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Open AccessArticle

Optimization of Sensitivity of GOES-16 ABI Sea Surface Temperature by Matching Satellite Observations with L4 Analysis

1
NOAA STAR, NCWCP, 5830 University Research Court, College Park, MD 20740, USA
2
Global Science and Technology, Inc., 7855 Walker Dr # 200, Greenbelt, MD 20770, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(2), 206; https://doi.org/10.3390/rs11020206
Received: 19 November 2018 / Revised: 25 December 2018 / Accepted: 17 January 2019 / Published: 21 January 2019
(This article belongs to the Collection Sea Surface Temperature Retrievals from Remote Sensing)
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Abstract

Monitoring of the diurnal warming cycle in sea surface temperature (SST) is one of the key tasks of the new generation geostationary sensors, the Geostationary Operational Environmental Satellite (GOES)-16/17 Advanced Baseline Imager (ABI), and the Himawari-8/9 Advanced Himawari Imager (AHI). However, such monitoring requires modifications of the conventional SST retrieval algorithms. In order to closely reproduce temporal and spatial variations in SST, the sensitivity of retrieved SST to SSTskin should be as close to 1 as possible. Regression algorithms trained by matching satellite observations with in situ SST from drifting and moored buoys do not meet this requirement. Since the geostationary sensors observe tropical regions over larger domains and under more favorable conditions than mid-to-high latitudes, the matchups are predominantly concentrated within a narrow range of in situ SSTs >2 85 K. As a result, the algorithms trained against in situ SST provide the sensitivity to SSTskin as low as ~0.7 on average. An alternative training method, employed in the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear-Sky Processor for Oceans, matches nighttime satellite clear-sky observations with the analysis L4 SST, interpolated to the sensor’s pixels. The method takes advantage of the total number of clear-sky pixels being large even at high latitudes. The operational use of this training method for ABI and AHI has increased the mean sensitivity of the global regression SST to ~0.9 without increasing regional biases. As a further development towards improved SSTskin retrieval, the piecewise regression SST algorithm was developed, which provides optimal sensitivity in every SST pixel. The paper describes the global and the piecewise regression algorithms trained against analysis SST and illustrates their performance with SST retrievals from the GOES-16 ABI. View Full-Text
Keywords: sea surface temperature; geostationary satellites; training regression SST algorithms; sensitivity to SSTskin; magnitude of diurnal cycle; in situ SST; L4 SST analysis sea surface temperature; geostationary satellites; training regression SST algorithms; sensitivity to SSTskin; magnitude of diurnal cycle; in situ SST; L4 SST analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Petrenko, B.; Ignatov, A.; Kihai, Y.; Pennybacker, M. Optimization of Sensitivity of GOES-16 ABI Sea Surface Temperature by Matching Satellite Observations with L4 Analysis. Remote Sens. 2019, 11, 206.

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