10 datasets found
  1. U

    CMAQ Grid Mask Files for 12km CONUS - US States and NOAA Climate Regions

    • dataverse-staging.rdmc.unc.edu
    • datasearch.gesis.org
    Updated Dec 12, 2019
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    UNC Dataverse (2019). CMAQ Grid Mask Files for 12km CONUS - US States and NOAA Climate Regions [Dataset]. http://doi.org/10.15139/S3/XDYYB9
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    Dataset updated
    Dec 12, 2019
    Dataset provided by
    UNC Dataverse
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Contiguous United States, United States
    Description

    Data Summary: US states grid mask file and NOAA climate regions grid mask file, both compatible with the 12US1 modeling grid domain. Note:The datasets are on a Google Drive. The metadata associated with this DOI contain the link to the Google Drive folder and instructions for downloading the data. These files can be used with CMAQ-ISAMv5.3 to track state- or region-specific emissions. See Chapter 11 and Appendix B.4 in the CMAQ User's Guide for further information on how to use the ISAM control file with GRIDMASK files. The files can also be used for state or region-specific scaling of emissions using the CMAQv5.3 DESID module. See the DESID Tutorial and Appendix B.4 in the CMAQ User's Guide for further information on how to use the Emission Control File to scale emissions in predetermined geographical areas. File Location and Download Instructions: Link to GRIDMASK files Link to README text file with information on how these files were created File Format: The grid mask are stored as netcdf formatted files using I/O API data structures (https://www.cmascenter.org/ioapi/). Information on the model projection and grid structure is contained in the header information of the netcdf file. The output files can be opened and manipulated using I/O API utilities (e.g. M3XTRACT, M3WNDW) or other software programs that can read and write netcdf formatted files (e.g. Fortran, R, Python). File descriptions These GRIDMASK files can be used with the 12US1 modeling grid domain (grid origin x = -2556000 m, y = -1728000 m; N columns = 459, N rows = 299). GRIDMASK_STATES_12US1.nc - This file containes 49 variables for the 48 states in the conterminous U.S. plus DC. Each state variable (e.g., AL, AZ, AR, etc.) is a 2D array (299 x 459) providing the fractional area of each grid cell that falls within that state. GRIDMASK_CLIMATE_REGIONS_12US1.nc - This file containes 9 variables for 9 NOAA climate regions based on the Karl and Koss (1984) definition of climate regions. Each climate region variable (e.g., CLIMATE_REGION_1, CLIMATE_REGION_2, etc.) is a 2D array (299 x 459) providing the fractional area of each grid cell that falls within that climate region. NOAA Climate regions: CLIMATE_REGION_1: Northwest (OR, WA, ID) CLIMATE_REGION_2: West (CA, NV) CLIMATE_REGION_3: West North Central (MT, WY, ND, SD, NE) CLIMATE_REGION_4: Southwest (UT, AZ, NM, CO) CLIMATE_REGION_5: South (KS, OK, TX, LA, AR, MS) CLIMATE_REGION_6: Central (MO, IL, IN, KY, TN, OH, WV) CLIMATE_REGION_7: East North Central (MN, IA, WI, MI) CLIMATE_REGION_8: Northeast (MD, DE, NJ, PA, NY, CT, RI, MA, VT, NH, ME) + Washington, D.C.* CLIMATE_REGION_9: Southeast (VA, NC, SC, GA, AL, GA) *Note that Washington, D.C. is not included in any of the climate regions on the website but was included with the “Northeast” region for the generation of this GRIDMASK file.

  2. PROCESSED DATA .nc (NetCDF Files)

    • figshare.com
    hdf
    Updated Apr 24, 2022
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    Kartika Wardani (2022). PROCESSED DATA .nc (NetCDF Files) [Dataset]. http://doi.org/10.6084/m9.figshare.19641777.v1
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    hdfAvailable download formats
    Dataset updated
    Apr 24, 2022
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Kartika Wardani
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset is a processed data in NetCDF (.nc) files, that used in our study. We used the SPI to determine meteorological drought conditions in the study area, that calculated by using the open-source module Climate and Drought Indices in Python.

  3. E

    EMSO Western Ligurian : MII, OXYGEN sensor (NetCDF files from 2017-09)

    • erddap.emso.eu
    • erddap.osupytheas.fr
    Updated Jul 18, 2021
    + more versions
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    SeaDataNet (2021). EMSO Western Ligurian : MII, OXYGEN sensor (NetCDF files from 2017-09) [Dataset]. https://erddap.emso.eu/erddap/info/Emso_Western_Ligurian_MII_Oxygen_NetCDF_2017/index.html
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    Dataset updated
    Jul 18, 2021
    Dataset authored and provided by
    SeaDataNet
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Sep 29, 2017 - Jul 29, 2019
    Area covered
    Variables measured
    DEPH, DOX1, OSAT, TEMP, time, DOX_QC, OSAT_QC, TEMP_QC, TIME_QC, latitude, and 5 more
    Description

