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42 results for “ECMWF”
Mediterranean Cyclone tracks between 1979-2018 (40 years) from a high-resolution perspective using ECMWF ERA5 dataset
<p>The present dataset presents the trajectories of the 13,157 cyclones identified within the Mediterranean Region (MR) between 1979 and 2018 (40 years). These cyclone tracks were obtained using the new Cyclone Detection and Tracking Method (CDTM) described in Aragão e Porcù (2021) to take advantage of the recent availability of a high-resolution reanalysis dataset of ECMWF ERA5. The CDTM uses hourly data of Geopotential Height at 1000 hPa with a spatial resolution of 0.25°x0.25°, and the analysis' domain covers the area within 15°W to 48° E and 21° N to 54°N. Additionally, trying to eliminate artificial low-pressure cores, short-living thermal-lows or too weak cyclones as much as possible, the present study only considered cyclones lasting more than 24h.<br> The dataset presents hourly information for all cyclones from the cyclogenesis time to the cyclolysis time. Each record presents: [1] Cyclone ID (integer, 8 digits), [2] Cyclone centre longitude position (°E, real, 8 digits, 3 decimal digits), [3] Cyclone centre latitude position (°N, real, 8 digits, 3 decimal digits), [4] Year (integer, 4 digits), [5] Month (integer, 2 digits), [6] Day (integer, 2 digits), [7] Hour (integer, 2 digits), [9] Cyclone centre Geopotential Height at 1000 hPa (m, real, 9 digits, 3 decimal digits).<br> The analyses presented in Aragão e Porcù (2021) revealed that the proposed CDTM is capable to capture almost the totality of the observed cyclones, as well as describing its respective area of cyclogenesis, trajectories, and durations. More than an adaptation to a high-resolution dataset, the method brings as its primary contribution a suitable set of parameters to systematically identify and track the cyclonic activities in the Mediterranean, where cyclones do not have sizeable horizontal pressure gradients and present a shorter lifetime compared to open-ocean cyclones.</p> <p>Cite this article</p> <p>Aragão, L., Porcù, F. Cyclonic activity in the Mediterranean region from a high-resolution perspective using ECMWF ERA5 dataset. <em>Clim Dyn</em> (2021). https://doi.org/10.1007/s00382-021-05963-x</p>
Input Runoff Data for RAPID Model Pre-Processor (RRR) from ECMWF ERA-Interim/Land
<p>This database can be used as the input runoff files in the RAPID model [<em>David et al.,</em> 2011] pre-processor (RRR). The runoff files were acquired/derived from the ECMWF ERA-Interim/Land [<em>Balsamo et al.,</em> 2015] outputs, available from ECMWF Data Server. The ERA-Interim/Land outputs are available in daily temporal resolution. The database contains the following files;</p> <p> ECMWF_Interim_Land_<strong><em>yyyy</em></strong>.tar.gz (Note: <strong><em>yyyy</em></strong> = 2000 to 2009)</p> <p> </p> <p>Note: These runoff data were used by <em>Sikder et al.</em> [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins.</p> <p> </p> <p>Other necessary links associated with this database:</p> <p>RAPID model: <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a></p> <p>RAPID model pre-processor (rrr): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a></p> <p>ECMWF outputs: <a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land">https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land</a></p> <p> </p> <p>References:</p> <p>Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., et al. [2015], ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, <a href="https://doi.org/10.5194/hess-19-389-2015">https://doi.org/10.5194/hess-19-389-2015</a></p> <p>David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a></p> <p>Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a></p>
