>To reproduce the box whisker plots in figures 2, 3, S7, and S8 one <br> needs to run the script CompStat. This scripts reads in the data <br> from all models (from the txt_files directory in the $ModelName <br> directory) and creates a box whisker plot for each model period and <br> plots them again the average lithospheric damage (gamma_L).</p> <p>For more details to each MATLAB script see the help comments within the<br> script. </p> <p>In case of any questions, do not hesitate to contact me via email: </p> <p>lukas.fuchs84 at gmail dot com</p> <p>% ==================================================================== %<br> % =============================== END ================================ %<br> % ==================================================================== %</p>
Data for "Dependence of Convective Cloud Properties and Their Transport on Cloud Fraction and GCM Resolution Diagnosed from a Cloud-Resolving Model Simulation"
<p>The datasets for the manuscript "Dependence of Convective Cloud Properties and Their Transport on Cloud Fraction and GCM Resolution Diagnosed from a Cloud-Resolving Model Simulation".</p> <p>model: WRF3.1.1</p> <p>location: Southern Great Plains</p> <p>time: from 2100 UTC 23 May to 0600 UTC 24 May</p> <p>time interval: 6 minutes</p> <p>domain size: 512km x 512km</p> <p>vertical layer: 500hpa</p> <p>variables: P, PB, PH, PHB, U, V, W, T, QCLOUD, QICE, QVAPOR</p> <p>calculated data: mse, up_only(only consider updraft), up_down(consider both updrafts and downdrafts)</p> <p> </p>
Intercomparison of Convective-Aggregation States with two Cloud Resolving Models: DATASET
<p>Radiative-Convective Equilibrium (RCE) is an important modeling paradigm for the tropical atmosphere. In this paradigm, cloud clustering can occur spontaneously, affecting the energy budget of the atmosphere. Here, two models, run in RCE, exhibiting this convective aggregation have been compared with each other and with the results of the Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP). The two models studied, the SAM (System for Atmospheric Modeling) and the ARPS (Advanced Regional Prediction System), are different in the physical and numerical formulation, allowing us to compare the sensitivity to processes related to the phenomenon of convective organization. In General, the two models present similarities in what concerns precipitation, warming, and drying of the atmosphere and anvil cloud area reduction. All these factors are also within the spread of the RCEMIP values. However, the two models differ both in the convective organization feedback and in the degree of organization. SAM is strongly organized and ARPS is weakly organized. SAM achieves convective organization through clouds-radiative feedback and ARPS achieves it through moisture-convection feedback. These differences can be traced back to the interaction between the microphysics and the sub-cloud layer properties. We suggest that when studying climate sensitivity, climate models should include both types of convective organization mechanisms.</p>
Intercomparison of Convective-Aggregation States with two Cloud Resolving Models: DATASET
<p>Radiative-Convective Equilibrium (RCE) is an important modeling paradigm for the tropical atmosphere. In this paradigm, cloud clustering can occur spontaneously, affecting the energy budget of the atmosphere. Here, two models, run in RCE, exhibiting this convective aggregation have been compared with each other and with the results of the Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP). The two models studied, the SAM (System for Atmospheric Modeling) and the ARPS (Advanced Regional Prediction System), are different in the physical and numerical formulation, allowing us to compare the sensitivity to processes related to the phenomenon of convective organization. In General, the two models present similarities in what concerns precipitation, warming, and drying of the atmosphere and anvil cloud area reduction. All these factors are also within the spread of the RCEMIP values. However, the two models differ both in the convective organization feedback and in the degree of organization. SAM is strongly organized and ARPS is weakly organized. SAM achieves convective organization through clouds-radiative feedback and ARPS achieves it through moisture-convection feedback. These differences can be traced back to the interaction between the microphysics and the sub-cloud layer properties. We suggest that when studying climate sensitivity, climate models should include both types of convective organization mechanisms.</p>
Model output dataset used in "Sensitivity of Heavy Convective Precipitation Simulations to Changes in Land-atmosphere Exchange Processes over China"
