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310 results for “Era5”
MED-GOLD Indicators for the Olives/olive oil pilot service over Iberian Peninsula from ERA5 Reanalysis 1979-2020
<p>Indicators of interest for the Olives/olive oil sector over the Iberian Peninsula using ERA5 Reanalysis data for the period 1979 to 2020:</p> <ol> <li>SPRTX Mean maximum temperature from Apr to May</li> <li>SPR32 Number of spring heat days with Tmax above 32°C during spring months (from 21 April to 21 June)</li> <li>SU36 Number of summer heat days with Tmax above 36°C during summer months (from 21 June to 21 September)</li> <li>SU40 Number of summer heat days with Tmax above 40°C during summer months (from 21 June to 21 September)</li> <li>WINRR Total winter (Oct-May) precipitation</li> </ol> <p>Datasets computed by ENEA, in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No. 776467</p>
MED-GOLD Indicators for the Wine pilot service over Iberian Peninsula from ERA5 Reanalysis 1979-2020
<p>Indicators of interest for the Wine sector over the Iberian Peninsula using ERA5 Reanalysis data for the period 1979 to 2020:</p> <ol> <li>Growing Season Temperature (GST) [Temp averaged between April and October]</li> <li>Spring Rain (SprR) [ Precip cumulated between 21 apr and 21 Jun],</li> <li>Harvest Rain (HarvestR) [ Precip cumulated between 21 aug and 21 Oct]</li> <li> (SU35) -number of days with temperature higher than 35°C [for April ot October],</li> <li>Warm Spell Duration Index (WSDI) [days with at least 6 consecutive days when the daily temperature maximum exceeds its 90th percentile for April to Oct]</li> </ol> <p>Wine risk indicators, implemented specifically for the MED-GOLD Wine pilot service by ENEA and SOGRAPE VINHOS S.A.:</p> <ol> <li>Sanitary Risk Index= offgts*offsp*(percentile(SprR))+offhart*percentile(HarvestR)+(100-percentile(GST); with offhart=1.; offsp=1; offgts=1.; if percentile (SprR)>= 60; offsp=1.5; if percentile (GST)<= 40; offhart=1.5; if percentile (GST)>=70; offgts=1.5; where the percentile are here computed starting from the distribution over the 1993-2106 hindcast period to be consistent and comparable with the seasonal forecast</li> <li>Heat Risk Index = percentile(GST)+percentile(SU35_AMJJASO) + percentile(WSDI_AMJJASO),where the percentile are here computed srarting from the distribution over the 1993-2106 hindcast period to be consistent and comparable with the seasonal forecast</li> </ol> <p>Datasets computed by ENEA, in collaboration with SOGRAPE VINHOS S.A. in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No. 776467</p>
A High Resolution (3km) Reanalysis Database for Mediterranean Coastal Winds Downscaled from ERA5, using the WRF Model
<p>A high resolution (3km) reanalysis database of Mediterranean coastal winds was constructed to support a research on potential sailing mobility in Antiquity. The database was created by downscaling the ERA5 reanalysis database using the WRF numerical prediction model.</p> <p>A detailed description of the reanalysis database is provided in the attached PDF file. The database format is GRIB version 2 and the total volume of the data files is 435GB. The GRIB files are hosted at <a href="https://coastalwinds.haifa.ac.il">https://coastalwinds.haifa.ac.il</a> as their total volume exceeds the volume that could be provided by Zenodo. Required files can therefore be downloaded from this location.</p> <p><strong>Link to the GRIB data files and index map:</strong></p> <p><strong><a href="https://coastalwinds.haifa.ac.il">https://coastalwinds.haifa.ac.il</a></strong></p> <p><strong>Acknowledgements:</strong></p> <p>The Data Science Research Center (DSRC) at Haifa University kindly provided funding towards the creation of this data set.</p>
Detection of Atmospheric Rivers in the Northern Hemisphere based on ERA5 reanalysis data and the IPART algorithm, 1979-2020
