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21 results for “Supercells”
ARMOR and NALMA data corresponding to "Observations of anomalous charge structures in supercell thunderstorms in the Southeastern United States"
<p>Dataset includes dual-polarization C-band University of Alabama in Huntsville (UAH) Advanced Radar for Meteorological and Operational Research (ARMOR) data in Raw and quality-controlled Universal Format (UF) from a selected period on 10 April 2009 as well as the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) North Alabama Lightning Mapping Array (NALMA) data in American Standard Code for Information Interchange (ASCII) format from selected period on 10 April 2009. </p> <p>The ARMOR is located at the Huntsville International Airport in Huntsville, Alabama at 34.64597, -86.77131, 200 m MSL. A set of 15 radar sampling volumes between 1712 UTC and 1821 UTC on 10 April 2009 are included in the dataset. Each of the raw and corrected UF files contains horizontal reflectivity (dBZ), differential reflectivity (dB), Doppler velocity (m s<sup>-1</sup>), spectrum width (m s<sup>-1</sup>), differential phase (°), and total power (dBZ) data. The corrected UF files additionally contain horizontal reflectivity and differential reflectivity data corrected for attenuation and differential attenuation following the methods of Bringi et al. (2001). The corrected files also contain estimated differential propagation phase (°) and computed specific differential phase (° km<sup>-1</sup>) data (Hubbert and Bringi 1995). </p> <p> </p> <p>ARMOR file naming conventions are as follows: </p> <p> </p> <p>RAW_NA_000_125_20090410171216.gz</p> <p>RAW: file format</p> <p>125: can scan type, where 125 indicates a full or sector volume plan position indicator </p> <p>20090410171216: date and time in the order of year, month, day, hour, minute, and second</p> <p> </p> <p>ARMOR_20090410171216_qc1.uf.gz</p> <p>ARMOR: radar name</p> <p>20090410171216: date and time in the order of year (YYYY), month (MM), day (DD), hour (HH), minute (MM), and second (SS)</p> <p>qc1: denotes ARMOR processed data</p> <p>uf: denotes the file format </p> <p> </p> <p>NALMA data consist of undecimated VHF source-level lightning measurements in hourly files. The center of the network is located at 34.72461, -86.64533. The network consisted of 11 sensors distributed throughout north Alabama and south-central Tennessee. Information about contributing stations is available in the header of each hourly file, including the station location, status, and the number of sources detected by each station. Further network-specific information documented by Koshak et al. (2004) while Rison et al. (1999) discuss LMA characteristics.</p> <p>Source data include information about the time the source was detected (UTC seconds of the day), latitude and longitude (decimal degrees), altitude (m), reduced chi<sup>2</sup> value associated with post-processing (unitless), power (dBW), and a network mask indicating the detecting stations (unitless). The format is (f15.9 f10.6 f11.6f 7.1 f5.2 f5.1 4x). </p> <p> </p> <p>Hourly file naming conventions are as follows:</p> <p> </p> <p>LYLOUT_090410_160000_3600.dat.gz</p> <p>LYLOUT: LMA file designator</p> <p>090410: date in order of last two digits of year (YY), month (MM), and day (DD)</p> <p>160000: time in order of hour (HH), minute (MM), and second (SS)</p> <p>3600: length of period covered in file in seconds (3600 s = 1 hour)</p> <p> </p> <p>Acknowledgments: </p> <p>Data were collected with support from NASA MSFC Award NNM05AA22A.</p> <p> </p> <p>References:</p> <p>Bringi, V. N., Keenan, T. D., & Chandrasekar, V. (2001). Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: A self-consistent method with constraints. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <em>39</em>(9), 1906–1915. https://doi.org/10.1109/36.951081</p> <p>Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements. <em>Journal of Atmospheric and Oceanic Technology</em>, <strong>12</strong>, 643–648. </p> <p>Koshak, W. J., Solakiewicz, R. J., Blakeslee, R. J., Goodman, S. J., Christian, H. J., Hall, J. M., … Cecil, D. J. (2004). North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses. <em>Journal of Atmospheric and Oceanic Technology</em>, <em>21</em>(4), 543–558. https://doi.org/10.1175/1520-0426(2004)021<0543:NALMAL>2.0.CO;2</p> <p>Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., & Harlin, J. (1999). A GPS-based three-dimensional lightning mapping system: Initial observations in Central New Mexico. <em>Geophysical Research Letters</em>, <em>26</em>(23), 3573–3576.</p>
ARMOR and NALMA data corresponding to 2008 storms analyzed in "Examining conditions supporting the development of anomalous charge structures in supercell thunderstorms in the Southeastern United States"
