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Dataset Title:  OSU Chlorophyll Bloom Product, VIIRS Suomi-NPP, Northwest US, 2012-2015 Subscribe RSS
Institution:  Oregon State University   (Dataset ID: osuBloomsViirsChla)
Information:  Summary ? | License ? | FGDC | ISO 19115 | Metadata | Background (external link) | Data Access Form
Graph Type:  ?
X Axis:  ?
Y Axis:  ?
Color:  ?
Dimensions ?    Start ?    Stop ?
time (UTC) ?     specify just 1 value →
    |< -
< <
altitude (m) ?     specify just 1 value →
< <
latitude (degrees_north) ?
< slider >
longitude (degrees_east) ?
< slider >
Graph Settings
Color Bar:   Continuity:   Scale: 
   Minimum:   Maximum:   N Sections: 
Draw land mask: 
Y Axis Minimum:   Maximum:   
(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 ? )
    Click on the map to specify a new center point. ?
[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 1.3261536e+9, 1.4512608e+9;
    String axis "T";
    Int32 fraction_digits 0;
    String ioos_category "Time";
    String long_name "Centered Time";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  altitude {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "up";
    Float64 actual_range 0.0, 0.0;
    String axis "Z";
    Int32 fraction_digits 0;
    String ioos_category "Location";
    String long_name "Altitude";
    String positive "up";
    String standard_name "altitude";
    String units "m";
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 actual_range 34.0, 52.0;
    String axis "Y";
    String coordsys "geographic";
    Int32 fraction_digits 4;
    String ioos_category "Location";
    String long_name "Latitude";
    String point_spacing "even";
    String standard_name "latitude";
    String units "degrees_north";
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 actual_range -134.0, -121.0;
    String axis "X";
    String coordsys "geographic";
    Int32 fraction_digits 4;
    String ioos_category "Location";
    String long_name "Longitude";
    String point_spacing "even";
    String standard_name "longitude";
    String units "degrees_east";
  prc_chla {
    Float32 _FillValue -32768.0;
    Float64 colorBarMaximum 150.0;
    Float64 colorBarMinimum -100.0;
    String colorBarPalette "BlueWhiteRed";
    String ioos_category "Ocean Color";
    String long_name "Percent Relative Change in Chlorophyll-a Concentration, OC3 Algorithm";
    Float32 missing_value -9999999.0;
    String units "percent";
    Float32 valid_max 10000.0;
    Float32 valid_min -100.0;
    String cdm_data_type "Grid";
    String contact "Morgaine McKibben <>";
    String Conventions "CF-1.6, COARDS, ACDD-1.3";
    String creation_date "2016-01-08 UTC";
    String creator_email "";
    String creator_name "Morgaine McKibben";
    String creator_type "person";
    Float64 Easternmost_Easting -121.0;
    Float64 geospatial_lat_max 52.0;
    Float64 geospatial_lat_min 34.0;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -121.0;
    Float64 geospatial_lon_min -134.0;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 0.0;
    Float64 geospatial_vertical_min 0.0;
    String geospatial_vertical_positive "up";
    String geospatial_vertical_units "m";
    String history 
    String infoUrl "";
    String institution "Oregon State University";
    String keywords "algorithm, bloom, change, chemistry, chlorophyll, chlorophyll-a, color, concentration, Earth Science > Oceans > Ocean Chemistry > Chlorophyll, npp, oc3, ocean, ocean color, oceans, oregon, osu, percent, product, relative, sea, seawater, state, suomi, university, viirs, water";
    String L2flags "L2 Flags 1,2,10,26 applied";
    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.";
    Float64 Northernmost_Northing 52.0;
    String product_version "1.0";
    String references "McKibben, S. M., P. G. Strutton, D. G. Foley, T. D. Peterson, and A. E. White (2012), Satellite-based detection and monitoring of phytoplankton blooms along the Oregon coast, J. Geophys. Res., 117, C12002, doi:10.1029/2012JC008114";
    String source_data "L2VIIRS_CHL";
    String sourceUrl "";
    Float64 Southernmost_Northing 34.0;
    String spatial_resolution "1 km";
    String standard_name_vocabulary "CF Standard Name Table v70";
    String summary "This product was developed for the Oregon coast based on the observed change between running 8-day composite chlorophyll-a (CHL) data obtained by the VIIRS aboard the Suomi-NPP spacecraft. This product was optimized to detect bloom onset via satellite in the coastal waters of Oregon, a region typified by high frequency biological variance yet pervasive cloud cover (see McKibben et al. 2012, JGR-Oceans). The product is based on running relative differences between successive 8-day products. A current composite (an average of the previous 8 days) and a reference composite (an average of the 8-day period immediately preceding the current composite). The reference is differenced from the current, then the result is normalized to the reference. Resulting daily imagery highlights the geographic locations of greatest relative change observed between weekly average CHL, providing a way to track onset and advection of active blooms over time. Note the product is optimized to work best in the coastal waters of Oregon, although it is operational further offshore and to the north or south as satellite coverage permits (coverage is increasingly better to the south and worst further offshore and to the north of Oregon). The bloom product accessible from this page is based on the VIIRS CHL product and shows the percent relative change in CHL over time. THIS IS AN EXPERIMENTAL PRODUCT: intended strictly for scientific evaluation by professional marine scientists.";
    String time_coverage_end "2015-12-28T00:00:00Z";
    String time_coverage_start "2012-01-10T00:00:00Z";
    String title "OSU Chlorophyll Bloom Product, VIIRS Suomi-NPP, Northwest US, 2012-2015";
    Float64 Westernmost_Easting -134.0;


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{?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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