<p># 1. Overview</p> <p>This is a catalogue of atmospheric river (AR) detections over the Northern Hemisphere, based on 6-hourly ERA5 reanalysis dataset and the Image-Processing based Atmospheric River Tracking (IPART) algorithm.</p> <p>Time domain of the data:</p> <ul> <li>From 1979-Jan-01 to 2020-Dec-31</li> <li>Temporal resolution is 6-hourly</li> </ul> <p>Spatial domain of the data:</p> <ul> <li>Northern Hemisphere, land and ocean</li> <li>Spatial resolution is 0.25 * 0.25 degrees latitude/longitude</li> </ul> <p>Input data from ERA5 include:</p> <ul> <li>Vertical integral of northward water vapour flux, in kg/(m s).</li> <li>Vertical integral of eastward water vapour flux, in kg/(m s).</li> </ul> <p>Data in the Northern Hemisphere domain (0 - 90 N), at 0.25 * 0.25 degrees latitude/longitude resolution are obtained from https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5.</p> <p>Version v3.0.8 of the IPART Python module used for detection and tracking of atmospheric rivers is preserved at <strong>10.5281/zenodo.4164826</strong>, available via Creative Commons Attribution 4.0 International license and developed openly at the Github repository https://github.com/ihesp/IPART.</p> <p># 2. File naming convention</p> <p>The data files are named using the following convention:</p> <p>ar_YYYYMM.nc</p> <p>where:</p> <ul> <li>YYYY: 4-digit year number</li> <li>MM: 2-digit month number</li> </ul> <p>E.g. `ar_199902.nc` means detections in Feb of 1999.</p> <p>Months are calendar months, including Feb-29th in leap-years.</p> <p># 3. Data format</p> <p>Data are saved in netCDF format.</p> <p>Each data file contains one 3-dimensional array, of a shape `(t, 360, 1440)`, where:</p> <ul> <li>`t`: length of the time dimension. Since data are 6-hourly, t equals 4 * num_of_days_in_month.</li> <li>`360`: latitude dimension, from 0 - 90N, with a 0.25-degree step.</li> <li>`1440`: longitude dimension, from 80 - 440 E (shifted eastward by 80 degrees to put both the Pacific and Atlantic oceans within the domain), with a 0.25-degree step.</li> </ul> <p>Each time slice of the data contains maps of the Northern Hemisphere, with integer values in grid cells. Possible values are:</p> <ul> <li>0: meaning no AR is detected in the grid cell.</li> <li>1, 2, ... ,n: integer labels, each corresponding to the region of an AR entity.</li> </ul> <p># 4. Important parameters in the IPART algorithm</p> <p>Here are the most important parameters used when detecting ARs from ERA5 data using the IPART python module:</p> <ul> <li> THR filtering kernel: `[16, 13, 13]`. `16` means 16 time slices, or equivalently 4 days given 6-hourly input data. `13` means 13 grid cells, or equivalently ~325 km, given 0.25 degrees latitude/longitude input data. Note that both of these temporal and spacial lengths are half of the sizes of the filtering kernel.</li> <li> minimum area: `50 * 1e4`, in km^2, minimum size of AR region candidates.</li> <li> maximum area: `1800 * 1e4`, in km^2, maximum size of AR region candidates.</li> <li> minimum L/W: `2.0`, minimum length/width ratio of AR region candiates.</li> <li> minimum length: `2000`, in km, minimum length of AR region candidates.</li> <li> minimum latitude: `20`, minimum latitude of the geometrical centroid of an AR region candidate.</li> <li> maximum latitude: `80`, maximum latitude of the geometrical centroid of an AR region candidate.</li> </ul> <p>For more details regarding these parameters, as well as the IPART algorithm, please refer to our published works:</p> <ul> <li>Xu, G., Ma, X., Chang, P., and Wang, L.: Image-processing-based atmospheric river tracking method version 1 (IPART-1), Geosci. Model Dev., 13, 4639–4662, https://doi.org/10.5194/gmd-13-4639-2020, 2020.</li> </ul> <p>Or the Github repository that houses the IPART module:</p> <ul> <li>https://github.com/ihesp/IPART</li> </ul>
Monsoon low-pressure system (LPS) tracks in ERA5 over India (1979-2019) with added environmental variables
