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9 results for “Tropical Cyclone Impacts”
WRF output for the Geophysical Research Letters publication "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific"
<p>This dataset is the Weather Research and Forecasting (WRF) model output for the <em>Geophysical Research Letters</em> publication entitled "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific". The dataset includes the azimuthal-averaged parameters with 0.2°resolution, three-day forecast, and two experiments for all cases analyzed in the publication. Due to the data size, only the variables used in the figures are uploaded (i.e., relative humidity, relative vorticity, temperature, and water vapor mixing ratio). Detailed information, composite calculation, and model settings can be found in the publication.</p>
Data for Tropical Cyclones flood hazards and impacts in Beira for study "Exploring coastal climate adaptation through storylines: Insights from Cyclone Idai in Beira, Mozambique"
<p>Data for Tropical Cyclones flood hazards and impacts in Beira for study "Exploring coastal climate adaptation through storylines: Insights from Cyclone Idai in Beira, Mozambique"<br><br><span><a href="../api/records/12664900/draft/files/hmax_idai_ifs_rebuild_bc_hist_rain_surge_noadapt.tiff/content" target="_blank" rel="noopener noreferrer">hmax_idai_ifs_rebuild*</a> -> Flood maps<br><a href="../api/records/12664900/draft/files/spatial_idai_ifs_rebuild_bc_3c-hightide_rain_surge_retreat.gpkg/content" target="_blank" rel="noopener noreferrer">spatial_idai_ifs_rebuild*</a> -> Impacts<br></span></p>
Response of a fringing reef coastline to the direct impact of a tropical cyclone
<p>The data contains observations from the impact of Tropical Cyclone Olwyn to northwest Western Australia in March 2015. A cross-shore array of 5 pressure sensors were deployed to measure wave heights and water levels at Point Jurabi (~5 km north of Tantabiddi), Ningaloo Reef; beach morphology was measured pre- and post-cyclone using RTK-DGPS; and a two-way coupled model was developed using Delft3D and SWAN. </p> <p> </p> <p>The data sets contains the raw pressure sensor measurements (.rsk), beach morphology grids (.txt), and model input/output files. Please see the 'Cuttler_etal_2017_Metadata.pdf', 'ReadMe.txt' file or contact michael.cuttler@uwa.edu.au for further information</p>
The impact of zonally symmetric and asymmetric climates on tropical cyclone genesis in coupled Aqua and Ridge Planets
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Data from: Impacts and recovery from Severe Tropical Cyclone Yasi on the Great Barrier Reef
Full recovery of coral reefs from tropical cyclone (TC) damage can take decades, making cyclones a major driver of habitat condition where they occur regularly. Since 1985, 44 TCs generated gale force winds (≥17 metres/second) within the Great Barrier Reef Marine Park (GBRMP). Of the hurricane strength TCs (≥H1—Saffir Simpson scale; ≥ category 3 Australian scale), TC Yasi (February, 2011) was the largest. In the weeks after TC Yasi crossed the GBRMP, participating researchers, managers and rangers assessed the extent and severity of reef damage via 841 Reef Health and Impact Surveys at 70 reefs. Records were scaled into five damage levels representing increasingly widespread colony-level damage (1, 2, 3) and reef structural damage (4, 5). Average damage severity was significantly affected by direction (north vs south of the cyclone track), reef shelf position (mid-shelf vs outer-shelf) and habitat type. More outer-shelf reefs suffered structural damage than mid-shelf reefs within 150 km of the track. Structural damage spanned a greater latitudinal range for mid-shelf reefs than outer-shelf reefs (400 vs 300 km). Structural damage was patchily distributed at all distances, but more so as distance from the track increased. Damage extended much further from the track than during other recent intense cyclones that had smaller circulation sizes. Just over 15% (3,834 km2) of the total reef area of the GBRMP is estimated to have sustained some level of coral damage, with ~4% (949 km2) sustaining a degree of structural damage. TC Yasi likely caused the greatest loss of coral cover on the GBR in a 24-hour period since 1985. Severely impacted reefs have started to recover; coral cover increased an average of 4% between 2011 and 2013 at re-surveyed reefs. The in situ assessment of impacts described here is the largest in scale ever conducted on the Great Barrier Reef following a reef health disturbance.
Economic impacts of tropical cyclone-induced multiple hazards in China
<p>The data and codes will be deposited in a repository that is appropriate for my scientific domain and that it is interoperable and reusable.</p>
Data from: Impacts and recovery from Severe Tropical Cyclone Yasi on the Great Barrier Reef
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Data from: Globally consistent impact of tropical cyclones on the structure of tropical and subtropical forests
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Datasets for Analyzing Tropical Cyclone Impacts on Hydrological Extremes in the Mid-Atlantic Region
<p>The data sets provided here were used for the climatological analysis of tropical cyclone impacts on hydrological extremes in the Mid-Atlantic region of the United States. This research was published in <em><strong>Environmental Research Letters</strong></em> <a href="http://doi.org/10.1088/1748-9326/ac2d6a">https://doi.org/10.1088/1748-9326/ac2d6a</a>.</p> <p>The data sets include:</p> <ul> <li><strong>hurdat_data/</strong> <ul> <li><strong>hurdat2-1950-2019_rev.txt:</strong> 6-hourly entires of hurricane tracks for all events occurring in the Atlantic region. The first column HURID matches the first column in the hurdat2-1950-2019_seq.txt</li> <li><strong>hurdat2-1950-2019_seq.txt</strong>: <ul> <li>1st column: HURID</li> <li>2nd column: event ID. For example, AL092011 means: AL (Spaces 1 and 2) – Basin – Atlantic; 09 (Spaces 3 and 4) – ATCF cyclone number for that year; 2011 (Spaces 5-8, before the first comma) – Year; </li> <li>3r column: event Name, if available, or else “UNNAMED” </li> <li>4th column: number of best track entries</li> </ul> </li> </ul> </li> <li><strong>usgs_gage/</strong> <ul> <li><strong>Content:</strong> daily streamflow records from USGS gages over 10/01/1950-9/30/2019</li> <li><strong>Format</strong>: <ul> <li>1st column: timestamp</li> <li>2nd column: daily mean flow (in cfs)</li> </ul> </li> </ul> </li> <li><strong>usgs_gage_list</strong> <ul> <li><strong>Content</strong>: Summary of USGS gages used in the analysis</li> </ul> </li> <li><strong>livneh_ppt.mat</strong> <ul> <li><strong>Content</strong>: Summary of precipitation data used in the analysis in Matlab structure array. The array includes statistics derived from precipitation records for 2857 grid cells (1/16 degree resolution) covering the Mid-Atlantic region. </li> </ul> </li> </ul>
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