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Storm Database Files for CLIMK–WINDS: A New Database of Extreme European Winter Windstorms
<p>This database is comprised of the four netCDF files containing the 50 most extreme European winter windstorms identified within the four input sources, with one netCDF file per source: ERA5 reanalysis, CCLM_ERA5_EUR-11 regional climate model simulation, COSMO-REA6 reanalysis, and CCLM_ERA5_CEU-3 regional climate model. This database was created by Clare Marie Flynn and its creation is described in the following paper: Flynn, C. M., Moemken, J., Pinto, J., Schutte, M., and Messori, G.: CLIMK–WINDS: A New Database of Extreme European Winter Windstorms, under review for final submission, Earth System Science Data, 2025.</p>
Accessible Oceans: Auditory Display. Ocean Response to Extratropical Storm Hermine
<p>The twenty-one tracks make up an auditory display of the ocean response to extratropical storm Hermine in 2016. The tracks in the auditory display are comprised of data sonifications and contextual audio supports (dialogue, auditory icons, and music). You may <a href="https://samply.app/p/VGWHypmrFPZ1NqzeqZuM">listen online here</a>.</p> <p>The ocean data comes from the National Science Foundation (NSF) Ocean Observatories Initiative (OOI) and the display is based on the <a href="https://datalab.marine.rutgers.edu/ooi-nuggets/extratropical-storm-hermine/">OOI Nugget</a> developed by Dr. Leslie Smith. Please note that the Ocean Labs data nugget does not include sea wave height as part of its graph that we sonified. Storms also impact sea wave height, and Dr. Leslie Smith acquired this data from the OOI so that we could include it in the sonification and auditory display.</p> <p>The “Accessible Oceans” AISL Pilots and Feasibility study aims to inclusively design auditory displays that support the perception and understanding of ocean data in informal learning environments (ILEs). More can be found on the project website: <a href="https://accessibleoceans.whoi.edu/">https://accessibleoceans.whoi.edu/</a></p>
Probabilistic-deterministic storm surge return level dataset for the Bengal delta
<p>Bengal delta shoreline, spanning Bangladesh and India, gets hit every 3 years on average by a major tropical cyclone. Although their occurrence is relatively moderate compared to other tropical regions (accounting for only 5% of global cyclones), the impact of these events is major, accounting for 50% of the victims recorded worldwide. This is due to the very low topography of the delta above sea level (less than 5 meters), high storm surge induced water level and flooding, combined with the high density of the vulnerable population. </p> <p>On one hand, the unavailability of long-term reliable water level data on a sparse tide-gauge network along the coastline has hindered the assessment of storm surge hazards. The application of hydrodynamic modelling to fill the data gap also suffers from the unavailability of a reliable long-term storm dataset over the region. The complex topography of the Bengal delta, with defence structures, and a dense network of rivers presents another modelling challenge. Finally, the interaction of tide, surge and wave further complicate the numerical complexity, needing a coupled modelling framework. </p> <p>Thanks to advancements made to acquire high-quality regional nearshore bathymetry and topography (Krien et al. 2016, Khan et al. 2019), as well as coupled storm surge modelling (Krien et al. 2017, Khan et al. 2021), the tidal and storm surge dynamics over the Bengal delta is now well captured by recent high-resolution coupled SCHISM-WWM Bay of Bengal model (Khan et al. 2021). To estimate the risk of storm surge and associated flooding across the Bengal delta, we have integrated the wave-coupled hydrodynamic model of Khan et al. (2021) for a large ensemble (~3600 cyclones, ~5000 years of storm activity) of synthetic cyclones generated through the statistical-deterministic method of Emanuel (2006). Our storm and surge ensemble covers the whole range of natural variability of storm frequency, size, intensity and track location, with a dense spatial distribution. The interactions among the tide, surge, and waves are modelled explicitly at high spatial resolution. The storm surge-induced water level at various return periods, up to 500 years, is then determined at high spatial resolution (250m at the coast) using a ranking-based technique.</p> <p>The dataset distributed here represents the storm surge water level estimate (e.g. total water level from the tide, surge, and wave computed dynamically through the model) at 25 to 500 year return period (25-year step). The corresponding variable in the self-describing netCDF data file is 'maxelev'. The estimated storm surge water level values are interpolated in a 30" (~1km at the equator) structured grid over the Bengal delta from the original unstructured-grid model outputs (250m resolution at the coast). </p> <p>This dataset is a part of a manuscript, currently being submitted to Natural Hazards and Earth System Sciences (https://nhess.copernicus.org/). Please cite the original paper, along with the dataset if used in your work as - Khan, M. J. U., Durand, F., Emanuel, K., Krien, Y., Testut, L., and Islam, A. K. M. S.: Storm surge hazard over Bengal delta: A probabilistic-deterministic modelling approach, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2021-329, in review, 2021.</p>