<p>Derived from the LPS v3.0 dataset (https://doi.org/10.5281/zenodo.7568990). Filtered to monsoon LPSs (majority of track lifetime between June and September), with genesis over the Bay of Bengal and making landfall over India. Temporal resolution also reduced from hourly to six-hourly. This dataset accompanies the paper "Using interpretable gradient-boosted decision-tree ensembles to uncover novel dynamical relationships governing monsoon low-pressure systems" (DOI to follow).</p> <p>Aside from the core variables described in the main LPS dataset (linked above), this version includes a large number of environmental variables, listed below. All are computed from ERA5 unless otherwise stated, "<em>mean</em>" means that the variable is computed as an average within 400 km of the LPS centre, "<em>mcz</em>" means that the variable is computed as an average in the box [75-85°E, 18.5-27°N].<br> <em>mean_u200</em>: 200 hPa zonal wind (m s<sup>-1</sup>)<br> <em>mean_u850</em>: 850 hPa zonal wind (m s<sup>-1</sup>)<br> <em>mean_skt</em>: surface temperature (K)<br> <em>mean_land_frac</em>: fraction of area within 400 km that is over land<br> <em>mcz_tcwv</em>: mean total column water vapour over monsoon trough (kg m<sup>-2</sup>)<br> <em>vortex_depth</em>: mean_vort_500 x mean_vort_700/mean_vort_850<sup>2</sup><br> <em>over_land</em>: flag for LPS centre (Boolean)<br> <em>dvo850_dt</em>: rate of change of mean_vort_850 (10<sup>-5</sup> s<sup>-1</sup> day<sup>-1</sup>) <br> <em>acc_land_time</em>: accumulated time where over_land = True (hours)<br> <em>total_land_time</em>: final value of acc_land_time} for a given LPS (hours)<br> <em>qshear_850</em>: meridional shear of 850 hPa specific humidity over India (m<sup>3</sup> m<sup>-3</sup> (°)<sup>-1</sup>)<br> <em>ushear_850</em>: meridional shear of 850 hPa zonal wind over India (m s<sup>-1</sup> (°)<sup>-1</sup>)<br> <em>mean_cape</em>: CAPE (J kg<sup>-1</sup>)<br> <em>mcz_cape</em>: mean CAPE over the monsoon trough (J kg<sup>-1</sup>)<br> <em>mean_dthetae_dp_900_750</em>: d(theta_e)/dp between 900 and 750 hPa, a measurement of atmospheric stability (K hPa<sup>-1</sup>)<br> <em>mean_dthetae_dp_750_500</em>: d(theta_e)/dp between 750 and 500 hPa, a measurement of atmospheric stability (K hPa<sup>-1</sup>)<br> <em>mean_land_skt</em>: land surface temperature (K; NaN over ocean)<br> <em>mean_sst</em>: sea surface temperature (K; NaN over land)<br> <em>mean_swvl1</em>: soil moisture in the top layer (m<sup>3</sup> m<sup>-3</sup>; <7 cm; NaN over ocean)<br> <em>mean_swvl2</em>: soil moisture in the second layer (m<sup>3</sup> m<sup>-3</sup>; 7-28 cm; NaN over ocean)<br> <em>mean_swvl1_grad</em>: mean absolute horizontal gradient of mean_swvl1 (m<sup>3</sup> m<sup>-4</sup>)<br> <em>mean_swvl2_grad</em>: mean absolute horizontal gradient of mean_swvl2 (m<sup>3</sup> m<sup>-4</sup>)<br> <em>olr_90</em>: 90th percentile of negative OLR (i.e. ~90th percentile of cloud top height) (W m<sup>-2</sup>)<br> <em>olr_75</em>: 75th percentile of negative OLR (W m<sup>-2</sup>)<br> <em>olr_50</em>: 50th percentile of negative OLR (W m<sup>-2</sup>)<br> <em>qshear_850_background</em>: qshear\_850 averaged over the previous ten days (m<sup>3</sup> m<sup>-3</sup> (°)<sup>-1</sup>)<br> <em>ushear_850_background</em>: ushear\_850 averaged over the previous ten days (m<sup>3</sup> s<sup>-1</sup> (°)<sup>-1</sup>)<br> <em>mean_q_850</em>: 850 hPa specific humidity (m<sup>3</sup> m<sup>-3</sup>)<br> <em>orography_height</em>: elevation of land surface under LPS centre (m)<br> <em>peak_vorticity</em>: largest value of mean_vort_850} attained by a given LPS (10<sup>-5</sup> s<sup>-1</sup>)<br> <em>reached_peak</em>: False if peak\_vorticity has not been reached yet, else True<br> <em>mean_prcp_400</em>: mean precipitation rate within 400 km of the LPS centre over the next six hours (kg m<sup>-2</sup> s<sup>-1</sup>)<br> <em>mean_prcp_800</em>: mean precipitation rate within 800 km of the LPS centre over the next six hours (kg m<sup>-2</sup> s<sup>-1</sup>)<br> <em>max_prcp_400</em>: maximum precipitation rate within 400 km of the LPS centre over the next six hours (kg m<sup>-2</sup> s<sup>-1</sup>)<br> <em>max_prcp_800</em>: maximum precipitation rate within 800 km of the LPS centre over the next six hours (kg m<sup>-2</sup> s<sup>-1</sup>)<br> <em>mean_vimfd_400</em>: vertically integrated moisture flux convergence (kg m<sup>-2</sup> s<sup>-1</sup>)<br> <em>mean_v200</em>: 200 hPa meridional wind speed (m s<sup>-1</sup>)<br> <em>mean_v500</em>: 500 hPa meridional wind speed (m s<sup>-1</sup>)<br> <em>mean_v850</em>: 850 hPa meridional wind speed (m s<sup>-1</sup>)<br> <em>mean_u500</em>: 500 hPa zonal wind speed (m s<sup>-1</sup>)<br> <em>zonal_speed</em>: zonal (x) component of LPS propagation velocity (m s<sup>-1</sup>)<br> <em>merid_speed</em>: meridional (y) component of LPS propagation velocity (m s<sup>-1</sup>)<br> <em>mean_prcp_imerg</em>: as mean_prcp_400 but computed using IMERG data, rather than ERA5 (kg m<sup>-2</sup> hr<sup>-1</sup>)</p> <p> </p> <p>qshear_850, ushear_850 and their backgrounds are averaged over 5° longitude either side of the LPS centre, with the gradient computed between 10°N and 27°N, reflecting the moisture and zonal wind gradients across the monsoon region.</p>
C3S SM COMBINED all versions (until v202212) with ERA5-Land vs ISMN FRMs 0-10 cm
QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202012 vs C3S SM combined v201912 vs C3S SM combined v201812 vs ERA5-Land v20190904 vs ISMN 20230110 global. URL: https://qa4sm.eu/ui/validation-result/2f865302-5e35-4c28-8549-1cc7e86f38cd. Produced on QA4SM (https://qa4sm.eu)
Bias corrected era5 skin temperature over the Arctic sea ice – 1981 to 2018 monthly means and climatology
<p>This dataset is generated in the context of the peer-reviewed study of Zampieri et al., 2023. The users can find a detailed description of the bias correction strategy and information on the scientific value of the dataset in the paper. Please, do not hesitate to contact me to obtain further information and suggestions on how to employ this dataset for your specific purpose. </p> <p><strong>References:</strong></p> <p>Zampieri, L.,<strong> </strong>Arduini, G., Holland, M., Keeley, S., Mogensen, K., Shupe, M., Tietsche, S. (2023) A machine learning correction model of the winter clear-sky temperature bias over the Arctic sea ice in atmospheric reanalyses. <em>Monthly Weather Review</em>. DOI:<a href="https://doi-org.cuucar.idm.oclc.org/10.1175/MWR-D-22-0130.1">10.1175/MWR-D-22-0130.1</a></p> <p><strong>Acknowledgments:</strong></p> <p>As part of the Virtual Earth System Research Institute (VESRI), funding for the Multiscale Machine Learning In coupled Earth System Modeling (M2LInES) project was provided to Lorenzo Zampieri by the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program. </p>
A new merged dataset for analyzing clouds, precipitation and atmospheric parameters based on ERA5 reanalysis data and the measurements of TRMM PR and VIRS
<p>This new merged gridded dataset (M-1B01-2A25-GD) contains precipitation, clouds and atmospheric parameters with 0.25° spatial resolution.</p> <p>It is produced by merging TRMM PR and VIRS measurements with the ERA5 reanalysis dataset at the same spatiotemporal resolution between 40° S and 40° N. The near-surface rain rate, profiles of rain rate and precipitation reflectivity factor, visible and infrared signals and atmospheric parameters (temperature, pressure, geopotential height, specific humidity, divergence, vertical velocity and so on) can be obtained in the dataset. The statistical results indicate that the merging and gridding will not dramatically distort the original data and the new dataset can be used to study the characteristics and distribution of the precipitation and clouds systems.</p>
ERA5 data by Canadian hail event 2005-2022
