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304 results for “lightning”
Negative Differential Resistance, Instability, and Critical Transition in Lightning Leader
<p>There is a challenging issue of die out and restrike of leaders in a burst of lightning event, such as multiplicity of strokes in grounded flash or recoil leader in cloud flash, although the argument involves the effects of channel characteristic of negative differential resistance instability, there is internal consistency about the instability, the bi-stability of insulation and induction and their critical transition from the view of bifurcation theory of nonlinear dynamics. In this paper, we examined the differential resistance characteristics of the leader-streams lighting system, we associate the leader-streamer channel differential resistance characteristics and sign change with channel state and instability transitions. The critical current and potential difference conditions for the stable transition of the leader-streamer channel are investigated. It is found that the necessary current required for the stable development of the channel is increased with the length of the leader channel, while the internal channel electric field of the leader decreases accordingly.</p>
Energy characteristics and the source of a ball lightning obtained by investigating its spectra
<p>This work, based on quantitative spectrometric analysis, is an exploratory research on the radiated power density and its evolution feature of a ball lightning. The radiated power density has shown a periodic pulse feature, like the spectral characteristics and temperature evolution of this BL. We proposed that this BL may be the discharge from the residual charge at the bottom of the previous cloud-to-ground (CG) lightning channel initiating it. Meanwhile, the electromagnetic field produced by the power line may be a potential outside energy source that supports the life of the BL. The optical radiation from soil constituent dominates the bright light of this BL.</p><p> </p>
Supporting information for the paper "High peak current lightning and the production of Elves"
<p>Supporting data for the paper "High peak current lightning and the production of Elves" by I. Bjørge-Engeland et al.</p>
Precise determination of lightning plasma parameters based on the collisional-radiative model
<p>Fine spectrum of lightning have been simulated by collisional-radiative model (CRM), and the radiation of core channel is mainly dominated by N II ion. The line-intensities<br>of N II dependent on electron temperature (Te) and electron density (ne) were obtained. And it is found that the intensity proportion of 500.52 nm in the 500.5 nm multiple spectral lines and that of 567.96 nm in 568.0 nm one, i.e., I(500.52nm)/ I(500.5multiple) and I(567.96nm)/ I(568.0multiple), tend to stabilize in the Te-ne variation space, which is an inherent physical property that can be applied in determining the experimental intensity of characteristic line from the total intensity of overlapped lines. Through accurately matching the theoretical intensityratio with the experimental one, e.g., I(500.52nm)/ I(444.70nm) and I(567.96nm)/ I(444.70nm), an intersection of contours of line-intensity ratios in the two-dimensional plane of Te-ne is derived, and then, the Te and ne in lightning channel were diagnosed simultaneously. Precise spectral analysis method and plasma diagnostic technique are the basis of researching the transmission characteristics of lightning discharge, this work also has application value in articially triggered lightning, laser-guided lightning, arc discharge, etc.</p>
Monthly averaged lightning data extracted from 1-year EMAC simulation including LCC-lightning (between 1 March, 2017 and 28 February, 2018, T42L90MA resolution).
<p>About Dataset<br> Monthly averaged lightning data extracted from 1-year EMAC simulation (between 1 March, 2017 and 28 February, 2018, T42L90MA resolution).</p> <p>Authors: Francisco J. Perez-Invernon, Heidi Huntrieser, Patrick Joeckel and Francisco J. Gordillo-Vazquez</p> <p>Description of the data<br> P_cth.tar: Lightning parameterization based on cloud top height. Scaling factor is applied.<br> L_cth.tar: Lightning parameterization based on cloud top height and modified over the oceans. A scaling factor of 0.571 have to be applied.<br> G_updr: Lightning parameterization based on updraft velocity. Scaling factor is applied.<br> A_prec: Lightning parameterization based on convective precipitation. A scaling factor of 0.76 have to be applied.<br> A_updr: Lightning parameterization based on Updraft strength at 440~hPa. A scaling factor of 0.01618 have to be applied.<br> P_cth + A_prec: Lightning parameterization based on cloud top height and updraft velocity. A scaling factor of 1.13 have to be applied.