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Dataset Title:  Mediterranean Sea, Water body phosphate.4Danl (DIVA 4D analysis of Water_body_
phosphate) [time][lat][lon], 0.125deg, 1971-2017
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Institution:  HCMR/HNODC   (Dataset ID: Mediterranean_PO_3D_0817_eb8b_4ef9)
Information:  Summary ? | License ? | FGDC | ISO 19115 | Metadata | Background (external link) | Data Access Form
 
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Things You Can Do With Your Graphs

Well, you can do anything you want with your graphs, of course. But some things you might not have considered are:

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 2.2128768e+9, 3.7196928e+9;
    String axis "T";
    String calendar "standard";
    String climatology "climatology_bounds";
    String ioos_category "Time";
    String long_name "Time";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 actual_range 30.0, 46.0;
    String axis "Y";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 actual_range -7.0, 36.5;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  Water_body_phosphate_deepest {
    Float32 _FillValue 9.96921e+36;
    Float64 colorBarMaximum 4.0;
    Float64 colorBarMinimum 0.0;
    String long_name "Deepest values of Water body phosphate";
    Float32 missing_value 9.96921e+36;
    String standard_name "mole_concentration_of_phosphate_in_sea_water";
    String units "umol/l";
  }
  Water_body_phosphate_deepest_L1 {
    Float32 _FillValue 9.96921e+36;
    String cell_methods "time: mean within years time: mean over years";
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String long_name "Deepest values of Water body phosphate masked using relative error threshold 0.3";
    Float32 missing_value 9.96921e+36;
    String standard_name "water_body_phosphate";
    String units "umol/l";
  }
  Water_body_phosphate_deepest_L2 {
    Float32 _FillValue 9.96921e+36;
    String cell_methods "time: mean within years time: mean over years";
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String long_name "Deepest values of Water body phosphate masked using relative error threshold 0.5";
    Float32 missing_value 9.96921e+36;
    String standard_name "water_body_phosphate";
    String units "umol/l";
  }
  NC_GLOBAL {
    String abstract 
"Moving 6-year analysis of Water body chlorophyll-a in the Mediterranean Sea for each season:
- winter: January-March,
- spring: April-June,
- summer: July-September,
- autumn: October-December. 
Every year of the time dimension corresponds to the 6-year centered average of the season.
6-years periods span from 1990-1995 until 2016-2021.
Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network.
Units: mg/m3.
Description of DIVA analysis: The computation was done with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.9, using GEBCO 30sec topography for the spatial connectivity of water masses.
The horizontal resolution of the produced DIVAnd maps grids is dx=dy=0.125 degrees (around 13.5km and 10.9km accordingly).
The vertical resolution is 20 depth levels: [0.,5.,10.,20.,30.,50.,75.,100.,125.,150.,200.,250.,300.,400.,500.,600.,700.,800.,900.,1000.].
The horizontal correlation length is 200km.
The vertical correlation length (in meters) was set twice the vertical resolution: [10.,10.,20.,20.,40.,50.,50.,50.,50.,100.,100.,100.,200.,200.,200.,200.,200.,200.,200.,200.].
Duplicates check was performed using the following criteria for space and time: dlon=0.001deg., dlat=0.001deg., ddepth=1m, dtime=1hour, dvalue=0.1.
The error variance (epsilon2) was set equal to 1 for profiles and 10 for time series to reduce the influence of close data near the coasts.
An anamorphosis transformation was applied to the data (function DIVAnd.Anam.loglin) to avoid unrealistic negative values: threshold value=200.
A background analysis field was used for all years (1990-2021) with correlation length equal to 600km and error variance (epsilon2) equal to 20.
