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256 results for “Cyclone”
Data for "The 20-year highest tropical cyclone-generated waves associated with the maximum energy of seismic noises" (Subset 1)
<p>This is dataset of ocean wave simulations used in the paper "The 20-year highest tropical cyclone-generated waves associated with the maximum energy of seismic noises" by Shimura et al. (submitted).</p> <p>The dataset contains </p> <ul> <li>significant wave height (Subset 1),</li> <li>wave induced surface pressure (Subset 2)</li> <li>long-period componet of wave heights (Subset 3)</li> <li>long-period component of surface pressure (Subset 4)</li> </ul> <p>during 2004 from 2023 summer. </p>
Data for "The 20-year highest tropical cyclone-generated waves associated with the maximum energy of seismic noises" (Subset 2)
<p>This is dataset of ocean wave simulations used in the paper "The 20-year highest tropical cyclone-generated waves associated with the maximum energy of seismic noises" by Shimura et al. (submitted).</p> <p>The dataset contains </p> <ul> <li>significant wave height (Subset 1),</li> <li>wave induced surface pressure (Subset 2)</li> <li>long-period componet of wave heights (Subset 3)</li> <li>long-period component of surface pressure (Subset 4)</li> </ul> <p>during 2004 from 2023 summer. </p>
Effects of Tropospheric Vertical Wind Shear on Gravity Waves Generated by Tropical Cyclones
<p>The configuration for running WRF, and the data and codes for ploting the figures in our manuscript are provided here.</p>
GEFS tropical cyclone track data by year and 5-day forecast data
<p>The df_master.pkl file contains the processed 5-day TC track forecast data. All remaining files contain the TC track data from ATCF (https://ftp.nhc.noaa.gov/atcf/archive/) by year (2008-2022).</p>
Variations in Rainfall Structure of Western North Pacific Landfalling Tropical Cyclones in Warming Climates
<div>This dataset contains the data used in the analyses for the paper titled <em>'Variations in Rainfall Structure of Western North Pacific Landfalling Tropical Cyclones in Warming Climates',</em> published in <em>Earth's Future.</em> The paper is authored by Thao Linh Tran, Elizabeth A. Ritchie, Sarah E. Perkins-Kirkpatrick, Hai Bui, and Thang M. Luong. Descriptions of the variables included in the data files are provided below.</div> <div> </div> <div>----------------------------------------------------------------------------------------</div> <div>Data_CMIP6_multimodel_mean_SST_7states.nc</div> <div><xarray.Dataset></div> <div>Dimensions: (state: 7, month: 12, lat: 181, lon: 360)</div> <div>Coordinates:</div> <div> * state (state) <U9 'CTR' 'med2030s' 'med2050s''med2090s' 'high2030s' 'high2050s' 'high2090s'</div> <div> * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12</div> <div> * lat (lat) int64 -90 -89 -88 -87 -86 -85 -84 ... 84 85 86 87 88 89 90</div> <div> * lon (lon) int64 0 1 2 3 4 5 6 7 8 ... 352 353 354 355 356 357 358 359</div> <div>Data variables:</div> <div> SST_cmip6 (state, month, lat, lon) float64 0.0 0.0 0.0 ... -1.699 -1.699</div> <div> SST_cmip6 (state, month, lat, lon) CMIP6 multimodel mean of sea surface temperature in each month in 7 climate states (deg Celcius)</div> <div> </div> <div> </div> <div>----------------------------------------------------------------------------------------</div> <div>Data_rain_PW_MFC_2methods_7states_AllTCs.nc</div> <div><xarray.Dataset></div> <div>Dimensions: (state: 7, radius: 20, nx: 61, ny: 61)</div> <div>Coordinates:</div> <div> * state (state) <U9 'CTR' 'med2030s' 'med2050s''med2090s' 'high2030s' 'high2050s' 'high2090s'</div> <div> * radius (radius) int64 25 50 75 100 125 150 ... 375 400 425 450 475 500</div> <div> * nx (nx) int64 0 18 36 54 72 90 ... 990 1008 1026 1044 1062 1080</div> <div> * ny (ny) int64 0 18 36 54 72 90 ... 990 1008 1026 1044 1062 1080</div> <div>Data variables:</div> <div> Rain_circle (state, radius) float64 22.66 24.94 26.54 ... 8.147 7.561 7.041</div> <div> Rain_ring (state, radius) float64 22.66 25.8 27.49 ... 2.689 2.436 2.231</div> <div> PW_ring (state, radius) float64 72.64 73.04 72.37 ... 69.81 68.99 68.27</div> <div> MFC_ring (state, radius) float64 0.01343 0.01539 ... 0.004405 0.004258</div> <div> MFC_hoz (state, nx, ny) float64 0.0002184 0.0003068 ... 