4 km NODC/RSMAS AVHRR Pathfinder v5.0 8-day Data for 2006 (NCEI Accession 0043758)

The Version 5.0 Pathfinder algorithm was used to produce these twice-daily, 5-day, 7-day, 8-day, monthly, and yearly global SST and related parameters (described below). This algorithm is an improved version of the previously most successful of many methods used to derive SST from AVHRR observations (see Barton (1995) for a review of earlier techniques), the University of Miami Pathfinder version v4.2 SST algorithm (described fully in Kilpatrick et al., 2001).

The v4.2 algorithm offered marked improvement over operational retrieval algorithms such as MCSST and was applied to AVHRR data to maximize accuracy and to minimize artificial fluctuations arising from the sequence of AVHRR instruments flown on NOAA's polar-orbiting satellites during the past 2 decades. The 9 km v4.2 Pathfinder SSTs have already been shown to be the highest quality product currently available for the construction of global climatologies (Casey and Cornillon, 1999) and longer-term SST trend determination (Casey and Cornillon, 2001), and have been demonstrated to be accurate within about 0.3 degrees C under optimal conditions (Kearns et al., 2000). Relative to the older 9 km v4.2 Pathfinder data, the new, ~ 4 km resolution Pathfinder Version 5.0 global SSTs increase detail roughly by a factor of four simply by virtue of the increased resolution. The increase in detail over widely used but relatively coarse SST datasets such as Optimally Interpolated SST Version 2 (OISSTv2; Reynolds et al., 2002) and the Hadley Centre's Global Sea Ice and SST (HadISST1; Rayner et al., 2003) is far greater.

In addition to the increased resolution, significant improvements have been made in the Version 5.0 which enhance the usefulness of the SST fields. Currently, these enhancements include the use of sea ice in the quality level determination scheme, inclusion of many inland water bodies, and the use of a greatly improved land mask. The greatest improvements are seen in coastal zones, marginal seas, and boundary current regions where SST gradients are often large and their impact on operational or research products is greatest. Separate SST products for daytime and nighttime AVHRR retrievals are made to better understand the differences in skin and bulk temperatures, since mean differences between AVHRR-measured skin temperatures and bulk temperatures of 0.1 to 0.2 degrees C (Schluessel et al., 1990) and locally varying differences of up to 1.8 degrees C (Minnett et al., 2000) have been observed.

In addition to SST values, the Pathfinder V5.0 Project makes six other parameters available, for a total of seven per time step:
1. All-pixel SST - The all-pixel SST files contain values for each pixel location, including those contaminated with clouds or other sources of error. The Overall Quality Flag values may be used to filter out these unwanted values. The SST value in each pixel location is an average of the highest quality AVHRR Global Area Coverage (GAC) observations available in each roughly 4 km bin.
2. First-guess SST - The Pathfinder algorithm uses a first guess SST provided by the Reynolds Optimally Interpolated SST Version 2 (OISSTv2) product. The OISSTv2 is also used in the quality control procedures.
3. Number of observations - This parameter indicates the number of AVHRR GAC observations falling in each approximately 4 km bin.
4. Standard deviation - This is the standard deviation of the observations in each 4 km bin.
5. Overall quality flag - The overall quality flag is a relative assignment of SST quality based on a hierarchical suite of tests. The Quality Flag varies from 0 to 7, with 0 being the lowest quality and 7 the highest. For more information regarding the suite of tests, see the Kilpatrick et al. (2001) paper cited above.
6. Mask 1 - These files contain a mask code, which along with Mask 2, can be used to determine the tests in the hierarchical suite that were passed or failed, resulting in the Overall Quality Flag.
7. Mask 2 - These files contain a mask code, which along with Mask 1, can be used to determine the tests in the hierarchical suite that were passed or failed, resulting in the Overall Quality Flag.

Note on Pathfinder Program: The Pathfinder program was jointly created by NASA and NOAA through the Earth Observing System (EOS) Program Office in 1990. The focus of the Pathfinder Program was to determine how existing satellite based datasets could be processed and used to study global change. The datasets were designed to be long time-series data processed with stable calibration and community consensus algorithms to better assist the research community. For more information, see: National Aeronautics and Space Administration (NASA). 1993. Earth Observing System (EOS) Reference Handbook, ed. G. Asrar and D. J. Dokken. Washington, D. C.: National Aeronautics and Space Administration, Earth Science Support Office, Document Resource Facility. The 4 km Pathfinder Project effort at the National Oceanic and Atmospheric Administration (NOAA) National Oceanographic Data Center (NODC) and the University of Miami's Rosenstiel School of Marine and Atmospheric Science (RSMAS) is an extension of and improvement on the sea surface temperature (SST) fields from that original NOAA/NASA AVHRR Oceans Pathfinder program.

NOTE: Due to an error with the AVHRR instrument on board NOAA-18, there was a problem with the daytime quality flags for NOAA-18 which resulted in removing some valid data within the sunglint region. All data from NOAA-18 were reprocessed in 2009 to correct this error, including final data from day 156 of 2005 through 2006, and interim data for 2007. These new data have replaced the original data in the following accessions: 0043748 (2005 Daily), 0043749 (2005 Monthly), 0043750 (2005 5-day), 0043751 (2005 7-day), 0043752 (2005 8-day), 0043753 (2006 Daily), 0043754 (2006 Yearly), 0043755 (2006 Monthly), 0043756 (2006 5-day), 0043757 (2006 7-day), 0043758 (2006 8-day), and 0045498 (2007 interim).

In this accession, NCEI has archived multiple versions of these data. The latest (and best) version of these data has the largest version number.