</p> <p>File format: netcdf</p> <p> Example:<br> <br> netcdf LCC_2017_______20170201_0000_mmlb_PaR_T {<br> dimensions:<br> time = UNLIMITED ; // (1 currently)<br> lon = 128 ;<br> lat = 64 ;<br> tbnds = 2 ;<br> variables:<br> double time(time) ;<br> time:long_name = "time" ;<br> time:bounds = "time_bnds" ;<br> time:units = "day since 2017-01-01 00:00:00" ;<br> time:calendar = "gregorian" ;<br> double YYYYMMDD(time) ;<br> YYYYMMDD:long_name = "time" ;<br> YYYYMMDD:units = "days as %Y%m%d.%f" ;<br> YYYYMMDD:calendar = "gregorian" ;<br> double dt(time) ;<br> dt:long_name = "delta_time" ;<br> dt:units = "s" ;<br> double nstep(time) ;<br> nstep:long_name = "current time step" ;<br> float lon(lon) ;<br> lon:long_name = "longitude" ;<br> lon:units = "degrees_east" ;<br> float lat(lat) ;<br> lat:long_name = "latitude" ;<br> lat:units = "degrees_north" ;<br> float aps(time, lat, lon) ;<br> aps:long_name = "surface pressure" ;<br> aps:units = "Pa" ;<br> aps:representation = "GP_2D_HORIZONTAL" ;<br> aps:grid_type = "gaussian" ;<br> aps:table = 128 ;<br> aps:code = 134 ;<br> aps:REFERENCE_TO = "g3b: aps" ;<br> aps:coordinates = "lon lat" ;<br> aps:cell_methods = "time: point" ;<br> float aps_ave(time, lat, lon) ;<br> aps_ave:long_name = "surface pressure" ;<br> aps_ave:units = "Pa" ;<br> aps_ave:representation = "GP_2D_HORIZONTAL" ;<br> aps_ave:grid_type = "gaussian" ;<br> aps_ave:table = 128 ;<br> aps_ave:code = 134 ;<br> aps_ave:REFERENCE_TO = "g3b: aps" ;<br> aps_ave:coordinates = "lon lat" ;<br> aps_ave:cell_methods = "time: mean" ;<br> float fpscg(time, lat, lon) ;<br> fpscg:long_name = "CG flash frequency" ;<br> fpscg:units = "1/s" ;<br> fpscg:REFERENCE_TO = "lnox_PaR_T_gp: fpscg" ;<br> fpscg:coordinates = "lon lat" ;<br> fpscg:cell_methods = "time: point" ;<br> float fpscg_ave(time, lat, lon) ;<br> fpscg_ave:long_name = "CG flash frequency" ;<br> fpscg_ave:units = "1/s" ;<br> fpscg_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpscg" ;<br> fpscg_ave:coordinates = "lon lat" ;<br> fpscg_ave:cell_methods = "time: mean" ;<br> float fpsic(time, lat, lon) ;<br> fpsic:long_name = "IC flash frequency" ;<br> fpsic:units = "1/s" ;<br> fpsic:REFERENCE_TO = "lnox_PaR_T_gp: fpsic" ;<br> fpsic:coordinates = "lon lat" ;<br> fpsic:cell_methods = "time: point" ;<br> float fpsic_ave(time, lat, lon) ;<br> fpsic_ave:long_name = "IC flash frequency" ;<br> fpsic_ave:units = "1/s" ;<br> fpsic_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsic" ;<br> fpsic_ave:coordinates = "lon lat" ;<br> fpsic_ave:cell_methods = "time: mean" ;<br> float fpsm2cg(time, lat, lon) ;<br> fpsm2cg:long_name = "CG flash density" ;<br> fpsm2cg:units = "1/s/m2" ;<br> fpsm2cg:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2cg" ;<br> fpsm2cg:coordinates = "lon lat" ;<br> fpsm2cg:cell_methods = "time: point" ;<br> float fpsm2cg_ave(time, lat, lon) ;<br> fpsm2cg_ave:long_name = "CG flash density" ;<br> fpsm2cg_ave:units = "1/s/m2" ;<br> fpsm2cg_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2cg" ;<br> fpsm2cg_ave:coordinates = "lon lat" ;<br> fpsm2cg_ave:cell_methods = "time: mean" ;<br> float fpsm2ic(time, lat, lon) ;<br> fpsm2ic:long_name = "IC flash density" ;<br> fpsm2ic:units = "1/s/m2" ;<br> fpsm2ic:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2ic" ;<br> fpsm2ic:coordinates = "lon lat" ;<br> fpsm2ic:cell_methods = "time: point" ;<br> float fpsm2ic_ave(time, lat, lon) ;<br> fpsm2ic_ave:long_name = "IC flash density" ;<br> fpsm2ic_ave:units = "1/s/m2" ;<br> fpsm2ic_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2ic" ;<br> fpsm2ic_ave:coordinates = "lon lat" ;<br> fpsm2ic_ave:cell_methods = "time: mean" ;<br> float fpslcc10(time, lat, lon) ;<br> fpslcc10:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10:units = "1/s" ;<br> fpslcc10:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc10" ;<br> fpslcc10:coordinates = "lon lat" ;<br> fpslcc10:cell_methods = "time: point" ;<br> float fpslcc10_ave(time, lat, lon) ;<br> fpslcc10_ave:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10_ave:units = "1/s" ;<br> fpslcc10_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc10" ;<br> fpslcc10_ave:coordinates = "lon lat" ;<br> fpslcc10_ave:cell_methods = "time: mean" ;<br> float fpslcc20(time, lat, lon) ;<br> fpslcc20:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20:units = "1/s" ;<br> fpslcc20:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc20" ;<br> fpslcc20:coordinates = "lon lat" ;<br> fpslcc20:cell_methods = "time: point" ;<br> float fpslcc20_ave(time, lat, lon) ;<br> fpslcc20_ave:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20_ave:units = "1/s" ;<br> fpslcc20_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc20" ;<br> fpslcc20_ave:coordinates = "lon lat" ;<br> fpslcc20_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc10(time, lat, lon) ;<br> fpsm2lcc10:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10:units = "1/s/m2" ;<br> fpsm2lcc10:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc10" ;<br> fpsm2lcc10:coordinates = "lon lat" ;<br> fpsm2lcc10:cell_methods = "time: point" ;<br> float fpsm2lcc10_ave(time, lat, lon) ;<br> fpsm2lcc10_ave:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10_ave:units = "1/s/m2" ;<br> fpsm2lcc10_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc10" ;<br> fpsm2lcc10_ave:coordinates = "lon lat" ;<br> fpsm2lcc10_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc20(time, lat, lon) ;<br> fpsm2lcc20:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20:units = "1/s/m2" ;<br> fpsm2lcc20:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc20" ;<br> fpsm2lcc20:coordinates = "lon lat" ;<br> fpsm2lcc20:cell_methods = "time: point" ;<br> float fpsm2lcc20_ave(time, lat, lon) ;<br> fpsm2lcc20_ave:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20_ave:units = "1/s/m2" ;<br> fpsm2lcc20_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc20" ;<br> fpsm2lcc20_ave:coordinates = "lon lat" ;<br> fpsm2lcc20_ave:cell_methods = "time: mean" ;<br> float fpssprite(time, lat, lon) ;<br> fpssprite:long_name = "Sprites flash frequency" ;<br> fpssprite:units = "1/s" ;<br> fpssprite:REFERENCE_TO = "lnox_PaR_T_gp: fpssprite" ;<br> fpssprite:coordinates = "lon lat" ;<br> fpssprite:cell_methods = "time: point" ;<br> float fpssprite_ave(time, lat, lon) ;<br> fpssprite_ave:long_name = "Sprites flash frequency" ;<br> fpssprite_ave:units = "1/s" ;<br> fpssprite_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpssprite" ;<br> fpssprite_ave:coordinates = "lon lat" ;<br> fpssprite_ave:cell_methods = "time: mean" ;<br> float fpsm2sprite(time, lat, lon) ;<br> fpsm2sprite:long_name = "Sprites flash density" ;<br> fpsm2sprite:units = "1/s/m2" ;<br> fpsm2sprite:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2sprite" ;<br> fpsm2sprite:coordinates = "lon lat" ;<br> fpsm2sprite:cell_methods = "time: point" ;<br> float fpsm2sprite_ave(time, lat, lon) ;<br> fpsm2sprite_ave:long_name = "Sprites flash density" ;<br> fpsm2sprite_ave:units = "1/s/m2" ;<br> fpsm2sprite_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2sprite" ;<br> fpsm2sprite_ave:coordinates = "lon lat" ;<br> fpsm2sprite_ave:cell_methods = "time: mean" ;<br> float bps(time, lat, lon) ;<br> bps:long_name = "BJ flash frequency" ;<br> bps:units = "1/s" ;<br> bps:REFERENCE_TO = "bluejetbPaR_T_gp: bps" ;<br> bps:coordinates = "lon lat" ;<br> bps:cell_methods = "time: point" ;<br> float bps_ave(time, lat, lon) ;<br> bps_ave:long_name = "BJ flash frequency" ;<br> bps_ave:units = "1/s" ;<br> bps_ave:REFERENCE_TO = "bluejetbPaR_T_gp: bps" ;<br> bps_ave:coordinates = "lon lat" ;<br> bps_ave:cell_methods = "time: mean" ;<br> float bpsm2(time, lat, lon) ;<br> bpsm2:long_name = "BJ flash density" ;<br> bpsm2:units = "1/s/m2" ;<br> bpsm2:REFERENCE_TO = "bluejetbPaR_T_gp: bpsm2" ;<br> bpsm2:coordinates = "lon lat" ;<br> bpsm2:cell_methods = "time: point" ;<br> float bpsm2_ave(time, lat, lon) ;<br> bpsm2_ave:long_name = "BJ flash density" ;<br> bpsm2_ave:units = "1/s/m2" ;<br> bpsm2_ave:REFERENCE_TO = "bluejetbPaR_T_gp: bpsm2" ;<br> bpsm2_ave:coordinates = "lon lat" ;<br> bpsm2_ave:cell_methods = "time: mean" ;<br> double time_bnds(time, tbnds) ;<br> time_bnds:long_name = "time bounds" ;<br> time_bnds:units = "days since 2017-01-01T00:00:00Z" ;<br> time_bnds:cell_methods = "time: point"</p>
All data of the manuscript "A self-sustained charge neutrality lightning model containing the channel decay and reactivation process" submitted to Geophysical Research Letters
<p>The data supports the manuscript entitled "A self-sustained charge neutrality lightning model containing the channel decay and reactivation process”. Microsoft Notepad can open the *.txt files, they contain the channel information of two intracloud flashes (IC1 and IC2) and the channel elctrical parameters at the first fork of positive or negative leader channels. A normal video player software can open Movies S1.avi, and it shows the entire development process of IC1 discharge.</p> <p>The data can be used freely for scientific purposes with the appropriate citation.</p>
The HistLight global lightning stroke density reconstruction (1836–2015)
