Evaluation and selection of SST regression algorithms for S-NPP VIIRS

B. Petrenko; A. Ignatov; Y. Kihai

doi:10.1117/12.2017454

3 June 2013 Evaluation and selection of SST regression algorithms for S-NPP VIIRS

B. Petrenko, A. Ignatov, Y. Kihai

Proceedings Volume 8724, Ocean Sensing and Monitoring V; 87240V (2013) https://doi.org/10.1117/12.2017454
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

Currently, two global Level 2 SST products are generated at NOAA from S-NPP VIIRS Sensor Data Records with two independent systems, JPSS Interface Data Processing Segment (IDPS) and Advanced Clear Sky Processor for Oceans (ACSPO) using different retrieval algorithms. The two products differently correlate with in situ SST and L4 analyses, and the performance of IDPS SST is suboptimal. In this context, evaluation of existing operational SST algorithms was undertaken to select the optimal algorithm for VIIRS. This paper describes methodology and results of the evaluation. For all tested algorithms, SST accuracy and precision are estimated from matchups of VIIRS brightness temperatures with in situ SST, and sensitivity of retrieved SST to true SST is calculated using the Community Radiative Transfer Model. These three retrieval characteristics are dependent on observational conditions and show significant spatial variability. Therefore, we evaluate the SST algorithms by quantifying favorability of spatial distributions of retrieval characteristics for global SST product. We define for this purpose Quality Retrieval Domain (QRD) as a part of the World Ocean, within which SST accuracy, precision and sensitivity meet predefined specifications on retrieval characteristics. We show that, given a set of specifications, the QRD significantly varies between the algorithms. This makes QRD an informative measure of the algorithms’ performance. Based on QRD estimates for a variety of specifications, we recommend for VIIRS the algorithms developed at the EUMETSAT Ocean and Sea Ice Satellite Application Facility as ones providing the maximum QRD under reasonable specifications on retrieval characteristics.

Citation Download Citation

B. Petrenko, A. Ignatov, and Y. Kihai "Evaluation and selection of SST regression algorithms for S-NPP VIIRS", Proc. SPIE 8724, Ocean Sensing and Monitoring V, 87240V (3 June 2013); https://doi.org/10.1117/12.2017454

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