Abstract:

This dataset contains satellite-derived chlorophyll-a from Visible Infrared Imaging Radiometer Suite (VIIRS) and forecasted salinity from the Gulf of Mexico 1km resolution Naval Research Laboratory-Navy Coastal Ocean Model (NRL-NCOM) from November 2-4, 2015. The data was used for adaptive sampling during the CONCORDE-Fall cruises. The dataset can be used by scientists for research and teachers to reproduce the student lesson plan presented in the associated manuscript. A video with instructions for the student lesson plan and how to use the KMZs for adaptive sampling is included. The data here has been processed to a KMZ format that can be visualized in Google Earth. Chlorophyll-a and salinity values used in the KMZ files are provided in netCDF files. This dataset supports the publication: Inia M. Soto, Mustafa Kemal Cambazoglu, Sabrina Parra, Jessie Kastler, Robert Arnone. (2018). Virtual Oceanography: Studying river plumes using data visualization tools, ocean model data, and satellite imagery. The Science Teacher Journal, Vol. 86, No. 04. doi: 10.2505/4/tst18_086_04_24

Suggested Citation:

Soto Ramos, Inia. 2019. Dataset for: Virtual Oceanography: Studying river plumes using data visualization tools, ocean model data, and satellite imagery. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/N75719N0

Publications:

Soto, I., Cambazoglu, M. K., Arnone, R., Kastler, J., & Parra, S. (2018). Virtual Oceanography. The Science Teacher, 086(04). doi:10.2505/4/tst18_086_04_24

Purpose:

The data was developed to characterize changes in the ocean color that can be used to identify river plumes, phytoplankton blooms and other oceanic features of interest. The data was used for adaptive sampling and it was coupled with circulation models.

Data Parameters and Units:

The KMZs files: Includes salinity and chlorophyll-a data for three consecutive days that can be visualized on ArcGIS Earth. File ArcGIS_Instructions.avi: A video with instructions for the student lesson plan and how to use the KMZs files for adaptive sampling. NetCDF files "ncom_1km_sss_11022015_11042015.nc" and "VIIRS_750m_chl_7dayslp_11022015_11042015.nc": Includes Chlorophyll-a and salinity values used in the KMZ files. Sea Surface Salinity (psu); Latitude (degrees_north); Longitude (degree_east); satellite-derived chlorophyll-a concentration (mg/m3).

Methods:

Satellite ocean color imagery from the VIIRS Suomi- National Polar-orbiting Partnership (SNPP) satellite was downloaded from the National Oceanographic and Atmospheric Administration (NOAA) Class website (http://www.class.ngdc.noaa.gov/)and process to Level-3 at 750m resolution using the Naval Research Laboratory's Automated Processing System (APS v5.1. Raw calibrated radiance products were orthorectified and processed by USM to correct for atmospheric components and obtain the radiance signal emitted from the ocean (Lw). The spectral Lw is the basis for which subsequent bio-optical algorithms are calculated. The full VIIRS dataset in netCDF format, including other ocean color products, for the same time period, can be found in dataset R4.x260.204:0006. The numerical model is the Navy Coastal Ocean Model (NCOM) (Barron et. al., 2006). The salinity model output in the Gulf of Mexico. The domain is forced by boundary conditions from the global operational HYCOM. This model experiment was forced hourly by Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS). Tidal potential forcing is applied to the inner domain, and tidal boundary conditions for water level and barotropic velocity are provided by the Oregon State University global Ocean Tide Inverse Solution (OTIS). Data assimilation is used in the experiment. River forcing is based on monthly climatological observed transports. The full 3D model data, including other model parameters in netCDF format, can be found in dataset R4.x260.000:0064.

Provenance and Historical References:

Barron, C. N., Kara, A. B., Martin, P. J., Rhodes, R. C., & Smedstad, L. F. (2006). Formulation, implementation and examination of vertical coordinate choices in the Global Navy Coastal Ocean Model (NCOM). Ocean Modelling, 11(3-4), 347–375. doi:10.1016/j.ocemod.2005.01.004

Individual Files: