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351 results for “jetting”
QuaLiKiz-v2.6.2 turbulent transport model evaluations based on JET experimental plasma profiles
<p>This dataset was used to train the QuaLiKiz-neural-network (QLKNN) model, QLKNN-jetexp-15D, described within the following published article: <a href="https://doi.org/10.1063/5.0038290">https://doi.org/10.1063/5.0038290</a>. It was generated with approximately 33 million standalone evaluations of QuaLiKiz-v2.6.2, each performed with a standard vector of 18 wavenumbers. Only approximately 21 million of these are kept for training due to various consistency checks applied to the code outputs. More information about the QuaLiKiz code can be found at <a href="https://www.qualikiz.com">www.qualikiz.com</a>.</p> <p>The data is saved under 3 keys in HDF5 format: "/input", "/output", and "/label". The inputs to the QuaLiKiz evaluations are provided under "/input", representing the local plasma parameters extracted from experimental measurements from the JET plasma device in Culham, UK, along with variations of select parameters according to propagated experimental uncertainties. Selected relevant outputs of the QuaLiKiz evaluations are provided under "/output", namely the local turbulent transport coefficients after applying a semi-empirical turbulent fluctuation saturation rule. Some useful metadata is provided under "/label", giving some degree of provenance tracking back to the JET experimental database, as well as describing the applied parameter variations and explaining why certain output rows were removed from the output structure.</p>
Pileup Jet Dataset for PUMML
<p>Dataset of events used in the PUMML paper. One file contains datasets with 2k events per pileup vertex number (mu) for mu=0-180 and the other contains 50k events for different signal processes (specified by the mass of a scalar particle decaying to quarks) all with mu=140.</p> <p>A notebook demonstrating how to use the files can be found at <a href="https://github.com/pkomiske/PUMML/blob/master/PUMML%20Events.ipynb">https://github.com/pkomiske/PUMML/blob/master/PUMML%20Events.ipynb</a>.</p>
CMS 2011A Open Data | Jet Primary Dataset | pT > 375 GeV | MOD HDF5 Format
<p>A dataset of 1,785,625 jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.UP77.P6PQ">Jet Primary Dataset of the CMS 2011A Open Data</a> reprocessed into the MOD HDF5 format. Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger and are required to have <span class="math-tex">\(p_T^\text{jet}>375\)</span> GeV, where <span class="math-tex">\(p_T^\text{jet}\)</span> includes a jet energy correction factor. Particle Flow Candidates (PFCs) for each jet are provided and include information about the PFC kinematics, PDG ID, and vertex. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.</p> <p>For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have <span class="math-tex">\(|\eta^\text{jet}|<1.9\)</span> to ensure tracking coverage and have "medium" quality to reject fake jets.</p> <p>The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>.</p> <p>There are corresponding datasets of simulated jets organized by hard parton <span class="math-tex">\(\hat p_T\)</span> also available on Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-<span class="math-tex">\(\infty\)</span> GeV</a></li> </ul>
QuaLiKiz-v2.6.2 linear instability spectra based on JET experimental plasma profiles
<p>This dataset was used to train the QuaLiKiz-neural-network (QLKNN) model, QLKNN-jetexp-15D, described within the following published article: <a href="https://doi.org/10.1063/5.0038290">https://doi.org/10.1063/5.0038290</a>. It was generated with approximately 33 million standalone evaluations of QuaLiKiz-v2.6.2, each performed with a standard vector of 18 wavenumbers. Only approximately 21 million of these are kept for training due to various consistency checks applied to the code outputs. More information about the QuaLiKiz code can be found at <a href="https://www.qualikiz.com">www.qualikiz.com</a>.</p> <p>The data is saved under 3 keys in HDF5 format: "/input", "/spectrum", and "/wavenumber". The '/input' key contains the inputs used for the QuaLiKiz evaluations, representing the local plasma parameters extracted from experimental measurements from the JET plasma device in Culham, UK, along with variations of select parameters according to propagated experimental uncertainties. The "/spectrum" key contains the linear growth rate and frequency spectra corresponding to the 2 most dominant microinstabilities determined by the calculation (s0 = dominant, s1 = sub-dominant). The "/wavenumber" key contains an array representing the standard set of 18 wavenumbers (<span class="math-tex">\(k_y \rho_s\)</span>) was used to generate the spectra (k0 = lowest wavenumber, k17 = highest wavenumber).</p>
