The PO.DAAC is pleased to announce the public release of the NASA JPL GHRSST Level-2P (L2P) VIIRS sea surface temperature (SST) version 2016.2 (v2016.2) dataset.
This dataset is produced by NASA’s Ocean Biology Processing Group (OBPG) with technical support from Dr. Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) and reformatted at JPL PO.DAAC to be in compliance with the Group for High-Resolution Sea Surface Temperature (GHRSST) Data Specification (GDS2). It provides daily global day and night coverage at 750 m spatial resolution, reporting in 6-minute granules in netCDF4 format.
The v2016.2 is the updated version from the current v2016.0. The new version has incorporated several enhancements, including (1) Improving GHRSST Level-2 compliant single sensor error statistics (SSES) of bias and standard deviation; (2) Adding a new ice test to fix a problem of thin/melting ice being misclassified as clear during the early summer melt season; (3) Increased the lower threshold for valid SST retrievals to be a more geophysically justifiable -1.8℃ for seawater rather than the previous -2.0℃; (4) Used the GHRSST Level 4 CMC (Canadian Meteorological Centre) Global Foundation SST product as the reference value in place of the NOAA 1/4° daily Optimum Interpolation SST product.
The datasets are described and discoverable via the PO.DAAC dataset information pages. The dataset information pages also provide access to the technical documentation, including the SST algorithm ATBD, data reprocessing overview, and guidance on how to cite the data.
Freund,Y. and Mason L., (1999) "The alternating decision tree learning algorithm", Proceedings of the 16th International Conference on Machine Learning, Bled, Slovenia , pp. 124-133
Pfahringer B., Holmes G., Kirkby R. (2001) "Optimizing the Induction of Alternating Decision Trees", In: Cheung D., Williams G.J., Li Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science, vol 2035. Springer, Berlin, Heidelberg, doi: https://doi.org/10.1007/3-540-45357-1_50
Kilpatrick, K.A., G. Podestá, E. Williams, S. Walsh, and P.J. Minnett, 2019: Alternating Decision Trees for Cloud Masking in MODIS and VIIRS NASA Sea Surface Temperature Products. J. Atmos. Oceanic Technol., 36, 387–407, doi: https://doi.org/10.1175/JTECH-D-18-0103.1