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85 results for “Microphysics”
Data used in the publication: Sensitivity of modeled microphysics to stochastically perturbed parameters
<p>These data support the results presented in the manuscript titled "Sensitivity of modeled microphysics to stochastically perturbed parameters". They consist of results from an idealized single vertical column atmospheric model run for a number of experiments that explore methods of representing model uncertainty. </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>
Processed microphysical profiles of convective cloud scenes from satellite over ATTO
<p class="MsoNormal">We present a new approach of analyzing and interpreting vertical profiles of cloud microstructure obtained by satellite remote sensing. The method is based on a spectral bin microphysics adiabatic parcel model and aims to elucidate the effects of aerosols on the evolution of convective clouds and related microphysical processes, including the activation of cloud condensation nuclei (CCN), the growth of cloud droplets, and the formation of precipitation. Characteristic features in the vertical profiles of effective radius (<em><span>r</span></em><sub>e</sub>) and temperature (<em>T</em>) reveal different microphysical zones in convective clouds related to the change increase of <em><span>r</span></em><sub>e</sub> with decreasing <em>T</em>. The classification of the different microphysical zones includes the (1) condensational growth of droplets, (2) growth by coalescence, (3) rainout, (4) secondary droplet activation zone (<em>SAZ</em>), (5) mixed-phase of ice particles and water droplets and (6) glaciation of the cloud. The detection of the <em>SAZ</em> is introduced here for the first time. This method allows us to identify the activation of aerosol particles above cloud base and their role in the invigoration of deep convective clouds.</p>
Postprocessed trajectory output for ice cloud microphysics - ICON and CLaMS-Ice models
<p>Postprocessed output from a trajectory module implemented in ICON v 2.3.0. The trajectories track density, temperature, pressure, specific humidity, cloud ice mass and number mixing ratios, cloud liquid mass and number mixing ratios, graupel mass and number mixing ratios, and ice sedimentation mass and number mixing ratios both into and out of the parcel. They are initiated over the Sichuan basin and allowed to flow for 51 hours westward during which the cross India into the Arabian Sea. This trajectory output is also used to run an offline microphysics box model, CLaMS-Ice. qih-Nih* files contain histograms of ice mass mixing ratio (qi) and ice crystal number concentration (Ni); het-hom-pre* files contain process tendencies from heterogeneous nucleation, homogeneous nucleation, and preexisting ice in CLaMS-Ice; qippmvNi-TRHi* files contain qi and Ni versus a range of cirrus temperatures and a range of supersaturations with respect to ice; qi_ppmv_abs* and Ni_abs* files contain probability distributions of qi and Ni differences over absolute time; and qi_ppmv_norm* and Ni_norm* files contain probability distributions of qi and Ni differences over normalized time. Suffixes in all cases indicate the cloud microphysical setup that the trajectory values were used to run with 1M = one-moment scheme, 2M = two-moment scheme, Tf = temperature fluctuation parameterization in CLaMS-Ice simulations, and noSHflux = no pseudo-mixing tendency included in CLaMS-Ice simulations.</p>
Code for APJAS publication - Numerical errors in ice microphysics parameterizations and their effects on simulated regional climate
<p>In this repository, we include the source codes for WRF microphysics parameterization used in the APJAS publication "Numerical errors in ice microphysics parameterizations and their effects on simulated regional climate"</p> <p>There are three WDM6 codes for simulations. The original WDM6 code (ORG) using parameter defined by Hong et al (2004), the revised WDM6 code (NEW) those revised by removing the numerical errors, and the additional WDM6 code (SEN) for sensitivity experiment adopting the column-shaped parameters.</p> <p>In supplement, several cloud-ice characteristics presented in the paper were induced in detail and compared with Hong et al (2004) and this study.</p>
Particle number concentrations and size distributions in the stratosphere: Implications of nucleation mechanisms and particle microphysics
<p>The data files of all figures for ACP-2022-487 entitled: "Particle number concentrations and size distributions in the stratosphere: Implications of nucleation mechanisms and particle microphysics"</p>
Training data and models for microphysics emulation
<p>Training data and models for microphysics emulation</p> <p>The training data is a subset of the full dataset described in the NeurIPS submission. Roughly speaking, 30 day runs with FV3GFS, with zhao carr microphysics. To keep the data reasonable in size, 1000 random netCDFs are sampled from the over 7000 files in the full training dataset. 200 test files are sampled.</p> <p>Also contains the trained ML models at models/</p> <p>Data behind the plots and tables is at plot-data/.</p> <p> </p>
Linkage between projected warm season precipitation systems and thermodynamic and microphysical changes over eastern China
<p>Data used in the manuscript "Linkage between projected warm season precipitation systems and thermodynamic and microphysical changes over eastern China" which was submitted to Journal of Geophysical Research: Atmospheres. </p>
