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optimization of sensitivity of goes 16 abi sea surface temperature by matching satellite observations with l4 analysis

descriptionPublicationkeyboard_double_arrow_right Article 21 Jan 2019Publisher:MDPI AGJournal:Remote Sensing, volume 11, page 206 (eissn: 2072-4292,

Authors: Boris Petrenko; Alexander Ignatov; Yury Kihai; Matthew Pennybacker;

doi: 10.3390/rs11020206

optimization of sensitivity of goes 16 abi sea surface temperature by matching satellite observations with l4 analysis

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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.

Related Organizations

University Research Co (United States)
United States
Global Science & Technology (United States)
United States

Keywords

L4 SST analysis, magnitude of diurnal cycle, sensitivity to SSTskin, training regression SST algorithms, Science, Q, sea surface temperature; geostationary satellites; training regression SST algorithms; sensitivity to SSTskin; magnitude of diurnal cycle; <i>in situ</i> SST; L4 SST analysis, sea surface temperature, <i>in situ</i> SST, geostationary satellites

13 Research products, page 1 of 2

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High latitude sea surface temperatures derived from MODIS infrared measurements
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