Sea Surface Temperature (SST)
Team Lead: Sasha Ignatov
SST is a priority JPSS product. It is used in many applications including monitoring of climate variability, operational weather and seasonal forecasting, military and defense operations, validation and/or forcing of the ocean and atmospheric models, ecosystem assessment, tourism, and fisheries. Satellite SST retrievals are assimilated into climate, mesoscale atmospheric, and sea surface numerical models, which form the cornerstone of the operational ocean forecasting systems.
Since launch of S-NPP in October 2011, and opening VIIRS cryoradiator doors in January 2012, the official JPSS SST Interface Data Product System (IDPS) SST EDR has been produced and archived at CLASS (www.class.noaa.gov). Simultaneously, the JPSS SST Team at STAR started producing an experimental SST product from VIIRS, using the NOAA heritage Advanced Clear Sky Processor for Ocean (ACSPO) system. In January 2014, based on two years of side-by-side comparisons in the NOAA online SST Quality Monitor (SQUAM; Dash et al, 2010), and users' feedback, the JPSS Program recommended to re-allocate the JPSS SST requirements from IDPS to ACSPO. In March 2014, ACSPO product became operational in the NOAA NPP Data Exploitation (NDE) system. It has been archived at the PO DAAC and NODC since May 2014.
ACSPO Product and Data Access
ACSPO L2P products and data:
L3U products and data:
EDR Long Term-Monitoring
- ACSPO SST ATBD, (PDF, 2.7 MB)
ACSPO system produces SST in each cloud-free pixel over water. ACSPO Clear-Sky Mask (Petrenko et al, 2010) is used. The JPSS SST algorithm is regression, stratified by day and night (Petrenko et al, 2014). Skin temperature of the ocean (at depths on the order of 10 microns) is retrieved. Level 2 product (in swath projection) has daily data volume of ~27 GB/day. Its gridded (0.02°, approximately 2km at equator; 0.85GB/Day) Level 3U (uncollated) counterpart was introduced in ACSPO v2.40 in May 2015. Both L2 and L3U products are organized in 10min granules and reported in the Group for High-Resolution SST (GHRSST) Data Specifications version 2 (GDS2) NetCDF4 format. In addition to SST, estimates of its systematic and random errors (bias and standard deviation) are also reported.
ACSPO VIIRS L2P SST is currently used in the NOAA Geo-Polar Blended L4 analysis, and in the Canadian Meteorological Centre (CMC) L4 analysis. Based on consideration of data volume, several L4 producers (including Australian Bureau of Meteorology, to support GAMSSA L4 analysis; Met Office, to support OSTIA L4 analysis; and Japanese Met Agency, to support their MGDSST L4 product; and CMC Team, to explore using low-volume L3 data instead of currently used L2) have requested a L3U product. The NOS, in conjunction with STAR, also explores the VIIRS L2/3 SSTs to improve their high- resolution analysis over the Chesapeake Bay.
Calibration and Validation
The primary objective of SST validation is to ensure that the product specifications are met long-term and in a full range of global retrieval conditions, and quantify the performance degradation in unfavorable conditions. The SST performance is assessed in the NOAA SST Quality Monitor system (SQUAM; Dash et al, 2010) against two types of global reference SSTs. The first is global daily L4 SST analyses fields computed from numerous satellite and in situ data (e.g., Reynolds OISST, OSTIA, and CMC). Retrievals are also routinely matched-up with quality controlled in situ SSTs from another NOAA online real-time system, in situ Quality Monitor (iQuam; Xu and Ignatov, 2014).
Along with SST product evaluation, clear-sky Brightness Temperatures (BT) associated with SSTs are also continuously monitored in the 3rd NOAA online system, Monitoring of IR Clear-sky Radiances over Oceans for SST (MICROS; Liang and Ignatov, 2011, 2013). Global SST monitoring in SQUAM is supplemented by the ACSPO Regional Monitor for SST (ARMS) system which focuses on geographical regions of interest, including US coastal regions and other regions worldwide.
ACSPO v2.40 (implemented in May 2015) uses destriped radiances in SST bands as input into ACSPO retrieval system (Bouali and Ignatov, 2014), and added a new L3U product requested by many L4 producers. Pattern recognition techniques are being explored for improved ACSPO clear-sky mask, which specifically target the most challenging coastal, dynamical, and high latitude areas (Gladkova et al, 2015). Improved quality SST imagery, and in particular, accurate processing of the bow-tie deletion zones, is a prerequisite for implementing these improvements (Gladkova et al, 2016). Improved SST retrieval algorithms are also being explored, including using the new window bands (e.g. centered at 8.6 microns).
Work is underway with the University of Wisconsin and in the STAR IT environment to establish ACSPO reprocessing capability. The near-term plan is to consistently reprocess all VIIRS SDR data and generate a uniform time series of ACSPO L2/3 products using the currently tested ACSPO version 2.40. The long term objective is to set up reprocessing capability, and periodically reprocess mission- life SDR data and maintain consistent, high quality JPSS SST EDR.
Bouali, M., and A. Ignatov, 2014: Adaptive Reduction of Striping for improved SST Imagery from S-NPP VIIRS. JTech, 31, 150-163, DOI: 10.1175/JTECH-D-13-00035.1.
Dash, P., A. Ignatov, Y. Kihai, and J. Sapper, 2010: The SST Quality Monitor (SQUAM). JTech, 27, DOI: 10.1175/2010JTECHO756.1
Gladkova, I., Kihai, Y., Ignatov, A., Shahriar, F., & Petrenko, B., 2015: SST Pattern Test in ACSPO Clear-Sky Mask for VIIRS. Remote Sensing of Environment, 160, 87-98. DOI: 10.1016/j.rse.2015.01.003
Gladkova, I.; Ignatov, A.; Shahriar, F.; Kihai, Y.; Hillger, D.; Petrenko, B., 2016: Improved VIIRS and MODIS SST Imagery. Remote Sens. 8, 79. DOI: 10.3390/rs8010079
Liang, X., and A. Ignatov, 2011: Monitoring of IR Clear-sky Radiances over Oceans for SST (MICROS). JTech, 28, 10, 1228-1242, DOI: 10.1175/JTECH-D-10-05023.1.
Liang, X., and A. Ignatov, 2013: AVHRR, MODIS and VIIRS Radiometric Stability and Consistency in SST bands. JGR, 118, 6, 3161-3171, DOI: 10.1002/jgrc.20205
Petrenko, B., A. Ignatov, Y. Kihai, and A. Heidinger, 2010: Clear-sky mask for the Advanced Clear-Sky Processor for Oceans. JTech, 27, 1609-1623. DOI: 10.1175/2010JTECHA1413.1
Petrenko, B., A. Ignatov, Y. Kihai, J. Stroup, and P. Dash, 2014: Evaluation and Selection of SST Regression Algorithms for JPSS VIIRS. JGR, 119, 8, 4580-4599, DOI: 10.1002/2013JD020637.
Xu, F., and A. Ignatov, 2014: In situ SST Quality Monitor (iQuam). JTech, 31, 164-180, DOI: 10.1175/jtech-d-13-00121.1.