10-day backward trajectories from ECMWF analysis data along the ship track of the Antarctic Circumnavigation Expedition in austral summer 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains 10-day backward trajectories along the ship track of the Antarctic Circumnavigation Expedition from Nov 2016 – April 2017 calculated with the Lagrangian analysis tool LAGRANTO using the 3D-wind fields from the European Centre for Medium Range Weather Forecasts (ECMWF) operational analysis data. The trajectories were started from up to 56 vertical levels between 0 and 500 hPa a.s.l. and various variables were interpolated along the trajectories.</p> <p><strong>Dataset contents</strong></p> <ul> <li>trajs_ACE.zip: lsl_${year}${month}${day}_${hour}, trajectory files (containing all trajectories starting at ${year}${month}${day} ${hour}UTC at the ACE track from different vertical levels), comma-separated values</li> <li>fig_map.zip: map_long10_${year}${month}${day}_${hour}.png, map plots of all trajectories starting at ${year}${month}${day} ${hour}UTC coloured by pressure, portable network graphics</li> <li>fig_cross.zip: cross10_q_${year}${month}${day}_${hour}.png, cross-section plots of all trajectories starting at ${year}${month}${day} ${hour}UTC coloured by specific humidity, portable network graphics</li> <li>data_file_header.txt, metadata for lsl-files, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This 10-day backward trajectory dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Equatorial wave filtering during May-September 2020 in the ECMWF OSE experiments with and without Aeolus data
<p>These are files with the global analyses produced by the observing system experiment with and without Aeolus data, and decomposed using the MODES software. Every file contain the zonal and meridional wind components and the pseudo-geopotential. Note that the paper makes use of the zonal winds only. Files including "hel1" in their titles belong to the OSE without Aeolus winds whereas the files with "hel4" in their names are from OSE including Aeolus winds. </p> <p>File names starting with KW belong to the Kelvin waves, file names starting with All contain the total fields, IGMRG denoted the non-Rossby modes whereas Rot belongs to file names containing only the signal associated with the Rossby modes. The Kelvin wave analyses are updated for the period from 1 May to 30 September, whereas other files are available for the periods discussed in the paper.</p> <p>The two movies are named MODES_KW_May2Sep2020.gif and MODES_BalancedUwind_May2Sep2020.gif for the Kelvin and balanced (Rossby modes) zonal winds averaged within 15 degrees N and 15 degrees S, respectively. Individual figures which constitute the movies are available at https://modes.cen.uni-hamburg.de.</p> <p> </p> <p> </p>
ECMWF ERA interim derived atmospheric mass, moisture and energy budget products
<p>As observations and atmospheric reanalyses have improved, the diagnostics that can be computed with confidence also increase. Accordingly, a new formulation of the energetics of the atmosphere is laid out, with a view to advancing diagnostic studies of Earth's energy budget and flows. It is utilized to produce assessments of the vertically integrated divergences in both the atmosphere and ocean. Careful conservation of mass is required, with special attention given to the hydrological cycle and redistribution of mass associated with precipitation and evaporation, and a new method for ensuring this is developed. It guarantees that the atmospheric divergence is associated with moisture and precipitation, unlike previous methods. A new term, identified as associated with the enthalpy of precipitation, is included in a preliminary way. It is sensitive to the formulation, and the use of temperature in degrees Celsius instead of Kelvin greatly reduces errors and produces the extra term with values up to about 65 W/m2. New results for 2000 to 2017 are presented for the vertical-mean and annual-mean diabatic atmospheric heating, atmospheric moistening, and total atmospheric energy divergence. Results for the atmospheric divergence are combined with top-of-atmosphere radiation observations to deduce total surface energy fluxes.</p> <p>These data files are monthly and span from 1979 to 2017, smoothed at T-106 resolution. The data format is NetCDF. A full dataset description is available at https://journals.ametsoc.org/view/journals/clim/31/16/jcli-d-17-0838.1.xml</p>
