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The goal of the NOAA AVHRR GAC Reanalysis (RAN) project is to create long-term time series of uniform sea surface temperature (SST) retrievals (Level 2 and 3 products) from AVHRR data using the Advanced Clear-Sky Processor for Oceans (ACSPO) system. During Phase 1 (‘RAN1’), data of several AVHRR/3s from 2002-2015 were reprocessed. Ongoing Phase 2 (‘RAN2’) aims to cover the full period of AVHRR GAC data from 1981-on. At the time of this writing, we reprocessed five AVHRR/2s onboard NOAA-07, -09, -11, -12 and -14 and two AVHRR/3s onboard NOAA- 15 and -16, and created an initial “beta” RAN2 data set (‘RAN2 B01’) spanning ~22 years from 1981-2003. The ACSPO algorithms for cloud masking and training SST regression coefficients, initially developed for operational SST processing, required modifications to mitigate the issues, specific to the RAN2 period: multiple sensor issues, and insufficient number of in situ SST data and their degraded quality. Another derived complexity, also related to insufficient and poor quality of satellite and in situ data, is the limited availability and suboptimal quality of first guess SSTs, which is used in ACSPO for cloud masking and quality control, and employed in the right part of the Non-Linear SST equations. The paper describes modifications to the ACSPO algorithms made for the RAN2 B01, and demonstrates the resulting improvements in the retrieved SST.
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