Labels for Emergency Response Imagery from Hurricane Barry, Delta, Dorian, Florence, Ida, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon
<p>The csv files contain human-generated labels for Emergency Response Imagery collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. All authors contributed to labeling the imagery. All labeling was done with an open-source labeling tool (Rafique et al., 2020).</p> <p>All csv files provide the userID (the ID of the anonymous labeler), the NOAA flight, the NOAA image, and 6 labels — allWater (if the image was all water), devType (if the image had buildings/development), washoverType (if the image had washover deposits), dmgType (if the image showed damage to built environment), impactType (if the labeler could identify the coastal impact, using the Storm Impact Scale from Sallenger, 2000), and terrainType (the type of physical environment).</p> <p>Images labeled here correspond to multiple NOAA flights — all listed in the csv file for each jpeg image. These jpeg images can be downloaded directly from NOAA (https://storms.ngs.noaa.gov/) or using Moretz et al. (2020a, 2020b).</p> <p>There are three csv files:</p> <p>ReleaseData_10172022.csv has 10,237 labels for 4250 images. These labels were generated by coastal scientists. The csv also contains the Latitude and Longitude of the image center (from NOAA).</p> <p>ReleaseDataQuads.csv has 400 labels for 100 images. These labels were generated by coastal scientists. The images labeled in this set correspond to original NOAA images that have been split into quadrants. Splitting images was done with ImageMagick. The command used to split the images was:</p> <p>`magick mogrify -crop 2x2@ +repage -path ../quadrants *.jpg`</p> <p>The naming convention corresponds to the image quarter — the *-0.jpg is upper left, *-1.jpg is upper right, *-2.jpg is lower left, and *-3.jpg is the lower right.</p> <p>ReleaseDataNCE.csv has 400 labels for 100 images. These images were labeled by non-coastal scientists. Note that the 100 images were also labeled by coastal scientists — those labels can be found in ReleaseData_v3.csv.</p> <p>There is another companion dataset to this, with slightly different labels (Goldstein et al., 2020).</p> <p>A zip file of images is also provided for demonstration purposes (images.zip). These are resized copies made with imagemagick, with the longest dimension set at 2000 pixels ( `mogrify -resize 2000x2000`). For full size images, please download the jpegs directly from NOAA.</p>
Stream Storm Sample Data from White Clay Creek (WCC) Watershed in 2021
These data were collected to explore Nitrogen update dynamics in the water column of small streams during storm events. Data was collected using a combination of passive sampling and grab sampling of suspended sediments from the stream channel. Analysis of samples include: dissolved and total chemistry, suspended sediment load, biological and microbiological characteristics, and Nitrogen uptake rates.
Quantifying the magnitude of storm events that have impacted the Virginia Coast Reserve (2009-2024) using the Cumulative Storm Impact Index (CSII)
This dataset contains a record of storm events along with quantified magnitudes that have impacted the Virginia Coast Reserve between 2009- 2024, minus 2010. We retrieved hourly water level data and monthly datums from the NOAA Tides and Currents database (tidesandcurrents.noaa.gov) for the tide station located in Wachapreague, VA (Station 8631044) to quantify the magnitude of storms using 1) the Storm Erosion Potential Index (SEPI; Zhang et al. 2001), and 2) the Cumulative Storm Impact Index (CSII; Fenster and Dominguez 2022). CSII incorporates the timing and magnitude of previous storms as a measure of cumulative impact, or "storminess". We identified storm events based on storm surge that exceeded two standard deviations (> 2SD) of the average surge and storm tide that exceeded the annual average Mean High Water (MHW) of a semi-diurnal tide (12 hours; SEPI). We then calculated the CSII for each storm as the sum of the SEPI and an exponentially decaying weighting factor (delta) from the previous storm's CSII that accounts for beach recovery that may have occurred between storm events. Here we use delta = 0.3 to best capture storm clustering during the 15 year period (Fenster and Dominguez 2022). Years missing >10% of data were excluded. For detailed methods on the data retrieval process, identifying storms, and quantifying storm magnitude, see Fenster and Dominguez (2022) and Dominguez et al. (2024). We identified a total of 208 storm events with an average of 14.3 events per year +/- 2.3 (SD) and an average annual CSII of 428.1 (m2hr) +/- 196.1 (SD).
