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183 results for “Nucleation”
Ice-Nucleating Particle Concentrations from the MC2/ISLAS 2021 campaign in Andenes, and NorESM2 simulations with observationally constrained INPs
<p>This dataset containts the data for the article <strong>Using a region-specific ice-nucleating particle parameterization improves the representation of Arctic clouds in a global climate model </strong>(https://doi.org/10.5194/acp-25-1617-2025), published in Atmospheric Chemistry and Physics (ACP). It consists of ice-nucleating particle (INP) measurements collected as part of the MC2/ISLAS campaign in Andenes, Norway (69<em>° </em>18’ N, 16<em>°</em> 07’ E) in 2021. Additionally, it consists of simultaneous aerosol measurements, back trajectories for the INP measurement times, and model data from the Norwegian Earth System model (Seland et al., 2020) where INP concentrations where constrained in the Arctic using these INP measurements. </p> <p><strong>Abstract:</strong></p> <p><em>Projections of global climate change and Arctic amplification are sensitive to the representation of low-level cloud phase in climate models. Ice-nucleating particles (INPs) are necessary for primary cloud ice formation at temperatures above approximately -38 °C, and thus significantly affect cloud phase and cloud radiative effect. Due to their complex and insufficiently understood variability, INPs constitute an important modelling challenge, especially in remote regions with few observations, such as the Arctic. In this study, INP observations were carried out at Andenes, Norway in March 2021. These observations were used as a basis for an Arctic-specific and purely temperature-dependent INP parameterization, and implemented into the Norwegian Earth System Model. This implementation results in an annual average increase in cloud liquid water path (CLWP) of 70 % for the Arctic, and improves the representation of cloud phase compared to satellite observations. The change in CLWP in boreal autumn and winter is found to likely be the dominant contributor to the annual average increase in net surface cloud radiative effect of 2 W m<sup>-2</sup>. This large surface flux increase brings the simulation into better agreement with Arctic ground-based measurements. Despite that the model cannot respond fully to the INP parameterization change due to fixed sea surface temperatures, Arctic surface air temperature increases with 0.7 °C in boreal autumn. These findings indicate that INPs could have a significant impact on Arctic climate, and that a region-specific INP parameterization can be a useful tool to improve cloud representation in the Arctic region.</em></p> <p>The dataset contains three subsets:</p> <p>i) <strong>aerosol_data:</strong> Observations of Ice-Nucleating Particles (INPs) in Andenes, Norway, March 2021, as well as simultaneous aerosol measurements</p> <p>ii) <strong>INP_trajectories: </strong>Back trajectories at the time of INP measurements</p> <p>iii) <strong>model_data: </strong>Simulations with NorESM2 using the Andenes 2021 INP observations to constrain INPs in the Arctic</p> <p>Additionally, scripts for visualizing the data and for reproducing the NorESM2 model setup can be found in the folder <strong>scripts. </strong>The data folders and scripts folder should be in the same repository when running the scripts.</p> <p>Some of the scripts use other openly available datasets. The availability of these are listed below. All the specific datasets can also be provided to the user upon request. </p> <p>The CALIOP L2 data used to derive SLF metrics (used in Fig07.py) and the CERES EBAF data (used in Fig12.py) can be downloaded freely at <a href="https://search.earthdata.nasa.gov/" rel="nofollow">https://search.earthdata.nasa.gov/</a>. The derived SLF metrics can also be found at Bruno (2022), and are also described in Hofer et al. (2024) and Shaw et al. (2022). The CALIPSO-GOCCP data product (used in Fig08.py) can be downloaded from https://climserv.ipsl.polytechnique.fr/cfmip-obs/Calipso_goccp.html. The surface radiation flux (used in Fig13.py) can be downloaded freely at <a href="https://www.pangaea.de/" rel="nofollow">https://www.pangaea.de/</a>. The ERA5 data used to produce the back trajectories can be found at https://doi.org/10.24381/cds.bd0915c6. The colormap from Crameri et al. (2020) was used when preparing the figures. </p> <p>References:</p> <ul> <li>Bruno, O. (2022). Distributions of supercooled liquid fraction from CALIOP V4 [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8289058" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8289058</a></li> <li>Crameri, F., Shephard, G.E. & Heron, P.J. The misuse of colour in science communication. <em>Nat Commun</em> <strong>11</strong>, 5444 (2020). https://doi.org/10.1038/s41467-020-19160-7</li> <li>Hofer, S., Hahn, L.C., Shaw, J.K. et al. Realistic representation of mixed-phase clouds increases projected climate warming. Commun Earth Environ 5, 390 (2024). <a href="https://doi.org/10.1038/s43247-024-01524-2" rel="nofollow">https://doi.org/10.1038/s43247-024-01524-2</a></li> <li>Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geoscientific Model Development, 13, 6165–6200, <a href="https://doi.org/10.5194/gmd-13-6165-2020" rel="nofollow">https://doi.org/10.5194/gmd-13-6165-2020</a>, 2020.