<p>This dataset is in HDF5 format. The data was pulled from the ECMWF reanalysis datasets "<a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview"><strong>ERA5 hourly data on single levels from 1940 to present</strong></a>" and "<a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview"><strong>ERA5 hourly data on pressure levels from 1940 to present</strong></a>." </p><p>Each file in "era5_by_canadian_hail_event" is associated to a specific Canadian hail event and combines the relevant data from both ERA5 datasets. The hail events were created based on the 7000 Canadian hail reports contained in "<a href="https://zenodo.org/records/8015925">Integrated Canadian Hail Database (2005-2022)</a>", where we considered reports to be of the same event if they were within a specified window in time and space (7 hours by 256 km). The 7000 reports were grouped into 2092 hail events. Thus, our ERA5 based dataset <strong>contains 2092 files</strong>. For more information on how the data was handled, see the GitHub repository "<a href="https://github.com/aconlon-eccc/era5-based-hail">era5-based-hail</a>." </p><p>Each file in this dataset contains the following variables from "<a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview"><strong>ERA5 hourly data on single levels from 1940 to present</strong></a>" : </p><ul><li>cp [m]: <a href="https://codes.ecmwf.int/grib/param-db/?id=143">Convective precipitation </a></li><li>d2m [K]: <a href="https://codes.ecmwf.int/grib/param-db/?id=168">Dewpoint temperature at 2m height </a></li><li>sp [Pa]: <a href="https://codes.ecmwf.int/grib/param-db/?id=134">Surface pressure</a></li><li>t2m [K]: Temperature at 2m height</li><li>tcc [100%]: <a href="https://codes.ecmwf.int/grib/param-db/?id=164">Total cloud cover</a></li><li>tciw [kg m-2]: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">Total column vertically-integrated cloud ice water</a></li><li>tclw [kg m-2]: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">Total column vertically-integrated cloud liquid water </a></li><li>tcrw [kg m-2]: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">Total column rain water</a></li><li>tcsw [kg m-2]: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">Total column snow water</a></li><li>tcwv [kg m-2]: <a href="https://codes.ecmwf.int/grib/param-db/?id=137">Total column vertically-integrated water vapour</a></li><li>tcw [kg m-2]: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">Total column water</a></li><li>tp [m]: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">Total precipitation</a></li><li>u10 [m/s]: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">u-component of wind at 10m height</a></li><li>v10 [m/s]: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">v-component of wind at 10m height</a></li></ul><p>And following variables from "<a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview"><strong>ERA5 hourly data on pressure levels from 1940 to present</strong></a>" :</p><ul><li>r [%]: <a href="https://codes.ecmwf.int/grib/param-db/?id=157">Relative humidity</a></li><li>t [K]: <a href="https://codes.ecmwf.int/grib/param-db/?id=130">Temperature</a></li><li>u [m/s]: <a href="https://codes.ecmwf.int/grib/param-db/?id=131">Longitudinal wind</a></li><li>v [m/s]: <a href="https://codes.ecmwf.int/grib/param-db/?id=132">Latitudinal wind</a></li><li>z [m^2/s^2]: <a href="https://codes.ecmwf.int/grib/param-db/?id=129">Geopotential</a></li></ul><p>at pressure levels [hPa]: 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 775, 800, 825, 850, 875, 900, 925, 950, 975, 1000.</p>
Exemplary validation of SMOS Level 3 version 339 Descending vs ERA5-Land v20190904 vs ISMN 20230110 global for EGU24
Exemplary QA4SM validation using FRMs for EGU24: SMOS Level 3 version 339 Descending vs ERA5-Land v20190904 vs ISMN 20230110 global. URL: https://qa4sm.eu/ui/validation-result/7f27889e-95e4-4e67-9a85-2e472cbb935c. Produced on QA4SM (https://qa4sm.eu)
NASA_ACCDAM_FLEXPART_ERA5_BackTrajectory_28yrOzone_WNA
