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.
Highlights
Diurnal variations in sea surface temperature (SST) play an important role in the energy exchange between the ocean and the atmosphere (e.g., [1,2])
The sensitivity of retrieved SST to TSKIN is a characteristic of an SST retrieval algorithm, which determines the reproduction of true spatial and temporal SST variations in retrieved SST
The sensitivity directly affects the magnitude of diurnal SST variations, estimated from geostationary sensors, and, it is of particular importance for the quantitative monitoring of the diurnal cycle in SST
Summary
Diurnal variations in sea surface temperature (SST) play an important role in the energy exchange between the ocean and the atmosphere (e.g., [1,2]). The most detailed hitherto satellite studies of the diurnal cycle [11,12,13,21] utilized the multiyear dataset of SST produced from the geostationary Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard METEOSAT-8 [22] with the Non-Linear SST (NLSST) algorithm [23], using two split-window bands at 10.8 and 12 μm It was shown, that the SEVIRI NLSST may include significant regional biases [24] and that the sensitivity of the NLSST may be suboptimal [16,25]. In the algorithm [28], currently used in the reprocessing of SEVIRI data at the EUMETSAT Ocean and Sea Ice Satellite Application Facility [29], a regression equation with coefficients derived by matching absolute SSTs with absolute brightness temperatures is applied to the retrieval of SST increments from brightness temperature increments. Trdhseonpetximt sitzeaptitoonwoafrdTsSKoIpNteimstizmaatitoens hoaf sTbSKeINenestthime dateevsehloapsmbeeennt tohfethdeepvieelcoepwmiseentreogfretshseionpiaelcgeowritshemr,ewgrhesicshiopnroavlgidoeristhompt,imwahliacnhdpurnovifiodrems soepntsimitiavlityanind euanchifoSrSmT speixnesli.tiIvnittyhisnpeaapcehr,SwSTe cpoimxepl.arIne tthheispperafpoerrm, awnececomf tphaergelothbealprergfroersmsiaoncaelgoofrtihthemgslotbraailnreedgaregsasiinosnt ainlgsoitruithanmds CtrMaiCneSdSTasg(aGinRs-tISinSsSiTtuananddGCRM-LC4 SSSSTT, sre(sGpRec-ItSiveSlSyT), and eGxRp-lLo4reStShTe,proetsepnetciatilvoeflyfu),rtahnedr sensitivity optimization by employing a PWR algorithm, trained against the CMC (PWR-L4)
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