<p><strong>The HistLight global lightning stroke density reconstruction (1836–2015)</strong></p> <p>This repository contains a global estimate of past lightning stroke density based on the <a href="https://doi.org/10.5281/zenodo.4774528">WGLC</a> and the <a href="https://www.psl.noaa.gov/data/gridded/data.20thC_ReanV3.html">20th Century Reanalysis</a>. The reconstruction was made by regressing monthly mean observed lightning density from the WGLC against monthly mean Convective Available Potential Energy (CAPE) from the 20th Century Reanalysis during the six-year period of overlap (2010-2015). The linear regression was made on a log(WGLC) : log(CAPE) basis. In areas where the explanatory power of the regression model was not statistically significant (P>0.05), no CAPE-based reconstruction was made, and the missing values were filled with the 2010-2015 climatological monthly mean lightning from the WGLC.</p> <p>Further details of the preparation of this dataset will be described in a forthcoming manuscript.</p> <p><strong>Technical features</strong></p> <p>The data are stored in a <a href="https://www.unidata.ucar.edu/software/netcdf/">NetCDF</a> (version 4) file and have the following attributes:</p> <ul> <li>Spatial extent: Entire Earth</li> <li>Spatial reference system (SRS): Unprojected (geographic, WGS84)</li> <li>Spatial resolution: half-degree</li> <li>Temporal extent: 1836-2015 (time coordinate refers to the first day of the month)</li> <li>Temporal resolution: monthly</li> </ul> <p><strong>Variables included in this release</strong></p> <ul> <li>Lightning density (lght) (strokes km<sup>-2</sup> day<sup>-1</sup>)</li> </ul>
Lightning Assimilation in the Weather Research and Forecasting (WRF) Model: Technique Updates and Assessment of the Applications from Regional to Hemispheric Scales
<p>Figure 1. The data is proprietary, but it can be purchased from Vaisala Inc. (https:// <a href="http://www.vaisala.com/en/products/systems/lightning-detection">www.vaisala.com/en/products/systems/lightning-detection</a>), and the WWLLN raw data are also available for purchase at <a href="http://wwlln.net">http://wwlln.net</a>.</p> <p>Figure 2. Maps, data is not applicable.</p> <p>Figure 3. Data file: NLDN_WWLLN_Prism_Rainfall_Analysis.xlsx</p> <p>Figure 4. Data file: NLDN_WWLLN_METVARS_T2_Jul_2016.xlsx</p> <p>Figure 5. Data file: CONUSall_METOBS_q_Jul_2016.xlsx</p> <p>Figure 6. Data file: CONUSall_METOBS_ws_Jul_2016.xlsx</p> <p>Figure 7. Created using the R script: Hemi_Rain_ModelOnlyWGPM.R based on the R object files: AnnualRainFall_CFC_WRF_Hemi_BASE_*.rds, AnnualRainFall_CFC_WRF_Hemi_LTA_*.rds, and GPM_WRF_Paired_rain2Hemispheric_July2016.rds.</p> <p>Figure 8. Created using the R script: Hemi_Rain_Aanlysis.R based on the R object files: AnnualRainFall_CFC_WRF_Hemi_BASE_*.rds and AnnualRainFall_CFC_WRF_Hemi_LTA_*.rds.</p> <p>Figure 9. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx</p> <p>Figure 10. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx</p> <p>Figure 11. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx</p> <p>Figure 12. Created using the R script: CreateCPCdataforUSdomain_vs_Prism.R based on the R oject files: Prism_CFC_WRF*.rds</p> <p>Figure 13. Data file: Hemi_lta_METOBS_T2_Jul_2016.xlsx</p> <p>Figure 14. Data file: Hemi_lta_METOBS_q_Jul_2016.xlsx</p> <p> </p>
Monthly averaged lightning and LCC lightning data extracted from present-day (2009-2011) and projected (2090-2095) EMAC simulations (T42L90MA resolution)
<pre>About Dataset Monthly averaged lightning and LCC lightning data extracted from present-day (2009-2011) and projected (2090-2095) EMAC simulations (T42L90MA resolution). Authors: Francisco J. Perez-Invernon, Francisco J. Gordillo-Vazquez, Patrick Joeckel and Heidi Huntrieser Description of the data PaR_T: Lightning parameterization based on cloud top height. PaR_L: Lightning parameterization based on cloud top height and modified over the oceans. Grewe: Lightning parameterization based on updraft velocity. AaP_P: Lightning parameterization based on convective precipitation. A AaP_M: Lightning parameterization based on Updraft strength at 440~hPa. PRaAP: Lightning parameterization based on cloud top height and updraft velocity. FinIF: Lightning parameterization based on updraft mass flux of ice at 440 hPa. extIF: Lightning parameterization based on updraft mass flux of ice at 440 hPa an isotherm. File format: netcdf Example: netcdf </pre> <p>2009_10h_______20090701_0000_mmlb_PRaAP.nc<br> netcdf \2009_10h_______20090701_0000_mmlb_PRaAP {<br> dimensions:<br> time = UNLIMITED ; // (1 currently)<br> lon = 128 ;<br> lat = 64 ;<br> tbnds = 2 ;<br> variables:<br> double time(time) ;<br> time:long_name = "time" ;<br> time:bounds = "time_bnds" ;<br> time:units = "day since 2009-01-01 00:00:00" ;<br> time:calendar = "gregorian" ;<br> double YYYYMMDD(time) ;<br> YYYYMMDD:long_name = "time" ;<br> YYYYMMDD:units = "days as %Y%m%d.