Hydrodynamic field data near Galveston, Texas wetland edges to help assess storm impacts and erosion
<p>Water free surface elevation measurements via submerged pressure transducers along transects near Galveston Bay wetland edges</p>
Data for: Ring Current Electron Precipitation During the 17 March 2013 Geomagnetic Storm: Underlying Mechanisms and Their Effect on the Atmosphere
<p>All data are included as MATLAB figure files, png files and MATLAB MAT files.</p><p>File precipitated_flux.mat contains a 4-D array of values of precipitated electron flux in [1/(s cm^2 keV)] for 289 time points from 16 March 2013 to 19 March 2013, with a 15 min time step; 100 values of energy in a range from 10 keV to 1 MeV, with a 10 keV step; on a spatial grid of 28 by 49 (P, R).</p><p>netCDF data can be opened with a variety of software tools, including Matlab, Origin or Python.</p>
Results of "Storm Time Data Assimilation in the Thermosphere Ionosphere with TIDA" CHAMP, GRACE-A, and GRACE-B neutral density data assimilation into CTIPe for 2003 Halloween Storms
<p># README</p> <p>Results for the article "Storm Time Neutral Density Assimilation in the Thermosphere Ionosphere with<br> TIDA".</p> <p>There are three storms presented here:</p> <p>1. 2003 storm: October 26-30, 2003<br> 2. 2004 storm: July 26-30, 2004<br> 3. 2002 storm: September 27 - October 2, 2002.</p> <p>For each of these three storms, there are four runs. For each storm, we've done a run<br> assimilating all satellites, and then three more assimilating each satellite individually and<br> comparing against the others.</p> <p>Each directory name before underscore identifies the date the run was<br> started. After the underscore identifies the date assimilated.</p> <p>This readme uses the notation that in curly brackets the satellites assimilated are given.</p> <p>- a stands for GRACE-A<br> - b stands for GRACE-B<br> - c stands for CHAMP</p> <p>The runs are summarized below:</p> <p>1. 2003 storm: {a, b, c}: 2021-12-29T1259_...<br> 3. 2003 storm: {a}: 2022-01-01T1654_...<br> 4. 2003 storm: {b}: 2022-01-02T1423_...<br> 2. 2003 storm: {c}: 2021-12-29T2342_...</p> <p>5. 2004 storm: {a, b, c}: 2022-01-03T1614_...<br> 6. 2004 storm: {a}: 2022-01-04T1027_...<br> 7. 2004 storm: {b}: 2022-01-04T2144_...<br> 8. 2004 storm: {c}: 2022-01-05T0502_...</p> <p>9. 2002 storm: {a, b, c}: 2022-01-02T2025_...<br> 10. 2002 storm: {a}: 2022-01-05T1056_...<br> 11. 2002 storm: {b}: 2022-01-05T1854_...<br> 12. 2002 storm: {c}: 2022-01-06T1017_...</p> <p>## Example result directory</p> <p>2021-12-29T1259_d2003-10-27<br> ├── density_champ_density.csv<br> ├── density_grace-a_density.csv<br> ├── density_grace-b_density.csv<br> └── inputs<br> ├── reference_2003-10-27_input.txt<br> └── special_2003-10-27_input.txt<br> <br> 1 directory, 5 files</p> <p>## Zenodo doesn't support directories</p> <p>So, the file structure has been flattened in the following way:</p> <p>Before: ./aaa/bbb/ccc.png</p> <p>After: ./aaa-bbb-ccc.png</p> <p>https://unix.stackexchange.com/~/45659</p> <p> </p>
Measurements and model simulations of iodine monoxide (IO) radical, water vapor (H2O), nitrogen dioxide (NO2) radical, formaldehyde (HCHO), gaseous elemental mercury (Hg0), and oxidized mercury (HgII) at Storm Peak Laboratory, Colorado, during April 2022
<p>This dataset was compiled to accompany the manuscript Lee et al., titled "Elevated Tropospheric Iodine over the Central Continental United States: Is Iodine a Major Oxidant of Atmospheric Mercury?", submitted to <em>AGU Geophysical Research Letters</em>.</p> <p> </p> <p><strong>file01</strong> contains two example spectral proofs for iodine monoxide (IO) radical measured by the University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument at Storm Peak Laboratory, CO (SPL; 3220 meters above sea level; 40.455 degrees North; 106.745 degrees West) during April 2022.</p> <p><strong>file02</strong> contains oxygen collision-induced absorption (O2-O2) slant column densities (SCDs) measured in a spectral fit window from 350 to 388 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file03</strong> contains O2-O2 SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file04</strong> contains IO SCDs measured in a spectral fit window from 417.5 to 438 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file05</strong> contains water vapor (H2O) SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file06</strong> contains nitrogen dioxide (NO2) radical SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file07</strong> contains formaldehyde (HCHO) SCDs measured in a spectral fit window from 328,5 to 359 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file08</strong> contains the profiles of pressure, temperature, O2-O2, ozone (O3), NO2, and H2O derived from ECMWF CAMS reanalysis (April 2022 at SPL) and used in the radiative transfer model McArtim3 to calculate weighting functions for the trace gas profile inversions of IO, H2O, NO2, and HCHO.</p> <p><strong>file09</strong> contains the a priori profiles used for the IO profile inversions during April 2022 at SPL. One profile assumes a "flat" profile shape with a constant volume mixing ratio of 0.10 pptv throughout the atmosphere. The other profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file10</strong> contains the a priori profile used for the H2O profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file11</strong> contains the a priori profile used for the NO2 profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file12</strong> contains the a priori profile used for the HCHO profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file13</strong> contains the IO tropospheric vertical column densities (VCDtrop; surface to 12 km), volume mixing ratios near instrument altitude (VMRinstr), and degrees of freedom (DoF) measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file14</strong> contains the H2O VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file15</strong> contains the NO2 VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file16</strong> contains the HCHO VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file17</strong> contains GEOS-Chem simulated temperature, relative humidity, IO VCDtrop & VMRinstr, H2O VCDtrop & VMRinstr, NO2 VCDtrop & VMRinstr, HCHO VCDtrop & VMRinstr, and bromine monoxide (BrO) radical VCDtrop & VMRinstr at SPL from April 1 to April 30, 2022.</p> <p><strong>file18</strong> contains the gaseous elemental mercury (Hg0) measured by the Utah State University dual-channel mercury system at SPL from April 1 to April 30, 2022.</p> <p><strong>file19</strong> contains the oxidized mercury (HgII) measured by the Utah State University dual-channel mercury system at SPL from April 1 to April 30, 2022.