AVHRR Pathfinder version 5 4km global sea surface temperature (SST) seasonal and annual day-night climatologies for 1982-2009 for the Gulf of Mexico (NCEI Accession 0072888)

The 4 km-resolution 5-day time series used to create these seasonal climatologies is computed from 1982-2009 using the Version 5.0 Pathfinder algorithm, an improved version of the previously most successful of the 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 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.

The following is information to be used when reading the DAY-NIGHT GeoTIFFs generated from the seasonal climatology data files:

GeoTIFF_Information:
Version: 1 Key_Revision: 1.0 Tagged_Information: ModelTiepointTag (2,3):0 0 0-180 90 0

ModelPixelScaleTag (1,3):0.0439453 0.0439453 0 End_Of_Tags.

Keyed_Information: GTModelTypeGeoKey (Short,1): ModelTypeGeographic

GTRasterTypeGeoKey (Short,1): RasterPixelIsArea GTCitationGeoKey (Ascii,17): 'LONG/LAT E005' GeographicTypeGeoKey
(Short,1): GCS_WGS_84 GeogAngularUnitsGeoKey (Short,1): Angular_Degree

ProjLinearUnitsGeoKey (Short,1): Linear_Meter End_Of_Keys.

End_Of_GeoTIFF. GCS: 4326/WGS 84 Datum: 6326/World Geodetic System

1984 Ellipsoid: 7030/WGS 84 (6378137.00,6356752.31) Prime Meridian: 8901/Greenwich (0.000000/ 0d 0' 0.00""E) Projection Linear Units:

9001/metre (1.000000m) Corner Coordinates:Upper Left (180d 0' 0.00"W, 90d 0' 0.00"N) Lower Left (180d 0' 0.00"W, 90d 0' 0.00"S) Upper Right
(180d 0' 0.00"E, 90d 0' 0.00"N) Lower Right (180d 0' 0.00"E, 90d 0' 0.00"S) Center ( 0d 0' 0.00"E, 0d 0' 0.00"N)

Note - the GeoTIFF data files for this accession are not colormapped; users may apply a colormap to these files as desired from within a GIS, Matlab, or orther software. The included .jpg and .png browse graphics provide representative views in color.

Additional processes required for SST climatologies: 1. The individual 5-day day-night cloudscreened files generated as part of the Version 5 Pathfinder Project for 1982-2009 were averaged into 3-month periods, or "seasons," to create a set of initial climatologies. For example, Season 1 of 1982, Season 1 of 1983, ...., Season 1 of 2009 were averaged to create a climatology for Season 1. In addition to the pre-screening of any data with a quality flag of less than 4, data with pixel values less than 16 were masked as NoData. This is because a pixel value translates to -1.8 degrees C, which is the coldest sea surface temperature possible. 2. Then, the temperature value was converted into degrees Celsius (Using the function New value = original value * 0.075 -3.0). 3. The time series maximum, minimum, average, number of observations and standard deviation were used for the final climatology data. 4. The final climatology maximum/minimum/average SST was the average of the time series maximum/minimum/average SST; the final climatology standard deviation was the standard deviation based on the seasonal average time series data; the final climatology number of observations was the total number of observations for all the seasonal time series data. Similar procedures were used for the annual climatology products.

These climatologies were developed from data in the following NODC accessions - NCEI accession numbers 001763, 001764, 001765, 001766, 001767, 001768, 001769, 001770, 001771, 001772, 001773, 001774, 001775, 001776, 001777, 001778, 001779, 0043733, 0043739, 0043745, 0043750, and 0043756.

Color classified images were developed for use online. For the color classified .PNG images, 1) a land mask was applied, 2) a an ArcGIS filtering and smoothing filter was utilized, and 3) the classification was done with a 0.5 degree interval starting from 16 to 32 celsius.

Included land mask developed from GSHHS v2.2.0, July 2011. Citation - Wessel, P., and W. H. F. Smith, A Global Self-consistent, Hierarchical, High-resolution Shoreline Database, J. Geophys. Res., 101, B4, pp. 8741-8743, 1996.

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