Imaging Temperature and Thickness of Thin Planar Liquid Water Jets in Vacuum - data
<p>Data set pertaining to the article "Imaging Temperature and Thickness of Thin Planar Liquid Water Jets in Vacuum", published in <em>Struct. Dyn.</em> 10, 034901 (2023), <a href="https://doi.org/10.1063/4.0000188" target="_blank" rel="noopener">https://doi.org/10.1063/4.0000188 </a>.</p> <p>The following data are provided:</p> <table> <tbody> <tr> <td>(zip-)file/Folder</td> <td>Description</td> <td>Format</td> <td>Extension</td> </tr> <tr> <td>IR_images/calibration_data/vacuum</td> <td> <p>Snapshots from a thermographic movie of our flat jet running in vacuum, at thirty different background temperature. (A snapshot shown in Fig. 3a, rhs.)</p> </td> <td> <p>temperature values per camera pixel (°C), 640 row * 480 columns, semicolon-separated ascii data</p> </td> <td>.asc</td> </tr> <tr> <td>IR_images/calibration_data/1atm</td> <td>As above, for our flat jet running in atmosphere. (Three snapshots shown in Fig. 2a.)</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_images/calibration_data/chipnozzle</td> <td>As above, for a flat jet produced from a chip nozzle, and running in atmosphere.</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_images/raw_data</td> <td>As above, for various conditions of the flat jet environment as detailed in table exp_settings.csv.</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_video</td> <td>Two thermographic movies recorded of our flat jet at varied conditions of the jet environment detailed in table chamber_pressure.pdf.</td> <td>Radiographic image stream, suitable for opening with free software Optris Pix Connect.</td> <td>.ravi</td> </tr> <tr> <td>FJ_cooling_2D.mph</td> <td>Input file for 2D finite element simulation of our flat jet.</td> <td>Input file suitable for Comsol software, proprietary format.</td> <td>.mph</td> </tr> <tr> <td>Y_Z_Temp_Comsol.txt</td> <td>Ascii representation of our simulated temperature profile (Fig. S7 (SI)).</td> <td>List of (y,z,T) tupels, with (y,z) in m and T in °C.</td> <td>.txt</td> </tr> </tbody> </table> <p> </p> <p>In case you have any questions regarding this data set please contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p>
Pythia8 Quark and Gluon Jets for Energy Flow
<p>Two datasets of quark and gluon jets generated with Pythia 8, one with all kinematically realizable quark jets and one that excludes charm and bottom quark jets (at the level of the hard process). The one without c and b jets was originally used in <a href="https://arxiv.org/abs/1810.05165">Energy Flow Networks: Deep Sets for Particle Jets</a>. Generation parameters are listed below:</p> <ul> <li>Pythia 8.226 (without bc jets), Pythia 8.235 (with bc jets), <span class="math-tex">\(\sqrt{s}=14\,\text{TeV} \)</span></li> <li>Quarks from WeakBosonAndParton:qg2gmZq, gluons from WeakBosonAndParton:qqbar2gmZg with the Z decaying to neutrinos</li> <li>FastJet 3.3.0, anti-ki jets with R=0.4</li> <li><span class="math-tex">\(p_T^\text{jet}\in[500,550]\,\text{GeV},\,|y^\text{jet} |<1.7\)</span></li> </ul> <p>There are 20 files in each dataset, each in compressed NumPy format. Files including charm and bottom jets have 'withbc' in their filename. There are two arrays in each file</p> <ul> <li>X: (100000,M,4), exactly 50k quark and 50k gluon jets, randomly sorted, where M is the max multiplicity of the jets in that file (other jets have been padded with zero-particles), and the features of each particle are its pt, rapidity, azimuthal angle, and pdgid.</li> <li>y: (100000,), an array of labels for the jets where gluon is 0 and quark is 1.</li> </ul> <p>If you use this dataset, please cite this Zenodo record as well as the corresponding paper:</p> <ul> <li>P. T. Komiske, E. M. Metodiev, J. Thaler, Energy Flow Networks: Deep Sets for Particle Jets, JHEP 01 (2019) 121, arXiv:1810.05165.</li> </ul> <p>For the corresponding dataset of Herwig jets, see <a href="https://zenodo.org/record/2664330">this Zenodo record</a>. The datasets can be downloaded and read into python automatically using the <a href="https://energyflow.network/docs/datasets/#quark-and-gluon-jets">EnergyFlow Python package</a>.</p> <p>Changes:</p> <ul> <li>v1 - Added files with b and c quark jets.</li> </ul>
Pythia Generated Jet Images with Alternative Rotation Scheme for Location Aware Generative Adversarial Network Training
<p>Dataset containing 300k jet images that can be used to train Location Aware Generative Adversarial Networks (LAGAN) for High Energy Physics, such as the one in [arXiv:1701.05927].</p> <p><strong>Format</strong>:</p> <p>HDF5 file with the following fields:</p> <ul> <li>'image' : array of dim (300000, 25, 25), contains the pixel intensities of each 25x25 image</li> <li>'signal' : binary array to identify signal (1, i.e. W boson) vs background (0, i.e. QCD)</li> <li>'jet_eta': eta coordinate per jet</li> <li>'jet_phi': phi coordinate per jet</li> <li>'jet_mass': mass per jet</li> <li>'jet_pt': transverse momentum per jet</li> <li>'jet_delta_R': distance between leading and subleading subjets if 2 subjets present, else 0</li> <li>'tau_1', 'tau_2', 'tau_3': substructure variables per jet (a.k.a. n-subjettiness, where n=1, 2, 3)</li> <li>'tau_21': tau<sub>2</sub>/tau<sub>1</sub> per jet</li> <li>'tau_32': tau<sub>3</sub>/tau<sub>2</sub> per jet</li> </ul> <p><strong>Details</strong>:</p> <ul> <li>Simulated using Pythia 8.219 at √ s = 14 TeV</li> <li>Image pre-processing using method from in L. de Oliveira et al., <em>Jet-Images -- Deep Learning Edition </em>[arXiv:1511.05190]</li> <li>scikit-image==0.10.0 implementation of cubic spline rotation with fewer low energy artifacts than scikit-image>=0.12.0</li> <li>Finite calorimeter granularity simulated with 0.1×0.1 grid in η and φ, with η × φ ∈ [−1.25, 1.25] × [−1.25, 1.25]</li> <li>Jet clustering with anti-k<sub>t</sub> algorithm with a radius R = 1.0 using FastJet 3.2.1; constituent re-clustering into R = 0.3 k<sub>t</sub> subjets</li> <li>Intensity of pixel = p<sub>T</sub> of cell</li> <li>60 GeV < m<sup>jet</sup> < 100 GeV</li> <li>250 GeV < p<sub>T</sub><sup>jet</sup> < 300 GeV</li> <li>Sparse images (~10% NNZ)</li> </ul> <p>Full dataset description in [arXiv:1701.05927].</p>
Dataset of "Liquid-Jet Photoemission Spectroscopy as a Structural Tool: Site-Specific Acid-Base Chemistry of Vitamin C"
<p>Liquid-jet photoemission spectroscopy (LJ-PES) directly probes the electronic structure of solutes<br>and solvents. It also emerges as a novel tool to explore chemical structure in aqueous solutions, yet<br>the scope of the approach has to be examined. Here, we present a pH-dependent liquid-jet photoelectron<br>spectroscopic investigation of ascorbic acid (vitamin C). We combine core-level photoelectron<br>spectroscopy and ab initio calculations, allowing us to site-specifically explore the acid-base chemistry<br>of the biomolecule. For the first time, we demonstrate the capability of the method to simultaneously<br>assign two deprotonation sites within the molecule. We show that a large change in chemical shift<br>appears even for atoms distant several bonds from the chemically modified group. Furthermore, we<br>present a highly efficient and accurate computational protocol based on a single structure using the<br>maximum overlap method for modeling core-level photoelectron spectra in aqueous environments.<br>This work poses a broader question: To what extent can LJ-PES complement established structural<br>techniques such as nuclear magnetic resonance? Answering this question is highly relevant in view<br>of the large number of incorrect molecular structures published.</p>
Source data for "Cyclic jetting enables microbubble-mediated drug delivery"
<p>This repository provides the source data associated with the paper <em>"</em><strong>Cyclic jetting enables microbubble-mediated drug delivery</strong><em>"</em> by Marco Cattaneo <em>et al.</em>, published in <em>Nature Physics</em>.</p> <ul> <li>The "<strong>Cattaneo_Fig_X.xlsx</strong>" files contain the data necessary for reproducing Fig. X.</li> <li>The "<strong>Cattaneo_VideoSourceData.zip</strong>" file includes the video source data not included within the article used to generate Fig. 4a-c. For further details, please refer to the "Cattaneo_Fig_4.xlsx" file.</li> </ul>
Datasets for submitted paper "Color appearance in rotational material jetting"
<p>This folder contains data for the submitted paper "Color appearance in rotational material jetting"<br> For more information, please contact Ali Payami Golhin (payami.ag@gmail.com)</p>
How does Mg2+(aq.) interact with ATP(aq.)? Observations through the lens of liquid-jet photoelectron spectroscopy - data
