The Climate and Fisheries Data Dashboard was developed by the Gulf of Maine Research Institute's Ocean Data Products team. The project was possible thanks to generous support from NOAA: specifically COCA, NCEI and CINAR programs.

The Fisheries and Climate Data Dashboard is a collection of tools to provide better access to climate data to help understand changes in the ecosystem. The geospatial footprint for data accessible through this tool is the Northeast Shelf Large Marine Ecosystem (NES LME). The region is then further subset the data into sub-regions that align with the Ecological Production Units for the NE U.S. Continental Shelf. The include: The Gulf of Maine, Georges Bank, Scotian Shelf, and Mid-Atlantic Bight. For each subset, the data have been aggregated and averaged over all grid points within the area.

The primary data set in the dashboard is the global NOAA OISST (Optimum Interpolation Sea Surface Temperature). Choose SST Climatologies from the menu to view OISST SST data presented in the context of historical normals or climatologies. We have created several interactive displays for the data, each with a slightly different focus. The data are refreshed daily to provide a dynamic picture of the ecosystem.

Methodology:

Acquiring data:
  1. Python NetCDF library script to acquire OISST NetCDF via OPeNDAP (THREDDS)
  2. Subset data by selecting parameters at lat/lon coordinates within bounding box
  3. Download subset in NetCDF [12,000 values ==> 30 years x # grids in BB]
  4. Produce a JSON file from full time series for each BB dates up to present
  5. Modify script to grab latest data, calculate SST/Anom on the fly and append JSON file
  6. Automate scripts run daily to keep data fresh
Spatial Averaging:
  1. From full time series, subset values for new bounding box
  2. Get SST value at each grid point for that day
  3. Take difference from mean (calculated in climatology) to get anomaly
  4. For all values in bounding box, average daily SST and Anomaly for regional bounding box
Build Reference level (i.e. climatology) for the data:
  1. Subset data from long time series for 30 year normal period [1/1/1982-12/31/2011]
  2. Run Pandas --> group data around 365 values [one for each day of the year] --> output new NetCDF
  3. From new local NetCDF file, run climatology statistics [for each day from long time series] (e.g. # observations, mean, mode, median, standard deviation, percentiles)]
  4. Repeat for sub-regional areas of interest as new bounding boxes

Data Sources:

  • NOAA OISST