    The European Multidisciplinary Seafloor and water column Observatory (EMSO) is a research infrastructure distributed throughout Europe for seabed and water column observatories. It aims to further explore the oceans, better understand the phenomena that occur on the seabed, and elucidate the critical role that these phenomena play in global Earth systems. This observatory is based on observation sites (or nodes) that have been deployed in strategic locations in European seas, from the Arctic to the Atlantic, from the Mediterranean to the Black Sea. There are currently eleven deep water nodes plus four shallow water test nodes. EMSO Western Ligurian is one of these permanent underwater observatories located in the Ligurian Sea and is deployed off Toulon, France. This region was chosen for its particular scientific interests such as: seismicity, topography, turbidity, biodiversity, water mass dynamics and organic matter flow. This underwater observation network is also part of KM3NeT (https://www.km3net.org/) which has a modular topology designed to connect up to 120 neutrino detection units. Earth and Sea Science (ESS) instrumentation connected to KM3NeT is based on two complementary components: an instrumented interface module (MII) and an autonomous instrumented line (ALBATROSS). The ALBATROSS line is an inductive line (2000 m) composed of an acoustic communication system, two inductive cables equipped with Conductivity, Temperature, Depth (CTD)-O2 sensors, current meters and two instrumented buoys. This line is deployed at a distance of 2-3 kilometers from the MII, and communication on land is done by an acoustic link with the MII, and electro-optical cable via the KM3NeT node. cdm_data_type=TimeSeries cdm_timeseries_variables=stationname,latitude,longitude citation=Lefevre Dominique, Libes Maurice, Mallarino Didier, Bernardet Karim, Gojak Carl (2023). EMSO-Ligure Ouest observatory data (MII) from 2019-08. SEANOE. https://doi.org/10.17882/75839 contributor_name=didier.mallarino@osupytheas.fr, maurice.libes@osupytheas.fr contributor_role=raw data conversion, and data formating in NetCDF Conventions=OceanSITES v1.4,SeaDataNet_1.0,COARDS, CF-1.6, ACDD-1.3, NCCSV-1.2 data_mode=R data_type=OceanSITES time-series data defaultGraphQuery=time%2CDOX1%2C&time>2019-07-23T00%3A00%3A00Z&time<2019-07-30T00%3A00%3A00Z&.draw=linesAndMarkers&.marker=3|5&.color=0x000000&.colorBar=|||||&.bgColor=0xffccccff description=http://www.emso-fr.org/EMSO-Western-Ligurian-Sea/Infrastructure-description doi=10.17882/75839 Easternmost_Easting=6.029 emso_facility=Western Ligurian Sea featureType=TimeSeries featuretype=TimeSeries format_version=1.5 geospatial_lat_max=42.805 geospatial_lat_min=42.805 geospatial_lat_units=degrees_north geospatial_lon_max=6.029 geospatial_lon_min=6.029 geospatial_lon_units=degrees_east history=fixed benthic node, https://www.emso-fr.org/EMSO-Western-Ligurian-Sea/Infrastructure-description infoUrl=http://www.emso-fr.org/EMSO-France infourl=http://www.emso-fr.org/EMSO-France insitution_ror_uri=https://ror.org/05258q350 institution=MIO UMR 7294 CNRS / OSU Pytheas institution_edmo_code=3078 institution_edmo_uri=https://edmo.seadatanet.org/report/3078 instrument_aquadopp=SDN:L22::TOOL0476 https://vocab.nerc.ac.uk/collection/L22/current/TOOL0476/ instrument_cstar=SDN:L22::TOOL1902 https://vocab.nerc.ac.uk/collection/L22/current/TOOL1902/ instrument_microcat=SDN:L22::TOOL1451 https://vocab.nerc.ac.uk/collection/L22/current/TOOL1451/ instrument_oxygen=SDN:L22::TOOL0036 https://vocab.nerc.ac.uk/collection/L22/current/TOOL0036/ keywords_vocabulary=GCMD Science Keywords license_uri=https://spdx.org/licenses/CC-BY-4.0.html lineage=The infrastructure is dedicated to long term in situ observation. Based on instrumentation of the water column, this infrastructure aims at obtaining continuous in situ and near real-time measurements. This infrastructure MII (Interface Instrumented Module) is a cabled infrastructure and ALBATROSS (Autonomous Line with a Broad Acoustic Transmission for Research in Oceanography and Sea Sciences) is a standalone deep sea mooring dedicated to the long term monitoring of hydrological and biogeochemical properties. The data are transmitted in real time to the shore using the inductive cable along the mooring line and an acoustic link with the MII. This infrastructure has been developed by the DT INSU, to take into account surface ocean circulation (i.e. North Mediterranean Current, water mass dynamics including deep water formation, surface-to-bottom material fluxes and associated biogeochemical processes. Real Time data taking is operational since May 2019 for MII and August 2019 for ALBATROSS. MII EQUIPMENTS : 1 Microcat SBE 37, 1 Aquadopp Nortek 2 MHz, 1 transmissiometer, Wetlab, 1 pressure sensor Aanderaa(HS), 1 oxygen optode Aanderaa (HS), 1 acoustic modem (Evologic). MII MEASURED PARAMETERS : (2436 m) : temperature/salinity/oxygen - current ALBATROSS EQUIPMENTS 6 Microcat SBE 37 ODO, 2 Microcat SBE 37, 6 Aquadopp 2Mhz Nortek, 1 acoustic modem Evologic, 2 inductive modem Seabird, 1 data acquisition unit. Target depths in meters 500, 1000, 1500, 2000,2200, 2400. metadata_contact=maurice.libes@osupytheas.fr, didier.mallarino@osupytheas.fr network=EMSO-ERIC Northernmost_Northing=42.805 platform_code=MII (Instrumented Interface Module) principal_investigator=Dominique Lefevre principal_investigator_email=Dominique.Lefevre@mio.osupytheas.fr project=EMSO west Ligurian Sea, Antares http://www.emso-fr.org/EMSO-France qc_indicator=probably good, raw data from sensor rfa=If you use these data in publications or presentation, please acknowledge the EMSO Project Office of MIO OSU Pytheas. Also, we would appreciate receiving a preprint and/or reprint of publications using the data for inclusion in our bibliography. Relevant publications should be sent to: Dominique Lefevre EMSO Project MIO UMR 7294 CNRS, Campus de Luminy 13288 Marseille cedex9 site_code=Western Ligurian Sea source=fixed benthic node, https://www.emso-fr.org/EMSO-Western-Ligurian-Sea/Infrastructure-description sourceUrl=(local files) Southernmost_Northing=42.805 standard_name_vocabulary=CF Standard Name Table v70 subsetVariables=stationname, latitude, longitude, TIME_QC, DOX_QC, OSAT_QC, TEMP_QC, DEPH, optodeType, optodeSerial, optode_QC time_coverage_end=2019-07-29T21:50:03Z time_coverage_start=2017-09-29T12:44:01Z update_interval=daily 24 hours Westernmost_Easting=6.029