0.000167</div> <div> Rain_circle (state, radius) Areal averaged TC rainfall within radii in 7 climate states calculated by the circle-average method (mm)</div> <div> Rain_ring (state, radius) Average TC rainfall within radial bands in 7 climate states calculated by the ring-average method (mm)</div> <div> PW_ring (state, radius) Average precipitable water within radial bands in 7 climate states calculated by the ring-average method (kg m-2)</div> <div> MFC_ring (state, radius) Average moisture flux convergence within radial bands in 7 climate states calculated by the ring-average method (g m-2 s-1)</div> <div> MFC_hoz (state, nx, ny) Integrated moisture flux convergence in the boundary layer in 7 climate states (kg m-2 s-1), TC center is located at nx=ny=540 km</div> <div> </div> <div> </div> <div>----------------------------------------------------------------------------------------</div> <div>Data_rain_PW_MFC_2methods_7states_TSs.nc</div> <div><xarray.Dataset></div> <div>Dimensions: (state: 7, radius: 20)</div> <div>Coordinates:</div> <div> * state (state) <U9 'CTR' 'med2030s' 'med2050s''med2090s' 'high2030s' 'high2050s' 'high2090s'</div> <div> * radius (radius) int64 25 50 75 100 125 150 ... 375 400 425 450 475 500</div> <div>Data variables:</div> <div> Rain_circle (state, radius) float64 17.86 16.98 15.74 ... 5.884 5.533 5.215</div> <div> Rain_ring (state, radius) float64 17.86 16.66 14.92 ... 2.704 2.461 2.28</div> <div> PW_ring (state, radius) float64 71.08 70.79 70.14 ... 70.67 69.9 69.22</div> <div> MFC_ring (state, radius) float64 0.00885 0.009635 ... 0.004318 0.004183</div> <div> Rain_circle (state, radius) Areal averaged TC rainfall within radii in 7 climate states calculated by the circle-average method (mm)</div> <div> Rain_ring (state, radius) Average TC rainfall within radial bands in 7 climate states calculated by the ring-average method (mm)</div> <div> PW_ring (state, radius) Average precipitable water within radial bands in 7 climate states calculated by the ring-average method (kg m-2)</div> <div> MFC_ring (state, radius) Average moisture flux convergence within radial bands in 7 climate states calculated by the ring-average method (g m-2 s-1)</div> <div> </div> <div> </div> <div>----------------------------------------------------------------------------------------</div> <div>Data_rain_PW_MFC_2methods_7states_TYs.nc</div> <div><xarray.Dataset></div> <div>Dimensions: (state: 7, radius: 20)</div> <div>Coordinates:</div> <div> * state (state) <U9 'CTR' 'med2030s' 'med2050s''med2090s' 'high2030s' 'high2050s' 'high2090s'</div> <div> * radius (radius) int64 25 50 75 100 125 150 ... 375 400 425 450 475 500</div> <div>Data variables:</div> <div> Rain_circle (state, radius) float64 21.55 23.26 24.55 ... 8.029 7.459 6.954</div> <div> Rain_ring (state, radius) float64 21.55 23.89 25.27 ... 2.73 2.473 2.273</div> <div> PW_ring (state, radius) float64 72.7 72.73 71.91 ... 69.23 68.42 67.71</div> <div> MFC_ring (state, radius) float64 0.01288 0.01458 ... 0.004363 0.004229</div> <div> Rain_circle (state, radius) Areal averaged TC rainfall within radii in 7 climate states calculated by the circle-average method (mm)</div> <div> Rain_ring (state, radius) Average TC rainfall within radial bands in 7 climate states calculated by the ring-average method (mm)</div> <div> PW_ring (state, radius) Average precipitable water within radial bands in 7 climate states calculated by the ring-average method (kg m-2)</div> <div> MFC_ring (state, radius) Average moisture flux convergence within radial bands in 7 climate states calculated by the ring-average method (g m-2 s-1)</div> <div> </div> <div> </div> <div>----------------------------------------------------------------------------------------</div> <div>Data_rain_PW_MFC_2methods_7states_STYs.nc</div> <div><xarray.Dataset></div> <div>Dimensions: (state: 7, radius: 20)</div> <div>Coordinates:</div> <div> * state (state) <U9 'CTR' 'med2030s' 'med2050s''med2090s' 'high2030s' 'high2050s' 'high2090s'</div> <div> * radius (radius) int64 25 50 75 100 125 150 ... 375 400 425 450 475 500</div> <div>Data variables:</div> <div> Rain_circle (state, radius) float64 31.83 39.28 44.65 ... 9.905 9.124 8.436</div> <div> Rain_ring (state, radius) float64 31.83 42.18 48.07 ... 2.552 2.304 2.071</div> <div> PW_ring (state, radius) float64 73.54 76.26 76.33 ... 71.1 70.22 69.44</div> <div> MFC_ring (state, radius) float64 0.01956 0.02383 ... 