<p>Total lightning and dual-polarization Doppler velocity data are available from the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) North Alabama Lightning Mapping Array (NALMA) and the C-band University of Alabama in Huntsville (UAH) Advanced Radar for Meteorological and Operational Research (ARMOR), respectively, over selected periods on 6 February 2008 and 11 April 2008. NALMA data are provided in American Standard Code for Information Interchange (ASCII) format and ARMOR data are provided in Raw and quality-controlled Universal Format (UF), where quality control methods are described below. </p> <p> </p> <p>The NALMA data are provided in hourly files which include undecimated point location (source-level) data corresponding to the detection of very high frequency (VHF) radiation emitted during the breakdown of lightning (Rison et al., 1999; Thomas et al., 2001). Source locations were reported from active sensors configured in an 11-sensor array distributed throughout North Alabama and South Central Tennessee, the center of which is located at 34.72641, -86.64533 (Koshak et al. 2004). Data files include information on the time that each source was detected (UTC seconds of the day), the latitude, longitude, and altitude of each source’s location (decimal degrees and m, respectively), the reduced chi<sup>2</sup> value associated with data processing (unitless), a station mask indicating which sensors contributed to the resolved location of each source (unitless). These data are provided in a line-by-line format of (f15.9 f10.6 f11.6f 7.1 f5.2 f5.1 4x). The 2008 data files additionally include a header section that provides further information about each sensor in the network and its relative contribution to the dataset. </p> <p> </p> <p>The hourly fine naming conventions are as follows for the February 2008 data:</p> <p>LMA_NA_6.2_125_2008-02-06_10-00-00.dat.gz</p> <p>LMA_NA: LMA file designator corresponding to the NALMA</p> <p>2008-02-06: year (YYYY)-month (MM)-day (DD)</p> <p>10-00-00: UTC time, (HH)-minute (MM)-second (SS)</p> <p> </p> <p>And for the April 2008 data:</p> <p>LYLOUT_080411_180000_3600.dat.gz</p> <p>LYLOUT: LMA file designator</p> <p>080411: date in order of last two digits of year (YY), month (MM), and day (DD)</p> <p>180000: UTC time in order of hour (HH), minute (MM), and second (SS)</p> <p>3600: length of period covered in file in seconds (3600 s = 1 hour)</p> <p> </p> <p>ARMOR data are provided as sets of 14 (14) sampling volumes corresponding to the 6 February 2008 (11 April 2008) periods between 1002 UTC and 1119 UTC (1844 UTC and 1952 UTC). Each RAW and processed UF file contains horizontal reflectivity (dBZ), differential reflectivity (dB), Doppler velocity (m s<sup>-1</sup>), spectrum width (m s<sup>-1</sup>), differential phase (º), and total power (dBZ) data. Horizontal reflectivity and differential reflectivity data were corrected for attenuation and differential attenuation, differential propagation phase (º) was estimated, and specific differential phase (º km<sup>-1</sup>) was calculated during post-processing (Hubbert and Bringi 1995, Bringi et al. 2001).</p> <p> </p> <p>Acknowledgments: </p> <p>NALMA data were collected with support from NASA MSFC Award NNM05AA22A.</p> <p> </p> <p>References:</p> <p>Bringi, V. N., Keenan, T. D., & Chandrasekar, V. (2001). Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: A self-consistent method with constraints. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <em>39</em>(9), 1906–1915. https://doi.org/10.1109/36.951081</p> <p>Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements. <em>Journal of Atmospheric and Oceanic Technology</em>, <strong>12</strong>, 643–648. </p> <p>Koshak, W. J., Solakiewicz, R. J., Blakeslee, R. J., Goodman, S. J., Christian, H. J., Hall, J. M., … Cecil, D. J. (2004). North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses. <em>Journal of Atmospheric and Oceanic Technology</em>, <em>21</em>(4), 543–558. https://doi.org/10.1175/1520-0426(2004)021<0543:NALMAL>2.0.CO;2</p> <p>Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., & Harlin, J. (1999). A GPS-based three-dimensional lightning mapping system: Initial observations in Central New Mexico. <em>Geophysical Research Letters</em>, <em>26</em>(23), 3573–3576.</p> <p>Thomas, R. J., Krehbiel, P. R., Hamlin, T., Harlin, J., & Shown, D. (2001). Observations of VHF source powers radiated by lightning. <em>Geophysical Research Letters</em>, <em>28</em>(1), 143–146. https://doi.org/10.1029/2000GL011464</p> <p> </p>
Supplementary data and code for: Non-Abelian Hyperbolic Band Theory from Supercells
