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Dataset Title:	Michigan Central Lower - Seasonal - GLISA - NCEI
Institution:	NOAA (NOAA) (Dataset ID: glisa_mi_d06_seasonal_division)
Range:	longitude = -85.0 to -85.0°E, latitude = 44.0 to 44.0°N, time = 1895-03-01T00:00:00Z to 2023-12-01T00:00:00Z
Information:	Summary \| License \| FGDC \| ISO 19115 \| Metadata \| Background \| Data Access Form \| Files

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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:

Web page authors can embed a graph of the latest data in a web page using HTML <img> tags.
Anyone can use ERDDAPs Slide Sorter to build a personal web page that displays graphs with the latest data (or other images or HTML content), each in its own, draggable slide.

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  platform {
    String cf_role "timeseries_id";
    String ioos_category "Identifier";
    String long_name "Station ID";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 actual_range 44.0, 44.0;
    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 -85.0, -85.0;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range -2.3616576e+9, 1.7013888e+9;
    String axis "T";
    String calendar "proleptic_gregorian";
    String ioos_category "Time";
    String long_name "Date";
    String source_name "Date";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  Year {
    Int32 _FillValue 9999;
    Int32 actual_range 1895, 2023;
    String ioos_category "Time";
    String long_name "Year";
  }
  Season {
    String ioos_category "Time";
    String long_name "Season";
  }
  Total_Precipitation_in {
    Float64 _FillValue -9999.0;
    Float64 actual_range 2.06, 18.29;
    Float64 colorBarMaximum 1.0;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Meteorology";
    String level_desc "Surface";
    String long_name "Annual Total Precipitation";
    Float64 missing_Value NaN;
    String standard_name "precipitation_amount";
    String units "inches";
    String var_desc "Total Precipitation";
  }
  Total_Precipitation_mm {
    Float64 _FillValue -9999.0;
    Float64 actual_range 52.32, 464.57;
    Float64 colorBarMaximum 1.0;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Meteorology";
    String level_desc "Surface";
    String long_name "Annual Total Precipitation";
    Float64 missing_Value NaN;
    String standard_name "precipitation_amount";
    String units "kg m-2";
    String var_desc "Total Precipitation";
  }
  Palmer_Drought_Severity_Index {
    Float64 _FillValue -9999.0;
    Float64 actual_range -5.97, 5.9;
    String ioos_category "Unknown";
    String level_desc "Surface";
    String long_name "Palmer Drought Severity Index";
    Float64 missing_Value NaN;
    String standard_name "palmer_drought_severity_index";
    String units "index";
    String var_desc "This is the Annual value (index) generated Annual that indicates the severity of a wet or dry spell. PDSI values 0 to -.5 = normal; -0.5 to -1.0 = incipient drought; -1.0 to -2.0 = mild drought; -2.0 to -3.0 = moderate drought; -3.0 to -4.0 = severe drought; and greater than - 4.0 = extreme drought.";
  }
  Modified_Palmer_Drought_Severity_Index {
    Float64 _FillValue -9999.0;
    Float64 actual_range -5.97, 6.36;
    String ioos_category "Unknown";
    String level_desc "Surface";
    String long_name "Modified Palmer Drought Severity Index";
    Float64 missing_Value NaN;
    String standard_name "palmer_drought_severity_index";
    String units "index";
    String var_desc "The modification of the Palmer Drought Severity Index (PMDI) incorporates a weighted average of the wet and dry index terms, using the probability as the weighting factor.This is the Annual value (index) generated Annual that indicates the severity of a wet or dry spell. PDSI values 0 to -.5 = normal; -0.5 to -1.0 = incipient drought; -1.0 to -2.0 = mild drought; -2.0 to -3.0 = moderate drought; -3.0 to -4.0 = severe drought; and greater than - 4.0 = extreme drought.";
  }
  Maximum_Temperature_degF {
    Float64 _FillValue -9999.0;
    Float64 actual_range 21.06, 84.55;
    String ioos_category "Temperature";
    String level_desc "Surface";
    String long_name "Annual Mean of Maximum Air Temperature";
    Float64 missing_Value NaN;
    String standard_name "max_air_temperature";
    String units "degree_Farhenheit";
    String var_desc "Maximum Air Temperature";
  }
  Maximum_Temperature_degC {
    Float64 _FillValue -9999.0;
    Float64 actual_range -6.08, 29.19;
    Float64 colorBarMaximum 40.0;
    Float64 colorBarMinimum -10.0;
    String ioos_category "Temperature";
    String level_desc "Surface";
    String long_name "Annual Mean of Maximum Air Temperature";
    Float64 missing_Value NaN;
    String standard_name "max_air_temperature";
    String units "degree_Celsius";
    String var_desc "Maximum Air Temperature";
  }
  Minimum_Temperature_degF {
    Float64 _FillValue -9999.0;
    Float64 actual_range 2.68, 58.84;
    String ioos_category "Temperature";
    String level_desc "Surface";
    String long_name "Annual Mean of Minimum Air Temperature";
    Float64 missing_Value NaN;
    String standard_name "min_air_temperature";
    String units "degree_Farhenheit";
    String var_desc "Minimum Air Temperature";
  }
  Minimum_Temperature_degC {
    Float64 _FillValue -9999.0;
    Float64 actual_range -16.29, 14.91;
    Float64 colorBarMaximum 40.0;
    Float64 colorBarMinimum -10.0;
    String ioos_category "Temperature";
    String level_desc "Surface";
    String long_name "Annual Mean of Minimum Air Temperature";
    Float64 missing_Value NaN;
    String standard_name "min_air_temperature";
    String units "degree_Celsius";