<p>Dataset pertaining to the article "How does Mg2+(aq) interact with ATP(aq)? Biomolecular Structure through the Lens of Liquid-Jet Photoemission Spectroscopy", published in Journal of the American Chemical Society (<a href="https://doi.org/10.1021/jacs.4c03174" target="_blank" rel="noopener">doi: 10.1021/jacs.4c03174</a>). Here, we arrive at new information on the interaction of ATP with Mg under physiological conditions by interpreting photoelectron spectra and intermolecular Coulombic decay from a liquid microjet.</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07, see<br>https://www.nexusformat.org/<br>https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br>NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br>* nexpy (distributed with python)<br>* https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>In each NeXus file-entry, two types of spectra are shown:<br>1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data'). For ATP spectra, the ADP overview spectrum, and ADP/Mg2+ Mg 2s spectra, a binding energy correction shifting the liquid 1b1 feature to 11.33 eV is applied.<br>2. As-measured data ('raw').</p> <p>Files with extension .txt are comma-separated ascii-files.</p> <p><br>The following files are provided:</p> <p>Photoemission data pertaining to adenosine phosphate PES measurements:<br>atp-mg.h5 - ATP photoemission spectra in the presence of Mg2+ cations in varying concentration<br>adp-mg.h5 - ADP photoemission spectra in the presence of Mg2+ cations in varying concentration<br>amp-mg.h5 - AMP photoemission spectra in the presence of Mg2+ cations (a single concentration)<br>atp-adp-amp.h5 - ATP, ADP, AMP photoemission without Mg admixture<br>mg-only.h5 - Mg 2s core level spectra without ATP<br>tham-only.h5 - VB band measured with only THAM (tris(hydroxymethyl)aminomethane), used as buffer for pH stabilization<br>atp-icd.h5 - ATP photoemission spectra in the presence of Mg2+ cations, kinetic energy range of ICD features (publication is based on the last three entries).<br><br></p> <p>Numeric representations of the traces shown in the article's figures:<br>Figure_3-data.txt<br>Figure_5a-Mg2p.txt<br>Figure_5a-Mg2s.txt<br>Figure_5a-Mgonly.txt<br>Figure_5a-P2p.txt<br>Figure_5a-P2s.txt<br>Figure_5b.txt<br>Figure_5c.txt<br>Figure_6-ADP.txt<br>Figure_6-AMP.txt<br>Figure_6-ATP.txt<br>Figure_8a-data.txt<br>Figure_S2-Tris.txt<br>Figure_S2-Tris_with_Mg2+.txt<br>Figure_S4-data.txt.</p> <p>Version history<br>3: updated to reflect changes in Figure numbering between ArXiv-post and version published in JACS, additional Figure 5-data added<br>2: NeXus-data added<br>1: initial upload</p> <p>Contact person for questions regarding this data set: Uwe Hergenhahn, uhe@fhi.mpg.de . If you use these data for your scientific work we are curious to learn about it.</p>
Mesoscale Low-Level Jet Climatology for the North and Baltic Seas
<p><strong>Mesoscale Low-Level Jet Climatology for the North and Baltic Seas</strong></p> <p>This dataset contains a mesoscale low-level jet (LLJ) climatology for the Baltic and North Seas.</p> <p>The dataset consists of many individual raster layers of LLJ characteristics zipped in the "llj_climatology.zip" file. Each layer is a netCDF4 file, which can be read directly by QGIS, Python, and many other tools. In the "figures" folder, plots showing most of the layers can be found. A more comprehensive description of the layers is given below.</p> <p>Some examples of layers contained:</p> <ul> <li>LLJ rate-of-occurrence</li> <li>LLJ height</li> <li>LLJ duration</li> <li>Wind speed and direction for at LLJ peak</li> <li>Max shear above and below the LLJ peak</li> <li>Wind speed and direction at 100, 150, 200 m</li> <li>Rotor-equivalent wind speed (REWS) for IEA 15 MW reference turbine</li> </ul> <p>Because of strong seasonality in offshore LLJ occurrences, most layers come as long-term means, including the full five years and seasonality-averaged layers. Several aggregate statistics are available for each layer, such as mean, median, and standard deviation. </p> <p>The data was created using the Weather Research and Forecasting model v4.2.1 running a five-year hindcast from 2019-06-26 to 2024-06-26. A two-domain setup was used to downscale ERA5 boundary data to 3 km horizontal grid spacing. See the associated paper for a full data generation process and validation description.