ECMWF Reanalysis
<p>The dataset consist of air_temperature at 2 metres for the month of May 2022. The dataset is of the type netCDF. </p>
VisMetHack2022: Visualizing winds and surface variables from the ECMWF IFS 1-km nature run
<p><strong>Overview</strong></p> <p>This data collection was contributed to the <a href="https://events.ecmwf.int/event/305/">Visualisation Hackathon 2022</a> (#VisMetHack2022), in conjunction with the Using <a href="https://events.ecmwf.int/event/296/">ECMWF's Forecasts (UEF2022</a>) workshop.</p> <p>The European Center for Medium-Range Weather Forecasts (ECMWF) and the Oak Ridge National Laboratory (ORNL) are pleased to announce access to the data collection from global 1-km nature run (NR) simulations using the Integrated Forecast System (IFS) with explicit convection. We invite you to join us in exploring this precursor to a digital twin of the earth!</p> <p>The NR simulations reveal unprecedented detail of the earth’s atmosphere [1], and the then outgoing Editor-in-Chief of AGU JAMES commended the project as one of “stunning ambitions,” enabled by computational capacity at scale [2]. The project also won the <em>2020 HPCwire Readers Choice Award </em>for Best Use of HPC in Physical Sciences. </p> <p>A set of two NR seasonal simulations have been completed, one corresponding to the northern hemispheric winter months (NDJF) and the other for the North Atlantic tropical cyclone season (ASO). The project used the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF). The simulations were facilitated with an INCITE award from the US Department of Energy Office of Science. </p> <p>For the first seasonal run of four months (NDJF), the hydrostatic IFS model was initialized at 00Z on 1 November 2018. The NR for the TC season (AS) was initialized at 00Z on 1 August 2019. The NR simulations were constrained only by sea surface temperatures (SST) at the lower boundary. The IFS output was saved every 3 hours.</p> <p>After feedback and interest from the scientific community, the simulations were rerun for four specific extreme events, with output every 15 minutes. The special cases include a tropical cycle and three severe storm events over the continental USA.</p> <p><strong>NR Data for visualizing winds</strong></p> <p>A small subset from the 1-km IFS NR collection is make available for #VisMetHack22. This subset is extracted from the tropical cyclone area in the North Atlantic from the ASO simulations. The 912 model time steps correspond to 97935 to 111600 in minutes since the NR reference time 2019-08-01 00:00:00. The time increment is 15 minutes, corresponding to the output frequency.</p> <p>The following variables are provided for #VisMetHack2022:<br> </p> <table> <tbody> <tr> <td> <p>Short Name</p> </td> <td> <p>Parameter ID</p> </td> <td> <p>Units</p> </td> <td> <p>Long Name</p> </td> </tr> <tr> <td> <p>10u</p> </td> <td> <p>165</p> </td> <td> <p>m/s</p> </td> <td> <p>10 metre U wind component</p> </td> </tr> <tr> <td> <p>10v</p> </td> <td> <p>166</p> </td> <td> <p>m/s</p> </td> <td> <p>10 metre V wind component</p> </td> </tr> <tr> <td> <p>2t</p> </td> <td> <p>167</p> </td> <td> <p>K</p> </td> <td> <p>2 metre temperature</p> </td> </tr> <tr> <td> <p>i10fg</p> </td> <td> <p>228029</p> </td> <td> <p>m/s</p> </td> <td> <p>Instantaneous 10 metre wind gust</p> </td> </tr> <tr> <td> <p>msl</p> </td> <td> <p>151</p> </td> <td> <p>Pa</p> </td> <td> <p>Mean sea level pressure</p> </td> </tr> <tr> <td> <p>xtprate</p> </td> <td> <p>99999</p> </td> <td> <p>kg m**-2 s**-1</p> </td> <td> <p>Total instantaneous precipitation rate. Summation of convective and large scale rain and snowfall rates. </p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Data processing</strong></p> <ol> <li> <p>The native model output was in the form of data objects consisting of GRIB1/2 16-bit AEC compressed messages. The messages were extracted from the FDB database instances into one or more files.