<p>We are planning to publish our model product on source-receptor relationship (SRR) simulations using FLEXPART-ERA5 in backward mode. This dataset spans 28 years (1994–2021) of ozone observations, covering altitudes from 900 hPa to 300 hPa over western North America.</p> <p>The resulting SRR allows users to investigate the similarities and differences in the origin locations of air parcels that contained, for instance, the highest and lowest ozone levels when sampled near or over western North America.</p> <p>We have uploaded an example of our product on this site, and the complete dataset will ultimately be archived at NASA's Atmospheric Science Data Center (ASDC). Specifically, this sample include: 1) a folder for one-month SRR with a corresponding readme file ("<a href="https://zenodo.org/api/records/14227019/draft/files/WUSA_201607_v2.zip/content" target="_blank" rel="noopener noreferrer">WUSA_201607_v2.zip</a>")<span>. 2) a monthly averaged NetCDF file with an accompanying readme file ("</span><a href="https://zenodo.org/api/records/14227019/draft/files/Example_montly_2001-02_NH.nc/content" target="_blank" rel="noopener noreferrer">Example_montly_2001-02_NH.nc</a><span>"). 3) three MATLAB scripts for binary-to-NetCDF conversion (connecting 1) and 2)). 4) the 28-year ozone data CSV file ("<a href="https://zenodo.org/api/records/14227019/draft/files/Receptor_western_NAmerica_ozone_obs_1994_2021_from900to300.csv/content" target="_blank" rel="noopener noreferrer">Receptor_western_NAmerica_ozone_obs_1994_2021_from900to300.csv</a>"). </span></p> <p>Additionally, we have prepared a to-be-submitted manuscript to describe this product in detail, including its associated applications.</p> <p> </p>
Observations assimilated in ERA5
<p>This animation shows observations assimilated in the ERA5 global reanalysis since January 1940 (Soci et al., 2024 <a href="https://doi.org/10.1002/qj.4803">https://doi.org/10.1002/qj.4803</a>). Each dot on a map shows the data counts at monthly resolution and horizontal resolution 2 deg. x 2 deg. latitude, longitude. The animation includes 12 frames per second, and is based on observation feedback data of the ERA5 reanalysis until December 2024. Note this animation does not show all the observations that supported the creation of forcings that are used by the model.</p>
Sample ERA5 Climate Reanalysis Data for UW Geospatial Data Analysis Course
<p>Used for Module 09: https://uwgda-jupyterbook.readthedocs.io/en/latest/modules/09_NDarrays_xarray_ERA5/</p> <p>Generated using Copernicus Climate Change Service information [2022]<br> Original license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</p>
Calibrated Western USA Noah-MP Parameter Set Forced by ERA5-WRF
<p>These three files include the default (original) Noah-MP parameter set for the Western USA, and parameter sets based on calibration to daily streamflow data and monthly streamflow data. </p>
EOOffshore: ERA5 Wind Data for the Irish Continental Shelf Region
<p><a href="https://eooffshore.github.io">EOOffshore</a> is a <a href="https://www.seai.ie/">Sustainable Energy Authority of Ireland (SEAI)</a> funded <a href="https://www.seai.ie/data-and-insights/seai-research/research-projects/details/building-upon-copernicus-earth-observation-services-to-augment-wind-measurement-coverage-of-the-oredp-offshore-renewable-energy-assessment-areas">project</a>, which commenced in June 2020 in the <a href="https://www.ucd.ie/physics/">School of Physics</a> in <a href="https://www.ucd.ie/">University College Dublin (UCD)</a>. It presents a case study that demonstrates the utility of the <a href="https://pangeo.io/">Pangeo</a> software ecosystem in the development of offshore wind speed and power density estimates, increasing wind measurement coverage of offshore renewable energy assessment areas in the <a href="https://www.marine.ie/Home/site-area/irelands-marine-resource/real-map-ireland">Irish Continental Shelf (ICS)</a> region. It has involved the creation of a new <a href="https://eooffshore.github.io/datasets.html">wind data catalog</a> for this region, consisting of a collection of analysis-ready, cloud-optimized (ARCO) datasets featuring up to 21 years of available in situ, reanalysis, and satellite observation wind data products.