%f" ;<br> YYYYMMDD:calendar = "gregorian" ;<br> double dt(time) ;<br> dt:long_name = "delta_time" ;<br> dt:units = "s" ;<br> double nstep(time) ;<br> nstep:long_name = "current time step" ;<br> float lon(lon) ;<br> lon:long_name = "longitude" ;<br> lon:units = "degrees_east" ;<br> float lat(lat) ;<br> lat:long_name = "latitude" ;<br> lat:units = "degrees_north" ;<br> float aps(time, lat, lon) ;<br> aps:long_name = "surface pressure" ;<br> aps:units = "Pa" ;<br> aps:representation = "GP_2D_HORIZONTAL" ;<br> aps:grid_type = "gaussian" ;<br> aps:table = 128 ;<br> aps:code = 134 ;<br> aps:REFERENCE_TO = "g3b: aps" ;<br> aps:coordinates = "lon lat" ;<br> aps:cell_methods = "time: point" ;<br> float aps_ave(time, lat, lon) ;<br> aps_ave:long_name = "surface pressure" ;<br> aps_ave:units = "Pa" ;<br> aps_ave:representation = "GP_2D_HORIZONTAL" ;<br> aps_ave:grid_type = "gaussian" ;<br> aps_ave:table = 128 ;<br> aps_ave:code = 134 ;<br> aps_ave:REFERENCE_TO = "g3b: aps" ;<br> aps_ave:coordinates = "lon lat" ;<br> aps_ave:cell_methods = "time: mean" ;<br> float fpscg(time, lat, lon) ;<br> fpscg:long_name = "CG flash frequency" ;<br> fpscg:units = "1/s" ;<br> fpscg:REFERENCE_TO = "lnox_PRaAP_gp: fpscg" ;<br> fpscg:coordinates = "lon lat" ;<br> fpscg:cell_methods = "time: point" ;<br> float fpscg_ave(time, lat, lon) ;<br> fpscg_ave:long_name = "CG flash frequency" ;<br> fpscg_ave:units = "1/s" ;<br> fpscg_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpscg" ;<br> fpscg_ave:coordinates = "lon lat" ;<br> fpscg_ave:cell_methods = "time: mean" ;<br> float fpsic(time, lat, lon) ;<br> fpsic:long_name = "IC flash frequency" ;<br> fpsic:units = "1/s" ;<br> fpsic:REFERENCE_TO = "lnox_PRaAP_gp: fpsic" ;<br> fpsic:coordinates = "lon lat" ;<br> fpsic:cell_methods = "time: point" ;<br> float fpsic_ave(time, lat, lon) ;<br> fpsic_ave:long_name = "IC flash frequency" ;<br> fpsic_ave:units = "1/s" ;<br> fpsic_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsic" ;<br> fpsic_ave:coordinates = "lon lat" ;<br> fpsic_ave:cell_methods = "time: mean" ;<br> float fpsm2cg(time, lat, lon) ;<br> fpsm2cg:long_name = "CG flash density" ;<br> fpsm2cg:units = "1/s/m2" ;<br> fpsm2cg:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2cg" ;<br> fpsm2cg:coordinates = "lon lat" ;<br> fpsm2cg:cell_methods = "time: point" ;<br> float fpsm2cg_ave(time, lat, lon) ;<br> fpsm2cg_ave:long_name = "CG flash density" ;<br> fpsm2cg_ave:units = "1/s/m2" ;<br> fpsm2cg_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2cg" ;<br> fpsm2cg_ave:coordinates = "lon lat" ;<br> fpsm2cg_ave:cell_methods = "time: mean" ;<br> float fpsm2ic(time, lat, lon) ;<br> fpsm2ic:long_name = "IC flash density" ;<br> fpsm2ic:units = "1/s/m2" ;<br> fpsm2ic:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2ic" ;<br> fpsm2ic:coordinates = "lon lat" ;<br> fpsm2ic:cell_methods = "time: point" ;<br> float fpsm2ic_ave(time, lat, lon) ;<br> fpsm2ic_ave:long_name = "IC flash density" ;<br> fpsm2ic_ave:units = "1/s/m2" ;<br> fpsm2ic_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2ic" ;<br> fpsm2ic_ave:coordinates = "lon lat" ;<br> fpsm2ic_ave:cell_methods = "time: mean" ;<br> float fpslcc10(time, lat, lon) ;<br> fpslcc10:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10:units = "1/s" ;<br> fpslcc10:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc10" ;<br> fpslcc10:coordinates = "lon lat" ;<br> fpslcc10:cell_methods = "time: point" ;<br> float fpslcc10_ave(time, lat, lon) ;<br> fpslcc10_ave:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10_ave:units = "1/s" ;<br> fpslcc10_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc10" ;<br> fpslcc10_ave:coordinates = "lon lat" ;<br> fpslcc10_ave:cell_methods = "time: mean" ;<br> float fpslcc20(time, lat, lon) ;<br> fpslcc20:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20:units = "1/s" ;<br> fpslcc20:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc20" ;<br> fpslcc20:coordinates = "lon lat" ;<br> fpslcc20:cell_methods = "time: point" ;<br> float fpslcc20_ave(time, lat, lon) ;<br> fpslcc20_ave:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20_ave:units = "1/s" ;<br> fpslcc20_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc20" ;<br> fpslcc20_ave:coordinates = "lon lat" ;<br> fpslcc20_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc10(time, lat, lon) ;<br> fpsm2lcc10:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10:units = "1/s/m2" ;<br> fpsm2lcc10:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc10" ;<br> fpsm2lcc10:coordinates = "lon lat" ;<br> fpsm2lcc10:cell_methods = "time: point" ;<br> float fpsm2lcc10_ave(time, lat, lon) ;<br> fpsm2lcc10_ave:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10_ave:units = "1/s/m2" ;<br> fpsm2lcc10_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc10" ;<br> fpsm2lcc10_ave:coordinates = "lon lat" ;<br> fpsm2lcc10_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc20(time, lat, lon) ;<br> fpsm2lcc20:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20:units = "1/s/m2" ;<br> fpsm2lcc20:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc20" ;<br> fpsm2lcc20:coordinates = "lon lat" ;<br> fpsm2lcc20:cell_methods = "time: point" ;<br> float fpsm2lcc20_ave(time, lat, lon) ;<br> fpsm2lcc20_ave:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20_ave:units = "1/s/m2" ;<br> fpsm2lcc20_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc20" ;<br> fpsm2lcc20_ave:coordinates = "lon lat" ;<br> fpsm2lcc20_ave:cell_methods = "time: mean" ;<br> float fpssprite(time, lat, lon) ;<br> fpssprite:long_name = "Sprites flash frequency" ;<br> fpssprite:units = "1/s" ;<br> fpssprite:REFERENCE_TO = "lnox_PRaAP_gp: fpssprite" ;<br> fpssprite:coordinates = "lon lat" ;<br> fpssprite:cell_methods = "time: point" ;<br> float fpssprite_ave(time, lat, lon) ;<br> fpssprite_ave:long_name = "Sprites flash frequency" ;<br> fpssprite_ave:units = "1/s" ;<br> fpssprite_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpssprite" ;<br> fpssprite_ave:coordinates = "lon lat" ;<br> fpssprite_ave:cell_methods = "time: mean" ;<br> float fpsm2sprite(time, lat, lon) ;<br> fpsm2sprite:long_name = "Sprites flash density" ;<br> fpsm2sprite:units = "1/s/m2" ;<br> fpsm2sprite:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2sprite" ;<br> fpsm2sprite:coordinates = "lon lat" ;<br> fpsm2sprite:cell_methods = "time: point" ;<br> float fpsm2sprite_ave(time, lat, lon) ;<br> fpsm2sprite_ave:long_name = "Sprites flash density" ;<br> fpsm2sprite_ave:units = "1/s/m2" ;<br> fpsm2sprite_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2sprite" ;<br> fpsm2sprite_ave:coordinates = "lon lat" ;<br> fpsm2sprite_ave:cell_methods = "time: mean" ;<br> float bps(time, lat, lon) ;<br> bps:long_name = "BJ flash frequency" ;<br> bps:units = "1/s" ;<br> bps:REFERENCE_TO = "bluejetbPRaAP_gp: bps" ;<br> bps:coordinates = "lon lat" ;<br> bps:cell_methods = "time: point" ;<br> float bps_ave(time, lat, lon) ;<br> bps_ave:long_name = "BJ flash frequency" ;<br> bps_ave:units = "1/s" ;<br> bps_ave:REFERENCE_TO = "bluejetbPRaAP_gp: bps" ;<br> bps_ave:coordinates = "lon lat" ;<br> bps_ave:cell_methods = "time: mean" ;<br> float bpsm2(time, lat, lon) ;<br> bpsm2:long_name = "BJ flash density" ;<br> bpsm2:units = "1/s/m2" ;<br> bpsm2:REFERENCE_TO = "bluejetbPRaAP_gp: bpsm2" ;<br> bpsm2:coordinates = "lon lat" ;<br> bpsm2:cell_methods = "time: point" ;<br> float bpsm2_ave(time, lat, lon) ;<br> bpsm2_ave:long_name = "BJ flash density" ;<br> bpsm2_ave:units = "1/s/m2" ;<br> bpsm2_ave:REFERENCE_TO = "bluejetbPRaAP_gp: bpsm2" ;<br> bpsm2_ave:coordinates = "lon lat" ;<br> bpsm2_ave:cell_methods = "time: mean" ;<br> double time_bnds(time, tbnds) ;<br> time_bnds:long_name = "time bounds" ;<br> time_bnds:units = "days since 2009-01-01T00:00:00Z" ;<br> time_bnds:cell_methods = "time: point" ;</p> <p>// global attributes:<br> :MESSy = "MESSy version d2.55.1-62-g7ea171fc6-dirty_7ea171fc682744f70976123b863cca166dd2023b_2021-05-19T16:21:08+00:00_2021-06-04T13:28:34+0200, http://www.messy-interface.org" ;<br> :MESSy_switch = "version 1.0" ;<br> :MESSy_channel = "version 2.4.3" ;<br> :MESSy_tracer = "version 2.6" ;<br> :MESSy_timer = "version 0.1" ;<br> :MESSy_qtimer = "version 3.0" ;<br> :MESSy_import = "version 1.0" ;<br> :MESSy_grid = "version v1.5" ;<br> :MESSy_rnd = "version 1.1" ;<br> :MESSy_aeropt = "version 2.0.2" ;<br> :MESSy_cloud = "version 2.2" ;<br> :MESSy_cloudopt = "version 2.1b" ;<br> :MESSy_convect = "version 2.0" ;<br> :MESSy_gwave = "version 1.0" ;<br> :MESSy_lnox = "version 3.0" ;<br> :MESSy_orbit = "version 0.9" ;<br> :MESSy_orogw = "version 1.1" ;<br> :MESSy_rad = "version 2.2" ;<br> :MESSy_e5vdiff = "version 1.2" ;<br> :MESSy_surface = "version 1.2" ;<br> :MESSy_tropop = "version 2.1" ;<br> :MESSy_viso = "version 2.3" ;<br> :MESSy_experiment = "2009_10h" ;<br> :EXEC_CHECKSUM = "45e5ddd17ae5992f931a9ba4bab4a921 bin/echam5.exe (md5sum)" ;<br> :GCM = "ECHAM5 version 5.3.02, Max-Planck Institute for Meteorology, Hamburg" ;<br> :GCM_spherical_trunc_n = 42 ;<br> :GCM_spherical_trunc_m = 42 ;<br> :GCM_spherical_trunc_k = 42 ;<br> :GCM_vertical_mode = "middle atmosphere (MA)" ;<br> :GCM_horizontal_mode = "global" ;<br> :GCM_advection = "Lin&Rood" ;<br> :GCM_start_date_time = "20090101 000000" ;<br> :GCM_timestep = 900.f ;<br> :F95_COMPILER_VERSION = "ifort (IFORT) 17.0.2 20170213" ;<br> :F95_COMPILER_CALL = "/opt/mpi/bullxmpi_mlx/1.2.9.2/bin/mpif90" ;<br> :F95_COMPILER_FLAGS = "-sox -fpp -g -O2 -xCORE-AVX2 -fp-model strict -align all -save-temps -DBULL -I/sw/rhel6-x64/sys/bullxlib-1.0.0/include -L/sw/rhel6-x64/sys/bullxlib-1.0.0/lib -Wl,-rpath,/sw/rhel6-x64/sys/bullxlib-1.0.0/lib -lbullxMATH -no-wrap-margin" ;<br> :F95_PREPROC_DEFINITIONS = "-DMESSY -DLITTLE_ENDIAN -D_LINUX64 -DHAVE_PNETCDF -DPNCREGRID -DMPIOM_13B -D_VCSREV_=\'d2.55.1-62-g7ea171fc6-dirty_7ea171fc682744f70976123b863cca166dd2023b_2021-05-19T16:21:08+00:00_2021-06-04T13:28:34+0200\'" ;<br> :F95_COMPILER_INCLUDES = "-I/sw/rhel6-x64/netcdf/netcdf_fortran-4.4.2-intel14/include -I/sw/rhel6-x64/netcdf/netcdf_fortran-4.4.2-intel14/include -I/sw/rhel6-x64/netcdf/parallel_netcdf-1.6.0-bullxmpi-intel14/include" ;<br> :operating_date_time = "20210615 081833" ;<br> :operating_system = "Linux 2.6.32-754.33.1.el6.x86_64 on x86_64" ;<br> :operating_host = "mlogin102" ;<br> :operating_user = "Francisco-Javier Perez-Invernon (b309171)" ;<br> :channel_io_pe = 172 ;<br> :channel_time_slo = 5182200.f ;<br> :channel_name = "mmlb_PRaAP" ;<br> :channel_file_type = "output" ;<br> :channel_file_name = "2009_10h_______20090701_0000_mmlb_PRaAP.nc" ;<br> :channel_netcdf_lib = "4.3.2 of May 5 2015 13:21:25 $" ;</p>