</p> <p><strong>file20</strong> contains the GEOS-Chem simulated Hg0 and HgII at SPL from April 1 to April 30, 2022.</p> <p><strong>file21</strong> contains the profiles of pressure, temperature, relative humidity, BrO, bromine atom (Br), methane (CH4), chlorine monoxide (ClO) radical, chlorine atom (Cl), carbon monoxide (CO), Hg0, peroxy radical (HO2), IO, iodine atom (I), NO2, hydroxyl radical (OH), and O3 used as constraints for the gas-phase mercury box model. All profiles except IO and I are adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average. The IO profile was calculated by scaling the GEOS-Chem April 2022 daytime (SZA < 85) average below 12 km by the average observed IO VCDtrop during April 2022. The I atom profile was calculated by multiplying the scaled IO profile by the ratio of unscaled I / unscaled IO profiles from GEOS-Chem.</p> <p> </p> <p><strong>file22</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file23</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file24</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file25</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file26</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file27</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file28</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file29</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file30</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file31</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file32</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file33</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file34</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file35</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file36</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file37</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file38</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file39</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p> </p> <p><strong>file40</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file41</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file42</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file43</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file44</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file45</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file46</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file47</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file48</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file49</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file50</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file51</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file52</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file53</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file54</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file55</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file56</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file57</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p>
MarsWRF imposed dust simulations for investigation of dust storm trajectories
<p>This dataset contains representative MarsWRF simulation outputs used to conduct the analysis discussed in Wang et al. [2023]. The model uses a staggered "C" computational grid, with 52 vertical eta layers and a 2º longitude × 2° latitude horizontal resolution. The model grid structure is summarized in a data object structure that is saved in the IDL software (NV5 Geospatial, Broomfield, CO) SAV file format as “wrfgrid.sav”. It can be read using the IDL software command “restore,'wrfgrid.sav'” or using the Python “scipy” library module that can interpret the IDL SAV file format, scipy.io.readsav.</p> <p>Files for each simulation are collected using the “tar” archive tool and compressed using the “gzip” tool to minimize storage requirements, and can be extracted similarly, (e.g., tar -xvzf *.tar.gz). Each file is written in NetCDF format and contains 30 sols of 2-hourly output (i.e., 360 output timesteps per file) for U (zonal wind), V (meridional wind), T (perturbation potential temperature with respect to 300 K, i.e., potential temperature – 300., which is a native WRF output field), PSFC (surface pressure), L_S (solar longitude), and UST (surface friction velocity). </p> <p>The no-storm control run simulation employs the dust optical depth scenario saved in dustscenario_nostorm.nc. This optical depth scenario is derived from the observationally-derived multiannual dust climatology [Montabone et al., 2015] by reducing the climatology to a single year and removing the influence of large dust storm episodes in the contributing years. The other MarsWRF simulations included in this archive impose additional dust optical depth over the base no-storm dust scenario in different latitudinal bands (i.e., spanning all longitudes) and for different L<sub>S</sub> time periods, as indicated by the archive file names. For example, ls200n230_45N75N_tau1.732.tar is the archive file for the simulation with additional imposed dust between 45ºN and 75ºN from Ls = 200º to Ls = 230º with the imposed optical depth amplitude of 1.732. Due to the large data volume, the archived files for each simulation can only cover the corresponding Ls period of interest for a representative Mars year. For details of the simulations, please refer to Wang et al. [2023 submitted] listed in the References section.</p>
Radar-derived storm characteristics and convective diagnostics associated with hourly maximum measured wind gusts around Australia
<p>The data in this record describes various characteristics associated with hourly measured surface wind gusts across various locations in Australia, with these characteristics and data sources described below.</p> <p>This record provides all data used in the preparation of Brown et al. (2023a), except for lightning data that can be obtained from the <a href="https://wwlln.net/">World Wide Lightning Location Network archive</a></p> <p><strong>Record contents</strong></p> <ul> <li><em>gust_observations.zip</em><br> Within this zip archive, a <em>.csv</em> file is provided for wind gust observations, along with associated storm statistics from radar, and convective diagnostics from a global reanalysis. These data are provided for each of the 20 radar domains listed in Brown et al. (2023a). The <em>.csv</em> files follow the structure: <em>gust_observations_x.csv, </em>where <em>x </em>is the identification number for each radar from the <a href="https://www.openradar.io/operational-network">Australian Unified Radar Archive</a>.