  4. Data and Software for Developing a General Comprehensive Evaluation Method...

    • zenodo.org
    bin, pdf, txt
    Updated Jul 6, 2024
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    Bing Zhang; Mingjian Zeng; Anning Huang; Zhengkun Qin; Couhua Liu; Wenru Shi; Xin Li; Kefeng Zhu; Chunlei Gu; Jialing Zhou; Bing Zhang; Mingjian Zeng; Anning Huang; Zhengkun Qin; Couhua Liu; Wenru Shi; Xin Li; Kefeng Zhu; Chunlei Gu; Jialing Zhou (2024). Data and Software for Developing a General Comprehensive Evaluation Method for Cross-Scale Precipitation Forecasts [Dataset]. http://doi.org/10.5281/zenodo.10951799
    Explore at:
    bin, txt, pdfAvailable download formats
    Dataset updated
    Jul 6, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Bing Zhang; Mingjian Zeng; Anning Huang; Zhengkun Qin; Couhua Liu; Wenru Shi; Xin Li; Kefeng Zhu; Chunlei Gu; Jialing Zhou; Bing Zhang; Mingjian Zeng; Anning Huang; Zhengkun Qin; Couhua Liu; Wenru Shi; Xin Li; Kefeng Zhu; Chunlei Gu; Jialing Zhou
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description
    Readme for GCEM Data and Code
    1 The data and code of two typical cases in Section 4.1
    1.1 Data
    1.1.1 Observed Precipitation Data
    1.1.1.1 /1_Two_Typical_Processes_data/1_1Observed_precipitation_data/2019071612
    (in the 1_Two_Typical_Processes_data.rar file)
    Hourly precipitation from 00:00 to 12:00 UTC on July 16, 2019 for Case 1
    surfr01h.nc
    surfr02h.nc
    surfr03h.nc
    surfr04h.nc
    surfr05h.nc
    surfr06h.nc
    surfr07h.nc
    surfr08h.nc
    surfr09h.nc
    surfr10h.nc
    surfr11h.nc
    surfr12h.nc
    1.1.1.2 /1_Two_Typical_Processes_data/1_1Observed_precipitation_data/2020061312
    (in the 1_Two_Typical_Processes_data.rar file)
    Hourly precipitation from 00:00 to 12:00 UTC on June 13,2020 for Case 2
    surfr01h.nc
    surfr02h.nc
    surfr03h.nc
    surfr04h.nc
    surfr05h.nc
    surfr06h.nc
    surfr07h.nc
    surfr08h.nc
    surfr09h.nc
    surfr10h.nc
    surfr11h.nc
    surfr12h.nc
    1.1.2 Forecasted Precipitation Data
    1.1.2.1 /1_Two_Typical_Processes_data/1_2Forecasted_precipitation_data/2019071612
    (in the 1_Two_Typical_Processes_data.rar file)
    Data for Case 1 during 00:00-12:00 UTC on July 16, 2019
    WRF3.2019071600000.nc (initial field at 12:00 UTC on July 16, 2019)
    WRF3.2019071600012.nc (12-hour accumulated precipitation during 00:00–12:00 UTC on July 16, 2019)
    1.1.2.2 /1_Two_Typical_Processes_data/1_2Forecasted_precipitation_data/2020061312
    (in the 1_Two_Typical_Processes_data.rar file)
    Data for Case 2 during 00:00-12:00 UTC on June 13,2020
    WRF3.2020061300000.nc (initial field at 12:00 UTC on June 13,2020)
    WRF3.2020061300012.nc (12-hour accumulated precipitation during 00:00–12:00 UTC on June 13,2020)
    1.2 Code and Configuration Files
    1.2.1 GCEM of Software and Configuration
    (/Code/1_Two_Typical_Processes/1Software_Configuration_of_GCEM in the Code.rar file)
    pastonc6hd2.f90 Main program, reads observed and forecasted precipitation data, performs GCEM verification, and outputs result files.
    module_skinput.f90 Subprogram, module for reading one or more observed precipitation grid file
    module_ybinput.f90 Subprogram, module for reading the start (or end) forecasted precipitation grid file
    mod_uxpasid2.f90 Subprogram, module for used to perform GCEM verification on forecasted data
    module_outnc.f90 Subprogram, module for outputting the verification results in netCDF file format
    compilePAS10mmd2.sh Used to compile source files to generate executable file under Linux
    r12hfile.txt Configuration file, used to specify the latitude and longitude range for data source and verification
    pastonc6hd2.exe Executable file
    1.2.2 TS of Software and Configuration
    (/Code/1_Two_Typical_Processes/2Software_Configuration_of_TS-Score in the Code.rar file)
    tsmain01.f90 Main program, reads observed and forecasted precipitation data, performs TS verification, and outputs result files.
    module_skinput.f90 Subprogram, module for reading one or more observed precipitation grid file module_ybinput.f90 Subprogram, module for reading the start (or end) forecasted precipitation grid file
    module_uxtsiTure.f90 Subprogram, module for used to perform TS verification on forecasted data
    compileTS.sh Used to compile source files to generate executable file under Linux
    r12hfile.txt Configuration file, used to specify the latitude and longitude range for data source and verification
    tsmain01.exe Executable file
    1.3 Output files
    1.3.1 GCEM verification results
    (/1_Two_Typical_Processes_data/1_3Results/1_3_1Results_GCEM in the 1_Two_Typical_Processes_data.rar file)