Accelerate the Parameterization of Unified Microphysics Across Scales (PUMAS) on the graphics processing unit (GPU) with directive-based methods
<p>Code used to produce results of paper titled "Accelerate the Parameterization of Unified Microphysics Across Scales (PUMAS) on the graphics processing unit (GPU) with directive-based methods" by Sun et al.</p> <p>Includes:</p> <p>- Source code of CAM to perform a CPU or GPU simulation</p> <p>- Source code of PUMAS stand-alone kernel for GPU porting (OpenACC and OpenMP offload), an example batch script for build/run on Casper (NCAR's cluster) and the input dataset</p> <p>- Dataset to reproduce the figures in the paper</p> <p> </p> <p>Contact details: Jian Sun (sunjian@ucar.edu)</p>
On the Role of Sub-Grid Variability and Microphysics in Km-Scale Simulations of Mixed-Phase Clouds during Cold Air Outbreaks
<p>Model output data used in manuscript "On the Role of Sub-Grid Variability and Microphysics in Km-Scale Simulations of Mixed-Phase Clouds during Cold Air Outbreaks" currently under review in Journal of Geophysical Research - Atmosphere.</p>
Data from: Limitations of separate cloud and rain categories in parameterizing collision-coalescence for bulk microphysics schemes
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Mixed-phase orographic cloud microphysics during StormVEx and IFRACS
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Processed microphysical profiles of convective cloud scenes from satellite over ATTO
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Data from: Insensitivity of the cloud response to surface warming under radical changes to boundary layer turbulence and cloud microphysics: results from the ultraparameterized CAM
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Data and scripts for 'Potential and limitations of machine learning for modeling warm-rain cloud microphysical processes'
<p>Data and scripts for "Potential and limitations of machine learning for modeling warm-rain cloud microphysical processes" by Axel Seifert and Stephan Rasp, J. Adv. Modeling Earth Systems, 12, 2020, https://doi.org/10.1029/2020MS002301</p>
How does the melting impact charge separation in squall line? A bin microphysics simulation study
<p>Data for paper of above title.</p>
Model and observational data for Morrison et al. (2020) JAMES paper entitled "Confronting the challenge of modeling cloud and precipitation microphysics"
This dataset was used to generate plots for a paper conditionally accepted in the Journal of Advances in Modeling Earth Systems (JAMES), an AGU journal. It comprises four different data sources: WRF simulations, radar observations, an idealized steady-state column model called the Bayesian Observationally-Constrained Statistical Scheme (BOSS), and idealized column rainshaft model using bin microphysics. The WRF simulations consist of output from a set of 11 runs using either bulk or bin microphysics schemes. The version of WRF is V3.9.1. To limit storage requirements, we have only saved a single time-slice of output at hour 6 of the simulations, along with the namelist.input file to generate these runs. The observational data are gridded and rotated composite NEXRAD reflectivity measurements from central Oklahoma. BOSS model output consists of microphysical parameter probability density functions and profiles of (as described in the meta-file for these data). The idealized bin microphysics rainshaft model output consists of profiles of drop mean size, reflectivity factor, precipitation rate, and drop concentration at two different time slices (described in the meta-data file for these data). We are requesting these data be added to the managed NCAR repository so they may be accessible to the community per AGU publication policy and in accordance with a U.S. DOE grant that partially supported this work.
Dataset for "McSnow 2.0: Explicit habit-prediction in a Lagrangian super-particle ice microphysics model"
<p>Data and plot scripts to create figures included in the paper draft "McSnow 2.0: Explicit habit-prediction in a Lagrangian super-particle ice microphysics model" (submitted to JAMES).</p> <p>This work has been funded by the German Science Foundation (DFG) under grant SE 1784/3-1, project ID 408011764 as part of the DFG priority program SPP 2115 on radar polarimetry.</p>
Microphysical complexity of black carbon particles restricts their warming potential
<p>The data for the figures in the journal article "Microphysical complexity of black carbon particles restricts their warming potential".</p>
Code for GMD publication - Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in WRF (v4.1.3) model during ICE-POP 2018
<p>In this repository, we include the source codes for WRF microphysics parameterization used in the GMD publication "Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in WRF (v4.1.3) model during ICE-POP 2018."</p> <p>The four 2-moment bulk microphysical parameterization codes, WDM6, WDM7, Thompson, and Morrison, are divided into 3 WDM, Thompson, and Morrison codes, and each code has been modified so that detailed microphysical processes can be checked in wrfout.</p> <p>The WDM6 and WDM7 schemes include numerical errors for ice microphysical parameterization (Kim and lim, 2021) and for cloud evaporation and melting processes (Lei et al., 2020).</p> <p>Namelist files shows the namelist.input for each case.</p> <p> </p> <p> </p>
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