0.004592 0.004396</div> <div> Rain_circle (state, radius) Areal averaged TC rainfall within radii in 7 climate states calculated by the circle-average method (mm)</div> <div> Rain_ring (state, radius) Average TC rainfall within radial bands in 7 climate states calculated by the ring-average method (mm)</div> <div> PW_ring (state, radius) Average precipitable water within radial bands in 7 climate states calculated by the ring-average method (kg m-2)</div> <div> MFC_ring (state, radius) Average moisture flux convergence within radial bands in 7 climate states calculated by the ring-average method (g m-2 s-1)</div> <div> </div> <div> </div> <div>----------------------------------------------------------------------------------------</div> <div>Data_Azimean_levelmean_thermodynamic_dynamic_variables_3states_AllCats.nc</div> <div><xarray.Dataset></div> <div>Dimensions: (state: 3, level: 38, radius: 252, tc: 117)</div> <div>Coordinates:</div> <div> * state (state) <U9 'CTR' 'med2090s' 'high2090s'</div> <div> * level (level) int64 25 83 156 250 ... 16300 17000 17550 18100</div> <div> * radius (radius) int64 0 2 4 6 8 10 ... 492 494 496 498 500 502</div> <div> * tc (tc) int64 0 1 2 3 4 5 6 ... 110 111 112 113 114 115 116</div> <div>Data variables: (12/16)</div> <div> cape_azimean (state, level, radius) object 1890.2525300742138 ... nan</div> <div> Ta_azimean (state, level, radius) object 27.644140005330815 ... -...</div> <div> Qv_azimean (state, level, radius) object 0.020813525216797777 ......</div> <div> Qtti_azimean (state, level, radius) object 5.566690902988369e-08 .....</div> <div> ttip (state, tc) object 7997552.5 20351432.0 ... 8139392.0</div> <div> SST_levmean (state, tc) float64 29.44 29.49 30.23 ... 31.55 31.01</div> <div> ... ...</div> <div> etheta_azimean (state, level, radius) object 367.2280704092775 ... 40...</div> <div> updraft_azimean (state, level, radius) object 0.08083254853909848 ... ...</div> <div> downdraft_azimean (state, level, radius) object -0.019081951314966872 .....</div> <div> mfc_azimean (state, level, radius) object 0.00029923238185347746 ....</div> <div> ss_azimean (state, radius) object 48.3689839795465 ... 47.3942474...</div> <div> vms_azimean (state, radius) object 371.7967310109822 ... 364.55313...</div> <div> cape_azimean (state, level, radius) Azimuthally averaged convective available potential energy (CAPE) at radii up to 500 km from the TC center (J kg-1)</div> <div> Ta_azimean (state, level, radius) Azimuthally averaged air temperature at radii up to 500 km from the TC center (deg Celcius)</div> <div> Qv_azimean (state, level, radius) Azimuthally averaged water vapor mixing ratio at radii up to 500 km from the TC center (g kg-1)</div> <div> Qtti_azimean (state, level, radius) Azimuthally averaged total ice-related (ice|graupel|snow) mixing ratio at radii up to 500 km from the TC center (g kg-1)</div> <div> ttip (state, tc) Total cloud ice path in 3 future states (kg m-2)</div> <div> SST_levmean (state, tc) Mean SST within 500 km of the TC center (deg Celcius)</div> <div> Ta_levmean (state, tc, level) Level averaged Ta within 500 km of the TC center at each level (deg Celcius)</div> <div> Qv_levmean (state, tc, level) Level averaged Qv within 500 km of the TC center at each level (g kg-1)</div> <div> vt_azimean (state, level, radius) Azimuthally averaged tangential wind at radii up to 500 km from the TC center (kt)</div> <div> vr_azimean (state, level, radius) Azimuthally averaged radial wind at radii up to 500 km from the TC center (kt)</div> <div> etheta_azimean (state, level, radius) Azimuthally averaged equivalent potential temperature at radii up to 500 km from the TC center (deg Kelvin)</div> <div> updraft_azimean (state, level, radius) Azimuthally averaged updraft at radii up to 500 km from the TC center (kt)</div> <div> downdraft_azimean (state, level, radius) Azimuthally averaged downdraft at radii up to 500 km from the TC center (kt)</div> <div> mfc_azimean (state, level, radius) Azimuthally averaged moisture flux convergence at radii up to 500 km from the TC center (kg m-2 s-1)</div> <div> ss_azimean (state, radius) Azimuthally averaged static stability at radii up to 500 km from the TC center (deg Kelvin)</div> <div> vms_azimean (state, radius) Azimuthally averaged convective stability at radii up to 500 km from the TC center (MJ m-1 s-2)</div> <div> </div> <div> </div> <div>----------------------------------------------------------------------------------------</div> <div>Data_Azimean_levelmean_thermodynamic_dynamic_variables_3states_TSs.nc</div> <div>Data variables: (12/14)</div> <div> Ta_azimean (state, level, radius) object 27.457257781178235 ... -...