Published in

American Meteorological Society, Journal of Atmospheric and Oceanic Technology, 11(27), p. 1899-1917, 2010

DOI: 10.1175/2010jtecho756.1

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The SST quality monitor (SQUAM)

Journal article published in 2010 by Prasanjit Dash, Alexander Ignatov ORCID, Yury Kihai, John Sapper
This paper is available in a repository.
This paper is available in a repository.

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Postprint: archiving allowed
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Data provided by SHERPA/RoMEO

Abstract

Abstract The National Environmental Satellite, Data, and Information Service (NESDIS) has been operationally generating sea surface temperature (SST) products (TS) from the Advanced Very High Resolution Radiometers (AVHRR) onboard NOAA and MetOp-A satellites since the early 1980s. Customarily, TS are validated against in situ SSTs. However, in situ data are sparse and are not available globally in near–real time (NRT). This study describes a complementary SST Quality Monitor (SQUAM), which employs global level 4 (L4) SST fields as a reference standard (TR) and performs statistical analyses of the differences ΔTS = TS − TR. The results are posted online in NRT. The TS data that are analyzed are the heritage National Environmental Satellite, Data, and Information Service (NESDIS) SST products from NOAA-16, -17, -18, and -19 and MetOp-A from 2001 to the present. The TR fields include daily Reynolds, real-time global (RTG), Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA), and Ocean Data Analysis System for Marine Environment and Security for the European Area (MERSEA) (ODYSSEA) analyses. Using multiple fields facilitates the distinguishing of artifacts in satellite SSTs from those in the L4 products. Global distributions of ΔTS are mapped and their histograms are analyzed for proximity to Gaussian shape. Outliers are handled using robust statistics, and the Gaussian parameters are trended in time to monitor SST products for stability and consistency. Additional TS checks are performed to identify retrieval artifacts by plotting ΔTS versus observational parameters. Cross-platform TS biases are evaluated using double differences, and cross-L4 TR differences are assessed using Hovmöller diagrams. SQUAM results compare well with the customary in situ validation. All satellite products show a high degree of self- and cross-platform consistency, except for NOAA-16, which has flown close to the terminator in recent years and whose AVHRR is unstable.