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Dataset Title:  Pseudo-Nitzschia Probability Subscribe RSS
Institution:  University of Californian Santa Cruz   (Dataset ID: HAB_PSEUDO_NITZSCHIA_NOWCAST)
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 _FillValue -99999.0;
    Float64 actual_range 1.3938048e+9, 1.5067296e+9;
    String axis "T";
    String calendar "gregorian";
    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";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float32 _FillValue 9999.0;
    Float32 actual_range 232.5, 243.0;
    String axis "X";
    String coordinate_reference_frame "urn:ogc:crs:EPSG:4326";
    String ioos_category "Location";
    String long_name "Longitude";
    String reference "WGS84";
    String reference_datum "Geographical Coordinates, WGS84 projections";
    String standard_name "longitude";
    String units "degrees_east";
    Float32 valid_max 360.0;
    Float32 valid_min 0.0;
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float32 _FillValue 9999.0;
    Float32 actual_range 31.3, 43.0;
    String axis "Y";
    String coordinate_reference_frame "urn:ogc:crs:EPSG:4326";
    String ioos_category "Location";
    String long_name "Latitude";
    String reference "WGS84";
    String reference_datum "Geographical Coordinates, WGS84 projections";
    String standard_name "latitude";
    String units "degrees_north";
    Float32 valid_max 90.0;
    Float32 valid_min -90.0;
  }
  pseudo_nitzschia {
    Float32 _FillValue -999.0;
    Float64 colorBarMaximum 1.5;
    Float64 colorBarMinimum 0.0;
    String content_type "modelResult";
    String ioos_category "Unknown";
    String long_name "Probability of Pseudo-Nitzschia > 10,000 cells/L ";
    Float32 missing_value -999.0;
    Float32 valid_max 1.0;
    Float32 valid_min 0.0;
  }
  NC_GLOBAL {
    String _CoordSysBuilder "ucar.nc2.dataset.conv.CF1Convention";
    String acknowledgment "This pre-operational project builds on funding from the NOAA Center for Sponsored Coastal Ocean Research Ecology and Oceanography of Harmful Algal Blooms (ECOHAB) https://oceanservice.noaa.gov/hazards/hab/ , the NOAA Montitoring and Event Response Programs (MERHAB), and NASA Applied Sciences Program.  Principal investigators are from UCSC with staff support from CeNCOOS/MBARI.";
    String cdm_data_type "Grid";
    String comment "Data produced by Dr. Clarissa Anderson (clrander@ucsc.edu), UCSC. Experimental Harmful Algal Bloom forecasts from of Pseudo-nitzschia and Domoic Acid (biotoxin) probabilities along the California coast are available in a beta form. Please use with caution as these model predictions are experimental.";
    String contributor "Fred Bahr";
    String contributor_email "flbahr@mbari.org";
    String contributor_name "Fred Bahr, Clarissa Anderson, Raphe Kudela";
    String contributor_role "author,principal investigator, principal investigator";
    String contributor_url "http://www.cencoos.org/";
    String Conventions "CF-1.6, COARDS, Unidata Dataset Discovery v1.0";
    String creator_email "clrander@ucsc.edu";
    String creator_name "Clarissa Anderson";
    String creator_url "http://www.cencoos.org/data/models/habs";
    String date_created "2017-09-22T12:15:05.673712";
    String date_issured "2017-09-22T12:15:05.673712";
    String date_modified "2017-09-22T12:15:05.673712";
    String date_updated "2017-09-22T12:15:05.673712";
    Float64 Easternmost_Easting 243.0;
    String file_version "1.0";
    Float64 geospatial_lat_max 43.0;
    Float64 geospatial_lat_min 31.3;
    Float64 geospatial_lat_resolution 0.03;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 243.0;
    Float64 geospatial_lon_min 232.5;
    Float64 geospatial_lon_resolution 0.03;
    String geospatial_lon_units "degrees_east";
    String history 
"Fri Sep 22 15:35:06 2017: ncea -d time,171 /mnt/gluster/data/netCDF/hab/pseudo_nitzschia/nowcast/hab_pseudo_nitzschia_nowcast_2017.265.22.org /mnt/gluster/data/netCDF/hab/pseudo_nitzschia/nowcast/processed//hab_pseudo_nitzschia_nowcast.736595.nc