Quality control of the observations was applied using the interpolated field (QCMETHOD=3). Residuals (differences between the observations and the analysis (interpolated linearly to the location of the observations) were calculated. Observations with residuals outside the minimum and maximum values of the 99% quantile were discarded from the analysis. 
Originators of Italian data sets-List of contributors:
- Brunetti Fabio (OGS)
- Cardin Vanessa, Bensi Manuel doi:10.6092/36728450-4296-4e6a-967d-d5b6da55f306
- Cardin Vanessa, Bensi Manuel, Ursella Laura, Siena Giuseppe doi:10.6092/f8e6d18e-f877-4aa5-a983-a03b06ccb987
- Cataletto Bruno (OGS)
- Cinzia Comici Cinzia (OGS)
- Civitarese Giuseppe (OGS)
- DeVittor Cinzia (OGS)
- Giani Michele (OGS)
- Kovacevic Vedrana (OGS)
- Mosetti Renzo (OGS)
- Solidoro C.,Beran A.,Cataletto B.,Celussi M.,Cibic T.,Comici C.,Del Negro P.,De Vittor C.,Minocci M.,Monti M.,Fabbro C.,Falconi C.,Franzo A.,Libralato S.,Lipizer M.,Negussanti J.S.,Russel H.,Valli G., doi:10.6092/e5518899-b914-43b0-8139-023718aa63f5
- Celio Massimo (ARPA FVG)
- Malaguti Antonella (ENEA)
- Fonda Umani Serena (UNITS)
- Bignami Francesco (ISAC/CNR)
- Boldrini Alfredo (ISMAR/CNR)
- Marini Mauro (ISMAR/CNR)
- Miserocchi Stefano (ISMAR/CNR)
- Zaccone Renata (IAMC/CNR)
- Lavezza, R., Dubroca, L. F. C., Ludicone, D., Kress, N., Herut, B., Civitarese, G., Cruzado, A., Lefevre, D.,Souvermezoglou, E., Yilmaz, A., Tugrul, S., and Ribera d'Alcala, M.: Compilation of quality controlled nutrient profiles from the Mediterranean Sea, doi:10.1594/PANGAEA.771907, 2011.";
    String acknowledgment "Aggregated data products are generated by EMODnet Chemistry under the support of DG MARE Call for Tenders EASME/2020/OP/0006 (EASME/EMFF/2020/3.1.11/Lot 5/SI2.846161)";
    String area_keywords "Mediterranean Sea";
    String area_keywords_urn "SDN:C19::3_1";
    String Author_e_mail "Athanasia (Sissy) Iona <sissy@hnodc.hcmr.gr>";
    String bathymetry_source "The GEBCO Digital Atlas published by the British Oceanographic Data Centre on behalf of IOC and IHO, 2003";
    String cdm_data_type "Grid";
    String citation "Usage is subject to mandatory citation: \"This resource was generated in the framework of EMODnet Chemistry, under the support of DG MARE Call for Tender EASME/EMFF/2020/3.1.11/European Marine Observation and Data Network (EMODnet) - Lot 5 - Chemistry\"";
    String Conventions "CF-1.6, COARDS, ACDD-1.3";
    String creator_email "NODC.Webmaster@noaa.gov";
    String creator_name "HCMR/HNODC";
    String creator_type "institution";
    String creator_url "https://www.nodc.noaa.gov/";
    String data_access "https://emodnet.ec.europa.eu/geoviewer";
    String date "2023-03-01T00:21:20";
    String DIVA_code_doi "10.5281/zenodo.7016823";
    String DIVA_references "Barth, A., Beckers, J.-M., Troupin, C., Alvera-Azcarate, A., and Vandenbulcke, L. (2014): divand-1.0: n-dimensional variational data analysis for ocean observations, Geosci. Model Dev., 7, 225-241, doi: 10.5194/gmd-7-225-2014";
    String DIVAnd_source "https://github.com/gher-uliege/DIVAnd.jl";
    String DIVAnd_version "v2.7.9";
    String doi "https://doi.org/10.13120/3a35ab00-b7b6-11ed-3f82-b9a73c08e1e1";
    Float64 Easternmost_Easting 36.5;
    String file_name "By_sea_regions/Mediterranean_Sea/Water_body_phosphate.4Danl.nc";
    Float64 geospatial_lat_max 46.0;
    Float64 geospatial_lat_min 30.0;
    Float64 geospatial_lat_resolution 0.125;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 36.5;
    Float64 geospatial_lon_min -7.0;
    Float64 geospatial_lon_resolution 0.125;
    String geospatial_lon_units "degrees_east";
    String history 
"Fri Sep  1 10:10:18 2023: ncks -O Water_body_phosphate.4Danl.nc Water_body_phosphate.4Danl.nc
2024-06-21T05:27:10Z http://opendap.oceanbrowser.net/thredds/dodsC/data/emodnet-domains/By_sea_regions/Mediterranean_Sea/Water_body_phosphate.4Danl.nc
2024-06-21T05:27:10Z https://erddap.emodnet.eu/griddap/Mediterranean_PO_3D_0817_eb8b_4ef9.das";