<br> </li> <li><em>station_details.csv</em><br> This file provides details on the automatic weather stations that measure the wind gusts, with station identifiers (column=Station_id) consistent between <em>station_details.csv </em>and<em> gust_observations_x.csv</em>.<br> </li> <li><em>Table1.pdf</em> <br> Descriptions of convective diagnostics from reanalysis, that are provided in <em>gust_observations_x.csv</em>. This table has been extracted from the supplementary information of Brown et al. (2023a), and references in this table can be found therein.<br> </li> <li><em>radar_details.pdf</em> <br> Taken from Table 1 from Brown et al. (2023a), showing the details of radars used here for storm statistics in <em>gust_observations_x.csv</em>.<br> </li> <li><em>Fig1.jpeg</em><br> Taken from from Brown et al. (2023a), showing a map of the radar domains used here for storm statistics in <em>gust_observations_x.csv</em>.</li> </ul> <p><strong>Wind gust data</strong></p> <p>Measured wind gusts here represent a 3-second average wind speed, at a height of 10 m above ground level. We also provide some derived quantities from the gust data (see table below). Gust data is provided by the <a href="http://www.bom.gov.au/climate/data/stations/">Australian Bureau of Meteorology</a>, from 204 automatic weather stations (<em>station_details.csv)</em>, chosen to be within 100 km of a weather radar with sufficient archived data. These data are originally provided by the Bureau of Meteorology at 1-minute frequency, representing a maximum over a 1-minute interval, but are resampled in this record to hourly frequency, for comparisons with other hourly data below (see Brown et al. (2023a) for details of this resampling). Note that any reuse of this data should properly acknowledge the Australian Bureau of Meteorology.</p> <p><strong>Radar data</strong></p> <p>Radar data is obtained by the <a href="https://www.openradar.io/">Australian Unified Radar Archive</a> (AURA), produced from operational weather radar within the Australian Bureau of Meteorology network. The level1b data used here is available from the AURA dataset on the Australian NCI under a CC4-BY-NC licence from <a href="https://dx.doi.org/10.25914/5f4c85732ee80">https://dx.doi.org/10.25914/5f4c85732ee80</a>. Various properties derived from radar reflectivity and Doppler velocity data is reported here in association with the wind gust observations. These properties are only reported if there is a storm object within 10 km and 10 minutes of the gust location (see Brown et al. (2023a) for storm object definition). Radar properties are described in the table below.</p> <p><strong>Environmental data</strong></p> <p>Various convective diagnostics are associated with wind gust observations, representing the convective environment and large-scale wind profile. These diagnostics are derived from a combination of pressure-level and surface-level ERA5 data (Hersbach et al. 2020), which is provided at hourly intervals on a 0.25-degree latitude-longitude grid, hosted on the Australian NCI (<a href="http://dx.doi.org/10.25914/5fb115b82e2ba">http:// dx.doi.org/10.25914/5fb115b82e2ba</a>). Details on these convective diagnostics are provided in Brown et al. (2023a), and<strong> </strong>in <em>Table1.pdf</em> as provided in this record.</p> <p><strong>Column descriptions</strong></p> <p>The following table provides descriptions of columns of <em>gust_observations_x.csv</em></p> <table> <thead> <tr> <th scope="col">Column name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>dt_utc</td> <td>Time of the measured wind gust, from the automatic weather station data (YYYY-MM-DD HH:MM:SS UTC)</td> </tr> <tr> <td>Station_id</td> <td>Identification number of the weather station that measured the gust. See <em>station_details.csv </em>for details of each station</td> </tr> <tr> <td>Wind_gust_observed</td> <td>The measured wind gust speed (m/s)</td> </tr> <tr> <td>Peak_to_mean_wind_gust_ratio</td> <td>Ratio of the measured wind gust to the 4-hour mean at that station (with the window centred on the gust time)</td> </tr> <tr> <td>SCW</td> <td>Is the measured gust a severe convective wind event?<br> 0: Gust is either less than 25 m/s, does not have a storm object within 10 km, or has a peak-to-mean wind gust ratio less than 2.<br> 1: Gust is greater than 25 m/s, has a storm object within 10 km, and has a peak-to-mean wind gust ratio greater than 2.</td> </tr> <tr> <td>Radar_id</td> <td>Radar identification number (see <em>radar_details.pdf)</em></td> </tr> <tr> <td>Storm_speed</td> <td>Translational speed of the parent storm object (m/s). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Storm_angle</td> <td>Angle of parent storm object movement. In units of degrees from N. Only defined if Storm_in10km=1</td> </tr> <tr> <td>Parent_storm_class</td> <td>The type of parent storm associated with a gust. Only defined if Storm_in10km=1. Possible types are:<br> "Non-linear"<br> "Linear"<br> "Cellular"<br> "Cell cluster"<br> "Supercellular"<br> "Embedded supercell"<br> See Brown et al. (2023a) for classification details</td> </tr> <tr> <td>Storm_in10km</td> <td>Is there a radar-derived storm object within 10 km of the gust, observed no more than 10 minutes prior to the gust?<br> 0: No<br> 1: Yes<br> See Brown et al. (2023a) for a definition of "storm object"</td> </tr> <tr> <td>Major_axis_length</td> <td>The length of the major axis of an ellipse fitted to the parent storm object (km). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Minor_axis_length</td> <td>The length of the minor axis of an ellipse fitted to the parent storm object (km). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Local_reflectivity_maxima</td> <td>Number of local reflectivity maxima within the parent storm object. Only defined if Storm_in10km=1</td> </tr> <tr> <td>Maximum_storm_altitude</td> <td>The maximum height of the parent storm radar reflectivity object (km). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Azimuthal_shear</td> <td>Azimuthal shear of the parent storm object derived from radar data (s<sup>-1 </sup>x 1000). Only defined if Storm_in10km=1. See Brown et al (2023a) for a discussion of azimuthal shear and processing applied to this quantity here.</td> </tr> <tr> <td>ERA5_time</td> <td>Time of the ERA5 environmental data that is associated with the measured gust, corresponding to the closest previous hour (YYYY-MM-DD HH:MM:SS UTC).