    rainverd2019071612012.nc Result file in netCDF format
    rainverd2019071612012.nc.txt Result explanation file in netCDF format
    outnc12hd22019071612.txt GCEM result file in text format
    rainverd2020061312012.nc Result file in netCDF format
    rainverd2020061312012.nc.txt Result explanation file in netCDF format
    outnc12hd22020061312.txt GCEM result file in text format
    1.3.2 TS verification results
    (/1_Two_Typical_Processes_data/1_3Results/1_3_2Results_TS in the 1_Two_Typical_Processes_data.rar file)
    ts12h2019071612.txt TS result file in text format
    ts12h2020061312.txt TS result file in text format
    1.4 Compiling Environment
    The verification program runs in a UNIX environment and requires the intel compiler (v2017) and the netCDF (v4.6.1) support library
    UNIX Environment Settings
    # .bashrc
    module load intel/intel-compiler-2017.5.239
    module load intelmpi/2019.6.154
    export F90=ifort
    export NETCDF=/public/software/mathlib/netcdf/4.6.1_intel-2017_mpi-2017_hdf5-1.8.20-intel2017
    export NETCDF_LIB=$NETCDF/lib
    export NETCDF_INC=$NETCDF/include
    export PATH=$NETCDF/bin:$PATH
    export LD_LIBRARY_PATH=$NETCDF/lib:$LD_LIBRARY_PATH
    1.5 Compiling and Running Steps
    1.5.1 The steps for case 1 during 00:00–12:00 UTC on July 16, 2019
    1. Creating an installation and running sub-directory
    mkdir p2019
    2. Copying data sources, code files and configuration files to this directory
    3. Running in this directory
    ./compilePAS10mmd2.sh Compile to generate executable file (pastonc6hd2.exe)
    ./compileTS.sh Compile to generate executable file (tsmain01.exe)
    4. Modifying the configuration file (r12hfile.txt)
    5. Run the executable files
    ./pastonc6hd2.exe > outnc12hd22019071612.txt
    Creating the GCEM result file (rainverd2019071612012.nc), procedure file (outnc12hd22019071612.txt)
    ./tsmain01.exe >ts12h2019071612.txt
    Creating the TS result and procedure file (ts12h2019071612.txt)
    1.5.2 The steps for case 2 during 00:00–12:00 UTC on June 13,2020
    1. Creating an installation and running sub-directory
    mkdir p2020
    2. Copying data sources, code files and configuration files to this directory
    3. Running in this directory
    ./compilePAS10mmd2.sh Compile to generate executable file (pastonc6hd2.exe)
    ./compileTS.sh Compile to generate executable file (tsmain01.exe)
    4. Modifying the configuration file (r12hfile.txt)
    5. Run the executable files
    ./pastonc6hd2.exe > outnc12hd22020061312.txt
    Creating the GCEM result file (rainverd2020061312012.nc), procedure file (outnc12hd22020061312.txt)
    ./tsmain01.exe >ts12h2020061312.txt
    Creating the TS result and procedure file (ts12h2019071612.txt)
    1.6 Module code main interface description
    1.6.1 skinput()
    subroutine skinput(skfile,skfilenum,rain,gridskx,gridsky,longitude,latitude)
    integer,intent(in) :: skfilenum
    character(len=200),dimension(skfilenum),intent(in) :: skfile
    real,dimension(:,:),allocatable,intent(out) :: rain
    integer,intent(out) :: gridskx,gridsky
    usage: Read a set of observed precipitation data files and output grid accumulated precipitation
    skfile, A set of filenames that are arrays of strings (input)
    skfilenum, Number of files (input)
    rain, Accumulated precipitation, rain(nx,ny) (output)
    gridskx, grid points, nx (output)
    gridsky, grid points, ny (output)
    gridlon, Longitude array, gridlon(nx) (output)
    gridlat, Latitude array, gridlat(ny) (output)
    1.6.2 ybinput()
    subroutine ybinput(ybfile,apcp,gridybx,gridyby,gridyblon,gridyblat)
    character(len=200),intent(in) :: ybfile
    real,dimension(:,:),allocatable,intent(out) :: apcp,gridyblat,gridyblon
    integer,intent(out) :: gridybx,gridyby
    usage: Read a set of forecasted precipitation data files and output forecast grid precipitation
    ybfile, Forecast file (input)
    apcp, forecasted precipitation array, apcp(nx, ny) (output)
    gridybx, Number of grid points for forecast data, nx (output)
    gridyby, Number of grid points for forecast data, ny (output)
    gridyblon, Longitude of forecast data, gridyblon(nx, ny) (output)
    gridyblat, Latitude of forecast data, gridyblat(nx, ny) (output)
    1.6.3 uxpasid2()
    subroutine uxpasid2(ui,xi,level,pas,iTure,iclass,ieps)
    real,intent(in) :: ui,xi,level
    real,intent(out) :: pas,ieps
    integer,intent(out) :: iTure,iclass
    usage: Read in the observed and forecasted precipitation, and output the PAS score result
    rainsk, Observed precipitation (input)
    rainyb, Forecasted precipitation (input)
    level, Specifing magnitude (input)
    ipas, Pas score value (0-1) or correct value of no precipitation forecast (1)

