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SST Reprocessing 2019.0

SST Reprocessing 2019.0

Introduction

The R2019.0 processing of MODIS 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. This methodology has been in use for the VIIRS-SNPP SST product since R2016.0 Additionally, a correction for atmospheric dust aerosol contaminated nighttime data based on the work of Luo et al., (2019) has been implemented.

Summary of Changes

Validation

A presentation of the r2019.0 validation of SST products was presented in June 2019 by the GHSST group. Download the poster here.

References

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 https://doi.org/10.1007/3-540-45357-1_50

Luo, B., Minnett, P. J., Gentemann, C. and Szczodrak, G., (2019) "Improving satellite retrieved night-time infrared sea surface temperatures in aerosol contaminated regions", Remote Sensing of Environment,Vol. 223, https://doi.org/10.1016/j.rse.2019.01.009

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