India Lightning Inventory
<p>India Lightning Inventory - Paper under review</p>
Supporting information for the paper: The temporal relationship between Terrestrial Gamma-ray flashes and associated optical pulses from lightning
<p>Supporting information for the paper: The temporal relationship between Terrestrial Gamma-ray flashes and associated optical pulses from lightning, consisting of 2 data files and 221 presentations of TGF-Optical emission events observed by ASIM between end of March 2019 and November 2020.</p> <p>See 0_READ_ME for information about the individual files and variables.</p>
Investigation on Dynamic Characteristics of Lightning Return Stroke Channel
<p>The dynamic characteristics in lightning discharge plasma channel are of great significance in studying the micro-physical mechanism of the discharge process. This paper established a simplified radiation-hydrodynamic model (RHM) of the return stroke channel radial expansion based on the fluid dynamics theory, and for the first time, considered the affect of the light radiation loss in the discharge process. The temporal evolutions of the characteristic parameters, such as temperature, pressure and expansion rate for return stroke channel were analyzed. The results indicated that the current peak value and the risetime are important factors in determining the channel dynamics characteristics. At the initial stage of return stroke, the light radiation loss has a distinct influence on channel temperature, which leads to a transitory drop in temperature forming a bimodal waveform structure. Following the peak current, the channel reaches peak pressure, which leads to a subsequent secondary temperature peak and accelerates the expansion of the channel. Peak current and the risetime are the indicator parameters of strong discharge and the main factors of lightning disaster. This work provides reference data for further research on the radial energy transport of lightning return stroke channel and the formation mechanism of shock waves.The dynamic characteristics in lightning discharge plasma channel are of great significance in studying the micro-physical mechanism of the discharge process. This paper established a simplified radiation-hydrodynamic model (RHM) of the return stroke channel radial expansion based on the fluid dynamics theory, and for the first time, considered the affect of the light radiation loss in the discharge process. The temporal evolutions of the characteristic parameters, such as temperature, pressure and expansion rate for return stroke channel were analyzed. The results indicated that the current peak value and the risetime are important factors in determining the channel dynamics characteristics. At the initial stage of return stroke, the light radiation loss has a distinct influence on channel temperature, which leads to a transitory drop in temperature forming a bimodal waveform structure. Following the peak current, the channel reaches peak pressure, which leads to a subsequent secondary temperature peak and accelerates the expansion of the channel. Peak current and the risetime are the indicator parameters of strong discharge and the main factors of lightning disaster. This work provides reference data for further research on the radial energy transport of lightning return stroke channel and the formation mechanism of shock waves.</p>
Fatiando a Terra Data: Lightning Creek Sill Complex, Australia - Airborne total-field magnetic anomaly grid
<p>Regular grid of total-field magnetic anomaly data from the Lightning Creek Sill Complex, featuring a text-book dipolar anomaly. This is a gridded version of the Lightning Creek anomaly from <a href="https://github.com/fatiando-data/osborne-magnetic">our Osborne Mine dataset</a>.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Slice to area of interest. Project data to UTM. Interpolate to a regular 50 m grid at 500 m height. Add CF-compliant metadata to the grid. Export to compressed netCDF 4.</p> <p><strong>Source: </strong>Geophysical Acquisition & Processing Section 2019. MIM Data from Mt Isa Inlier, QLD (P1029), magnetic line data, AWAGS levelled. Geoscience Australia, Canberra. <a href="http://pid.geoscience.gov.au/dataset/ga/142419">http://pid.geoscience.gov.au/dataset/ga/142419</a></p> <p><strong>Source license: </strong><a href="http://pid.geoscience.gov.au/dataset/ga/142419">CC-BY</a></p> <p><strong>Repository: </strong><a href="https://github.com/fatiando-data/lightning-creek-magnetic-grid">https://github.com/fatiando-data/lightning-creek-magnetic-grid</a></p>
Data for "Numerical simulation study of the evolution of lightning channel decay and reactivation processes" by Zheng et al.