The harmful algal bloom forecast and nowcast products were first produced in 2014.  These data sets are part of a pre-operational prototype system with the intention of transferring operations to NOAA and NASA channels
2018-08-17T22:52:49Z (local files)
2018-08-17T22:52:49Z http://erddap.axiomdatascience.com/griddap/HAB_PSEUDO_NITZSCHIA_NOWCAST.das";
    String infoUrl "http://www.cencoos.org/";
    String institution "University of Californian Santa Cruz";
    String ioos_regional_association "CeNCOOS";
    String keyword_vocabulary "GCMD Science Keywords";
    String keywords "aquarium, bay, california, californian, cells, cells/l, central, cruz, domoic acid, earth science>biological classification>plants>microalgae>diatoms, institut, models, monterey, nitzschia, northern, observing, ocean, probability, pseudo, pseudo-nitzschia, research, santa, university";
    String license "Warning - Prototype Product - Use Cautiously.  The data may be used and redistributed for free but is not intended for operational decisions or legal use, since it may contain inaccuracies.  Neither the data Contributor, UCSC, MBARI, CeNCOOS, NOAA, nor the United States Government, nor any of their employees or contractors make 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 Metadata_Conventions "CF-1.6, COARDS, Unidata Dataset Discovery v1.0";
    String nameing_authority "cencoos.org";
    Int32 nco_openmp_thread_number 1;
    Float64 Northernmost_Northing 43.0;
    String processing_level "Model data computed from model and statellite observation data";
    String project "UC Santa Cruz Harmful Algal Bloom Forecasting";
    String publisher_email "flbahr@mbari.org";
    String publisher_name "CeNCOOS";
    String publisher_url "http://www.cencoos.org";
    String references "http://www.cencoos.org/data/models/habs, https://oceanservice.noaa.gov/hazards/hab/";
    String sea_name "North Pacific Ocean";
    String source "Model output";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 31.3;
    String standard_name_vocabulary "CF-v25";
    String summary "With NASA funding, UCSC researchers and CeNCOOS have been working to operationalize a predictive Harmful Algal Bloom (HAB) model for the region.  Nowcasts (current state) and forecasts (future states) from this model of Pseudo-nitzschia (a marine diatom) blooms and Domoic Acid (the potentially harmful biotoxin Pseudo-nitzschia releases) probabilities are available daily along the California coast.  Predicted \"nowcasts\" of harmful algal bloom (HAB) conditions are created through a combination of sophisticated circulation models that predict the ocean physics, satellite remote-sensing data of the ocean \"color\" and chlorophyll patterns, and statistical models for predicting bloom and toxin likelihoods. These predictions are generated daily to provide a snapshot of where you might encounter a Pseudo-nitzschia bloom and/or domoic acid event. Forecasts - Predictions of harmful algal bloom (HAB) conditions are created through a combination of sophisticated circulation models that predict the ocean physics, satellite remote-sensing data of the ocean \"color\" and chlorophyll patterns, and statistical models for predicting bloom and toxin likelihoods. These predictions are generated daily to provide a forecast of where you might encounter a Pseudo-nitzschia bloom and/or domoic acid event in the next one to three days.";
    String time_coverage_duration "P179.0D";
    String time_coverage_end "2017-09-30T00:00:00Z";
    String time_coverage_resolution "point";
    String time_coverage_start "2014-03-03T00:00:00Z";
    String title "Pseudo-Nitzschia Probability";
    String unlimited_dimension "time";
    Float64 Westernmost_Easting 232.5;
  }
}

 

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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