</p> <p>Based on user feedback, future versions could be expanded to hold additional layers/variables, such as sector-wise Weibull parameters or time-series samples for representative points. Contact btol@dtu.dk for feedback and requests for future versions. </p> <p><strong>Full list of variables</strong></p> <ul> <li><strong>ws100, ws150, ws200</strong>: wind speed at 100, 150, and 200 meters</li> <li><strong>wd100, wd150, wd200</strong>: wind direction at 100, 150, 200 meters</li> <li><strong>rews_iea15mw</strong>: rotor-equivalent wind speed for the IEA 15 MW reference turbine</li> <li><strong>cf_iea15mw</strong>: capacity factor for the IEA 15 MW reference turbine</li> <li><strong>llj_rate</strong>: LLJ detection rate </li> <li><strong>height_of_llj_max</strong>: height of LLJ peak in meters</li> <li><strong>llj_ws_max</strong>: wind speed of LLJ peak in meters per second</li> <li><strong>llj_wind_direction</strong>: wind direction of LLJ peak in degree</li> <li><strong>llj_duration</strong>: LLJ duration in hours</li> <li><strong>llj_most_prevalent_hour</strong>: most prevalent hour-of-day during LLJ events as hour integers (0-23)</li> <li><strong>llj_most_prevalent_hour_freq</strong>: relative frequency of most prevalent hour-of-day during LLJ events</li> <li><strong>llj_most_prevalent_season</strong>: most prevalent month-of-year during LLJ events as 0-based month integers (0-11 JAN-DEC)</li> <li><strong>llj_most_prevalent_season_freq</strong>: relative frequency of most prevalent month-of-year during LLJ events </li> <li><strong>llj_max_shear_below</strong>: maximum shear between the LLJ peak and the minimum below</li> <li><strong>llj_min_shear_above</strong>: minimum (maximum negative) shear between the LLJ peak and the minimum above</li> <li><strong>height_of_max_shear_below_llj</strong>: height of maximum shear detected below the LLJ peak in meters</li> <li><strong>height_of_min_shear_above_llj</strong>: height of minimum shear detected above the LLJ peak in meters</li> <li><strong>llj_depth</strong>: the depth of the LLJ measured from "height_of_max_shear_below_llj" to "height_of_min_shear_above_llj" in meters</li> <li><strong>llj_rews_iea15mw</strong>: rotor-equivalent wind speed for the IEA 15 MW reference turbine during LLJ events</li> <li><strong>llj_cf_iea15mw</strong>: capacity factor for the IEA 15 MW reference turbine during LLJ events</li> <li><strong>llj_abs_falloff_above</strong>: absolute wind speed fall-off above the LLJ peak in meters per second</li> <li><strong>llj_rel_falloff_above</strong>: relative wind speed fall-off above the LLJ peak </li> <li><strong>llj_abs_falloff_below</strong>: absolute wind speed fall-off below the LLJ peak in meters per second</li> <li><strong>llj_rel_falloff_below</strong>: relative wind speed fall-off below the LLJ peak </li> </ul> <p><strong>Several layers exist for different aggregation and seasons for each variable. Suffixes describe the aggregation (_mean, _median, _std) and season (_DJF, _MAM, _JJA, _SON)</strong></p> <p>This work is part of the FLOW project and was supported by the European Union Horizon Europe Framework Programme (HORIZON-CL5-2021-D3-03-04) under grant agreement no. 101084205.</p>
X-ray radiography 4D particle tracking of heavy spheres suspended in a turbulent jet
<p>This database report 3d trajectories of heavy spheres suspended in a turbulent upward jet. A cylindrical tank is filled with water and the jet nozzle is placed on its axis on the bottom wall, and a constant flowrate (Q) of water is fed through the nozzle. Conditions at 1700 and 2200 mL/min are considered, and the number of spheres is varied between 1 and 12 (Nsphere). The spheres are glass and are detected using X-ray radiography at 60Hz. The 4d kinematics are obtained with this setup using radioSphere (E. Ando et<br> al., Measurement Science and Technology, 32(9), 095405, 2021). Each condition has a series of files named based on the number of spheres in the tank Nsphere and the flowrate Q, with each sphere of index isphere having its own file. Each file is 3 columns of doubles representing the 3d coordinates x, y, and z of the sphere, in mm, where z is the axis of the cylinder and the points up, against gravity.</p> <p>Results from this database are published here: https://doi.org/10.1016/j.ijmultiphaseflow.2023.104406<br> O. Stamati, B. Marks, E. Ando, S. Roux, N. Machicoane, X-ray radiography 4D particle tracking of heavy spheres suspended in a turbulent jet, <em>International Journal of Multiphase Flow</em> 162, 104406, 2023.</p>
The short gamma-ray burst population in a quasi-universal jet scenario: MCMC chains
<p>The paper "The short gamma-ray burst population in a quasi-universal jet scenario" (https://arxiv.org/abs/2306.15488) described an effort in modelling the short gamma-ray burst population under the assumption that all jets share the same angular profile.</p> <p>This repository contains <strong>emcee </strong>hdf5 files with the MCMC chains corresponding to the "full sample" and "flux-limited sample" analyses described in the paper.</p>
JUMP - Data collection - Part II: Zonal jets using three different approaches, laboratory - Global Climate Models - observations.