</td> </tr> <tr> <td>ERA5_latitude</td> <td>Location of closest ERA5 (land) grid point to measured gust, in degrees of latitude</td> </tr> <tr> <td>ERA5_longitude</td> <td>Location of closest ERA5 (land) grid point to measured gust, in degrees of longitude</td> </tr> <tr> <td>Environmental_cluster</td> <td>Event type, based on statistical clustering of environmental data (Brown et al. 2023b)<br> 0: Strong background wind cluster<br> 1: Steep lapse rate cluster<br> 2: High moisture cluster</td> </tr> <tr> <td>Umean06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Umean01</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>U10</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WindGust10</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>S06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>EBWD</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Umeanwindinf</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SRHE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SRH06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DMI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR_subcloud</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR_freezing</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR03</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR13</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WMSI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>BDSD</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ConvGust_wet</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ConvGust_dry</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>GUSTEX</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DmgWind</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DmgWind_fixed</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DCAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WMPI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WINDEX</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DowndraftTemp</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ThetaeDiff</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>TEI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WNDG</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DCP</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SCP</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SCP_fixed</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SHERB</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SHERBE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SWEAT</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MUCS6</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MLCS6</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>EffCS6</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>T_Totals</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>K_Index</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Eff_CAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Eff_LCL</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MLCAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ML_LCL</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MUCAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MU_LCL</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Qmean01</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Qmean06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> </tbody> </table> <p><strong>References</strong></p> <p>Brown, A., Dowdy, A., Lane, T. P., & Hitchcock, S. (2023b). Types of Severe Convective Wind Events in Eastern Australia. <em>Monthly Weather Review</em>, <em>151</em>(2), 419–448. https://doi.org/10.1175/MWR-D-22-0096.1</p> <p>Brown, A., A. Dowdy, T. P. Lane, & Hitchcock, S. (2023a). Long-term observational characteristics of different severe convective wind types around Australia. <em>Wea. Forecasting</em>, <a href="https://doi.org/10.1175/WAF-D-23-0069.1">https://doi.org/10.1175/WAF-D-23-0069.1</a>, in press.</p> <p>Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., et al. (2020). The ERA5 Global Reanalysis. <em>Quarterly Journal of the Royal Meteorological Society</em>, qj.3803. https://doi.org/10.1002/qj.3803</p>
DeepSurge storm surge predictions for HighResMIP tropical cyclones
<p>DeepSurge is a newly presented deep-learning approach to modeling the storm surge generated by a tropical cyclone (TC). This dataset is a collection of DeepSurge outputs for synthetic TCs in the North Atlantic generated by the HighResMIP project (Haarsma et al. 2016) for a simulated historical (1950-2014) and future (2015-2050) climate under the climate scenario SSP585.</p> <p>The data generation process and data analysis is detailed in an upcoming publication. The storm surge data presented here intentionally does not include the effects of sea level rise, rainfall, or other factors, in order to isolate the effects of changing TC climatology on future storm surge risk.</p> <h4>Dataset format</h4> <p>The data comes in the form of maximum surge levels at 2846 near-coastal locations for each synthetic TC. Each TC is defined by the corresponding track in the HighResMIP TempestExtremes dataset (Roberts 2019). The data is presented in NetCDF format, with two dimensions: </p> <ul> <li>'nodes', the number of near-coastal locations, always 2846.</li> <li>'tracks', the number of tracks in the simulation, which is different in each file.</li> </ul> <p>There are 6 variables in each file:</p> <ul> <li>'lons' and 'lats', the coordinates of the nodes in degrees North and East respectively.</li> <li>'track_valid' is a binary indicator (zero for false, one for true) indicating whether the TC occurs within the region of interest (HighResMIP tracks are global, but we only simulate those in the North Atlantic)</li> <li>'track_done' is another binary indicator for whether the track has been simulated. It should indicate true for all tracks for which 'track_valid' is true.</li> <li>'max_zeta' provides the predicted maximum surge height, in meters, for each storm at all 2846 nodes. This data is only valid in entries for which the corresponding 'track_done' and 'track_valid' indicators are true.</li> <li>'years' is the year in which each simulated TC occurs.</li> </ul>
Oxygen-18 isotope ratios, chemistry, and tracers in storm water from WS10 hillslope experiment in the HJ Andrews Experimental Forest, 2001-2003
Oxygen-18 ratios, expressed as Delta-18O or D18O (parts per hundred), measured in WS10 discharge and WS10 precipitation. The time series spans from January 2001 to February 2003, period during which precipitation and streamflow were sampled mostly manually and weekly. The time series also includes some periods of higher frequency automatic sampling in streamflow and precipitation in winter 2001 and winter 2002. The precipitation Delta-18O time series corresponds to the Delta-18O measured at PRIMET station located about 1 km from WS10 outlet at a similar altitude, except from 01-Sep-2002 to 04-Feb-2003, when the Delta-18O of precipitation sampled sequentially on an experimental hillslope of WS10, was used (the collector was located on the south aspect, 91 m upstream from the outlet).
