Development and evaluation of SST algorithms for GOES-R ABI using MSG SEVIRI as a proxy

a r t i c l e i n f o Cross-evaluation of sea surface temperature (SST) algorithms was undertaken using split-window channels of Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (SEVIRI) as a proxy for the Geostationary Operational Environmental Satellites-R (GOES-R) Advanced Baseline Imager (ABI). The goal of the study was to select the algorithm which provides the highest and the most uniform SST accuracy within the area observed by the geostationary sensor. The previously established algorithms, such as Non-Linear Regression (NLR) and Optimal Estimation (OE) were implemented along with two new algorithms, Incre-mental Regression (IncR) and Corrected Non-Linear Regression (CNLR), developed within preparations for the GOES-R ABI mission. OE, IncR and CNLR adopt the first guesses for SST and brightness temperatures (BT) and retrieve deviations of SST from the first guess (increments). OE retrieves SST increments with inversion of the radiative transfer model, whereas CNLR and IncR use regression equations. The difference between CNLR and IncR is that CNLR uses NLR coefficients, whereas IncR implies optimization of coefficients specifically for incremental formulation. Accuracy and precision of SST retrievals were evaluated by comparison with drifting buoys. The major observations from this study are as follows: 1) all algorithms adopting first guesses for SST and BTs are capable of improving SST accuracy and precision over NLR; and 2) IncR delivers the highest overall SST precision and the most uniform distributions of regional SST accuracy and precision. This paper also addresses implementation and validation issues such as bias correction in simulated BTs; preserving sensitivity of incremental SST retrievals to true SST variations; and selection of criteria for optimization and validation of incremental algorithms. Sea surface temperature (T S , SST; see Tables 1 and 2 for list of abbreviations and notations used in the paper), derived from satellite observations of top-of-the atmosphere thermal infrared radiation, is used in many environmental applications. SST retrievals from geostationary platforms such as Meteosat Second Generation (MSG — e. benefit from continuous observations of a vast ocean area over the full diurnal cycle. Another aspect of a geostationary sensor is that each individual element on the earth's surface within the observed area is viewed at a nearly constant view zenith angle (VZA, θ). This emphasizes the need for taking special care to ensure uniformity of accuracy and precision of SST retrievals within a wide range of VZA, compared with polar-orbiting sensors. SST will be …