</p> </li> <li> <p>The files containing the GRIB messages were interpolated to 0.02 x 0.02 a regular latitude-longitude grid using ECMWF Meteorological Interpolation and Regridding (MIR), and then written out to files as GRIB messages.</p> </li> <li> <p>The MIR output files were extracted to the area of interest (AOI) from global fields, and converted to Netcdf-4 (NC).</p> </li> <li> <p>The metadata in NC4 files were selectively edited or added.</p> </li> <li> <p>Finally, the NC4 files were compressed to reduce volume using the ncks utility from Netcdf Operators (NCO), with lossless L1 compression.</p> </li> <li> <p>The variable ‘xtprate’ was calculated by a summation of instantaneous and large scape rainfall and snowfall rates.</p> </li> </ol> <p><strong>Contact</strong></p> <p>Valentine Anantharaj <<a href="mailto:vga@ornl.gov">vga@ornl.gov</a>> or <vga1.ornl@gmail.com> </p> <p>Samuel Hatfield <<a href="mailto:Samuel.Hatfield@ecmwf.int">Samuel.Hatfield@ecmwf.int</a>></p> <p> </p> <p><strong>Citation and references</strong></p> <p>Please cite the following manuscript as well as the DOI provided by Zenodo:</p> <p>[1] Wedi, N. P., Polichtchouk, I., Dueben, P., Anantharaj, V. G., Bauer, P., Boussetta, S., et al. (2020). A baseline for global weather and climate simulations at 1 km resolution. Journal of Advances in Modeling Earth Systems, 12, e2020MS002192. <a href="https://doi.org/10.1029/2020MS002192">https://doi.org/10.1029/2020MS002192</a></p> <p>[2] Anantharaj, V., Hatfield, S. and Vukovic, Milana (2022). VisMetHack2022: Visualizing winds and surface variables from the ECMWF IFS 1-km nature run. https://doi.org/10.5281/zenodo.6633929</p> <p><strong>Acknowledgements</strong></p> <p>This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725.</p> <p>ECMWF also benefited from collaborations funded via ESCAPE-2 (No. 800897), MAESTRO (No. 801101), EuroEXA (No. 754337), and ESiWACE-2 (No. 823988) projects funded by the European Union's Horizon 2020 future and emerging technologies and the research and innovation programmes. </p>
Atmospheric CO2 simulations over Indian sites using STILT driven by ECMWF meteorology.
<p>This data contains atmospheric CO2 simulations at a temporal resolution of 3 hours over 15 Indian sites using a Lagrangian transport model, STILT driven by ECMWF meteorology during May 2017.</p> <p>Note: You are encouraged to contact the creator before using this data in any presentation or publication. </p> <p>Reference:</p> <p>Jithin Sukumaran, Dhanyalekshmi Pillai, Vishnu Thilakan, Saradambal Lekshmi, Gokul Udayakumar, Thara Anna Mathew, Aparnna Ravi, Manoj M G. How critical is the accuracy of the atmospheric transport modelling to improve the urban CO2 emission in India? - A Lagrangian-based approach. (2024), JGR Atmospheres. [Under review]</p> <p> </p>
ECMWF ERA5 Monthly surface air temperature anomalies (Celcius) relative to 1981-2010
<p><em>Monthly global-mean and European-mean surface air temperature anomalies relative to 1981-2010, from January 1979 to August 2019. Data source: ERA5. Credit: Copernicus Climate Change Service/ECMWF.</em></p> <p>See <a href="https://climate.copernicus.eu/surface-air-temperature-august-2019">https://climate.copernicus.eu/surface-air-temperature-august-2019</a> for more information.</p> <p><br> </p>
An Urban Scheme for the ECMWF Integrated Forecasting System: Global Forecasts and Residential CO2 Emissions - Dataset
<p>These data support the journal article : An Urban Scheme for the ECMWF Integrated Forecasting System: Global Forecasts and Residential CO2 Emissions (Journal of Advances in Modeling Earth Systems).</p> <p>The files provided are as follows:</p> <p>SITE_RMSE* - These files provided the computed RMSE values for SYNOP site evaluation using the control IFS and the urban IFS. Results are given for different forecast lead times, different seasons and for both 2 m and 10 m wind speed. </p> <p>DIURNAL* - These files provide the diurnal 2 m temperature output from the model and the comparison of those with observations.</p> <p>For more information please contact or access to alternative data related to the publication please contact: joe.mcnorton@ecmwf.int</p> <p> </p>