    iTure, 0 indicates that the rating is correct for a no precipitation

  5. E

    EMSO Ligure Ouest : MII capteur AQUADOPP (NetCDF files)

    • erddap.emso-fr.org
    Updated Dec 18, 2023
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    Dominique Lefevre (2023). EMSO Ligure Ouest : MII capteur AQUADOPP (NetCDF files) [Dataset]. https://erddap.emso-fr.org/erddap/info/Emso_Ligure_Ouest_MII_Aquadopp_NetCDF/index.html
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    Dataset updated
    Dec 18, 2023
    Authors
    Dominique Lefevre
    Time period covered
    May 23, 2019 - Dec 18, 2023
    Area covered
    Variables measured
    TIS, Roll, Z_up, time, Pitch, Speed, depth, X_East, Heading, Y_North, and 10 more
    Description

    EMSO Ligure Ouest (Network Common Data Format (NetCDF) files). EMSO Ligure Ouest : MII traitement temps reel de fichiers de mesures provenant de capteurs AQUADOPP in situ sur une station de fond MII cdm_data_type=TimeSeries cdm_timeseries_variables=stationname,latitude,longitude comment=Scientific objectives at Ligurian site http://www.emso-fr.org/EMSO-Western-Ligurian-Sea/Scientific-objectives contact=Dominique Lefevre (dominique.lefevre@mio.osupytheas.fr) contributor_name=maurice.libes@osupytheas.fr contributor_role=raw data conversion, and data formating in NetCDF Conventions=CF-1.6, COARDS, ACDD-1.3, NCCSV-1.2 defaultGraphQuery=time%2CSpeed_mean&time>=now-7days&time<=now&.draw=lines&.color=0x000000&.bgColor=0xffccccff description=http://www.emso-fr.org/EMSO-Western-Ligurian-Sea/Infrastructure-description Easternmost_Easting=6.029 featureType=TimeSeries geospatial_lat_max=42.805 geospatial_lat_min=42.805 geospatial_lat_units=degrees_north geospatial_lon_max=6.029 geospatial_lon_min=6.029 geospatial_lon_units=degrees_east geospatial_vertical_max=2436.0 geospatial_vertical_min=2436.0 geospatial_vertical_positive=down geospatial_vertical_units=m history=The infrastructure is dedicated to long term in situ observation. Based on instrumentation of the water column, this infrastructure aims at obtaining continuous in situ and near real-time measurements. This infrastructureMII (Interface Instrumented Module) is a cabled infrastructure and ALBATROSS (Autonomous Line with a Broad Acoustic Transmission for Research in Oceanography and Sea Sciences) is a standalone deep sea mooring dedicated to the long term monitoring of hydrological and biogeochemical properties. The data are transmitted in real time to the shore using the inductive cable along the mooring line and an acoustic link with the MII. This infrastructure has been developed by the DT INSU, to take into account surface ocean circulation (i.e. North Mediterranean Current, water mass dynamics including deep water formation, surface-to-bottom material fluxes and associated biogeochemical processes. Real Time data taking is operational since May 2019 for MII and August 2019 for ALBATROSS. MII EQUIPMENTS : 1 Microcat SBE 37, 1 Aquadopp Nortek 2 MHz, 1 transmissiometer, Wetlab, 1 pressure sensor Aanderaa(HS), 1 oxygen optode Aanderaa (HS), 1 acoustic modem (Evologic). MII MEASURED PARAMETERS : (2436 m) : temperature/salinity/oxygen - current ALBATROSS EQUIPMENTS 6 Microcat SBE 37 ODO, 2 Microcat SBE 37, 6 Aquadopp 2Mhz Nortek, 1 acoustic modem Evologic, 2 inductive modem Seabird, 1 data acquisition unit. Target depths in meters 500, 750,1000,1250, 1500, 2000,2200, 2400. DOI: 10.17882/75839 Created file : 18/12/23 infoUrl=http://www.emso-fr.org/EMSO-France institution=MIO UMR7294 CNRS / OSU Pytheas keywords_vocabulary=GCMD Science Keywords lineage=ALBATROSS, the Autonomous Line with a Broad Acoustic Transmission for Research in Oceanography and Sea Sciences, is a standalone deep sea mooring dedicated to the long term monitoring of hydrological and biogeochemical properties. The data are transmitted in real time to the shore using the inductive cable along the mooring line and an acoustic link with the MII Northernmost_Northing=42.805 production=MIO UMR 7294 CNRS / OSU Pytheas project=EMSO http://www.emso-fr.org/EMSO-France references=http://www.emso-fr.org/EMSO-France Request_for_acknowledgement=If you use these data in publications or presentation, please acknowledge the EMSO Project Office of MIO/OSU Pytheas. Also, we would appreciate receiving a preprint and/or reprint of publications using the data for inclusion in our bibliography. Relevant publications should be sent to: Dominique Lefevre EMSO Project MIO UMR 7294 CNRS, Campus de Luminy 13288 Marseille cedex9 source=sea bottom measurements - MII robot - Aquadopp sensor sourceUrl=(local files) Southernmost_Northing=42.805 standard_name_vocabulary=CF Standard Name Table v70 subsetVariables=stationname, latitude, longitude, depth testOutOfDate=now-1day time_coverage_end=2023-12-18T11:28:03Z time_coverage_start=2019-05-23T19:10:40Z Westernmost_Easting=6.029