</li> <li>Shaw, J., McGraw, Z., Bruno, O., Storelvmo, T., & Hofer, S. (2022). Using satellite observations to evaluate model microphysical representation of Arctic mixed-phase clouds. Geophysical Research Letters, 49, e2021GL096191. <a href="https://doi.org/10.1029/2021GL096191" rel="nofollow">https://doi.org/10.1029/2021GL096191</a></li> </ul>
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>
Early Detection of Nucleation Events from Solution in LC-TEM by Machine Learning
<p>These data are images taken with a liquid cell transmission electron microscope and annotation data of particles.</p> <p>See <a href="https://github.com/hiroyasukatsuno/Early-Detection-of-Nucleation-Events-LC-TEM">this page (GitHub)</a>.</p> <p> </p>
Data set for the replication package of the paper "Simulations of DNA-origami self-assembly reveal design-dependent nucleation barriers"
<p>Data set for the replication package of the paper "Simulations of DNA-origami self-assembly reveal design-dependent nucleation barriers".</p>
Importance of high-latitude sources for ice-nucleating particles in cold-air outbreaks and the implications for cloud-phase feedback
<p>This dataset contains FLEXPART results and codes for data analysis and visualization used in the MRes research project. </p> <p>Numpy arrays for FLEXPART data are in "data_flexpart.zip" with longitude and latitude coordination files.</p> <p>Codes for data analysis and visualization are in "ipynb.zip". Codes were written in Python language. Codes are categorized by figures in the article. </p>
Data and code availibility for the paper "Ice-nucleating agents in sea spray aerosol identified and quantified with a holistic multi-modal freezing model" by Alpert et al.
<p>The data and codes used in the paper "Ice-nucleating agents in sea spray aerosol identified and quantified with a holistic multi-modal freezing model" by Alpert et al., published in <em>Science Advances</em> are included in this collection. Detailed descriptions of the files are given in the readme file.</p>
Modeling the hydrological cycle in the atmosphere of Mars: Influence of a bimodal size distribution of aerosol nucleation particles
<p>Data from figures.</p>
Final data for paper "A nonperturbative test of nucleation calculations for strong phase transitions"
<p>In this Zenodo deposit we include the data used to make three key figures in our paper, showing our final nucleation rate results. The columns of the raw data files are labelled. In each case, the quantity being tabulated is the logarithm of the nucleation rate, typically labelled <em>logGamma</em> in our data files. We also include plotting scripts to generate the figures from the data.</p> <ul> <li> <p>The directory <code>a_limit</code> contains the extrapolation to zero lattice spacing, with the raw data in the file <code>a_limit_data</code>. The temperature <em>T</em> is fixed to our benchmark point of 93.121 GeV. The linear size <em>Nx</em> and the lattice spacing <em>a</em> are varied to give a constant total volume. The file <code>a_limit_lin_op_data</code> contains our comparison measurement using the linear order parameter. The script <code>plot_a_limit.py</code> performs a nonlinear least squares fit to a cubic, writes the continuum extrapolated nuclation rate result to stdout, and plots the data and the fit.</p> </li> <li> <p>The directory <code>vol_limit</code> contains the extrapolation to infinite volume, with the raw data in the file <code>vol_limit_data</code>. Here the temperature <em>T</em>=93.121 GeV and lattice spacing <em>a</em>=1.5 are fixed, while the linear size <em>Nx</em> is varied. The script <code>plot_vol_limit.py</code> performs a nonlinear least squares fit to an exponential function, writes the infinite volume limit nucleation rate result to stdout, and again plots the data and the fit.