Seasonal forecasts of ocean heat content in ECMWF-SEAS5 and CMCC-SPS3
<p>Seasonal forecasts of ocean heat content in the upper 300m from two Copernicus Climate Change Service Systems: ECMWF SEAS5 and CMCC SPS3. Data was used for the following study:</p> <p>McAdam, R., Masina, S., Balmaseda, M. <em>et al.</em> Seasonal forecast skill of upper-ocean heat content in coupled high-resolution systems. <em>Clim Dyn</em> 58, 3335–3350 (2022). https://doi.org/10.1007/s00382-021-06101-3</p>
Representing Model Uncertainty for Global Atmospheric CO2 Flux Inversions Using ECMWF-IFS-46R1
<p>Data used in the work "Representing Model Uncertainty for Global Atmospheric CO2 Flux Inversions Using ECMWF-IFS-46R1" - McNorton et al. (2020)</p> <p>All data generated using version 46R1 of the Integrated Forecast System based at the European Centre for Medium-Range Weather Forecasts, with work funded as part of the European Commission CO2 Human Emissions Project.</p> <p>Data includes global total standard errors for the total column CO2 mixing ratios at 3 hourly intervals for 2015 and both total column and surface transport errors at hourly intervals for January and July 2015, derived from a 50 member ensemble. It is suggested that the data are used by the inverse modelling community to account for transport model errors.</p> <p>Please view the README.txt file for a full description.</p> <p> </p> <p>###########################<br> ## EXPERIMENTAL SETUP ##<br> ###########################</p> <p># FLUXES #</p> <p>CHE-EDGAR-2015 EMISSIONS<br> CHE-TIER-2-FIRE/OCEAN<br> ONLINE CHTESSEL BIOGENIC FLUXES (FOR TRANSPORT ERROR THESE USE THE CONTROL MEMBER FLUXES)</p> <p># MODEL #</p> <p>IFS-CYCLE 46R1<br> RESOLUTION TCO399 (~25km)<br> 137 VERTICAL LEVELS<br> ALL DATA PROVIDED HERE ARE EITHER COLUMN INTEGRATED MIXING RATIO (XCO2) OR SURFACE (LEVEL 137)<br> ALL DATA PROVIDED HERE ARE STANDARD DEVIATION ACROSS 50 ENSEMBLE MEMBERS<br> </p>
Global ECMWF Fire Forecasting system - sample data for wildfires in Attica (Greece) on 23-26 July 2018
<p>The European Centre for Medium-Range Weather Forecasts (<a href="https://www.ecmwf.int/">ECMWF</a>) produces daily fire danger forecasts and reanalysis products from the Global ECMWF Fire Forecast (<a href="https://git.ecmwf.int//projects/CEMSF/repos/geff/browse">GEFF</a>) model. Reanalysis is available through the Copernicus Climate Data Store (<a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical">CDS</a>) while the medium-range real-time forecast is available through the <a href="https://effis.jrc.ec.europa.eu/static/effis_current_situation/public/index.html">EFFIS</a> and <a href="https://gwis.jrc.ec.europa.eu/static/gwis_current_situation/public/index.html">GWIS</a> platforms.</p> <p>This repository provides sample datasets for the assessment of the fire danger during the Attica (Greece) wildfires occurred on 23-26 July 2018:</p> <ul> <li> <p>ECMWF_EFFIS_20180723_1200_en.tar<br> (ensemble forecasts issued on 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723_1200_hr.tar<br> (deterministic forecasts issued on 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723-26_1200_hr_e5.tar<br> (deterministic reanalysis based on ERA5 issued for 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723-26_1200_en_e5.tar<br> (probabilistic reanalysis based on ERA5 issued for 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723-26_e5.tar<br> (probabilistic and deterministic reanalysis based on ERA5 issued for 2018-07-23/26, global coverage, FWI only)</p> </li> <li> <p>bbox.tar, containing 1 index (FWI) for the bounding box:</p> <ul> <li> <p>GEFF-reanalysis, which provides historical records of fire danger conditions in the period 23-26 July 2018</p> <ul> <li> <p>e5_hr, this folder contains deterministic model outputs</p> </li> <li> <p>e5_en, this folder contains probabilistic model outputs (made of 10 ensemble members)</p> </li> </ul> </li> <li> <p>GEFF-realtime provides real-time forecasts (in the period 14-26 July 2018) generated using weather forcings from the latest model cycle of the ECMWF’s Integrated Forecasting System (IFS).