</p> <p><a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5">ERA5</a> is the fifth generation global reanalysis data set produced by the <a href="https://www.ecmwf.int/">European Centre for Medium-Range Weather Forecasts (ECMWF)</a>. It is a component of the <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service (C3S)</a>, where data products are publicly available in the <a href="http://As requested by the ECMWF - Licence to Use Copernicus Products, this Zarr store was: Generated using Copernicus Climate Change Service information [2001 - 2021]">C3S Climate Data Store</a>. This particular catalog data set (<em>eooffshore_ics_era5_single_level_hourly_wind.zarr.tar.gz</em>) contains 2001-2021 products for the ICS region from the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview"><em>ERA5 hourly data on single levels from 1979 to present</em></a> data set, which provides hourly data from 1979 to the present day, at single levels (atmospheric, ocean-wave and land surface quantities). Wind speed and direction have been calculated from the <em>uX</em> and <em>vX</em> variables, where <em>X = 10 m</em> and <em>100 m</em> above sea level. This ERA5 data set was used in the EOOffshore project outputs presented (<em><a href="https://meetingorganizer.copernicus.org/EGU22/EGU22-2746.html">Scalable Offshore Wind Analysis With Pangeo</a></em>) at the <em><a href="https://meetingorganizer.copernicus.org/EGU22/session/42046">Meeting Exascale Computing Challenges with Compression and Pangeo</a></em> <a href="https://www.egu22.eu/">2022 EGU General Assembly</a> session.</p> <p>Description and example usage of the ERA5 data set in EOOffshore:</p> <ul> <li><a href="https://eooffshore.github.io/ERA5_ICS_Wind_Data.html">ERA5 Wind Data for Irish Continental Shelf region</a></li> <li><a href="https://eooffshore.github.io/Offshore_Wind_AOI.html">Offshore Wind in Irish Areas Of Interest</a></li> <li><a href="https://eooffshore.github.io/Comparison_Wind_Power.html">Comparison of Offshore Wind Speed Extrapolation and Power Density Estimation</a></li> </ul> <p>As requested by the <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">ECMWF - Licence to Use Copernicus Products</a>, this Zarr store was:</p> <ul> <li> <p>Generated using Copernicus Climate Change Service information [2001 - 2021]</p> </li> </ul>
South Pacific precipitation dataset: PACRAIN, TRMM, ERA5 and calibrated precipitation series.
<p>This dataset contains a set of stations located in the South Pacific region, obtained from the PACRAIN (Pacific Rainfall Database). Similarly, the TRMM (Tropical Rainfall Measuring Mission) satellite precipitation series and the ERA5 reanalysis precipitation series have been extracted for the aforementioned stations. For the stations corresponding to IDs NZ75400, NZ82400, NZ84317, NZ99701, SP00646, US14000 and US14690, the following <strong>daily</strong> variables are included:</p> <p> </p> <ul> <li>TRMM raw</li> <li>ERA5 raw</li> <li>PACRAIN </li> <li>Weather type associated</li> <li>TRMM calibrated (scaling) </li> <li>TRMM calibrated (empirical quantile mapping) </li> <li>TRMM calibrated (scaling conditioned) </li> <li>TRMM calibrated (empirical quantile mapping conditioned) </li> </ul> <p>where the last four are a consequence of the application of 4 different calibration methods. A summary of the stations is shown in the following table:</p> <table> <caption> </caption> <tbody> <tr> <td>Station Id</td> <td>Station Name</td> <td>Longitude</td> <td>Latitude</td> <td>Start</td> <td>End</td> <td>% Missing Data</td> <td>Altitude</td> </tr> <tr> <td>NZ75400</td> <td>Kolopelu (Wallis and Futuna)</td> <td>-178.12</td> <td>-14.32</td> <td>1998-01-01</td> <td>2012-01-01</td> <td>9.74</td> <td>36</td> </tr> <tr> <td>NZ82400</td> <td>Alofi (Niue)</td> <td>-163.93</td> <td>-19.07</td> <td>1998-01-01*</td> <td>2010-09-02</td> <td>2.68</td> <td>59</td> </tr> <tr> <td>NZ84317</td> <td>Rarotonga (Cook Islands)</td> <td>-159.8</td> <td>-21.2</td> <td>1999-09-28</td> <td>2012-01-12</td> <td>11.36</td> <td>4</td> </tr> <tr> <td>NZ99701</td> <td>Raoul