AVHRR GAC Sea Surface Temperature Reanalysis Version 2 - Boris Petrenko; Victor Pryamitsyn; Alexander Ignatov; Olafur Jonasson; Yury Kihai (Remote Sensing, 2022)
The 40+ years-long sea surface temperature (SST) dataset from 4 km Global Area Coverage (GAC) data of the Advanced Very High-Resolution Radiometers (AVHRR/2s and/3s) flown onboard ten NOAA satellites (N07/09/11/12/14/15/16/17/18/19) has been created under the NOAA AVHRR GAC SST Reanalysis 2 (RAN2) Project. The data were reprocessed with the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) enterprise SST system. Two SST products are reported in the full ~3000 km AVHRR swath: ‘subskin’ (highly sensitive to true skin SST, but debiased with respect to in situ SST) and ‘depth’ (a closer proxy for in situ data, but with reduced sensitivity). The reprocessing methodology aims at close consistency of satellite SSTs with in situ SSTs, in an optimal retrieval domain. Long-term orbital and calibration trends were compensated by daily recalculation of regression coefficients using matchups with drifters and tropical moored buoys (supplemented by ships for N07/09), collected within limited time windows centered at the processed day. The nighttime Sun impingements on the sensor black body were mitigated by correcting the L1b calibration coefficients. The Earth view pixels contaminated with a stray light were excluded. Massive cold SST outliers caused by volcanic aerosols following three major eruptions were filtered out by a modified, more conservative ACSPO clear-sky mask. The RAN2 SSTs are available in three formats: swath L2P (144 10-min granules per 24 h interval) and two 0.02° gridded (uncollated L3U, also 144 granules/24 h; and collated L3C, two global maps per 24 h, one for day and one for the night). This paper evaluates the RAN2 SST dataset, with a focus on the L3C product and compares it with two other available AVHRR GAC L3C SST datasets, NOAA Pathfinder v5.3 and ESA Climate Change Initiative v2.1. Among the three datasets, the RAN2 covers the global ocean more completely and shows reduced regional and temporal biases, improved stability and consistency between different satellites, and in situ SSTs.