</p> </li> <li> <p>The directory <code>rate_reweighted</code> contains the data needed to plot the nucleation rate as a function of temperature. This plot combines lattice data and various perturbative estimates of the nucleation rate. The lattice data are in three files:</p> <ul> <li><code>rate_lattice_BM2_vol_limit</code> contains the infinite volume limit result at the simulated lattice temperature <em>T</em>=93.121 GeV and with lattice spacing <em>a</em>=1.5.</li> <li><code>rate_lattice_BM2</code> reweights the lattice data to different temperatures and then takes the infinite volume limit.</li> <li><code>rate_lattice_BM2_Nx40_a1.5</code> contains data reweighted from the simulation at fixed volume <em>Nx</em>=40. In each lattice data file, the temperature, continuum potential parameters and nucleation rate are given.</li> </ul> <p>The perturbative results are in three files, <code>rate_perturbative_BM2_rg_low</code>, <code>rate_perturbative_BM2</code> and <code>rate_perturbative_BM2_rg_high</code>, corresponding to the three RG scales mentioned in the text. These files again contain the temperature and continuum lattice parameters. The column <em>eps</em> gives the dimensionless loop expansion parameter around the metastable phase within the EFT. The column <em>logGamma_0</em> gives the tree level result, <em>logGamma_A</em> and <em>logGamma_B</em> the LPA results with the two options for dealing with the imaginary parts described in the text, and <em>logGamma_1</em> is the one loop result. The script <code>plot_rate_reweighted.py</code> plots all of these data together. For the tree level and one loop cases, the error bands plotted correspond to the minimum and maximum values of the nucleation rate across the three RG scales. For the LPA results, the bands include the extreme values for all six options including the two approaches to handling the imaginary parts.</p> </li> </ul> <p>In each case, the plots are saved to PDF and PNG plot files.</p> <p>The figures used in this deposit and in the paper used the following package versions (obtained with the <code>pipreqs</code> package):</p> <pre><code>matplotlib==3.5.1 numpy==1.21.5 scipy==1.8.0 seaborn==0.11.2</code></pre> <p>Note that in <a href="https://doi.org/10.5281/zenodo.10891524">Version v1</a> there was an error in the normalisation of our perturbative tree-level and LPA results which has now been fixed.</p>
Supplementary Material for: "Kink-Helium Interactions in Tungsten: Opposing Effects of Assisted Nucleation and Hindered Migration"
<p>This uploaded contains the supporting material and files for the pre-print titled: ”Kink-Helium Interactions in Tungsten: Opposing Effects of Assisted Nucleation and Hindered Migration”.</p> <p> </p> <p>MN is supported by a studentship funded by the UK Engineering and Physical Sciences Research Council–supported Centre for Doctoral Training in Modelling of Heterogeneous Systems, Grant No. EP/S022848/1 and the Atomic Weapons Establishment. JRK acknowledges funding from the Leverhulme Trust under grant RPG-2017-191. APB acknowledges support from the CASTEP-USER project, funded by the Engineering and Physical Sciences Research Council under the grant agreement EP/W030438/1. JRK and APB acknowledge funding from the NOMAD Centre of Excellence funded by the European Commission under grant agreement 951786. We acknowledge the University of Warwick Scientific Computing Research Technology Platform for assisting the research described within this study. Some of the calculations were performed using the Sulis Tier 2 HPC platform hosted by the Scientific Computing Research Technology Platform at the University of Warwick. Sulis is funded by EPSRC Grant EP/T022108/1 and the HPC Midlands+ consortium. We are grateful for computational support from the UK national high performance computing service, ARCHER2, for which access was obtained via the UKCP consortium and funded by EPSRC Grant No. EP/X035891/1.</p>
From nucleation to fat crystal network: effect of stearic-palmitic sucrose ester on static crystallization of palm oil
<p>Dataset belonging to publication 'From nucleation to fat crystal network: effect of stearic-palmitic sucrose ester on static crystallization of palm oil', <a href="https://doi.org/10.3390/foods13091372">https://doi.org/10.3390/foods13091372</a>.</p> <p> </p> <p>PLM = polarized light microscopy</p> <p>CryoSEM = cryo-scanning electron microscopy</p> <p>> data obtained after de-oiling fat samples with isobutanol (4x) and aceton (1x), see publication</p> <p>SAXS = small-angle X-ray scattering</p> <p>> data obtained after subtraction of intensity of empty capillary, see publication</p> <p>> for SE heating and cooling cycles, data is recorded from 70°C (1h) to 20°C (1h), and 4 repeated cycles </p> <p>WAXS = wide-angle X-ray scattering</p> <p>> data obtained after subtraction of intensity of empty capillary, see publication</p> <p>> for SE heating and cooling cycles, data is recorded from 70°C (1h) to 20°C (1h), and 4 repeated cycles </p> <p>USAXS = ultra-small-angle X-ray scattering</p> <p>> data obtained after subtraction of intensity of the capillary at 70°C, see publication</p> <p>DSC = differential scanning calorimetry</p> <p>> Samples are heated at 70°C for 10 min, and then crystallized following a certain protocol (see publication).</p> <p>> Samples are maintained one hour at their respective isothermal crystallization temperature.</p> <p>> Samples are rehaeted at 5°C/min to 70°C.</p> <p>SE = sucrose ester (SP30, HLB6)</p> <p>PO = palm oil</p> <p>POE = palm oil + 0.5 wt% SE</p> <p>FC = fast cooling (20°C/min)</p> <p>SC = slow cooling (1°C/min)</p>