<p>This data and code collection contains all necessary code and data required to reproduce the results of the accompanied publication. To run the code contained in this collection GAP, the HyperCells package, Mathematica, the NCAlgebra (version >= 6.0) and the HyperBloch package are required. Please refer to the included Readme file and the links therein for more details.</p> <p>Changes: add NCAlgebra to list of dependencies.</p>
Data for "Tropical Cyclone Supercell Response to the Coast using a Climatology of Radar-Derived Azimuthal Shear"
<p>This dataset contains a .csv file published alongside the article entitled "Tropical Cyclone Supercell Response to the Coast using a Climatology of Radar-Derived Azimuthal Shear" for consideration in <em>Geophysical Research Letters</em>.</p>
Data for "Examining outer band supercell environments in landfalling tropical cyclones using ground-based radar analyses" v3
<p>The data here are archived for open data access for the publication entitled "Examining outer band supercell environments in landfalling tropical cyclones using ground-based radar analyses" submitted to <em>Monthly Weather Review</em>.</p> <p>Radar data are archived in netCDF format in which variables are identified by their radar moment. The radar data are separated by SR1 and KLCH for Hurricane Laura. For Hurricane Frances, the relevant SR data are contained in the frances_sr_data.tar.gz file.</p> <p>The csv archive contains the track information for objectively identified supercell storms from the manuscript.</p> <p>Questions about the data may be directed to addison.alford@noaa.gov.</p>
Properties of negative initial leaders and lightning flash size in a cluster of supercells
<p>It is the figure data of the paper titled as "Properties of negative initial leaders and lightning flash size in a cluster of supercells". The abstract of this paper is as follows:</p> <p>Properties of negative initial leaders (NILs) and flash size in a cluster of supercells with generally inverted charge structure in Oklahoma on 10 − 11 May 2010 are examined, primarily using Lightning Mapping Array data. A method to identify NILs from LMA source is proposed, and helps to reveal the multiple NILs properties and their distributions. The NILs in the supercell cluster have smaller speed (median 3D displacement speed: 0.65 × 10<sup>5</sup> m s<sup>−1</sup>), relative to the previous reports in “normal” thunderstorms. Furthermore, median NIL speeds initially decrease with increasing height, but begin increasing above 12 km. The NILs tend to decelerate during the early stage. The parameters characterizing flash duration and spatial size are also investigated. It is found they all follow lognormal distributions and the spatial flash size is relatively small on average (median horizontal distance: 5.54 km). Most flashes (83.18%) extend primarily in the horizontal direction. Flash area shows an inverse relationship with flash density at their fast changes during storm evolution. Although large flash initiation density (FID) generally occurs in regions with small flash size, the smallest flash size is nearly not collocated with large FID value. In the regions with large FID, average flash duration roughly increases with increasing FID, while average flash area changes little. We proposed that the pattern of charge pockets and variation of charge density dominated by the strong kinematics are responsible for some new findings about the properties of NIL and flash size in the supercell cluster.</p>
Synthetic 4D STEM dataset based on a SrTiO3 supercell with two additional artificial spatial frequencies
<p>This dataset allows to investigate phase contrast methods for 4D scanning transmission electron microscopy, such as ptychography.</p> <p>A synthetic dataset has been simulated, based on an SrTiO<sub>3</sub> unit cell as a starting point. Then, a five by five super cell was created by repetition. Two artificial spatial frequencies were added to the phase grating, one with a wavelength of a single unit cell and one with a wavelength of the super cell. To eliminate dynamical scattering, a 4D-STEM simulation with 20 × 20 scan points per unit cell was performed using only one slice with a thickness of one unit cell along electron beam direction [001].</p> <p><strong>Files</strong></p> <ul> <li><em>conf_01.mat</em>: HDF5 file with the phase grating.</li> <li><em>Data extraction and plot of the phase grating.ipynb</em>: Jupyter notebook showing how to access the phase grating file and plot the data.</li> <li><em>slice_00001_thick_1.9525_nm_blocksz100.raw</em>: Simulated 4D STEM dataset as a raw binary file. Shape 100 x 100 x 596 x 596, dtype float32.</li> <li><em>ssb-example.ipynb</em>: Jupyter notebook showing first moment analysis and ptychography with the dataset.</li> </ul> <p><strong>Simulation parameters</strong></p> <ul> <li>Scan points: 100x100</li> <li>Field of view: 1.9525nm</li> <li>Convergence angle: 23mrad, 136 px</li> <li>Acceleration voltage: 300 kV</li> <li>Center: (297, 297)</li> <li>Rotation angle: 0°</li> </ul>