</p> <ul> <li> <p>rt_hr, this folder contains high-resolution deterministic forecasts (~9 Km)</p> </li> <li> <p>rt_en, this folder contains probabilistic forecasts (~18Km)</p> </li> </ul> </li> </ul> </li> <li> <p>lon_min = 23, lon_max = 25, lat_min = 37, lat_max = 39</p> </li> </ul> <p><strong>Please note, the sample data provided in this repository is intended to be used for education purposes only (e.g. training courses).</strong></p> <p>These products have been developed as part of the EU-funded Copernicus Emergency Management Services (<a href="https://emergency.copernicus.eu/">CEMS</a>) and complement other Copernicus products related to fire, such as the biomass-burning emissions made available by the Copernicus Atmosphere Monitoring Service (<a href="https://atmosphere.copernicus.eu/">CAMS</a>). The development of the GEFF modelling system was funded through a third-party agreement with the European Commission’s Joint Research Centre (<a href="https://ec.europa.eu/info/departments/joint-research-centre_en">JRC</a>). </p> <p>GEFF produces fire danger indices based on the Canadian Fire Weather index as well as the US and Australian fire danger models. GEFF datasets are under the Copernicus license, which provides users with free, full and open access to environmental data.</p> <p>For more information, please refer to the documentation on the <a href="http://datastore.copernicus-climate.eu/c3s/published-forms/c3sprod/cems-fire-historical/Fire_In_CDS.pdf">CDS</a> and on the <a href="https://effis.jrc.ec.europa.eu/about-effis/technical-background/fire-danger-forecast/">EFFIS website</a>.</p>
Global ECMWF Fire Forecasting system - sample data for wildfires in Sweden on 15-20 July 2018
<p>The European Centre for Medium-Range Weather Forecasts (<a href="https://www.ecmwf.int/">ECMWF</a>) produces daily fire danger forecasts and reanalysis products from the Global ECMWF Fire Forecast (<a href="https://git.ecmwf.int//projects/CEMSF/repos/geff/browse">GEFF</a>) model. Reanalysis is available through the Copernicus Climate Data Store (<a href="https://cds.climate.copernicus.eu/cdsapp#%21/dataset/cems-fire-historical">CDS</a>) while the medium-range real-time forecast is available through the <a href="https://effis.jrc.ec.europa.eu/static/effis_current_situation/public/index.html">EFFIS</a> and <a href="https://gwis.jrc.ec.europa.eu/static/gwis_current_situation/public/index.html">GWIS</a> platforms.</p> <p>This repository provides FWI sample datasets for the assessment of the wildfires occurred in Sweden on 15-20 July 2018:</p> <ul> <li> <p>GEFF-reanalysis, which provides historical records of fire danger conditions</p> <ul> <li> <p>e5_hr, this folder contains deterministic model outputs</p> </li> <li> <p>e5_en, this folder contains probabilistic model outputs (made of 10 ensemble members)</p> </li> </ul> </li> <li> <p>GEFF-realtime provides real-time forecasts generated using weather forcings from the model cycle 45r1 of the ECMWF’s Integrated Forecasting System (IFS).</p> <ul> <li> <p>rt_hr, this folder contains high-resolution deterministic forecasts (~9 Km)</p> </li> <li> <p>rt_en, this folder contains probabilistic forecasts (~18Km)</p> </li> </ul> </li> <li> <p>Geographical bounding box: lon_min = 10.1, lon_max = 24.8, lat_min = 55, lat_max = 69</p> </li> </ul> <p><strong>Please note, the sample data provided in this repository is intended to be used for education purposes only (e.g. training courses).</strong></p> <p>These products have been developed as part of the EU-funded Copernicus Emergency Management Services (<a href="https://emergency.copernicus.eu/">CEMS</a>) and complement other Copernicus products related to fire, such as the biomass-burning emissions made available by the Copernicus Atmosphere Monitoring Service (<a href="https://atmosphere.copernicus.eu/">CAMS</a>). The development of the GEFF modelling system was funded through a third-party agreement with the European Commission’s Joint Research Centre (<a href="https://ec.europa.eu/info/departments/joint-research-centre_en">JRC</a>). </p> <p>GEFF produces fire danger indices based on the Canadian Fire Weather index as well as the US and Australian fire danger models. GEFF datasets are under the Copernicus license, which provides users with free, full and open access to environmental data.