SST Reprocessing 2016.0

Introduction

The R2016.0 processing of VIIRS Sea Surface Temperature (SST) data by the OBPG incorporates a revised cloud classification scheme based on the theory of Alternating Decision Trees (ADtree) developed by Freund and Mason 1999 and modified by Pfahringer et. al. 2000. A full description of parameters, training, and validation of the ADtree cloud mask can be found in Kilpatrick et. al. 2019.

Summary of Changes

• Implemented a new "Alternating Decision Trees" methodology for cloud classification
• Updated SST coefficients
• Implemented VIIRS-specific sensor error statistics (SSES) LUTs

R2016.1 update

This is a minor update that includes:

• Added an L2 GHRSST compliant single sensor error statistics (SSES) of bias and standard deviation, based on 5 years of collocated satellite and in situ data in the Miami VIIRS matchup database, using the same hypercube methodology as used for MODIS
• Added a new ice test to fix a problem of thin/melting ice being misclassified as clear during the early summer melt season
• Increased the lower threshold for valid SST retrievals to be a more geophysically justifiable -1.8℃ for seawater rather than the previous -2.0℃
• minor bug fixes to the cloud classifier tests

R2016.2 update

This is a minor update that includes:

• Apply the changes from R2016.1 to the entire mission time series (R2016.1 was a forward-stream update only)
• Use the GHRSST Level 4 CMC (Canadian Meterological Centre) Global Foundation Sea Surface Temperature product as the reference SST value in place of the NOAA 1/4° daily Optimum Interpolation Sea Surface Temperature product
• Implement a new file naming convention

References

Y. Freund, L. Mason, "The alternating decision tree learning algorithm", Proceedings of the 16th International Conference on Machine Learning, Bled, Slovenia (1999), 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: 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: 10.1175/JTECH-D-18-0103.1