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Dataset Title:  GLCFS, Lake Erie, Forecast, 3D (Lake Erie, 3D, Best Time Series) [time][ny][
nx], 2017-present
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Institution:  NOAA/GLERL   (Dataset ID: glos_tds_0824_db24_91d2)
Information:  Summary ? | License ? | Metadata | Background (external link) | Data Access Form
Graph Type:  ?
X Axis:  ?
Y Axis:  ?
Color:  ?
Dimensions ?    Start ?    Stop ?
time (UTC) ?
    << - +
< slider >
ny (count) ?     specify just 1 value →
    << -
< <
nx (count) ?     specify just 1 value →
    << -
< <
Graph Settings
Marker Type:   Size: 
Color Bar:   Continuity:   Scale: 
   Minimum:   Maximum:   N Sections: 
Y Axis Minimum:   Maximum:   Ascending: 
(Please be patient. It may take a while to get the data.)
Then set the File Type: (File Type information)
or view the URL:
(Documentation / Bypass this form ? )
    Time range:    <<    -              
[The graph you specified. Please be patient.]


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 1.5147756e+9, 1.5223248e+9;
    String axis "T";
    String calendar "proleptic_gregorian";
    String ioos_category "Time";
    String long_name "Forecast time for ForecastModelRunCollection";
    Float64 missing_value NaN;
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  ny {
    Int16 actual_range 0, 86;
    String ioos_category "Statistics";
    String long_name "NY";
    String units "count";
  nx {
    Int16 actual_range 0, 192;
    String ioos_category "Statistics";
    String long_name "NX";
    String units "count";
  eta {
    String ioos_category "Unknown";
    String long_name "Eta";
    String units "m";
    String _CoordSysBuilder "ucar.nc2.dataset.conv.CF1Convention";
    String author "";
    String cdm_data_type "Grid";
    String comment1 "Lake Erie  2 km bathymetric grid";
    String comment2 "3-hourly model 3D output starting at validtime plus 3 hr";
    String comment3 "Generated 2x per day following the 0,12 Z Forecast runs";
    String contributor_name "GLOS DMAC, GLERL";
    String contributor_role "distributor, producer";
    String Conventions "CF-1.6, COARDS, ACDD-1.3";
    String creation_date "Thu Mar  8 01:35:06 2018 GMT";
    String creator_email "";
    String creator_name "Dr. Dave Schwab";
    String creator_type "person";
    String creator_url "";
    String data_source "NDFD";
    String disclaimer "";
    String history 
"FMRC Best Dataset
    String id "glos.glcfs.erie.fcfmrc-3d.Lake_Erie_-_3D_best.ncd";
    String infoUrl "";
    String institution "NOAA/GLERL";
    String keywords "best, best time series, coastal, data, environmental, erie, eta, forecast, forecasting, glcfs, glerl, glos, great, great lakes, laboratory, lake, lakes, noaa, observing, research, series, system, time, time series";
    String license "No usage restrictions";
    String location "Proto fmrc:Lake_Erie_-_3D";
    String metadata_link "";
    String model "Princeton Ocean Model-Great Lakes";
    String naming_authority "GLOS";
    String publisher_email "";
    String publisher_name "GLOS DMAC";
    String publisher_type "institution";
    String publisher_url "";
    String references "";
    String sourceUrl "";
    String standard_name_vocabulary "CF Standard Name Table v29";
    String summary "GLCFS - Lake Erie - Forecast - 3D. Best time series, taking the data from the most recent run available. Great Lakes Coastal Forecasting System. Lake Erie  2 km bathymetric grid. 3-hourly model 3D output starting at validtime plus 3 hr. Generated 2x per day following the 0,12 Z Forecast runs";
    String time_coverage_end "2018-03-29T12:00:00Z";
    String time_coverage_start "2018-01-01T03:00:00Z";
    String title "GLCFS, Lake Erie, Forecast, 3D (Lake Erie, 3D, Best Time Series) [time][ny][nx], 2017-present";
    String validtime "08-MAR-2018 00:00 GMT";
    String validtime_DOY "067, 2018 00:00 GMT";


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{?query}
For example,[(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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