</div> <div> Qv_azimean (state, level, radius) object 0.020375530778800895 ......</div> <div> Qtti_azimean (state, level, radius) object 5.639538301516413e-08 .....</div> <div> SST_levmean (state, tc) float64 29.44 29.49 30.23 ... 31.55 31.01</div> <div> Ta_levmean (state, tc, level) float32 28.13 27.51 ... -83.1 -83.41</div> <div> Qv_levmean (state, tc, level) float32 20.09 19.83 ... 0.00155</div> <div> ... ...</div> <div> etheta_azimean (state, level, radius) object 360.9125096202158 ... 40...</div> <div> updraft_azimean (state, level, radius) object 0.07054015061042855 ... ...</div> <div> downdraft_azimean (state, level, radius) object -0.018065747224214473 .....</div> <div> mfc_azimean (state, level, radius) object 0.00020737332807555921 ....</div> <div> ss_azimean (state, radius) object 46.39805195710858 ... 47.228965...</div> <div> vms_azimean (state, radius) object 361.6881448325401 ... 365.20869...</div> <div> Ta_azimean (state, level, radius) Azimuthally averaged air temperature at radii up to 500 km from the TC center (deg Celcius)</div> <div> Qv_azimean (state, level, radius) Azimuthally averaged water vapor mixing ratio at radii up to 500 km from the TC center (g kg-1)</div> <div> Qtti_azimean (state, level, radius) Azimuthally averaged total ice-related (ice|graupel|snow) mixing ratio at radii up to 500 km from the TC center (g kg-1)</div> <div> SST_levmean (state, tc) Mean SST within 500 km of the TC center (deg Celcius)</div> <div> Ta_levmean (state, tc, level) Level averaged Ta within 500 km of the TC center at each level (deg Celcius)</div> <div> Qv_levmean (state, tc, level) Level averaged Qv within 500 km of the TC center at each level (g kg-1)</div> <div> vt_azimean (state, level, radius) Azimuthally averaged tangential wind at radii up to 500 km from the TC center (kt)</div> <div> vr_azimean (state, level, radius) Azimuthally averaged radial wind at radii up to 500 km from the TC center (kt)</div> <div> etheta_azimean (state, level, radius) Azimuthally averaged equivalent potential temperature at radii up to 500 km from the TC center (deg Kelvin)</div> <div> updraft_azimean (state, level, radius) Azimuthally averaged updraft at radii up to 500 km from the TC center (kt)</div> <div> downdraft_azimean (state, level, radius) Azimuthally averaged downdraft at radii up to 500 km from the TC center (kt)</div> <div> mfc_azimean (state, level, radius) Azimuthally averaged moisture flux convergence at radii up to 500 km from the TC center (kg m-2 s-1)</div> <div> ss_azimean (state, radius) Azimuthally averaged static stability at radii up to 500 km from the TC center (deg Kelvin)</div> <div> vms_azimean (state, radius) Azimuthally averaged convective stability at radii up to 500 km from the TC center (MJ m-1 s-2)</div> <div> </div> <div> </div> <div>----------------------------------------------------------------------------------------</div> <div>Data_Azimean_levelmean_thermodynamic_dynamic_variables_3states_TYs.nc</div> <div><xarray.Dataset></div> <div>Dimensions: (state: 3, level: 38, radius: 252, tc: 117)</div> <div>Coordinates:</div> <div> * state (state) <U9 'CTR' 'med2090s' 'high2090s'</div> <div> * level (level) int64 25 83 156 250 ... 16300 17000 17550 18100</div> <div> * radius (radius) int64 0 2 4 6 8 10 ... 492 494 496 498 500 502</div> <div> * tc (tc) int64 0 1 2 3 4 5 6 ... 110 111 112 113 114 115 116</div> <div>Data variables: (12/14)</div> <div> Ta_azimean (state, level, radius) object 27.515624558698093 ... -...</div> <div> Qv_azimean (state, level, radius) object 0.020579266032935868 ......</div> <div> Qtti_azimean (state, level, radius) object 5.815300849925888e-08 .....</div> <div> SST_levmean (state, tc) float64 29.44 29.49 30.23 ... 31.55 31.01</div> <div> Ta_levmean (state, tc, level) float32 28.13 27.51 ... -83.1 -83.41</div> <div> Qv_levmean (state, tc, level) float32 20.09 19.83 ... 0.00155</div> <div> ... ...</div> <div> etheta_azimean (state, level, radius) object 366.45252777008426 ... 4...</div> <div> updraft_azimean (state, level, radius) object 0.08167928852011325 ... ...