Influence of temperature on the molecular composition of ions and charged clusters during pure biogenic nucleation
<p>Data of Figures in manuscript:</p> <p>Influence of temperature on the molecular composition of ions and charged clusters during pure biogenic nucleation.</p> <p>Frege et al., Atmos. Chem. Phys. 18, 65–79, 2018</p> <p>https://doi.org/10.5194/acp-18-65-2018</p> <p> </p>
Experimental–Computational Analysis of Nucleation Sites for Primary Static Recrystallization
<p>This repository contains supplementary material to our paper. Specifically, the Matlab, Python,a and Shell scripts and cellular automaton source code we used to run and post-process the simulations as well as the simulation results:</p> <p><strong>MTEXEBSDMappingStructureInitialization.zip</strong><br> Specifies, using MTex v5.0.3, how we converted the measured SEM/EBSD mapping to a synthetic 2d microstructure.</p> <p><strong>SCORESourceCode.zip</strong><br> Specifies the source code of SCORE. Version 1.2.1. Demands a local HDF5 installation. MPI/OpenMP parallelized.<br> Inspect www.github.com/mkuehbach/SCORE for further details on how to compile and background to the model<br> an implementation.</p> <p><strong>ExecuteSimulations.zip</strong><br> Specifies shell scripts and UDS input files to execute the simulations. Details via these UDS files also all parameter<br> settings we used to reproduce the runs.</p> <p><strong>ComparisonXaXv.tar.gz</strong><br> Compares in summarized form, and extracted from the RXAreaFractionDepthProfile folder files, the area vs<br> volume fraction at specified time snapshots for the z= [0.0, 0.5, 1.0] RDTD section.<br> <br> <strong>Inherited_GrainSizeMicrostructure.zip</strong><br> ANG-like serial sectioning snapshot results and IPF visualization of microstructure evolution for those<br> simulation cases in which the nuclei inherited the orientation from their site.</p> <p><strong>Random_GrainSizeMicrostructure.zip</strong><br> ANG-like serial sectioning snapshot results and IPF visualization of microstructure evolution for those<br> simulation cases in which the nuclei had random orientations form the SO3.</p> <p><strong>RXAreaFractionDepthProfile.zip</strong><br> Specifies the evolution of the area fraction recrystallized with grains in cross-sectional area >=13px<br> for every RDTD layer.</p> <p>The corresponding parameterization is detailed in the *.uds input file which specifies all constitutive parameter<br> and log settings of the automaton. The simulation is executed by compiling the program and linking to<br> HDF5. The OMP_NUM_THREADS environment variable should be set to not more than 10.<br> The SCORE is executed as follows:<br> mpirun -np 1 ./score <simid> <udsfile> <KAM Ang EBSD file> 1>STDOUT.txt 2>STDERR.txt<br> <br> <strong>Profiling.zip</strong><br> Details the execution log of the automaton ie runtime individual composition of nuclei volume transformation<br> progression, interfacial area evolution, etc.</p> <p><strong>SingleGrainData.zip</strong><br> Details the volume consumption / volume gain kinetics of every single deformed / recrystallized grain.</p> <p><strong>TemperatureTimeProfile.zip</strong><br> Details the time/temperature and step profile of the numerical integration.<br> This allows to map integration time steps to simulated microstructural states.</p> <p><strong>ThreadProfilingGrowth.zip</strong><br> Details the evolution of the recrystallized volume versus time and number of active cells per thread sub-domain.</p> <p><strong>MartinPostprocessingScripts.zip</strong><br> Is a collection of Python and MTex scripts to compile the area size distribution and compute ODFs.</p>
Data and metadata associated with ice-nucleating particles during the 2022 Arctic Cold Air Outbreak Campaign
<p>Data and metadata associated with analysis of ice-nucleating particle concentrations and aerosol-size distributions measured during the March 2022 Arctic Cold Air Outbreak campaign.</p> <p>Contains INP concentrations and aerosol-size normalisations of these, parametrisations of these, SEM data and metadata and backtrajectory modelling inputs and results.</p>
