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20 results for “IFS”
Model intercomparison of COSMO 5.0 and IFS 45r1 at kilometer-scale grid spacing
<p>Simulation output data from COSMO and IFS used to produce the figures in the model intercomparison paper (https://doi.org/10.5194/gmd-2021-31), as well as the data used for initializing the soil in COSMO.</p> <p>The output data is partitioned into different parts:</p> <ol> <li>cosmo_hordiff.tar:<br> Model output from COSMO for the horizontal diffusion experiment.</li> <li>cosmo_standard.tar:<br> Model output from COSMO for the standard experiment.</li> <li>ifs_standard.tar:<br> Model output from IFS for the standard experiment.</li> <li>soil_cosmo_ini_vergara2021_avg_mayjune_12km.nc:<br> Initial conditions for the soil model used in COSMO.</li> </ol>
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>
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>
Namelist files and settings for multi-year km-scale nextGEMS Cycle 3 simulations with IFS-FESOM/NEMO
<p>This dataset contains the used atmosphere, land, wave, and ocean model namelist files and settings for the multi-year km-scale nextGEMS Cycle 3 simulations with IFS-FESOM (TCo2559 atmosphere at 4.4km resolution + 5km ocean; TCo1279 atmosphere at 9km resolution + 5km ocean) and IFS-NEMO (TCo1279 atmosphere at 9km resolution, 0.25 degree ocean) as described in the study</p><p>Rackow, Pedruzo-Bagazgoitia, Becker, Milinski, Sandu et al. : <strong>Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5/NEMOV3.4</strong></p><p>This work was supported by the European Union's Horizon 2020 collaborative project NextGEMS (grant number 101003470). For more infos about the nextGEMS project, see https://nextgems-h2020.eu (last access: 29.11.2023) </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>
Wind gust during tropical cyclone Ida, with and without deep convection parametrization from 1.4km global simulation with ECMWF IFS
<p>Animations of wind gust during tropical cyclone Ida, from global TCo7999L137 (1.4km horizontal grid-spacing) simulation with hydrostatic IFS from INCITE2022 project. </p> <p>One animation shows simulation with deep convection parametrization on and the other with off.</p>
A Study Called EPI VITRAKVI to Compare Treatment Results in Patients With Infantile Fibrosarcoma (IFS), a Type of Connective Soft Tissue Cancer, Who Received a Treatment Called Larotrectinib From a St
ClinicalTrials.gov study NCT05236257. IPD Sharing: NO. Countries: 1. Publications: 1.
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>
FVM 1.0: A nonhydrostatic finite-volume dynamical core for the IFS
<p>Simulation output data used to produce the figures in the paper.</p> <p>Four different files are provided. These refer to the IFS-FVM and IFS-ST results for the dry and moist configurations of the baroclinic instability benchmark.</p> <p>The paper is available at: https://doi.org/10.5194/gmd-12-651-2019</p>
IFS-COMPO data related to the surface evaluation of Secondary Inorganic Aerosol distribution and wet deposition terms
<p>These datasets are used for the GMD submission related to the evaluation of the impact of applying EQSAM4Clim in the IFS-COMPO model on Secondary Inorganic Partcile surafec distributions and the resulting wet deposition totals for selected global regions.</p>
IFS CAT EDR Ensemble Data
<p>This dataset contains ECMWF IFS ensemble forecast data of CAT-EDR index (https://www.ecmwf.int/en/newsletter/168/meteorology/forecasting-clear-air-turbulence) analyzed in Gisinger, Bramberger, Dörnbrack, and Bechtold (2024, GRL).</p> <p> </p>
Feasibility of iFS™ for Intrastromal Arcuate Keratotomy (ISAK) Procedures
ClinicalTrials.gov study NCT01210820. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Safety and Effectiveness of Arcuate Incisions Performed With the iFS Femtosecond Laser System
ClinicalTrials.gov study NCT01348854. IPD Sharing: Not stated. Countries: 3. Publications: 0.
Global Controlled Trial on Effects of an Online Self-Help Program for of Ambitious Altruists on Their Mental Health, Wellbeing, and Productivity: Comparing Versions With IFS vs. CBT, Buddy- vs. Group-
ClinicalTrials.gov study NCT06442072. IPD Sharing: NO. Countries: 1. Publications: 0.
Corneal Incisions With the IntraLase iFS Femtosecond Laser System
ClinicalTrials.gov study NCT01713660. IPD Sharing: Not stated. Countries: 0. Publications: 0.
NovaLign Intramedullary Fixation System (IFS) for the Treatment of Humeral Fractures
ClinicalTrials.gov study NCT00969839. IPD Sharing: Not stated. Countries: 1. Publications: 0.
An Outcomes Study Comparing the Intralase FS 60 to the Intralase iFS When Performing LASIK Surgery for Nearsightedness
ClinicalTrials.gov study NCT01365728. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Feasibility of the Ultravision™ System in Low Pressure Laparoscopic Cholecystectomy Compared to Airseal® IFS
ClinicalTrials.gov study NCT04162106. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of the Efficacy of Internal Family Systems (IFS) Therapy
ClinicalTrials.gov study NCT05155930. IPD Sharing: NO. Countries: 0. Publications: 0.
Survey on the Current Status of IFD Diagnosis and Treatment by Intensive Care Physicians in Sichuan Province (IFS)
ClinicalTrials.gov study NCT06346951. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
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