    String id "DatasetScan/emodnet-domains/By_sea_regions/Mediterranean_Sea/Water_body_phosphate.4Danl.nc";
    String infoUrl "http://opendap.oceanbrowser.net/thredds/dodsC/data/emodnet-domains/By_sea_regions/Mediterranean_Sea/Water_body_phosphate.4Danl.nc.html";
    String institution "HCMR/HNODC";
    String institution_urn "SDN:EDMO::269";
    String keywords "analysis, body, chemistry, concentration, data, deepest, diva, earth, Earth Science > Oceans > Ocean Chemistry > Phosphate, error, hcmr, hnodc, latitude, longitude, masked, mediterranean, mole, mole_concentration_of_phosphate_in_sea_water, ocean, oceans, phosphate, phosphate.4danl, po4, relative, science, sea, seawater, threshold, time, using, values, water, Water_body_phosphate_deepest, Water_body_phosphate_deepest_L1, Water_body_phosphate_deepest_L2";
    String keywords_vocabulary "GCMD Science Keywords";
    String license 
"The data may be used and redistributed for free but is not intended
for legal use, since it may contain inaccuracies. Neither the data
Contributor, ERD, NOAA, nor the United States Government, nor any
of their employees or contractors, makes any warranty, express or
implied, including warranties of merchantability and fitness for a
particular purpose, or assumes any legal liability for the accuracy,
completeness, or usefulness, of this information.";
    String NCO "netCDF Operators version 4.9.9 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco)";
    Float64 Northernmost_Northing 46.0;
    String parameter_keyword "Water body phosphate";
    String parameter_keyword_urn "SDN:P35::EPC00007";
    String product_code "HNODC-Mediterranean Sea-Water body phosphate-v2023-ANA";
    String product_id "3a35ab00-b7b6-11ed-3f82-b9a73c08e1e1";
    String product_version "2.0";
    String project "EMODnet Chemistry phase 5";
    String search_keywords "Phosphate concentration parameters in the water column";
    String search_keywords_urn "SDN:P02::PHOS";
    String source "observational data from SeaDataNet/EMODnet Chemistry Data Network";
    String sourceUrl "http://opendap.oceanbrowser.net/thredds/dodsC/data/emodnet-domains/By_sea_regions/Mediterranean_Sea/Water_body_phosphate.4Danl.nc";
    Float64 Southernmost_Northing 30.0;
    String standard_name_vocabulary "CF Standard Name Table v70";
    String summary "DIVA 4D analysis of Water_body_phosphate. Every year of the time dimension corresponds to a 6-year running average for Winter (January - March), Spring (April - June), Summer (July - September) or Automn (October - December). Horizontal resolution 0.125deg X 0.125deg.";
    String time_coverage_end "2087-11-15T00:00:00Z";
    String time_coverage_start "2040-02-15T00:00:00Z";
    String title "Mediterranean Sea, Water body phosphate.4Danl (DIVA 4D analysis of Water_body_phosphate) [time][lat][lon], 0.125deg, 1971-2017";
    String WEB_visualisation "https://emodnet.ec.europa.eu/geoviewer";
    Float64 Westernmost_Easting -7.0;
  }
}

 

Using griddap to Request Data and Graphs from Gridded Datasets

griddap lets you request a data subset, graph, or map from a gridded dataset (for example, sea surface temperature data from a satellite), via a specially formed URL. griddap uses the OPeNDAP (external link) Data Access Protocol (DAP) (external link) and its projection constraints (external link).

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

griddap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/griddap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/griddap/jplMURSST41.htmlTable?analysed_sst[(2002-06-01T09:00:00Z)][(-89.99):1000:(89.99)][(-179.99):1000:(180.0)]
Thus, the query is often a data variable name (e.g., analysed_sst), followed by [(start):stride:(stop)] (or a shorter variation of that) for each of the variable's dimensions (for example, [time][latitude][longitude]).

For details, see the griddap Documentation.


 
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