Two global level 2 sea surface temperature (SST) products are generated at NOAA from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) sensor data records (L1) with two independent processing systems, the Joint Polar Satellite System (JPSS) Interface Data Processing Segment (IDPS) and the NOAA heritage Advanced Clear-Sky Processor for Oceans (ACSPO). The two systems use different SST retrieval and cloud masking algorithms. Validation against in situ and L4 analyses has shown suboptimal performance of the IDPS product. In this context, existing operational and proposed SST algorithms have been evaluated for their potential implementation in IDPS. This paper documents the evaluation methodology and results. The performance of SST retrievals is characterized with bias and standard deviation with respect to in situ SSTs and sensitivity to true SST. Given three retrieval metrics, all being variable in space and with observational conditions, an additional integral metric is needed to evaluate the overall performance of SST algorithms. Therefore, we introduce the Quality Retrieval Domain (QRD) as a part of the global ocean, where the retrieval characteristics meet predefined specifications. Based on the QRDs analyses for all tested algorithms over a representative range of specifications for accuracy, precision, and sensitivity, we have selected the algorithms developed at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF) for implementation in IDPS and ACSPO. Testing the OSI-SAF algorithms with ACSPO and IDPS products shows the improved consistency between VIIRS SST and Reynolds L4 daily analysis. Further improvement of the IDPS SST product requires adjustment of the VIIRS cloud and ice masks.

[1] The fast Community Radiative Transfer Model (CRTM) has been integrated into National Environmental Satellite Data and Information Service's newly developed Advanced Clear-Sky Processor for Oceans (ACSPO). CRTM is used in conjunction with the National Centers for Environmental Prediction (NCEP) Global Forecast System atmospheric profiles and Reynolds weekly version 2 sea surface temperatures (SST) to simulate clear-sky brightness temperatures (BT). Model BTs are used to improve the ACSPO clear-sky mask, monitor quality of advanced very high resolution radiometer (AVHRR) BTs, and explore physical SST retrievals. This paper documents CRTM implementation in ACSPO version 1 and evaluates nighttime ''model minus observation'' (M-O) BT biases in three bands (3.7, 11, and 12 mm) of four AVHRR/3 instruments onboard NOAA-16, NOAA-17, NOAA-18, and MetOp-A. With careful treatment of input atmospheric and SST data, the agreement is generally good, showing only weak dependencies of M-O biases on view zenith angle, column water vapor, and wind speed. The agreement improves if Reynolds weekly SST is used instead of NCEP SST. Including surface reflection also reduces the M-O bias. After all optimizations, the M-O biases are within several tenths of a Kelvin. Consistency between different platforms is $0.1K, except for NOAA-16 channel 3B, which is biased low compared to other platforms by $0.4K. Our future plans include extending the analyses to daytime data and exploring physical SST retrievals. A web-based tool is being established to continuously monitor the M-O biases and physical SSTs. The validation methodology employed in this paper will be used to quantitatively measure the effect of each improvement on the M-O bias and physical SST.

The National Environmental Satellite, Data, and Information Service (NESDIS) has been operationally generating sea surface temperature (SST) products (T S ) from the Advanced Very High Resolution Radiometers (AVHRR) onboard NOAA and MetOp-A satellites since the early 1980s. Customarily, T S are validated against in situ SSTs. However, in situ data are sparse and are not available globally in near-real time (NRT). This study describes a complementary SST Quality Monitor (SQUAM), which employs global level 4 (L4) SST fields as a reference standard (T R ) and performs statistical analyses of the differences DT S 5 T S 2 T R . The results are posted online in NRT. The T S data that are analyzed are the heritage National Environmental Satellite, Data, and Information Service (NESDIS) SST products from NOAA-16, -17, -18, and -19 and MetOp-A from 2001 to the present. The T R fields include daily Reynolds, real-time global (RTG), Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA), and Ocean Data Analysis System for Marine Environment and Security for the European Area (MERSEA) (ODYSSEA) analyses. Using multiple fields facilitates the distinguishing of artifacts in satellite SSTs from those in the L4 products. Global distributions of DT S are mapped and their histograms are analyzed for proximity to Gaussian shape. Outliers are handled using robust statistics, and the Gaussian parameters are trended in time to monitor SST products for stability and consistency. Additional T S checks are performed to identify retrieval artifacts by plotting DT S versus observational parameters. Cross-platform T S biases are evaluated using double differences, and cross-L4 T R differences are assessed using Hovmö ller diagrams. SQUAM results compare well with the customary in situ validation. All satellite products show a high degree of self-and cross-platform consistency, except for NOAA-16, which has flown close to the terminator in recent years and whose AVHRR is unstable.

The Advanced Clear Sky Processor for Oceans (ACSPO) generates clear-sky products, such as SST, clear-sky radiances, and aerosol, from Advanced Very High Resolution Radiometer (AVHRR)-like measurements. The ACSPO clear-sky mask (ACSM) identifies clear-sky pixels within the ACSPO products. This paper describes the ACSM structure and compares the performances of ACSM and its predecessor, Clouds from AVHRR Extended Algorithm (CLAVRx). ACSM essentially employs online clear-sky radiative transfer simulations enabled within ACSPO with the Community Radiative Transfer Model (CRTM) in conjunction with numerical weather prediction atmospheric [Global Forecast System (GFS)] and SST [Reynolds daily high-resolution blended SST (DSST)] fields. The baseline ACSM tests verify the accuracy of fitting observed brightness temperatures with CRTM, check retrieved SST for consistency with Reynolds SST, and identify ambient cloudiness at the boundaries of cloudy systems. Residual cloud effects are screened out with several tests, adopted from CLAVRx, and with the SST spatial uniformity test designed to minimize misclassification of sharp SST gradients as clouds. Cross-platform and temporal consistencies of retrieved SSTs are maintained by accounting for SST and brightness temperature biases, estimated within ACSPO online and independently from ACSM. The performance of ACSM is characterized in terms of statistics of deviations of retrieved SST from the DSST. ACSM increases the amount of ''clear'' pixels by 30% to 40% and improves statistics of retrieved SST compared with CLAVRx. ACSM is also shown to be capable of producing satisfactory statistics of SST anomalies if the reference SST field for the exact date of observations is unavailable at the time of processing.