Movies of the simulations performed in Baïsset et al. paper entitled « Weakening induced by phase nucleation in metamorphic rocks: insights from numerical models »
<p>Here you can find the movies of the evolution of the accumulated plastic strain of the simulations performed in the study. You will also find the table that summarizes the conditions of the different simulations (Table 1), as well as the colorbar (legend.png) corresponding to all the movies.</p>
Dataset for manuscript "Gaps in our understanding of ice-nucleating particle sources exposed by global simulation of the UK Earth System Model"
<p>Datasets and Jupyterlab python script for plotting all figures relevant to the mansucript "Gaps in our understanding of ice-nucleating particle sources exposed by global simulation of the UK Earth System Model" by Herbert et al.</p> <p>https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1538/</p> <p>Data needs to be unzipped and paths (input and output) updated in the jupyterlab python script.</p> <p> </p>
A new parameterisation for homogeneous ice nucleation driven by highly variable dynamical forcings
<p>This archive contains the data associated with the preprint of the article <em>"A New Parameterization for Homogeneous Ice Nucleation Driven by Highly Variable Dynamical Forcings."</em> The collection includes datasets used to construct the initial conditions for forcing an air parcel model with ice physics, outputs produced by the parcel model, and data necessary for generating the plots presented in the paper. A README file is provided, offering a detailed explanation of the contents and structure of the archive,</p>
Supporting datasets used in the paper entitled "Increasing Arctic dust suppresses the reduction of ice nucleation in the Arctic lower troposphere by warming"
<p>This archive contains datasets used in the paper entitled "Increasing Arctic dust suppresses the reduction of ice nucleation in the Arctic lower troposphere by warming".</p>
Data for the publication "Evaluation of climatic impacts of ice nucleating particles through precipitation process with a GCM"
<p>These data are a set of annual-mean values for 5yr simulations using the MIROC6-SPRINTARS global aerosol-climate model with different treatments of precipitation (i.e., diagnostic and prognostic). The outputs include diagnostics from the satellite simulator COSP2.<br>The data are used in the manuscript entitled "Evaluation of climatic impacts of ice nucleating particles through precipitation process with a GCM". All data used in this study are available from the corresponding author upon request.</p>
The Relationship of Aerosols and Ice Nucleating Particles in Beijing - Data
<p>The data of INP concentrations from the CFDC, aerosol size distributions from the APS and mass concentrations of PM10, PM2.5 and PM1 are displayed. The data in episodes are also displayed.</p>
Data for: The conserved centrosomin motif, γTuNA, forms a dimer that directly activates microtubule nucleation by the γ-tubulin ring complex (γTuRC)
<p>To establish the microtubule cytoskeleton, the cell must tightly regulate when and where microtubules are nucleated. This regulation involves controlling the initial nucleation template, the γ-tubulin ring complex (γTuRC). Although γTuRC is present throughout the cytoplasm, its activity is restricted to specific sites including the centrosome and Golgi. The well-conserved γ-tubulin nucleation activator (γTuNA) domain has been reported to increase the number of microtubules (MTs) generated by γTuRCs. However, previously we and others observed that γTuNA had a minimal effect on the activity of antibody-purified Xenopus γTuRCs in vitro (Thawani et al., eLife, 2020; Liu et al., 2020). Here we instead report, based on improved versions of γTuRC, γTuNA, and our TIRF assay, the first real-time observation that γTuNA directly increases γTuRC activity in vitro, which is thus a bona fide γTuRC activator. We further validate this effect in Xenopus egg extract. Via mutation analysis, we find that γTuNA is an obligate dimer. Moreover, efficient dimerization as well as γTuNA's L70, F75, and L77 residues are required for binding to and activation of γTuRC. Finally, we find that γTuNA's activating effect opposes inhibitory regulation by stathmin. In sum, our improved assays prove that direct γTuNA binding strongly activates γTuRCs, explaining previously observed effects of γTuNA expression in cells and illuminating how γTuRC-mediated microtubule nucleation is regulated.</p>
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