    String var_desc "Minimum Air Temperature";
  }
  Average_Temperature_degF {
    Float64 _FillValue -9999.0;
    Float64 actual_range 11.87, 70.57;
    Float64 colorBarMaximum 40.0;
    Float64 colorBarMinimum -10.0;
    String ioos_category "Temperature";
    String level_desc "Surface";
    String long_name "Annual Mean of Air Temperature";
    Float64 missing_Value NaN;
    String standard_name "air_temperature";
    String units "degree_Farhenheit";
    String var_desc "Air Temperature";
  }
  Average_Temperature_degC {
    Float64 _FillValue -9999.0;
    Float64 actual_range -11.18, 21.43;
    Float64 colorBarMaximum 40.0;
    Float64 colorBarMinimum -10.0;
    String ioos_category "Temperature";
    String level_desc "Surface";
    String long_name "Annual Mean of Air Temperature";
    Float64 missing_Value NaN;
    String standard_name "air_temperature";
    String units "degree_Celsius";
    String var_desc "Air Temperature";
  }
  Heating_Degree_Days {
    Float64 _FillValue -9999.0;
    Float64 actual_range 54.0, 4782.0;
    String ioos_category "Temperature";
    String level_desc "Surface";
    String long_name "Heating Degree Days";
    Float64 missing_Value NaN;
    String standard_name "heating_degree_days";
    String units "days";
    String var_desc "Heating Degree Days (HDD) accumulate on days colder than 65 degrees Farhenheit when heat is required.";
  }
  Cooling_Degree_Days {
    Float64 _FillValue -9999.0;
    Float64 actual_range 0.0, 604.0;
    String ioos_category "Temperature";
    String level_desc "Surface";
    String long_name "Cooling Degree Days";
    Float64 missing_Value NaN;
    String standard_name "cooling_degree_days";
    String units "days";
    String var_desc "Cooling Degree Days (CDD) accumulate on days warmer than 65 degrees Farhenheit, when cooling is required.";
  }
 }
  NC_GLOBAL {
    String cdm_data_type "TimeSeries";
    String cdm_timeseries_variables "platform,latitude,longitude";
    String Conventions "CF-1.6, ACDD-1.3, IOOS-1.2, COARDS";
    String creator_country "United States";
    String creator_email "glisa-info@umich.edu";
    String creator_institution "University of Michigan, Michigan State University";
    String creator_name "GLISA, the NOAA Great Lakes Climate Adaptation Partnership (CAP)";
    String creator_sector "academic";
    String creator_type "institution";
    String creator_url "https://glisa.umich.edu";
    Float64 Easternmost_Easting -85.0;
    String featureType "TimeSeries";
    Float64 geospatial_lat_max 44.0;
    Float64 geospatial_lat_min 44.0;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -85.0;
    Float64 geospatial_lon_min -85.0;
    String geospatial_lon_units "degrees_east";
    String history 
"2024-11-13T06:27:54Z (local files)
2024-11-13T06:27:54Z https://seagull-erddap.glos.org/tabledap/glisa_mi_d06_seasonal_division.das";
    String infoUrl "https://glisa.umich.edu";
    String institution "NOAA (NOAA)";
    String ioos_ingest "false";
    String keywords "administration, aggregated, atmospheric, CDDCDV, centers, climate, cooling, cooling_degree_days, data, date, days, degree, divisions, drought, environmental, great, great lakes, HDDCDV, heating, heating_degree_days, index, information, lakes, meteorology, modified, national, ncei, nesdis, noaa, oceanic, palmer, palmer_drought_severity_index, PDSI, PMDI, PRCP_inches, PRCP_mm, region, severity, temperature, time, TMAX_degC, TMAX_degF, TMIN_degC, TMIN_degF, TMP_degC, TMP_degF, US, values, year";
    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, GLOS, 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 naming_authority "org.glos";
    Float64 Northernmost_Northing 44.0;
    String publisher_country "United States";
    String publisher_email "info@glos.org";
    String publisher_institution "GLOS";
    String publisher_sector "nonprofit";
    String publisher_url "https://glos.org";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 44.0;
    String standard_name "CF Standard Name Table v77";
    String standard_name_vocabulary "CF Standard Name Table v70";
    String summary "The aggregated seasonal values of the National Centers for Environmental Information (NCEI) US Climate Divisions in the Great lakes region - Michigan Central Lower";
    String time_coverage_end "2023-12-01T00:00:00Z";
    String time_coverage_start "1895-03-01T00:00:00Z";
    String title "Michigan Central Lower - Seasonal - GLISA - NCEI";
    Float64 Westernmost_Easting -85.0;
  }
}

Using tabledap to Request Data and Graphs from Tabular Datasets

tabledap lets you request a data subset, a graph, or a map from a tabular dataset (for example, buoy data), via a specially formed URL. tabledap uses the OPeNDAP (external link)

Data Access Protocol (DAP) (external link)

and its selection 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.

(easy) You can get data by using the dataset's Data Access Form or Subset form. They make the URL for you.
(easy) You can make a graph or map by using the dataset's Make A Graph form. It makes the URL for you.
(not hard) You can bypass the forms and get the data or make a graph or map by generating the URL by hand or with a computer program or script.

Tabledap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/pmelTaoDySst.htmlTable?longitude,latitude,time,station,wmo_platform_code,T_25&time>=2015-05-23T12:00:00Z&time<=2015-05-31T12:00:00Z
Thus, the query is often a comma-separated list of desired variable names, followed by a collection of constraints (e.g., variable<value), each preceded by '&' (which is interpreted as "AND").

For details, see the tabledap Documentation.

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