</p> <p>For more information, please refer to the documentation on the <a href="http://datastore.copernicus-climate.eu/c3s/published-forms/c3sprod/cems-fire-historical/Fire_In_CDS.pdf">CDS</a> and on the <a href="https://effis.jrc.ec.europa.eu/about-effis/technical-background/fire-danger-forecast/">EFFIS website</a>.</p>
ECMWF data for analysing smoked-charged vortices after the 2019-2020 Australian wildfires
<p>The dataset contains GRIB2 files produced from the operational IFS model and assimilation system of the European Centre for Medium Range Weather Forecast (ECMWF).</p> <p>OPZLWDA2020mmdd-SH.grd files contain log of surface pressure, zonal wind, meridional wind, temperature, relative vorticity and ozone mixing ratio for the 137 levels of the model on a 1°x1° grid in the southern hemisphere for the long window analysis at 6UTC and 18UTC every day from 1st January 2020 to 31 March 2020.</p> <p>OPZFCST2020mmdd-SH.grd files contain log of surface pressure, temperature, relative vorticity and ozone mixing ratio for the 137 levels of the model on a 1°x1° grid in the southern hemisphere every day for the 10-day forecast run starting at 00UTC every four days from 7 January 2020 to 31 March 2020.</p> <p>For basic access, these files are readable using python tools. The package pygrib available under conda-forge is recommended. The eccode library that allows read from C or Fortran is freely available from ECMWF and is installed along pygrib. Reading with eccode library is also possible in C and Fortran.</p> <p>A simple reader using pygrib is provided. </p> <p>Dedicated packages for the project are available on github/bernard-legras/STC/STC-Australia with dependencies in github/bernard-legras/STC/STC/pylib. The package that reads and process ECMWF data is github/bernard-legras/STC/STC/pylib/ECMWF_N.py. This package needs setup modification to discover the files where they have been copied.</p> <p>All requirements should be made to bernard.legras@lmd.ipsl.fr</p>
Greenland Ice Sheet precipitation and surface temperature from CloudSat and ECMWF
<p>This dataset contains code, data, and instructions for recreating the figures and analysis in Thompson-Munson et al. (submitted), "An Observational Constraint for Future Greenland Rainfall in a Warmer Atmosphere". Please see the readme for instructions and descriptions of the data and code.</p>
Preprocessed EUMETSAT H-SAF h61 Satellite and ECMWF HRES 24h Forecast Precipitation Datasets for Hydrometeorological Applications over Central Europe
<p>This dataset contains preprocessed precipitation data from the ECMWF's high resolution HRES forecast (24h) and EUMETSAT's blended infrared and microwave remotely sensed data for use in hydrological and meteorological research. <br><br><em>* Preprocessing procedure and codes are accessible <a href="https://gitlab.jsc.fz-juelich.de/kiste/atmoscorrect/-/blob/master/HRES_PP.ipynb?ref_type=heads">here for HRES</a>, and <a href="https://gitlab.jsc.fz-juelich.de/kiste/atmoscorrect/-/blob/master/HSAF_PP.ipynb?ref_type=heads">here for H-SAF</a> datasets.</em></p> <p><strong>HRES Data (HRES_pr.nc):</strong></p> <ul> <li><strong>Source:</strong> <a href="https://confluence.ecmwf.int/display/FUG/Section+2.1.2.4+HRES+-+High+Resolution+Forecasts">ECMWF's high-resolution, deterministic HRES 24-hour precipitation forecast at 12UTC.</a></li> <li><strong>Resolution and domain:</strong> 0.1° × 0.1° grid in (longmin: -1.1, longmax: 18.4, latmin: 44.1, latmax: 56.5)</li> <li><strong>Preprocessing Steps:</strong> <ol> <li>Extracted the precipitation variable (tp) out of variables.</li> <li>Converted precipitation units from meters (m) to millimeters (mm).</li> <li>Changed cumulative precipitation to instantaneous.