Methane and carbon dioxide flux in a tidal freshwater marsh recovering from three years of experimental seawater additions and following the Hurricane Irma storm surge
Methane (CH4) and carbon dioxide (CO2) flux rates were measured in a tidal freshwater marsh using static flux chambers. The experimental field site, SALTEx (Seawater Addition Long-Term Experiment) is part of the Georgia Coastal Ecosystems (GCE) LTER and is located on the Altamaha River, GA. The marsh was experimentally dosed with brackish water additions for 3 years, from 2014 – 2017. There are three treatments groups (Press, Pulse, and Fresh) and two control groups (with and without siding on the plots), each with six replicates. Press treatment plots received brackish water throughout the year, Pulse plots received brackish water in September and October and fresh water the rest of the year, Fresh plots received fresh river water throughout the year. The two control groups, one with siding on the plots and one without, received no water addition manipulations. All dosing ceased in January 2018, at which point we began to study the recovery of the marsh. In this study, the Hurricane Irma Rapid Grant evaluated additional effects of the Hurricane Irma storm surge that occurred in October 2017. Greenhouse gas measurements were taken seasonally beginning in March 2018 and ending in March 2019.
Record of storm events and associated water levels for the Virginia Coast Reserve, 1980-2013
This empirical storm record for the Virginia Coast Reserve was created using a 34-year record of hourly wave hindcast data - including wave height (Hs) and wave period (Tp) - from the USACE's Wave Information Studies buoy offshore Hog Island in the Virginia Coast Reserve (Station 63183, 22 m water depth) and hourly records of water level from the nearest NOAA tide gauge (Station 8631044, Wachapreague, VA). The record includes wave and water level statistics for each event relevant for coastal modeling applications: storm start and end times, duration, total water level, still water level, as well as concurrent tidal amplitude, non-tidal residual, Hs, and Tp. The raw data is processed by first removing the 1 yr running median, which accounts for non-stationarity in wave and water level parameters due to inter-annual and decadal variability while maintaining seasonality. The median of the last 3 years is then applied to the entire time series such that the new time series is representative of the current climate. A year-by-year tidal analysis is performed to obtain the tidal amplitude and non-tidal residual. Lastly, water elevations are calculated following the run-up equations of Stockdon et al. (2006). Storm events are then extracted from the corrected time series by conditioning on Hs: events are identified as periods of 8 or more consecutive hours with deep-water significant wave heights greater than 2.1 m, which is the minimum monthly averaged wave height for periods in which waters levels exceeded the measured average dune toe elevation (1.9 m NAVD88) of barriers in the Virginia Coast Reserve. In total, we identify 282 independent sea-storm events over the 34-year record, resulting in an average of 8.3 events per year. See Reeves et al. (2021; https://doi.org/10.1029/2021GL092958) and supplementary information therein for complete details of the methodology.
Energy transfers and reflexion of infragravity waves at a dissipative beach under storm waves.
