Following the launch of the Suomi National Polar-orbiting Partnership satellite in October 2011 with the Visible Infrared Imager Radiometer Suite (VIIRS) sensor onboard, National Oceanic and Atmospheric Administration (NOAA) started generating a global level 2 preprocessed (L2P) sea surface temperature (SST) product. The NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) L2P data are organized into 144 10-min granules per day, with a total volume of ∼27 GB. The L2P product has been successfully assimilated in several level 4 (L4) analyses. At the same time, some other users requested a gridded level 3 (L3) product with a reduced data volume. An L3U “uncollated” product (in which multiple passes over the same grid are independently saved) was produced by mapping the L2P product into equal 0.02° grids. Similar to the L2P, the L3U data are also reported in 10-min granules, but with a daily volume <1 GB. Currently, the NOAA VIIRS L3U SST product is operationally used or tested in several major international numerical weather prediction centers. The L3U shows comparable performance with L2P, suggesting that both products can be used interchangeably as input into L4 analyses. The original L2P pixel-level swath data continue to be produced and available to interested users from NOAA (NCEI) and JPL (Physical Oceanography) data archives.
In response to a request from the NOAA Coral Reef Watch Program, NOAA SST Team initiated reprocessing of 4 km resolution GAC data from AVHRRs flown onboard NOAA and MetOp satellites. The objective is to create a longterm Level 2 Advanced Clear-Sky Processor for Oceans (ACSPO) SST product, consistent with NOAA operations. ACSPO-Reanalysis (RAN) is used as input in the NOAA geo-polar blended Level 4 SST and potentially other Level 4 SST products. In the first stage of reprocessing (reanalysis 1, or RAN1), data from NOAA-15, -16, -17, -18, -19, and Metop-A and -B, from 2002-present have been processed with ACSPO v2.20, and matched up with quality controlled in situ data from in situ Quality Monitor (iQuam) version 1. The ~12 years time series of matchups were used to develop and explore the SST retrieval algorithms, with emphasis on minimizing spatial biases in retrieved SSTs, close reproduction of the magnitudes of true SST variations, and maximizing temporal, spatial and inter-platform stability of retrieval metrics. Two types of SST algorithms were considered: conventional SST regressions, and recently developed incremental regressions. The conventional equations were adopted in the EUMETSAT OSI-SAF formulation, which, according to our previous analyses, provide relatively small regional biases and well-balanced combination of precision and sensitivity, in its class. Incremental regression equations were specifically elaborated to automatically correct for model minus observation biases, always present when RTM simulations are employed. Improved temporal stability was achieved by recalculation of SST coefficients from matchups on a daily basis, with a ±45 day window around the current date. This presentation describes the candidate SST algorithms considered for the next round of ACSPO reanalysis, RAN2.