<p>The formation of large scale structures in three-dimensional (3D) turbulent flows. How small-scale dynamics organize in turbulent flows to grow large scale coherent circulation? is at the heart of fundamental studies in fluid dynamics. It appears to be equally important for our understanding of atmospheric dynamics, oceanography, meteorology and more generally geophysical fluid dynamics. Here, we deliver a data collection that <strong>(1)</strong> gathers measurements of 3D turbulent flows that emulate planetary atmospheres of the gas giants. Turbulent flows are explored using three different approaches, laboratory experiments, numerical simulations and direct planetary observations. All data set are computed in order to easily extract flow properties, i.e. high resolution maps of the different velocity components and flow vorticity (useful for further diagnostic). The data collected are fully discribed in Cabanes et al GRL (2020) "Revealing the intensity of turbulent energy transfer in planetary atmospheres" and can be used to compute <strong>(2)</strong> theoretical diagnostics with the numerical codes that allow to reveal the physical meaning of flow measurements. Numerical codes are available on https://github.com/scabanes</p> <p>We deliver (1) data collection and (2) numerical codes in the following files attached:</p> <p>(1) Data collection:</p> <ul> <li>A PDF file named <strong>JUMP-zonal-jets-data-collection-GRL.pdf</strong> that describes the following data files and nomenclature.</li> <li>A zip File of the velocity fields in the lab, interpolated on Polar and Cartesian grids <ul> <li><strong>JUMP-JetsInTheLab.zip</strong></li> </ul> </li> <li>A netcdf file of velocity fields of our Saturn reference simulation <ul> <li><strong>uvData-SRS-istep-312000-nstep-50-niz-12.nc</strong></li> </ul> </li> <li>Two netcdf files of velocity fields from Cassini observations of Jupiter<strong> </strong> <ul> <li><strong>uvData-JupObs-istep-0-nstep-4-niz-1.nc</strong></li> <li><strong>StatisticalData-JupObs.nc</strong></li> </ul> </li> <li>A zip file of potential vorticity profiles for Saturn and Jupiter observations <ul> <li><strong>IPV-QGPV-Jupiter-Saturn.zip</strong></li> </ul> </li> </ul> <p>(2) Numerical codes:</p> <ul> <li>Codes for statistical analysis in spherical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FPOST&sa=D&sntz=1&usg=AFQjCNFuDU0eij4XGxQfReO92CHfJz6PBA">https://github.com/scabanes/POST</a></li> <li>Codes for statistical analysis in cylindrical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> <li>Codes for statistical analysis in cartesian geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> </ul> <p> </p> <p>The purpose of this data collection is to reveal statistical properties of planetary flows. By computing the same analysis on different data sets the researcher allows direct confrontation of planetary observations with idealized laboratory and numerical models. Idealized models are specially designed to sweep on a large array of parameters in order to understand what parameters control planetary global circulation. The data collected and generated by the researcher deliver <strong>(1)</strong> velocity measurements of 3D turbulent flows using the different approaches (observations-laboratory-numerics) and <strong>(2)</strong> guidelines to compute the appropriate statistical analysis through the PTST. Here, the ground-breaking novelty is that the researcher deliver the possibility to compute statistical diagnostics adapted to the different geometries: the spherical geometry of planetary flows, i.e. 2D latitude-longitude maps, the cylindrical geometry of laboratory experiments, i.e. 2D flows in a rotating cylindrical tank, and the Cartesian geometry of idealized numerical simulations. Indeed, the math behind each statistical diagnostics must account for the different geometrical configurations in order to properly confront the different approaches. The PTST is also designed to be easily re-used by different communities such as experimentalists, numericists and atmosphericists that deal with 3D or 2D turbulent flows.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement N° 797012.</p>
Dataset for: Wood et al Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing
<p>This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article Wood et al (2020) 'Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing' published in Environmental Research Letters (<a href="https://doi.org/10.1088/1748-9326/abce27">https://doi.org/10.1088/1748-9326/abce27</a>).</p> <p>To isolate the role of sea surface temperature (SST) patterns for the Southern Hemisphere circulation response in the abrupt-4xCO2 experiments in CMIP5 and CMIP6, we perform experiments using IGCM4.</p> <p>Five 120-year long simulations were performed following a 5-year spin-up period. In the control simulation (CTRL) we prescribe an annually repeating cycle of climatological monthly mean SSTs using the multi-model mean (MMM) of the ‘ts’ field for the first 200 years of the CMIP5 piControl simulations. Following the CMIP6 protocol (Eyring et al., 2016), greenhouse gas (CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O) concentrations are set at preindustrial (year 1850) values and ozone is prescribed as a zonally averaged monthly mean preindustrial climatology.</p> <p>In two perturbation simulations (4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub>) the same boundary conditions are used as in CTRL, but with an annually repeating cycle of climatological monthly mean SST anomalies added using the MMM ‘ts’ field for either the CMIP5 or CMIP6 FAST (years 4-10) responses. In both the 4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub> simulations CO<sub>2</sub> is quadrupled from its preindustrial concentration. This enables a like-for-like comparison with the CMIP5 and CMIP6 abrupt-4xCO2 simulations. Two further perturbation simulations (SHET-only<sub>CMIP5</sub> and SHET-only<sub>CMIP6</sub>) are used to isolate the effect of differences in SH extratropical SST patterns alone. In both simulations CO<sub>2</sub> is kept at preindustrial values, and CTRL SSTs are used with the SST anomalies from either 4xCO2-FULL<sub>CMIP5</sub> or 4xCO2-FULL<sub>CMIP6</sub> added poleward of 18°S. Similarly to McCrystall et al. (2020), the SST anomalies are smoothed between 18°S and 29°S using a cosine squared weighting function with weights of 0 at 18°S and 1 at 29°S. This minimizes sharp gradients in SST across the tropical-extratropical boundary.</p> <p>To enable a clean determination of the effects of SST patterns alone, in all perturbation simulations we keep sea ice fixed at preindustrial values by only adding SST anomalies where the MMM sea ice concentration in the CMIP5 piControl simulations is less than 15% (i.e., equatorward of the sea ice edge). Furthermore, to remove the effect of differences in the change in global mean SST, the SST anomalies in each CMIP model are normalised by the respective global mean SST anomaly and then scaled to a global mean value of 2.2 K (the pooled MMM of CMIP5 and CMIP6). The CMIP6 FAST SST anomalies are added to the CMIP5 preindustrial control SSTs, so as to isolate the effect of differences in the fast SST responses between CMIP5 and CMIP6, and not the effect of differences in the base state.</p>
North Atlantic jet stream clusters: daily and seasonal occurence
<p>This dataset contains the time series used in Madonna et al 2020 (Reconstructing winter climate anomalies in the Euro-Atlantic sector using circulation patterns, DOI: 10.5194/wcd-2021-6)</p> <p><br> Filenames:</p> <p>1) seasonal_timeseries.txt</p> <p>Time series of the occurrence (in % = days/season*100) of time during winter of each jet cluster, blocking and the NAO.<br> Winters are defined as December, January and February (DJF). The season name is given by the last month (i.e. 1980 is December 1979, January 1980 and February 1980). 29 February is removed from the data so that each winter season has 90 days.</p> <p>Jet clusters are calculated following Madonna et al 2017. The five clusters are named as in Madonna et al 2017: Northern (N), Central (C), Mixed (M), Southern (S) and Tilted (T).<br> Blocking are calculated following Scherrer et al. 2006 and averaged over Greenland (GB), offshore of the Iberian Peninsula also called Iberian wave breaking (IWB) and over Scandinavia (SBL). The exact definition of the regions can be found in Madonna et al 2020.</p> <p>The NAO index was downloaded from ftp://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.nao.index.b500101.current.ascii. Positive (NAO+) and negative (NAO-) days are defined as those that exceed 0.5 DJF standard deviation, corresponding to values greater than 0.613 and lower than -0.177, respectively.</p> <p>Example: during winter 1980, 7.78% of the days were in the North jet cluster. This is equivalent to 7 days -> 7.78 * 90 (days per season) /100</p> <p><br> 2) daily_inverse_distance_from_centroid.txt contains information about the similarity of the 2D zonal wind field to the cluster centroids which is used to determine the jet state.</p> <p>The file has 12 columns, labelled as follow:<br> date, lat, speed, N4, C4, M4, S4, N5, C5, M5, S5, T5</p> <p>The first column (date) shows the day in YYYYMMDD format, the second (lat) is the latitude (in °N) of the maximum zonal wind in the 60°W-0°W sector (i.e. the jet latitude index, see Woollings et al. 2010 or Madonna et al. 2017 for more details), and the third (speed) is the zonal averaged (60°W-0°) zonal wind speed (in m/s) at the latitude given by column 2.