</div> <div> downdraft_azimean (state, level, radius) object -0.017788485333689888 .....</div> <div> mfc_azimean (state, level, radius) object 0.00028844761894039205 ....</div> <div> ss_azimean (state, radius) object 47.93880505618239 ... 47.189455...</div> <div> vms_azimean (state, radius) object 369.8002440991976 ... 364.41387...</div> <div> Ta_azimean (state, level, radius) Azimuthally averaged air temperature at radii up to 500 km from the TC center (deg Celcius)</div> <div> Qv_azimean (state, level, radius) Azimuthally averaged water vapor mixing ratio at radii up to 500 km from the TC center (g kg-1)</div> <div> Qtti_azimean (state, level, radius) Azimuthally averaged total ice-related (ice|graupel|snow) mixing ratio at radii up to 500 km from the TC center (g kg-1)</div> <div> SST_levmean (state, tc) Mean SST within 500 km of the TC center (deg Celcius)</div> <div> Ta_levmean (state, tc, level) Level averaged Ta within 500 km of the TC center at each level (deg Celcius)</div> <div> Qv_levmean (state, tc, level) Level averaged Qv within 500 km of the TC center at each level (g kg-1)</div> <div> vt_azimean (state, level, radius) Azimuthally averaged tangential wind at radii up to 500 km from the TC center (kt)</div> <div> vr_azimean (state, level, radius) Azimuthally averaged radial wind at radii up to 500 km from the TC center (kt)</div> <div> etheta_azimean (state, level, radius) Azimuthally averaged equivalent potential temperature at radii up to 500 km from the TC center (deg Kelvin)</div> <div> updraft_azimean (state, level, radius) Azimuthally averaged updraft at radii up to 500 km from the TC center (kt)</div> <div> downdraft_azimean (state, level, radius) Azimuthally averaged downdraft at radii up to 500 km from the TC center (kt)</div> <div> mfc_azimean (state, level, radius) Azimuthally averaged moisture flux convergence at radii up to 500 km from the TC center (kg m-2 s-1)</div> <div> ss_azimean (state, radius) Azimuthally averaged static stability at radii up to 500 km from the TC center (deg Kelvin)</div> <div> vms_azimean (state, radius) Azimuthally averaged convective stability at radii up to 500 km from the TC center (MJ m-1 s-2)</div> <div> </div> <div> </div> <div>----------------------------------------------------------------------------------------</div> <div>Data_Azimean_levelmean_thermodynamic_dynamic_variables_3states_STYs.nc</div> <div><xarray.Dataset></div> <div>Dimensions: (state: 3, level: 38, radius: 252, tc: 117)</div> <div>Coordinates:</div> <div> * state (state) <U9 'CTR' 'med2090s' 'high2090s'</div> <div> * level (level) int64 25 83 156 250 ... 16300 17000 17550 18100</div> <div> * radius (radius) int64 0 2 4 6 8 10 ... 492 494 496 498 500 502</div> <div> * tc (tc) int64 0 1 2 3 4 5 6 ... 110 111 112 113 114 115 116</div> <div>Data variables: (12/14)</div> <div> Ta_azimean (state, level, radius) object 28.677196671837233 ... -...</div> <div> Qv_azimean (state, level, radius) object 0.022842606379269957 ......</div> <div> Qtti_azimean (state, level, radius) object 4.0012758302765466e-08 ....</div> <div> SST_levmean (state, tc) float64 29.44 29.49 30.23 ... 31.55 31.01</div> <div> Ta_levmean (state, tc, level) float32 28.13 27.51 ... -83.1 -83.41</div> <div> Qv_levmean (state, tc, level) float32 20.09 19.83 ... 0.00155</div> <div> ... ...</div> <div> etheta_azimean (state, level, radius) object 376.0869964585824 ... 40...</div> <div> updraft_azimean (state, level, radius) object 0.0844076324643325 ... 0...</div> <div> downdraft_azimean (state, level, radius) object -0.02603523353916364 ......</div> <div> mfc_azimean (state, level, radius) object 0.0004255960070462955 .....</div> <div> ss_azimean (state, radius) object 52.07136400532476 ... 48.142035...</div> <div> vms_azimean (state, radius) object 389.7398573564309 ... 364.59206...