</li> <li>Selected the first 24 hours of forecast data from the available 90-hour forecasts.</li> <li>Merged all processed files into a single NetCDF file.</li> </ol> </li> </ul> <p><strong>H-SAF Data (HSAF_pr.nc):</strong></p> <ul> <li><strong>Source:</strong> <a href="https://hsaf.meteoam.it/Products/Detail?prod=H61B">EUMETSAT's H-SAF h61B</a></li> <li><strong>Resolution and domain:</strong> Original product: ~4.8 km at nadir, ~8km in Europe; preprocessed product: resampled to 0.1° × 0.1° (~10 km) grid in (longmin: -1.1, longmax: 18.4, latmin: 44.1, latmax: 56.5).</li> <li><strong>Preprocessing Steps:</strong> <ol> <li>Trimmed the MSG coverage data to cover the study domain.</li> <li>Calculated the grid correspondance using lat/lon information from MSG grid using a <a href="https://www-cdn.eumetsat.int/files/2020-04/pdf_conf_2018_s1_mueller_p.pdf">reference method</a>.</li> <li>Merged all processed files into a single NetCDF file.</li> <li>Regridded the data to the 0.1° × 0.1° resolution using bilinear function in <a href="https://code.mpimet.mpg.de/projects/cdo">CDO</a></li> </ol> </li> </ul> <p><strong>Data Period:</strong> 01/07/2020-25/04/2023</p>
Dealing with discontinuous meteorological forcing in operational ocean modelling: a case study using ECMWF-IFS and GETM (v2.5)
<p>Data used in manuscript "Dealing with discontinuous meteorological forcing in operational ocean modelling: a case study using ECMWF-IFS and GETM (v2.5)" for Geoscientific Model Development. </p> <p>Uploaded data includes GETM output and used source code (FABM+GOTM+GETM).</p>
Code and extensive data for training neural networks for radiation, used in "Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0""
<p>Data and code used in a paper submitted to JAMES titled :<em> Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System</em></p> <p>1) The files <strong>ml_training_*.7z</strong> contain extensive datasets (in NetCDF format) for training neural network versions of the RRTMGP gas optics scheme as described in the paper. The datasets are read by <a href="https://github.com/peterukk/rte-rrtmgp-nn/blob/main/examples/rrtmgp-nn-training/ml_train.py">ml_train.py.</a></p> <p>2) The ML datasets were in turn generated using the input profiles (in NetCDF format) inside <strong>inputs_to_RRTMGP.zip </strong>by running the Fortran programs <code>rrtmgp_sw_gendata_rfmipstyle.F90 and rrtmgp_lw_gendata_rfmipstyle.F90 </code>in <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em>, which call the RRTMGP gas optics scheme, The input profiles contain <strong>millions of columns, hundreds of perturbation experiments (including hypercube-sampled gas concentrations), are derived from several different data sources (including CAMS reanalysis, GCM, and CKDMIP-MMM), and span present-day, preindustrial, and future atmospheric conditions.</strong> They could be used to generate training data for developing emulators of the full RTE+RRTMGP radiation scheme, not just gas optics (see nn_dev on the <a href="https://github.com/peterukk/rte-rrtmgp-nn">RTE+RRTMGP-NN repository on Github</a>, used in a previous paper where different emulation methods were compared)</p> <p>3) The Fortran and Python code used for data generation and NN training are found in<a href="https://github.com/peterukk/rte-rrtmgp-nn/tree/main/examples/rrtmgp-nn-training"> <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em> </a>on the main branch on Github; <strong>an archived version is also included here </strong>(<strong>rte-rrtmgp-nn-2.0.zip</strong>). See the readme in the above sub-directory for further information.</p> <p> </p>
Video supplement for Lagrangian transport simulations using the extreme convection parametrization: an assessment for the ECMWF reanalyses
<p>We provide a video supplement for the paper "Lagrangian transport simulations using the extreme convection parametrization: an assessment for the ECMWF reanalyses" submitted to the journal Geoscientific Model Development.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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