<p>%%% Author: Xavier Bertin (xbertin@univ-lr.fr) %%%<br> %%% Date: 15/04/2020 %%% <br> %%% Purpose: This repository provides the field observations and XBeach model input %%%<br> %%% required to reproduce the results presented in paper referred below. %%%<br> %%% Reference: Bertin, X., Martins, K., de Bakker, A., Guérin, T., Chataigner, T., %%%<br> %%% Coulombier, T. et de Viron, O., 2020. Energy transfers and reflexion of %%%<br> %%% infragravity waves at a dissipative beach under storm waves. In press %%%<br> %%% to Journal of Geophysical Research-Ocean. %%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>*The directory Obs includes:<br> -The wave bulk parameters computed as explained in the paper for the 10 sensores used in this<br> study: the offshore ADCP1, the intertidal PT1, PT2, ADCP2/PT3, PT4, PT5, ADV/PT6, PT7/Altus, PT8<br> and PT9. Each file has the same format and includes: the date (YYYY MM DD), the time (HH MM SS), <br> the mean water depth, the spectral significant wave height Hm0, mean wave periods Tm01 and Tm02, <br> the discrete and continuous peak periods, the energetic wave period Tm0,-2 and the spectral<br> significant height of IG waves Hm0,IG. <br> -The spectral significant height Hm0,IG+ and mean wave period Tm02,IG+ of incoming IG waves<br> separated at the ADCP2 and ADV using the method of Guza et al. (1984). The two files have the same <br> format and includes the date (YYYY MM DD), the time (HH MM SS), Hm0,IG+ and Tm02,IG+.<br> -The position of each sensore measured with a geodetic GNSS and provided in the same datum as the <br> bathymetry used in the model (Lambert93 and mean sea level). </p> <p>*The directory XBeach includes all the necessary files required to reproduce the simulations presented <br> in this study<br> -The bathymetry interpolated over a rectilinear grid, with X and Y given in Lambert93 coordinates (files<br> X_L93.grd and Y_L93.grd) and Z referred with respect to mean sea level (Z_L93.grd).<br> -The water level fluctuations measured at ADCP1 (WLevel_ADCP_201702.dat).<br> -The XBeach input file (params.txt) and a file providing the list of directional wave spectra<br> provided in the directory "Spectra_WWIII". These spectra were computed from a regional application of <br> WaveWatchIII over the North Atlantic Ocean and forced with CFSR wind fields but they were converted<br> in the format of SWAN, readable by XBeach.</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>
Extreme to phenomenal storm wave impacts on a steep rocky coast, north Mayo, Ireland: video data, image analysis, runup and flow velocity calculations for waves of storms Fionn and Gareth.
<p>The primary data are video (.mp4) files of extreme storm wave impacts on the sites of high elevation (>=20m above high water mark) coastal boulder deposits, recorded during storms Fionn (16/01/2018) and Gareth (12/03/2019), at (54.320355, -9.569633) on the north Mayo coast of Ireland, while the significant wave height was in the range [11m,14m]. There are also .png and .jpg files derived from frames of some of the videos, relating to the analysis of the impacting wave kinematics (runup/landward propagation and flow velocities), together with physical measurements for scale determination and runup/velocity/measurement uncertainty calculations in Excel. The files EventX.mp4 are the primary data for the wave impacts EventX. The files EventX_Frame_Y.jpg are frames sampled from EventX.mp4 at constant time intervals in the temporal vicinity of the impact. The files EventX_Edges_Y.png are the edges derived from the frames with the Canny edge detector. The files EventX_Registration_Y.jpg are the impacting wavefront edges with topographical edges registered on the file ReferenceImage.jpg The files EventX.jpg are the stacked registrations for all Y, from which the impact kinematics are derived. The file Scale_Registration_Position_Velocity_Measurements_AndUncertainty.xlsx contains physical measurements for scale determination, measurements of registration error, and the calculations of impact runup/landward displacement and flow velocities, with their uncertainties. The files JetX_Leacht_a_Chúil.mp4/g are videos of large jet-producing impacts at another site.</p> <p>The files DSCN0066.MP4-DSC0085.MP4 are the raw video observations of Storm Gareth, recorded from 15:35-18:41 UT on 12 March 2019 with a Nikon Coolpix W100, while the significant wave height increased from 12m to in excess of 14m (the timestamp of these videos in Properties->Details->Media Created is one hour later than the UT of creation, because the camera's clock was set to Irish Summer Time). The file GPO15366.MP4 is an example of the GoPro (Hero 5) videos recorded simultaneously.</p>
A subset of the EMARS dataset in MY24 and MY26 converted from the sigma-p hybrid coordinate to the pressure coordinate and a list of local dust storms detected during the MYs in western Arcadia Planitia
<p>This dataset includes a subset of EMARS' background mean data (Greybush et al., 2019) converted from the sigma-p hybrid coordinate to the pressure coordinate. Only MY24 and MY26 were used to generate the figures shown in Ogohara (submitted to JGR Planets). <br>Updates from the original EMARS are:</p> <ul> <li>The vertical coordinate has been converted from the sigma-p hybrid coordinate to the pressure coordinate.</li> <li>The variables expressing the Earth date (e.g., year, month, day, etc.) have been combined into one variable, earth_date.</li> <li>A new variable, emars_date, has been created from emars_sol and mars_hour.</li> </ul> <p>In addition, this dataset provides two lists of local dust storms events during MY24 and MY26 which were detected in western Arcadia Planitia using a deep learning-based method proposed by Ogohara and Gichu (2022). The lists are:</p> <ul> <li>[Data Set S1] List of global image swath files examined. Only file names of MGS/MOC red band images are listed. The list consists of 5 columns indicating image ID, observation date, orbit number, solar longitude, and filter name (RED).</li> <li>[Data Set S2] List of global image swath files containing identified dust storms, as well as some attributes of the detected dust storms. Only file names of red band images are listed. The list consists of 7 columns indicating image ID, observation date, orbit number, solar longitude, center longitude and latitude, and area (km2.)</li> </ul>
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