</div> <div> Ta_azimean (state, level, radius) Azimuthally averaged air temperature at radii up to 500 km from the TC center (deg Celcius)</div> <div> Qv_azimean (state, level, radius) Azimuthally averaged water vapor mixing ratio at radii up to 500 km from the TC center (g kg-1)</div> <div> Qtti_azimean (state, level, radius) Azimuthally averaged total ice-related (ice|graupel|snow) mixing ratio at radii up to 500 km from the TC center (g kg-1)</div> <div> SST_levmean (state, tc) Mean SST within 500 km of the TC center (deg Celcius)</div> <div> Ta_levmean (state, tc, level) Level averaged Ta within 500 km of the TC center at each level (deg Celcius)</div> <div> Qv_levmean (state, tc, level) Level averaged Qv within 500 km of the TC center at each level (g kg-1)</div> <div> vt_azimean (state, level, radius) Azimuthally averaged tangential wind at radii up to 500 km from the TC center (kt)</div> <div> vr_azimean (state, level, radius) Azimuthally averaged radial wind at radii up to 500 km from the TC center (kt)</div> <div> etheta_azimean (state, level, radius) Azimuthally averaged equivalent potential temperature at radii up to 500 km from the TC center (deg Kelvin)</div> <div> updraft_azimean (state, level, radius) Azimuthally averaged updraft at radii up to 500 km from the TC center (kt)</div> <div> downdraft_azimean (state, level, radius) Azimuthally averaged downdraft at radii up to 500 km from the TC center (kt)</div> <div> mfc_azimean (state, level, radius) Azimuthally averaged moisture flux convergence at radii up to 500 km from the TC center (kg m-2 s-1)</div> <div> ss_azimean (state, radius) Azimuthally averaged static stability at radii up to 500 km from the TC center (deg Kelvin)</div> <div> vms_azimean (state, radius) Azimuthally averaged convective stability at radii up to 500 km from the TC center (MJ m-1 s-2)</div> <div> </div>
Datasets and codes for "The Upper Ocean Biogeochemical Response Due to Three Sequential Cyclones in the Bay of Bengal"
Open the record for dataset details and reuse information.
Database of upper ocean mixing enhanced by tropical cyclones in the northern South China Sea in 2023
<p>Data reported in the manuscript "Identification and quantification of upper ocean mixing enhanced by tropical cyclones in the northern South China Sea" can be downloaded here.</p> <p>Matlab (versions later than 9.12.0.1884302 (R2022a)) is necessary to run the postprocessing codes.</p>
North Atlantic EXtratropical CYCLONE TRAcks and Lagrangian-Derived MOisture Uptake Dataset: ExCyclone-TRAMO (Part I)
<h3>Abstract:</h3> <p>This dataset focuses on <strong>extratropical cyclones (ETCs)</strong> over the <strong>North Atlantic Ocean (NATL)</strong> during the extended winter seasons, spanning from October to April, between 1985 and 2022. The relationship between moisture uptake and precipitation in various mesoscale ETC structures remains an active research area. The provision of moisture parameters through <strong>tracer dispersion models</strong> enhances our understanding of moisture dynamics, the behaviour of ETCs, and the associated meteorological fields.</p> <h3>Description:</h3> <p>The dataset was constructed through <strong>dynamic downscaling</strong> of ERA5 reanalysis data using the Weather Research and Forecasting (WRF) model, in conjunction with the <strong>Lagrangian dispersion model FELXPART-WRF</strong>. It consists of <strong>6-hourly </strong>time intervals with a horizontal resolution of <strong>0.18°</strong>. This database is completely described in <em>Coll-Hidalgo, P., Gimeno-Sotelo, L., Fernández-Alvarez, J.C. et al. North Atlantic Extratropical Cyclone Tracks and Lagrangian-Derived Moisture Uptake Dataset. Sci Data 11, 1258 (2024). <a href="https://doi.org/10.1038/s41597-024-04091-5" target="_blank" rel="noopener">https://doi.org/10.1038/s41597-024-04091-5</a></em></p> <p>The dataset includes:</p> <ul> <li>Detailed tracking of ETCs across the North Atlantic: <ul> <li>Date in year-month-day format</li> <li>hh (in UTC): Hour in Coordinated Universal Time</li> <li>latitude (degrees north): Latitude in degrees north</li> <li>longitude (degrees west): Longitude in degrees west</li> <li>MSLP (hPa): Mean sea level pressure in hectopascals (hPa)</li> <li>Radius (km): Cyclone radius in kilometres (km)</li> <li>Last closed isobar (hPa): Pressure at the last closed isobar in hectopascals (hPa)</li> <li>Low-level thermal wind parameter (VTL)</li> <li>High-level thermal wind parameter (VTU)</li> <li>The thermal asymmetry parameter (B)</li> </ul> </li> </ul> <ul> <li>Two-Dimensional Masks Shaping ETCs: <ul> <li>Radius</li> <li>Warm Conveyor Belt (WCB) footprint Related Areas</li> <li>A theoretical boundary encompassing a broader extent of the ETC circulation within a spiral shape </li> </ul> </li> </ul> <ul> <li>Cyclone Moisture Uptake-Related Variables by ETCs life timesteps: <ul> <li>Total moisture uptake</li> <li>Discrete Moisture Uptake (10-Day Backward)</li> <li>Moisture uptake by layers from the surface to 100 hPa.