<p>This dataset accompanies the paper by Zhang et al. "Sensitivity of Heavy Convective Precipitation Simulations to Changes in Land-atmosphere Exchange Processes over China".</p> <p>Three heavy precipitation events were modeled using the WRF v3.9 model:</p> <p>(1) The_21_July_Beijing_Rainstorm_Simulation<br> (2) The_30_July_Ningxia_rainstorm_Simulation<br> (3) The_19_June_Jiangxi_rainstorm_Simulation</p> <p>Furthermore, three cases were designed for each heavy precipitation event: (1) control experiment (DEFAULT), using the default M-O option (<em>C<sub>zil</sub></em> ~ 0); (2) constant <em>C<sub>zil</sub></em> (CZIL0.01, CZIL0.05, CZIL0.1, CZIL0.3, CZIL0.5 and CZIL0.8), with <em>C<sub>zil</sub></em> values of 0.01, 0.05, 0.1, 0.3, 0.5, and 0.8; (3) a dynamic canopy-height dependent <em>C<sub>zil</sub></em> (NEWCZIL).</p> <p>Plain Language Summary for this paper:<br> Over recent decades, the frequent occurrence of heavy precipitation events has caused devastating ecological and socioeconomic impacts, such as agriculture losses, infrastructure damage, and casualties. High-resolution atmospheric modeling at a convection-permitting grid spacing (≤4 km) provides valuable applications for predicting heavy precipitation. Precipitation can be strongly affected by the energy and moisture exchanges between land surface and atmosphere. However, the representation of land-atmosphere interactions in atmospheric models and the responses of precipitation to land-atmosphere exchange efficiency remain great uncertainties. This study performed 3-km high-resolution atmospheric modeling with a dynamic vegetation-type-dependent land-atmosphere exchange scheme for three typical heavy precipitation events that occurred over areas with different dominant land-cover types. The results showed that land-atmosphere exchange efficiency mainly affected the precipitation intensity as well as the onset and peak time of precipitation. The dynamic exchange scheme modifies the efficiency of land-atmosphere exchanges to match local land cover conditions and could reproduce well the field observations, especially the intensity and location of the heaviest rainfall which usually serve as the most concerned variable in a major rainstorm event. Our findings show that the dynamical scheme could help achieve more accurate precipitation simulations.</p>
Confronting the convective gray zone in the global configuration of the Met Office Unified Model
<p>Supporting data for the manuscript "Confronting the convective gray zone in the global configuration of the Met Office Unified Model" by L. Tomassini et al.</p>
(NEW) Supporting Dataset for 'Role of convection in upper ocean mixing during a tropical cyclone'.
<p>Contains the results from the LES and PWP runs</p>
Planetary core-style rotating convective flows in paraboloidal laboratory experiments
<p><span>Turbulent convection in a planet's outer core is simulated here using a thermally-driven free surface paraboloidal laboratory annulus. We show that the rapidly rotating convection dynamics in free-surface paraboloidal annuli are similar to those in planetary spherical shell geometries. Three experimental cases are carried out, respectively, at 35 revolutions per minute (rpm), 50 rpm and 60 rpm.Thermal Rossby waves are detected in full</span><span> disk thermographic images of the fluid's free surface.</span> <span>Ultrasonic flow velocity measurements reveal the presence of multiple azimuthal (zonal) jets, with successively more jets</span><span> forming in higher rotation rate cases.</span> <span>The jets' cylindrical radial extent is well ap</span><span>proximated by the Rhines scale.</span> <span>Over time, the zonal jets migrate to larger radial po</span><span>sition with migration rates in good agreement with prior theoretical estimates.</span> <span>Our re</span><span>sults suggest that planetary core rotating convection will be comprised of flow structures found</span><span> in other turbulent geophysical fluid dynamical systems:</span> <span>convective turbulence dom</span><span>inates the small-scale flow field, and also act to flux energy into larger-scale, slowly evolv</span><span>ing zonal flow structures.</span> <span>How the ambient magnetic fields in planetary core settings af</span><span>fect such turbulent flows remains an open question.</span></p>
Data: Quasi-global Convection-permitting Simulations of Tidally Locked Rocky Planets
<p>Here are the data and scripts used for creating the figures in the paper, "Quasi-global Convection-permitting Simulations of Tidally Locked Rocky Planets".</p>
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