</p> <p>Columns 4-7 give the inverse distance from each cluster centroids using four (4) clusters: Northern (N4), Central (C4), Mixed (M4), Southern (S4) and is normalized from 0 to 1. Values close to 1 means that the clusters are similar to its centroid. The distances sum up to 1.</p> <p>Columns 8-12 show similar to 4-7 the inverse distance from the centroids using five (5) clusters: Northern (N5), Central (C5), Mixed (M5), Southern (S5) and Tilted (T5). Distances are also normalized and sum up to 1.</p> <p>In the study of Madonna et al 2020, a day has a defined cluster X (X=N, C, M, S, T), if the inverse distance from the cluster centroid X exceeds 0.5 and it clearly dominates over the other clusters.</p> <p><br> Example: 1 January 1979, the zonal mean zonal wind is maximum at 47°N and has a value of 15.61 m/s.<br> Considering 4 clusters, the jet resembles most the Mixed cluster (M4=0.36), followed by the Southern (S4=0.23), Northern (N4=0.22) and Central (C4=0.18). The sum of the distances (0.36 + 0.23 + 0.22 + 0.18 = 0.99 due to decimal approximation) is equal to 1. Using 4 clusters, this day would be assigned to cluster M4. The day is, however, not clearly identified as a Mixed jet, as the inverse distance (M4=0.36) is smaller than 0.5. The threshold of 0.5 is set to identify days where a centroid clearly leads over the others.<br> If we consider 5 clusters, the jet on 1 Jan 1979 resembles the tilted jet (T5 = 0.73) and has very little in common with the other centroids (values of 0.05-0.08). Thus, considering 5 clusters, this day is classified as a tilted jet. It is also clearly defined, as 0.73 > 0.5.</p> <p><br> References:</p> <p>Madonna, E., Li, C., Grams, C.M. and Woollings, T. (2017), The link between eddy‐driven jet variability and weather regimes in the North Atlantic‐European sector. Q.J.R. Meteorol. Soc, 143: 2960-2972. https://doi.org/10.1002/qj.3155</p> <p>Scherrer, S. C., Croci‐Maspoli, M., Schwierz, C., and Appenzeller, C. (2006). Two‐dimensional indices of atmospheric blocking and their statistical relationship with winter climate patterns in the Euro‐Atlantic region. International Journal of Climatology, 26(2), 233-249</p> <p>Woollings T, Hannachi A and Hoskins B. (2010). Variability of the North Atlantic eddy‐driven jet stream. Q. J. R. Meteorol. Soc. 136: 856– 868.</p> <p> </p> <p> </p> <p> </p>
Resources for The Fundamental Limit of Jet Tagging
<p>Resources related to The Fundamental Limit of Jet Tagging (arxiv:2411.02628)</p> <p>Includes:</p> <p>-In total 11,200,0000 qcd and 11,200,0000 top jets generated from corresponding transformer-based models (Tmodels) trained using the JetClass Dataset.</p> <p>-The trained Tmodels.</p> <p>-The LLR predictions from the Tmodels, for different number of constituents.</p> <p>-The predictions corresponding to the classifier-based jet taggers.</p>
Data for "Schlieren and BOS velocimetry of a round turbulent helium jet in air"
<p>This is a subset of data used in our publication <a href="https://arxiv.org/abs/2202.04122">Schlieren and BOS velocimetry of a round turbulent helium jet in air</a> </p> <p> </p> <p>In the four helium-jet schlieren-image datasets given below, the nozzle diameter is 1.4 mm, the camera frame rate is 6000 fps and the individual image exposure is 0.0001667 s. 3000 images are provided for each dataset, or 1/2 s in real-time, which we determined to provide statistically-adequate data. Please see the <a href="https://arxiv.org/abs/2202.04122">paper</a> for more detail.</p> <p><strong>20210125-Run1</strong>: traditional mirror-schlieren, $Re_d = 5,890$, $U_j = 436$ m/s, and the scale is 0.29 mm/pixel.</p> <p><strong>20210206-Run1</strong>: traditional mirror-schlieren, $Re_d = 11,300$, $U_j = 682$ m/s, and the scale is 0.29 mm/pixel.</p> <p><strong>20210419-Run1</strong>: background-oriented schlieren (BOS), $Re_d = 5,890$, $U_j = 436$ m/s, and the scale is 0.26 mm/pixel. These are raw BOS images that must be processed with a reference image in order to yield pseudo-schlieren results. The reference (flow-off, tare) image is the first image in the sequence and is so named.</p> <p><strong>20210420-Run1</strong>: background-oriented schlieren (BOS), $Re_d = 11,300$, $U_j = 682$ m/s, and the scale is 0.26 mm/pixel. These are raw BOS images that must be processed with a reference image in order to yield pseudo-schlieren results. The reference (flow-off, tare) image is the first image in the sequence and is so named.</p>
Data files for: Meteorological factors in the production of Gigantic Jets by tropical thunderstorms in Colombia
<p>Data includes:</p> <ul> <li>Gigantic jet locations and times</li> <li>Vertical profiles for GJ and null cases</li> <li>CSV files with meteorological variables per GJ event and null case</li> </ul>
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