</li> </ul> </li> </ul> <h3>Dataset Repository Information</h3> <p>This repository contains the dataset for the years 1985-1999. For users interested in datasets from subsequent periods, please refer to the following links:</p> <p>Years 2000-2014:<a href="https://zenodo.org/records/13844450"> Access the dataset here</a><br>Years 2015-2022: <a href="https://zenodo.org/records/13847453" target="_blank" rel="noopener">Access the dataset here</a></p> <p>A <a href="https://github.com/ECMOISTDATABASE/North-Atlantic-Extratropical-Cyclones-database.git">GitHub repository</a> is available, providing example code that demonstrates how to use and handle the dataset effectively. </p>
North Atlantic EXtratropical CYCLONE TRAcks and Lagrangian-Derived MOisture Uptake Dataset: ExCyclone-TRAMO (Part II)
<h3>Abstract:</h3> <p>This dataset focuses on <strong>extratropical cyclones (ETCs)</strong> over the <strong>North Atlantic Ocean (NATL)</strong> during the extended winter seasons, spanning from October to April, between 1985 and 2022. The relationship between moisture uptake and precipitation in various mesoscale ETC structures remains an active research area. The provision of moisture parameters through <strong>tracer dispersion models</strong> enhances our understanding of moisture dynamics, the behaviour of ETCs, and the associated meteorological fields.</p> <h3>Description:</h3> <p>The dataset was constructed through <strong>dynamic downscaling</strong> of ERA5 reanalysis data using the Weather Research and Forecasting (WRF) model, in conjunction with the <strong>Lagrangian dispersion model FELXPART-WRF</strong>. It consists of <strong>6-hourly </strong>time intervals with a horizontal resolution of <strong>0.18°</strong>. This database is completely described in<em> Coll-Hidalgo, P., Gimeno-Sotelo, L., Fernández-Alvarez, J.C. et al. North Atlantic Extratropical Cyclone Tracks and Lagrangian-Derived Moisture Uptake Dataset. Sci Data 11, 1258 (2024). <a href="https://doi.org/10.1038/s41597-024-04091-5" target="_blank" rel="noopener">https://doi.org/10.1038/s41597-024-04091-5</a></em></p> <p>The dataset includes:</p> <ul> <li>Detailed tracking of ETCs across the North Atlantic: <ul> <li>Date in year-month-day format</li> <li>hh (in UTC): Hour in Coordinated Universal Time</li> <li>latitude (degrees north): Latitude in degrees north</li> <li>longitude (degrees west): Longitude in degrees west</li> <li>MSLP (hPa): Mean sea level pressure in hectopascals (hPa)</li> <li>Radius (km): Cyclone radius in kilometres (km)</li> <li>Last closed isobar (hPa): Pressure at the last closed isobar in hectopascals (hPa)</li> <li>Low-level thermal wind parameter (VTL)</li> <li>High-level thermal wind parameter (VTU)</li> <li>The thermal asymmetry parameter (B)</li> </ul> </li> </ul> <ul> <li>Two-Dimensional Masks Shaping ETCs: <ul> <li>Radius</li> <li>Warm Conveyor Belt (WCB) footprint Related Areas</li> <li>A theoretical boundary encompassing a broader extent of the ETC circulation within a spiral shape </li> </ul> </li> </ul> <ul> <li>Cyclone Moisture Uptake-Related Variables by ETCs life timesteps: <ul> <li>Total moisture uptake</li> <li>Discrete Moisture Uptake (10-Day Backward)</li> <li>Moisture uptake by layers from the surface to 100 hPa.</li> </ul> </li> </ul> <h3>Dataset Repository Information</h3> <p>This repository contains the dataset for the years 2000-2016. For users interested in datasets from subsequent periods, please refer to the following links:</p> <p>Years 1985-1999: <a href="https://zenodo.org/records/13844378">Access the dataset here</a><br>Years 2015-2022: <a href="https://zenodo.org/records/13847453" target="_blank" rel="noopener">Access the dataset here</a></p> <p>A <a href="https://github.com/ECMOISTDATABASE/North-Atlantic-Extratropical-Cyclones-database.git">GitHub repository</a> is available, providing example code that demonstrates how to use and handle the dataset effectively. </p>
Incorporating Hourly Convective Cloud data into Tropical Cyclone Rapid Intensification Forecasting with Machine Learning
