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Dataset Title:  NYHOPS New York Bight Apex Subdomain, NYHOPS Forecast Collection for the New
York Bight Apex, NYHOPS Forecast Collection for the New York Bight Apex, Best
Time Series [time], 2018
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Institution:  Stevens Institute of Technology   (Dataset ID: stevens_tech_dl_633f_146d_7ecc)
Information:  Summary ? | License ? | 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 1.517271e+9, 1.517745e+9;
    String axis "T";
    String ioos_category "Time";
    String long_name "forecast time coordinate";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  time_offset {
    String ioos_category "Time";
    String long_name "hour offset from start of run for coordinate = time";
    String standard_name "forecast_period";
    String units "hour";
    String _CoordinateModelBaseDate "2018-02-17T00:00:00Z";
    Int32 base_date 2018, 1, 27, 0;
    String CDI "Climate Data Interface version 1.7.0 (";
    String cdm_data_type "Grid";
    String CDO "Climate Data Operators version 1.7.0 (";
    String comment "Once-daily 24hr Hindcast and 48hr Forecast";
    String Conventions "CF-1.6, _Coordinates, COARDS, ACDD-1.3";
    String Conventions_comment "The version is CF-1.4";
    String creation_date "20180131";
    String creator_email ",";
    String creator_name "Drs. Alan F. Blumberg, Nickitas Georgas";
    String creator_url "";
    String file_type "Full_Grid";
    String grid_type "Curvilinear";
    String history 
"Thu Feb 01 02:02:33 2018: cdo selindexbox,58,69,24,45 /mnt/cstor01/home/nyhops/NYHOPS/www/NETCDF/gcmplt.cdf /mnt/cstor01/home/nyhops/NYHOPS/www/NETCDF/
ECOMSED/Princeton Ocean Model; Synthetic dataset from TDS fmrc (best) aggregation, original data from fmrc/NYBightApex
    String hormix_type "CLOSURE";
    Float32 hormix_value 0.01;
    String id "fmrc/NYBightApex/NYHOPS_Forecast_Collection_for_the_New_York_Bight_Apex_best.ncd";
    String infoUrl "";
    String institution "Stevens Institute of Technology";
    String keywords "apex, best, best time series, bight, collection, coordinate, data, forecast, forecast_period, harbor, hour, institute, model, new, new york, nyhops, observing, offset, period, prediction, results, run, series, start, stevens, subdomain, system, technology, time, time series, time_offset, york";
    String license "The NYHOPS model, presently at its 3rd generation, has been the culmination of a multiyear Stevens Institute of Technology project funded by multiple federal and local agencies. Please contact the people referenced below if you are planning to use this data for any purpose. The data is provided without warranty of any kind.";
    String model "New York Harbor Observing and Prediction System (NYHOPS)";
    String references ",";
    Float32 refl_coeff 0.1;
    String related_url "";
    String source "S_ECOM_vs8";
    String sourceUrl "";
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "New York Harbor Observing and Prediction System (NYHOPS) Forecast Model Results. Best time series, taking the data from the most recent run available. 3D Marine Nowcast/Forecast System for the New York Bight Apex NYHOPS subdomain. Currents, waves, surface meteorology, and water conditions.";
    String time_coverage_end "2018-02-04T11:50:00Z";
    String time_coverage_start "2018-01-30T00:10:00Z";
    String title "NYHOPS New York Bight Apex Subdomain, NYHOPS Forecast Collection for the New York Bight Apex, NYHOPS Forecast Collection for the New York Bight Apex, Best Time Series [time], 2018";
    String type_of_run "PROGNOSTIC";
    String vrtmix_type "CLOSURE";
    Float32 vrtmix_value 1.0e-6;
    String z_type "Sigma";


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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