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182 results for “Tropical Cyclones”
The role of tropical cyclone on Changjiang River subaqueous delta geomorphology: a numerical investigation of Tropical Cyclone Danas (2019)
<p>ecs_mesh: Model domain used for numerical simulation in Matlab data format.</p> <p>Variable description of ecs_mesh.mat:</p> <p>nodex/nodey: Longitude/Latitude of the vertices of the triangles</p> <p>cellx/celly: Longitude/Latitude of the faces of the triangles</p> <p>nodeh: Bathymetry of the vertices</p> <p>nv: Vertices composition of the triangles</p> <p> </p> <p>model_result_baroclinic: Used FVCOM model output from 2019-07-01 to 2019-08-01 in Matlab data format.</p> <p>Variable description of model_result_baroclinic.mat:</p> <p>SSC: Total suspended sediment concentration of all sediment classes, units: g/L</p> <p>deposition_flux: Deposition flux of suspended sediment, units: kg/m^2/h</p> <p>divergence_sed_flux: depth-integrated divergence of sediment flux, units: kg/m^2/h</p> <p>erosion_flux: Erosion flux of sea-bed surface sediment, units: kg/m^2/h</p> <p>salinity: sea water salinity, units: psu</p> <p>taub: Total bed stress, units: Pa</p> <p>time_model: Time of the results, time zone: LST</p> <p>u: Eastward water velocity, units: m/s</p> <p>v: Northward water velocity, units: m/s</p> <p>zeta: Water surface elevation</p> <p> </p> <p>model_verify: Bed elevation data from ADV, wave parameters from buoy, and bottom suspended sediment concentration from OBS in Matlab data format.</p> <p>Variable description of model_verify.mat:</p> <p>ADV_BEC_xx: Bed Elevation measurement from ADV at Station xx, units: mm</p> <p>ADV_time_xx: Measurement time of bed elevation of ADV at Station xx, time zone: LST</p> <p>Buoy_hs_S1: Significant wave height measurement from Buoy at Station S1, units: m</p> <p>Buoy_time_S1: Measurement time of Buoy at Station S1, time zone: LST</p> <p>Buoy_tpeak_S1: Peak wave period measurement from Buoy at Station S1, units: s</p> <p>OBS_SSC_S1: Suspended sediment concentration measurement from OBS at Station S1, units: g/L</p> <p>OBS_time_S1: Measurement time of OBS at Station S1, time zone: LST</p> <p>tauc_mtke_S1: In-situ bottom shear stress calculated from turbulence kinetic energy method, units: Pa</p> <p>time_mtke_S1: Time of calculated bottom shear stress, time zone: LST</p> <p>time_wind: Time of surface wind at S1 from CFSv2 model from 2019-06-01 to 2019-08-01 time zone: LST</p> <p>u_wind: Eastward velocity of surface wind at S1 from CFSv2 model, units: m/s</p> <p>v_wind: Northward velocity of surface wind at S1 from CFSv2 model, units: m/s</p>
Modulation of Cyclones with Tropical and Extratropical Origins by Mesoscale SSTs in the Kuroshio Extension Region
<p>Datasets for <strong>Modulation of Cyclones with T</strong><strong>ropical and Extratropical </strong><strong>Origins </strong><strong>by Mesoscale SSTs in the Kuroshio Extension Region.</strong></p>
E3SM simulations to assess the projected changes in tropical cyclone activity during active and inactive North Atlantic hurricane seasons
<p>This dataset compiles the results of high resolutions E3SM experiments performed to assess the projected changes in the North Atlantic tropical cyclone activity in a warming climate. The results of these experiments are currently in revision under the title "<strong>Future Changes in Active and Inactive Atlantic Hurricane Seasons in the Energy Exascale Earth System Model</strong>" </p> <p>The file tracks_teca.zip compiles the TC tracks detected in the experiments, using the Toolkit for Extreme Climate Analysis (TECA), available at https://teca.readthedocs.io/en/latest/applications.html#teca-tc-stats</p> <p>GPI_LaNinaAMMp.zip and GPI_ElNinoAMMn.zip includes the genesis potential index and its components for each of the experiments.</p> <p>For further information, please contact the author at anasena@iastate.edu</p> <p>Acknowledgements:</p> <p><strong>This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231. E3SM simulations were performed using BER Earth System Modeling program’s Compy computing cluster located at Pacific Northwest National Laboratory. PNNL is operated by Battelle for the U.S. Department of Energy under Contract DE-AC05-76RL01830. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), Earth and Environmental Systems Modeling (EESM) Program, under Early Career Research Program Award Number DE‐SC0021109.</strong></p>
Dataset used in Sea surface wind structure observed by wave gliders during tropical cyclones
<p>Sea surface wind vector observed by three wave gliders deployed in the Western Pacific Ocean. The observation level is 1.2 meter.</p>
(NEW) Supporting Dataset for 'Role of convection in upper ocean mixing during a tropical cyclone'.
<p>Contains the results from the LES and PWP runs</p>
Analysed data set for rapidly intensifying tropical cyclones in the western North Pacific
<p>These are the extracted data set from (1) GPM IMERG; (2) Himawati-8 brightness temperature in infrared bands; (3) Himawari-8 cloud properties for the rapidly intensifying tropical cyclones in the western North Pacific.</p>
ECMWF IFS potential salinity and temperature data interpolated to ALAMO float positions. Results of tropical cyclone tracking algorithm for TC Irma, Florence, Teddy and Ida simulations with ECMWF IFS.
<p>ECMWF IFS potential salinity and temperature data interpolated to ALAMO float positions. The data is from forecasts of tropical cyclone Irma, Florence, Teddy and Ida performed at horizontal atmosphere resolutions of TCo1279, TCo2559, TCo3999, TCo7999 and ocean resolutions of eORCA025 and eORCA025.</p> <p> </p> <p>Data also contains the results of tracking these tropical cyclones in the ECMWF IFS simulations. </p>
Spatio-temporal Characterization of Coherent Structures in Simulated Tropical Cyclone Boundary Layer
<p>Jupyter-notebooks to read and analyze processed outputs from three Tropical Cyclone (TC) simulations (CONTROL, LOWOR, and NONPARAM) and plot figures. The datasets are large and can be made available by the authors upon request.</p>
Supporting files for "Towards understanding the differences between mesoscale and large-eddy simulations of tropical cyclones"
<p>This deposit includes the time- and azimuth-averaged velocity fields for the five idealized tropical cyclones described in "Towards understanding the differences between mesoscale and large-eddy simulations of tropical cyclones". The horizontal wind speed magnitude, radial velocity, tangential velocity, vertical velocity, and potential temperature fields are included for the mesoscale (d01) and LES (d02) domains.</p>
Treasure Bowl: PM2.5 Aggregation in the Eye of a Tropical Cyclone
<p>The measured data for the manuscript "Treasure Bowl: PM2.5 Aggregation in the Eye of a Tropical Cyclone"</p>
Data for "The 20-year highest tropical cyclone-generated waves associated with the maximum energy of seismic noises" (Subset 4)
<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 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>
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>
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>
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>
ScienceDex guides
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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.