<p>These models were developed to predict both the probability of RI and the binary RI classification for tropical cyclones. They are a weighted average of probabilities derived from logistic regression, random forest, decision tree, and extremely randomized tree algorithms within the standard Python scikit-learn package. The uploaded files include the hyperparameters and weights for each individual machine learning model.</p>
Contributions of Tropical Cyclones and Internal Tides to Deep Near-Inertial Kinetic Energy Under Eddy Modulation
<p>This dataset contains the velocity data observed by the mooring at 130°E, 15°N. It is supplementary of the paper "<em><strong>Contributions of Tropical Cyclones and Internal Tides to Deep Near-Inertial Kinetic Energy Under Eddy Modulation</strong></em>" submitted to the <em>Geophysical Research Letters</em>. If you need any more information or want to utilize the data for research purpose, please contact the author first (Dr. Zhixiang Zhang; E-mail: zzx@qdio.ac.cn).</p>
TempestExtremes Tracking of Extratropical Cyclones in CESM2-LE and CESM2-SF
<p>Individual raw extratropical cyclone trajectories created by TempestExtremes as part of <strong><a href="https://doi.org/10.1175/JCLI-D-23-0455.1">Aerosol-Induced Changes in Atmospheric and Oceanic Heat Transports in the CESM2 Large Ensemble</a></strong> by Needham et. al (2024).</p>
ClimateNet Dataset as used in "Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data"
<p>ClimateNet dataset as it was used by us for the study: "Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data" (https://gmd.copernicus.org/preprints/gmd-2024-60/).</p> <p> </p> <p>For the original dataset refer to: https://portal.nersc.gov/project/ClimateNet/</p>
Figure 4 in Tropical cyclones and reproductive ecology of Crocodylus acutus Cuvier, 1807 (Reptilia: Crocodilia: Crocodylidae) on a Caribbean atoll in Mexico
Figure 4. Fluctuation of temperature in (A) three nests of 2007 and (B) three nests of 2008 of American crocodiles in Banco Chinchorro. Note: Data-logger in Nest 6 ceased functioning on 1 June 2008 during tropical storm Arthur.
Figure 3 in Tropical cyclones and reproductive ecology of Crocodylus acutus Cuvier, 1807 (Reptilia: Crocodilia: Crocodylidae) on a Caribbean atoll in Mexico
Figure 3. Relationship between gap fraction and incident solar radiation intensity (ISRI) for 34 sites in Cayo Centro, Banco Chinchorro, showing 9 successful nests (black squares); 7 unsuccessful nests (grey squares); and 18 control sites (white squares).
Figure 2 in Tropical cyclones and reproductive ecology of Crocodylus acutus Cuvier, 1807 (Reptilia: Crocodilia: Crocodylidae) on a Caribbean atoll in Mexico
Figure 2. Climatic diagram from the meteorological station of Mahahual, with periods of courtship and mating (thin line), laying (dashed line), and hatching (thick line) of American crocodiles in Banco Chinchorro.
Figure 1 in Tropical cyclones and reproductive ecology of Crocodylus acutus Cuvier, 1807 (Reptilia: Crocodilia: Crocodylidae) on a Caribbean atoll in Mexico
Figure 1. Localisation map of Banco Chinchorro biosphere reserve and of Cayo Centro, with nesting areas and nests location.
Figure 5 in Tropical cyclones and reproductive ecology of Crocodylus acutus Cuvier, 1807 (Reptilia: Crocodilia: Crocodylidae) on a Caribbean atoll in Mexico
Figure 5. Monthly mean direct solar radiation intensity (DSRI) at nine nesting sites of American crocodiles in Banco Chinchorro, with incubation period indicated.
Building pseudo proxy paleotempestology networks using synthetic North Atlantic tropical cyclones
<p>This release contains the past millennium synthetic storm datasets (MPI-ESM; CESM-LME) and scripts needed to develop pseudo paleohurricane compilations used in Wallace et al. (2021). Version 2 includes updated storm datasets that are re-calibrated to have long term mean frequency that matches the satellite era tropical cyclone frequency. The site properties (resolution, radius) are also updated. </p>
MATLAB scripts for reproducing figures in "Atlantic Tropical Cyclones Downscaled from Climate Reanalyses Show Increasing Activity Through the Late 19th and 20th Centuries"
<p>A set of MATLAB scripts that contain the data for all 4 figures of "<strong>Atlantic Tropical Cyclones Downscaled from Climate Reanalyses Show Increasing Activity Through the Late 19<sup>th</sup> and 20<sup>th</sup> Centuries" </strong> and allows the user to plot these data. Please read the very short ReadMe file before using the scripts. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.