Numerical simulation of supercell thunderstorms (at 50 meter resolution) associated with above anvil cirrus plumes
<p>Four-dimensional data from high-resolution simulations (50 meter grid spacing) of supercell thunderstorms conducted on the Frontera supercomputer are contained in this archive. Output from two simulations is included, named "Strong" and "Weak". In the Strong simulation, data from parcel trajectories is also included. </p> <p>The four-dimensional (time, and three dimensions of space) data and parcel data is saved in Network Common Data Format (NetCDF), version 4, an open-source self-describing scientific data format commonly used in the atmospheric sciences. </p>
Data for "From Langmuir Turbulence to Supercells: the Role of Longitudinal Alignment between Surface and Bottom Forcing"
<p>The files here contains the data shown in the article. The main part of data can be find in v4. v5 include a new result of conditional averaged turbulent kinetic energy.</p>
Numerical simulation of supercell thunderstorms (at 50 meter resolution) associated with above anvil cirrus plumes
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Spanish Supercell Dataset - Hail
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Properties of negative initial leaders and flash size in a cluster of supercells
<p>The data is associated with the paper titled "Properties of negative initial leaders and flash size in a cluster of supercells" . The abstract is as follows:</p> <p>Properties of negative initial leaders (NILs) and flash size in a cluster of supercells with generally inverted charge structure in Oklahoma on 10−11 May 2010 are examined, primarily using Lightning Mapping Array data. A method to identify NILs from LMA source is proposed. Median values of 18.00 ms, 1.02 km, 0.65 × 10<sup>5</sup> m s<sup>−1</sup>, and 47.24° are found for NIL duration, three-dimensional (3D) displacement, 3D displacement speed, and the angle between the displacement and the vertical directions, respectively. Median NIL speeds initially decrease with increasing height, but begin increasing above 12 km, possibly related to the rapid reduction in air density. The NILs tend to decelerate during the early stage, particularly during the first 6 ms. The NILs in small-size charge regions tend to have low speeds, vice versa. The flash duration, horizontal distance, vertical distance, flash area and flash volume have median values of 0.27 s, 5.54 km, 3.67 km, 9.67 km<sup>2</sup>, and 14.37 km<sup>3</sup>, respectively. All parameters follow lognormal distributions. Most flashes (83.18%) extend primarily in the horizontal direction. Flash area shows an inverse relationship with flash density at their fast changes during storm evolution. Although large flash initiation density (FID) generally occurs in regions with small flash size, the smallest flash size is not collocated with large FID value. Flash duration changes independently of flash area in regions with large FID. A hypothesis is proposed to explain the observations in this study on the correlations among dynamic process, flash activity, flash size, and flash duration.</p>
Revealing Key Dynamical Mechanisms of a Severe Supercell within a QLCS using Rapid Update 4DVar Assimilation of C-band Phased Array Weather Radar Data
<div> <p>These are data supporting the conclusions of the paper. The observation dataset including sounding data, automatic weather station data and radar data. And Vdras outputs are also provided here. The corresponding scripts are based on python(version 3.9) and NCL (version 6.6.2).</p> <p> </p> </div> <h2>Files</h2>
Data and movies for Finley et al. "Impact of the Streamwise Vorticity Current on Low-level Mesocyclone Development in a Simulated Supercell"
<p>The netcdf data files and python files used to make the figures, and the movies provided in the supplemental material in Finley et al. Netcdf software is available from https://www.unidata.ucar.edu/software/netcdf/. Figures 1-4 and movies were made with VisIt, which is available from https://visit-dav.github.io/visit-website/. </p>
Assessing the comparative effects of storm-relative helicity components within right-moving supercell environments