Island (New Zealand)</td> <td>-177.93</td> <td>-29.23</td> <td>1998-01-01*</td> <td>2012-01-01</td> <td>0.72</td> <td>49</td> </tr> <tr> <td>SP00646</td> <td>Port Vila (Vanuatu)</td> <td>168.3</td> <td>-17.72</td> <td>2000-01-26</td> <td>2013-06-01</td> <td>18.13</td> <td>24</td> </tr> <tr> <td>US14000</td> <td>Aoloau (American Samoa)</td> <td>-170.77</td> <td>-14.3</td> <td>1998-01-01*</td> <td>2019-12-31*</td> <td>21.72</td> <td>408</td> </tr> <tr> <td>US14690</td> <td>Nu'uuli (American Samoa)</td> <td>-170.70</td> <td>-14.32</td> <td>1998-01-01*</td> <td>2019-12-31*</td> <td>0.037</td> <td>3</td> </tr> </tbody> </table> <p> </p>
Transformed Eulerian mean data from the ERA5 reanalysis (daily means)
<p>This dataset provides <strong>daily</strong> and zonal mean variables derived from the ERA5 reanalysis, including terms of the transformed Eulerian mean (TEM) momentum budget.</p> <p>All variables (zonal, meridional and vertical wind speed, temperature, zonal wind tendencies from Eliassen-Palm (EP) flux divergence and advection, EP fluxes and the residual streamfunction) are obtained from 6-hourly and native vertical and 0.5 degrees spatial resolution data. Zonal mean wind tendency from parameterizations is also provided (from the forecasts). Data are obtained from the MARS archive.</p> <p>Data are provided as one .zip file per decade (only partial for the 2020s and 1950s). <strong>Monthly</strong> means of the same quantities are provided in a companion dataset (10.5281/zenodo.7081721).</p> <p>The data and related documentation are provided 'as is' and without any warranty of any kind. Users are invited to report any issue or inconsistency they may find. Please cite the reference publication when using this dataset.</p> <p> </p> <p>Known issues:</p> <p>- All TEM terms divided by<em> <span>\(\cos(\phi)\)</span></em>, where <span>\(\phi\)</span> is latitude, diverge at the north and south poles (where <span>\(\phi = \pm \pi/2\)</span>), so they should not be considered. If variables at the poles are needed, values at neighbouring latitudes should be taken.</p>
Transformed Eulerian mean data from the ERA5 reanalysis (monthly means)
<p>This dataset provides <strong>monthly</strong> and zonal mean variables derived from the ERA5 reanalysis, including terms of the transformed Eulerian mean (TEM) momentum budget.</p> <p>All variables (zonal, meridional and vertical wind speed, temperature, zonal wind tendencies from Eliassen-Palm (EP) flux divergence and advection, EP fluxes and the residual streamfunction) are obtained from 6-hourly and native vertical and 0.5 degrees spatial resolution data. Zonal mean wind tendency from parameterizations is also provided (from the forecasts). Data are obtained from the MARS archive.</p> <p>Data are provided as one .zip file per decade (only partial for the 2020s and 1950s). <strong>Daily</strong> means of the same quantities are provided in a companion dataset (10.5281/zenodo.7081436).</p> <p>The data and related documentation are provided 'as is' and without any warranty of any kind. Users are invited to report any issue or inconsistency they may find.</p> <p> </p> <p>Known issues:</p> <p>- All TEM terms divided by<em> <span>\(\cos(\phi)\)</span></em>, where <span>\(\phi\)</span> is latitude, diverge at the north and south poles (where <span>\(\phi = \pm \pi/2\)</span>), so they should not be considered. If variables at the poles are needed, values at neighbouring latitudes should be taken.</p>
Validation of H-SAF ASCAT SSM CDR H119 vs SMOS Level 2 v700 vs ERA5 v20190613
QA4SM validation: H-SAF ASCAT SSM CDR H119 vs SMOS Level 2 v700 vs ERA5 v20190613. URL: https://qa4sm.eu/ui/validation-result/033a3604-b935-4864-93a7-0497e1f2e61b. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904
QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904. URL: https://qa4sm.eu/ui/validation-result/ab9a564e-8c55-4ba2-828a-9577e9948803. Produced on QA4SM (https://qa4sm.eu)
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.