<p>All data of the manuscript "Numerical simulation study of the evolution of lightning channel decay and reactivation processes" submitted to Journal of Geophysical Research: Atmospheres.</p> <p>The data supports the manuscript entitled "Numerical simulation study of the evolution of lightning channel decay and reactivation processes”. Microsoft Notepad can open the *.txt files and the *.DAT files, they contain the channel information of two intracloud flashes (IC1 and IC2) and the channel elctrical parameters at different channel segments. </p> <p>The data can be used freely for scientific purposes with the appropriate citation. </p>
Lightning Observations with the1 Horus Polarimetric Phased Array Radar Supplemental Material
Open the record for dataset details and reuse information.
Photometric Measurements of Lightning Phenomena with Simultaneous Radio Frequency Data
<p>This data was collected with a novel multi-band optical instrument alongside simultaneous radio frequency measurements for 16 lightning flashes.</p> <p>The transmissivity of 3 narrowband optical filters used to collect photometric data for temperature analysis are stored in the filterData folder along with the responsivity of the photodiode used to collect the data. </p> <p>The flash data is stored by flash in folders labeled with the UTC day and time in a MonthDDYYY_HHMMSS format. For each flash, there is an overview flashFigure.pdf that quickly summarizes the flash by plotting the Lightning Mapping Array (LMA) data and some of the data stored in PicoScope.csv including the Fast Antenna data, the Slow Antenna data and the 777 nm data measured at the ground. Some of the flashes were also seen by the Geostationary Lightning Mapper (GLM), this data is included on the flashFigure.pdf file when available as well as fully reproduced in the GLM.csv file. For each flash, the LMA data is stored in the LMA.dat file, the photodiode and electric field antenna data are stored in the PicoScope.csv file as well as the time in milliseconds after the trigger occurs in UTC time (the millisecond precise data is stored in the name of the time column in the PicoScope.csv). Each PicoScope file has some pre-trigger data, this is denoted with a negative time value in the time column as it is before the trigger event. Finally, the temperature is reported in the Temperature.csv file, it was based off of a decimated version of the PicoScope file to reduce the computational time but it's time units are still milliseconds after the trigger occurs in UTC time like the data in PicoScope.csv.</p>
Xin_RS_2021_GOES_lightning_data
<p>Core data used in <a href="https://github.com/zxdawn/Xin_RS_2021_GOES_lightning">Xin_RS_2021_GOES_lightning</a> repository.</p> <p>For more detail, please check the README.md file.</p>
Effects Of Atmospheric Attenuation On The Lightning Spectrum
<p>Spectrum carries important information reflecting atomic and molecular processes inside a light source. Accurate spectral diagnosis is vital in revealing the microscopic physical mechanism of the lightning discharge process. Spectral correction was applied considering the influence of atmospheric attenuation, grating efficiency, and camera response on the observed spectrum, thereby solving the problems of atmospheric attenuation in long distance lightning spectrum observations. Based on the restored spectrum, the temperature of the lightning return stroke channel was calculated by the ionic and atomic lines respectively. The result showed that corrected temperature at the initial stage of the return stroke, calculated by the ionic line, could reach up to 40,000 K, which was about 10,000 K higher than the values directly obtained from the observed spectrum. Atmospheric attenuation of the atomic spectral line in the nearinfrared band is relatively weak; therefore, atmospheric attenuation was inferred to have a relatively less effect on the channel temperature that was calculated by the atomic spectral lines. This work provided the attenuation ratio of the characteristic lines in lightning spectra with distance, and can used for more precisely quantitative investigation on the physical characteristics of the lightning process. It also has application value for improving the spectral diagnostic techniques on celestial body and other natural luminous process.<br> </p>
M81 post-processed results from Lightning SED fitting
<p>The post-processed results from the Lightning SED fitting code as fit by the authors for the spatially resolved map of M81. The results are for the MCMC sampler (m81_map_mcmc.fits.gz) and the MPFIT minimizer (m81_map_mpfit.fits.gz). Fitting of this example was performed on a 32-core node of the Pinnacle cluster at the Arkansas High Performance Computing Center.</p>
Lightning and volcanic plume data from the climactic eruption of Hunga Volcano, Tonga, in January 2022
<p>This dataset contains lightning and volcanic plume data for the eruption of Hunga Volcano in Tonga from 13–15 January 2022. The dataset consists of two files. The first is a spreadsheet containing four tabs: (1) Ground-based flashes, which include lightning flashes from combined ground-based networks from 13–15 January 2022; (2) Ground-based rates, which include flash rates and pulse rates in one-minute bins from 13–15 January 2022 using the combined networks; (3) Optical GLM flashes & rates, which include GLM flashes and per-minute rates from 15 January 2022; and (4) Volcanic plume dimensions, which include maximum plume heights and umbrella radii through time on 15 January 2022. The second file is a Google Earth KMZ file of umbrella cloud areas outlined from stereoscopic cloud height retrievals from 04:17–07:07 UTC on 15 January 2022. Refer to journal article "Lightning rings and gravity waves: Insights into the giant eruption plume from Tonga’s Hunga Volcano on 15 January 2022" published in Geophysical Research Letters for further details about data processing. </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.