<p>Supercell thunderstorms develop low-level rotation via tilting of environmental horizontal vorticity (ω<sub>h</sub>) by the updraft. This rotation induces dynamic lifting that can stretch near-surface vertical vorticity into a tornado. Low-level updraft rotation is generally thought to scale with 0–500 m storm-relative helicity (SRH): the combination of storm-relative flow, |SRF|, |ω<sub>h</sub>|, and cosφ (where φ is the angle between SRF and ω<sub>h</sub>). It is unclear how much influence each component of SRH has in intensifying the low-level mesocyclone. This study surveys these three components using self-organizing maps (SOMs) to distill 15,906 proximity soundings for observed right-moving supercells. Statistical analyses reveal the component most highly correlated to SRH and to streamwise vorticity (ω<sub>s</sub>) in the observed profiles is |ω<sub>h</sub>|. Furthermore, |ω<sub>h</sub>| and SRF are themselves highly correlated due to their shared dependence on the hodograph length. The representative profiles produced by the SOMs were combined with a common thermodynamic profile to initialize quasi-realistic supercells in a cloud model. The simulations reveal that, across a range of real-world profiles, intense low-level mesocyclones are most closely linked to ω<sub>h</sub> and SRF, while the angle between them appears to be mostly inconsequential.</p>
Assessing the comparative effects of storm-relative helicity components within right-moving supercell environments
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Data from: From cellular characteristics to disease diagnosis: uncovering phenotypes with supercells
Cell heterogeneity and the inherent complexity due to the interplay of multiple molecular processes within the cell pose difficult challenges for current single-cell biology. We introduce an approach that identifies a disease phenotype from multiparameter single-cell measurements, which is based on the concept of "supercell statistics", a single-cell-based averaging procedure followed by a machine learning classification scheme. We are able to assess the optimal tradeoff between the number of single cells averaged and the number of measurements needed to capture phenotypic differences between healthy and diseased patients, as well as between different diseases that are difficult to diagnose otherwise. We apply our approach to two kinds of single-cell datasets, addressing the diagnosis of a premature aging disorder using images of cell nuclei, as well as the phenotypes of two non-infectious uveitides (the ocular manifestations of Behçet's disease and sarcoidosis) based on multicolor flow cytometry. In the former case, one nuclear shape measurement taken over a group of 30 cells is sufficient to classify samples as healthy or diseased, in agreement with usual laboratory practice. In the latter, our method is able to identify a minimal set of 5 markers that accurately predict Behçet's disease and sarcoidosis. This is the first time that a quantitative phenotypic distinction between these two diseases has been achieved. To obtain this clear phenotypic signature, about one hundred CD8+ T cells need to be measured. Although the molecular markers identified have been reported to be important players in autoimmune disorders, this is the first report pointing out that CD8+ T cells can be used to distinguish two systemic inflammatory diseases. Beyond these specific cases, the approach proposed here is applicable to datasets generated by other kinds of state-of-the-art and forthcoming single-cell technologies, such as multidimensional mass cytometry, single-cell gene expression, and single-cell full genome sequencing techniques.
Left-moving Supercells with Polarimetric Data Available, 2011-2022
<p>This is a quality-controlled dataset of left-moving supercell storms that are associated with polarimetric weather radar data collected by the U.S. WSR-88D network. Cases are from 2011-2022, and include a combination of cases from SPC's Storm Modes dataset (Smith et al. 2012) from 2011-2015, and manually-derived cases systematically collected by all of the dataset creators. Mesoanticyclone strength is noted following the Andra (1997) nomogram. Start/end time and lat/lon are specified for each case to ensure good radar data quality. Quality control consists of (i) ensuring that each storm is separated from others; (ii) the storm is within 200 km of a WSR-88D; (iii) updraft rotation is anticyclonic and consistent through time and over depth; (iv) the low-level reflectivity gradient is on the left flank relative to storm motion. </p> <p>From version 1 to version 2, the following changes were made: </p> <p>REMOVED: storms 89, 93, 107, 216, 564, 673, 698 [These were duplicated in the original dataset]</p> <p>REVISED: storm 53 (new end time 0021 UTC; new end lat/lon 34.62/-102.25) [Incorrect storm was tracked in the original dataset] </p>
Data for "Supercells and Tornado-like Vortices in an Idealized Global Atmosphere Model"
<p>Data used in a manuscript on supercells and tornado-like vortices, for submission to ESS.</p>
Data from: From cellular characteristics to disease diagnosis: uncovering phenotypes with supercells
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