  6. Data from: Radiative Forcing of Nitrate Aerosols from 1975 to 2010 as...

    • zenodo.org
    nc
    Updated Jun 18, 2021
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    Zheng; Zheng (2021). Radiative Forcing of Nitrate Aerosols from 1975 to 2010 as Simulated by MOSAIC Module in CESM2-MAM4 [Dataset]. http://doi.org/10.5281/zenodo.4979841
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    ncAvailable download formats
    Dataset updated
    Jun 18, 2021
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Zheng; Zheng
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Netcdf outputs from CESM2 simulations for publication: Radiative Forcing of Nitrate Aerosols from 1975 to 2010 as Simulated by MOSAIC Module in CESM2-MAM4

  7. E

    EMSO Western Ligurian : Albatross mooring, MICROCAT sensor (NetCDF files...

    • erddap.emso.eu
    • erddap.osupytheas.fr
    Updated Dec 6, 2024
    + more versions
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    SeaDataNet (2024). EMSO Western Ligurian : Albatross mooring, MICROCAT sensor (NetCDF files from 2024-03) [Dataset]. https://erddap.emso.eu/erddap/info/Emso_Western_Ligurian_Albatross_Microcat_NetCDF_2024/index.html
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    Dataset updated
    Dec 6, 2024
    Dataset authored and provided by
    SeaDataNet
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Mar 28, 2024 - Dec 6, 2024
    Area covered
    Variables measured
    CNDC, DENS, DEPH, DOX1, DOX2, OSAT, POTT, PRES, PSAL, TEMP, and 13 more
    Description

