Determination of AATSR biases using the OSTIA SST analysis system and a matchup database

Sea surface temperature (SST) analyses are produced on a daily basis at the Met Office using the Operational SST and Sea Ice Analysis (OSTIA) system. OSTIA uses satellite SST data, provided by international agencies via the Global Ocean Data Assimilation Experiment (GODAE) High-Resolution SST Pilot Project (GHRSST-PP) regional/global task sharing (R/GTS) framework, which includes an estimate of bias error (available online at http://www.ghrsst-pp.org). The OSTIA system produces a foundation SST estimate (SSTfnd), which is the SST that is free of diurnal variability, at a resolution of 1/20 degrees (similar to 6 km). Global coverage outputs are provided each day in GHRSST-PP L4 netCDF format. The verification and intercomparison of the OSTIA analysis, with observations and analyses, has revealed a cold bias of approximately 0.1 K in the OSTIA outputs. Because OSTIA uses the operational 1-km Envisat Advanced Along-Track Scanning Radiometer (AATSR) ATS_NR_2P data [via the GHRSST-PP/European Space Agency (ESA) Medspiration Project, available online at http://www.medspiration.org] as a reference dataset for bias adjustment of other satellite data, the AATSR data were identified as the likely cause of the observed bias. To test this, a series of experiments were carried out in June 2006 using the Medspiration AATSR observations in which the Single Sensor Error Statistics (SSES) bias estimate was assigned fixed magnitudes of 0.0, 0.05, 0.15, and 0.2 K. The authors find that the AATSR data have approximately zero bias relative to in situ buoys. Because AATSR measures the SST skin temperature (SSTskin) and was given a mean global SSTskin deviation of -0.17 K (based on in situ radiometer data), this result suggests that ATS_NR_2P SSTskin data have a warm bias of 0.17 K. Using a matchup database of near-contemporaneous 10 arc min AATSR and in situ data, the authors find that the AATSR SSTskin dual- and triple-window retrievals have a warm bias of 0.14 and 0.17 K, respectively, between August 2002 and July 2006. The results of the experiments confirm that the current Medspiration SSES bias correction provided with the Medspiration AATSR L2P observations is poorly specified. The database was not configured to test the relationship between the cloud proximity confidence value and the AATSR bias error. Based on the matchup database and reanalysis results, the authors suggest that Medspiration be modified to use an SSES bias estimate of 0.17 K for all category 2-6 proximity confidence values for the current AATSR dual-view SST ATS_NR_2P products to provide a correct SSTskin estimate. In response to the results presented in this study, operational changes have been made to the Medspiration processing, which improve the bias estimates provided in the AATSR data. The authors suggest that a concerted effort be invested to develop the most appropriate SSES for the AATSR class of sensors that have specific characteristics that must be included in the SSES estimation scheme. The main elements of such a scheme are presented in this paper.