    The European Multidisciplinary Seafloor and water column Observatory (EMSO) is a research infrastructure distributed throughout Europe for seabed and water column observatories. It aims to further explore the oceans, better understand the phenomena that occur on the seabed, and elucidate the critical role that these phenomena play in global Earth systems. This observatory is based on observation sites (or nodes) that have been deployed in strategic locations in European seas, from the Arctic to the Atlantic, from the Mediterranean to the Black Sea. There are currently eleven deep water nodes plus four shallow water test nodes. EMSO Western Ligurian is one of these permanent underwater observatories located in the Ligurian Sea and is deployed off Toulon, France. This region was chosen for its particular scientific interests such as: seismicity, topography, turbidity, biodiversity, water mass dynamics and organic matter flow. This underwater observation network is also part of KM3NeT (https://www.km3net.org/) which has a modular topology designed to connect up to 120 neutrino detection units. Earth and Sea Science (ESS) instrumentation connected to KM3NeT is based on two complementary components: an instrumented interface module (MII) and an autonomous instrumented line (ALBATROSS). The ALBATROSS line is an inductive line (2000 m) composed of an acoustic communication system, two inductive cables equipped with Conductivity, Temperature, Depth (CTD)-O2 sensors, current meters and two instrumented buoys. This line is deployed at a distance of 2-3 kilometers from the MII, and communication on land is done by an acoustic link with the MII, and electro-optical cable via the KM3NeT node cdm_data_type=TimeSeries cdm_timeseries_variables=stationname,latitude,longitude citation=Lefevre Dominique, Libes Maurice, Laus Celine, Bernardet Karim, Gojak Carl(2024). EMSO-Ligure Ouest observatory data (mooring ALBATROSS) from 2021-08. SEANOE. https://doi.org/10.17882/99982 contributor_name=maurice.libes@osupytheas.fr contributor_role=raw data conversion, and data formating in NetCDF Conventions=OceanSITES v1.4,SeaDataNet_1.0,COARDS, CF-1.10, ACDD-1.3, NCCSV-1.2 data_mode=R data_type=OceanSITES time-series data defaultGraphQuery=time,TEMP&sensor_theoric_depth=500.0&.draw=linesAndMarkers&.marker=6|3&.color=0x000000&.colorBar=|||||&.bgColor=0xffccccff&time>=max(time)-2days&.timeRange=2,day(s) deploiement_date=2024-03 description=http://www.emso-fr.org/EMSO-Western-Ligurian-Sea/Infrastructure-description doi=10.17882/99982 Easternmost_Easting=6.1019 emso_facility=Ligurian Sea featureType=TimeSeries featuretype=TimeSeries format_version=1.5 geospatial_lat_max=42.9611 geospatial_lat_min=42.9611 geospatial_lat_units=degrees_north geospatial_lon_max=6.1019 geospatial_lon_min=6.1019 geospatial_lon_units=degrees_east geospatial_vertical_max=2400 geospatial_vertical_min=500 history=fixed benthic node, https://www.emso-fr.org/EMSO-Western-Ligurian-Sea/Infrastructure-description infoUrl=http://www.emso-fr.org/EMSO-France infourl=http://www.emso-fr.org/EMSO-France institution=MIO UMR 7294 CNRS / OSU Pytheas institution_edmo_code=3078 institution_edmo_uri=https://edmo.seadatanet.org/report/3078 institution_ror_uri=https://ror.org/05258q350 keywords_vocabulary=GCMD Science Keywords license_uri=https://spdx.org/licenses/CC-BY-4.0.html lineage=The infrastructure is dedicated to long term in situ observation. Based on instrumentation of the water column, this infrastructure aims at obtaining continuous in situ and near real-time measurements. This infrastructure MII (Interface Instrumented Module) is a cabled infrastructure and ALBATROSS (Autonomous Line with a Broad Acoustic Transmission for Research in Oceanography and Sea Sciences) is a standalone deep sea mooring dedicated to the long term monitoring of hydrological and biogeochemical properties. The data are transmitted in real time to the shore using the inductive cable along the mooring line and an acoustic link with the MII. This infrastructure has been developed by the DT INSU, to take into account surface ocean circulation (i.e. North Mediterranean Current, water mass dynamics including deep water formation, surface-to-bottom material fluxes and associated biogeochemical processes. Real Time data taking is operational since May 2019 for MII and August 2019 for ALBATROSS. MII EQUIPMENTS : 1 Microcat SBE 37, 1 Aquadopp Nortek 2 MHz, 1 transmissiometer, Wetlab, 1 pressure sensor Aanderaa(HS), 1 oxygen optode Aanderaa (HS), 1 acoustic modem (Evologic). MII MEASURED PARAMETERS : (2436 m) : temperature/salinity/oxygen - current ALBATROSS EQUIPMENTS 6 Microcat SBE 37 ODO, 2 Microcat SBE 37, 6 Aquadopp 2Mhz Nortek, 1 acoustic modem Evologic, 2 inductive modem Seabird, 1 data acquisition unit. Target depths in meters 500, 1000, 1500, 2000,2200, 2400. metadata_contact=maurice.libes@osupytheas.fr, celine.quentin@mio.osupytheas.fr network=EMSO-ERIC Northernmost_Northing=42.9611 platform_code=ALBATROSS principal_investigator=Dominique Lefevre principal_investigator_email=Dominique.Lefevre@mio.osupytheas.fr project=EMSO West Ligurian Sea, Antares http://www.emso-fr.org/EMSO-France rfa=If you use these data in publications or presentation, please acknowledge the EMSO Project Office of MIO OSU Pytheas. Also, we would appreciate receiving a preprint and/or reprint of publications using the data for inclusion in our bibliography. Relevant publications should be sent to: Dominique Lefevre EMSO Project MIO UMR 7294 CNRS, Campus de Luminy 13288 Marseille cedex9 site_code=LO source=subsurface mooring sourceUrl=(local files) Southernmost_Northing=42.9611 standard_name_vocabulary=CF Standard Name Table v70 subsetVariables=stationname, latitude, longitude, TIME_QC, TEMP_QC, CNDC_QC, PRES_QC, DOX_QC, sensor_theoric_depth, PSAL_QC, OSAT_QC, DENS_QC testOutOfDate=now-1day time_coverage_end=2024-12-06T23:01:12Z time_coverage_start=2024-03-28T00:00:01Z update_interval=daily 24 hours Westernmost_Easting=6.1019

  8. c

    Global distributions of pteropods (Gymnosomata, Thecosomata,...

    • compendiumkustenzee.be
    Updated Sep 30, 2013
    + more versions
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    (2013). Global distributions of pteropods (Gymnosomata, Thecosomata, Pseudothecosomata) abundance and biomass - Gridded data product (NetCDF) - Contribution to the MAREDAT World Ocean Atlas of Plankton Functional Types [Dataset]. http://www.compendiumkustenzee.be/en/imis-mog?module=dataset&dasid=4375&printversion=1&dropIMIStitle=1
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    Dataset updated
    Sep 30, 2013
    Description

    The aim of this study was to collect and synthesize existing pteropod (Gymnosomata, Thecosomata and Pseudothecosomata) abundance and biomass data, in order to evaluate the global distribution of pteropod carbon biomass, with a particular emphasis on its seasonal, temporal and vertical patterns.

  9. H

    National Water Model HydroLearn Python Notebooks

    • hydroshare.org
    • search.dataone.org
    zip
    Updated Nov 14, 2023
    + more versions
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    Dan Ames; Justin Hunter (2023). National Water Model HydroLearn Python Notebooks [Dataset]. http://doi.org/10.4211/hs.5949aec47b484e689573beeb004a2917
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    zip(1.8 MB)Available download formats
    Dataset updated
    Nov 14, 2023
    Dataset provided by
    HydroShare
    Authors
    Dan Ames; Justin Hunter
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    This resource contains Jupyter Python notebooks which are intended to be used to learn about the U.S. National Water Model (NWM). These notebooks explore NWM forecasts in various ways. NWM Notebooks 1, 2, and 3, access NWM forecasts directly from the NOAA NOMADS file sharing system. Notebook 4 accesses NWM forecasts from Google Cloud Platform (GCP) storage in addition to NOMADS. A brief summary of what each notebook does is included below:

    Notebook 1 (NWM1_Visualization) focuses on visualization. It includes functions for downloading and extracting time series forecasts for any of the 2.7 million stream reaches of the U.S. NWM. It also demonstrates ways to visualize forecasts using Python packages like matplotlib.

    Notebook 2 (NWM2_Xarray) explores methods for slicing and dicing NWM NetCDF files using the python library, XArray.

    Notebook 3 (NWM3_Subsetting) is focused on subsetting NWM forecasts and NetCDF files for specified reaches and exporting NWM forecast data to CSV files.

    Notebook 4 (NWM4_Hydrotools) uses Hydrotools, a new suite of tools for evaluating NWM data, to retrieve NWM forecasts both from NOMADS and from Google Cloud Platform storage where older NWM forecasts are cached. This notebook also briefly covers visualizing, subsetting, and exporting forecasts retrieved with Hydrotools.

    NOTE: Notebook 4 Requires a newer version of NumPy that is not available on the default CUAHSI JupyterHub instance. Please use the instance "HydroLearn - Intelligent Earth" and ensure to run !pip install hydrotools.nwm_client[gcp].

    The notebooks are part of a NWM learning module on HydroLearn.org. When the associated learning module is complete, the link to it will be added here. It is recommended that these notebooks be opened through the CUAHSI JupyterHub App on Hydroshare. This can be done via the 'Open With' button at the top of this resource page.

  10. Data layers for FORCOAST service module A3 Limfjorden 2009-2017

    • data.europa.eu
    unknown
    Updated Apr 17, 2024
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    Zenodo (2024). Data layers for FORCOAST service module A3 Limfjorden 2009-2017 [Dataset]. https://data.europa.eu/data/datasets/oai-zenodo-org-10987354?locale=fr
    Explore at:
    unknown(1534982)Available download formats
    Dataset updated
    Apr 17, 2024
    Dataset authored and provided by
    Zenodohttp://zenodo.org/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The environmental bottom data layers behind the FORCOAST service module A3 is generated by the 3D FlexSem model consisting of a hydrodynamic model coupled to the biogeochemical model ERGOM. The original data is on an unstructured grid (varying size of polygons), but for this purpose the data was interpolated to a structured grid and converted to netcdf files. The data layers are: 1) bottom temperature 2) bottom salinity 3) bottom Chl a 4) resuspension of detritus 5) bottom oxygen 6) bottom detritus

  11. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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UNC Dataverse (2019). CMAQ Grid Mask Files for 12km CONUS - US States and NOAA Climate Regions [Dataset]. http://doi.org/10.15139/S3/XDYYB9

CMAQ Grid Mask Files for 12km CONUS - US States and NOAA Climate Regions

Explore at:
Dataset updated
Dec 12, 2019
Dataset provided by
UNC Dataverse
License

CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically

Area covered
Contiguous United States, United States
Description

Data Summary: US states grid mask file and NOAA climate regions grid mask file, both compatible with the 12US1 modeling grid domain. Note:The datasets are on a Google Drive. The metadata associated with this DOI contain the link to the Google Drive folder and instructions for downloading the data. These files can be used with CMAQ-ISAMv5.3 to track state- or region-specific emissions. See Chapter 11 and Appendix B.4 in the CMAQ User's Guide for further information on how to use the ISAM control file with GRIDMASK files. The files can also be used for state or region-specific scaling of emissions using the CMAQv5.3 DESID module. See the DESID Tutorial and Appendix B.4 in the CMAQ User's Guide for further information on how to use the Emission Control File to scale emissions in predetermined geographical areas. File Location and Download Instructions: Link to GRIDMASK files Link to README text file with information on how these files were created File Format: The grid mask are stored as netcdf formatted files using I/O API data structures (https://www.cmascenter.org/ioapi/). Information on the model projection and grid structure is contained in the header information of the netcdf file. The output files can be opened and manipulated using I/O API utilities (e.g. M3XTRACT, M3WNDW) or other software programs that can read and write netcdf formatted files (e.g. Fortran, R, Python). File descriptions These GRIDMASK files can be used with the 12US1 modeling grid domain (grid origin x = -2556000 m, y = -1728000 m; N columns = 459, N rows = 299). GRIDMASK_STATES_12US1.nc - This file containes 49 variables for the 48 states in the conterminous U.S. plus DC. Each state variable (e.g., AL, AZ, AR, etc.) is a 2D array (299 x 459) providing the fractional area of each grid cell that falls within that state. GRIDMASK_CLIMATE_REGIONS_12US1.nc - This file containes 9 variables for 9 NOAA climate regions based on the Karl and Koss (1984) definition of climate regions. Each climate region variable (e.g., CLIMATE_REGION_1, CLIMATE_REGION_2, etc.) is a 2D array (299 x 459) providing the fractional area of each grid cell that falls within that climate region. NOAA Climate regions: CLIMATE_REGION_1: Northwest (OR, WA, ID) CLIMATE_REGION_2: West (CA, NV) CLIMATE_REGION_3: West North Central (MT, WY, ND, SD, NE) CLIMATE_REGION_4: Southwest (UT, AZ, NM, CO) CLIMATE_REGION_5: South (KS, OK, TX, LA, AR, MS) CLIMATE_REGION_6: Central (MO, IL, IN, KY, TN, OH, WV) CLIMATE_REGION_7: East North Central (MN, IA, WI, MI) CLIMATE_REGION_8: Northeast (MD, DE, NJ, PA, NY, CT, RI, MA, VT, NH, ME) + Washington, D.C.* CLIMATE_REGION_9: Southeast (VA, NC, SC, GA, AL, GA) *Note that Washington, D.C. is not included in any of the climate regions on the website but was included with the “Northeast” region for the generation of this GRIDMASK file.

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