A separate land mask developed from the Global Self-Consistent High-Resolution Shoreline Database (GSHHS v 2.2.0, Wessel and Smith, 2011) is included with the data files. The color-classified versions of the climatologies were developed for use in visual online applications and are annual and seasonal means only.
Kenneth S. Casey, National Oceanic and Atmospheric Administration (NOAA) National Oceanographic Data Center (NODC), and Edward J. Kearns, Vicki Halliwell, and Robert Evans, University of Miami, Rosenstiel School of Marine and Atmospheric Science (RSMAS), 20060920, 4 km NODC/RSMAS AVHRR Pathfinder Version 5 Seasonal and Annual Day-Night Sea Surface Temperature Climatologies for 1982-2009 for the Gulf of Mexico (NODC Accession 0072888): not applicable Pathfinder Version 5.0, NOAA National Oceanographic Data Center, Silver Spring, Maryland, USA.
This is a Raster data set. It contains the following raster data types:
Horizontal positions are specified in geographic coordinates, that is, latitude and longitude. Latitudes are given to the nearest 0.0439453125. Longitudes are given to the nearest 0.0439453125. Latitude and longitude values are specified in Decimal Degrees.
The horizontal datum used is WGS84.
The ellipsoid used is WGS84.
The semi-major axis of the ellipsoid used is 6378137.
The flattening of the ellipsoid used is 1/298.257223563.
|Range of values|
|Range of values|
|sea surface temperature||The .HDF files are 16-bit files, and pixel values can range from 0 to 65535 (2 to the 16th power). However,realistic pixel values for SST will always be less than 600 or so. SST in degrees C = (0.075 *pixel value) - 3.0, so a pixel value of 600 equals 42 degrees C, a temperature which exceedsnormal SST limits. Temperatures are represented in 0.075 degree C increments|
Kenneth S. Casey, National Oceanographic Data Center (NODC), and Edward J. Kearns, Vicki Halliwell, and Robert Evans, University of Miami, Rosenstiel School of Marine and Atmospheric Science (RSMAS)
(301)713-3272 x197 (voice)
To provide high resolution climatologies of SST with high accuracy and spatial resolution for climate change and ecological studies.
Kenneth S. Casey, National Oceanic and Atmospheric Administration (NOAA) National Oceanographic Data Center (NODC), and Edward J. Kearns, Vicki Halliwell, and Robert Evans, University of Miami, Rosenstiel School of Marine and Atmospheric Science (RSMAS), 20060920, Pathfinder Version 5 5-day Sea Surface Temperature day/night cloudscreened SST data 1982-2009: not applicable Pathfinder Version 5, NOAA National Oceanographic Data Center, Silver Spring, Maryland.
To create the "cloud-screened" 5-day files used to create the seasonal and annual climatologies in this collection, original Pathfinder Version 5 day and night data of quality flags 4-7 were averaged to create global daily SST files, which were then averaged into 5day temporal bins. Version 5.1 data were used for 1982-1984, and Version 5.0 data were used for 1985-2009. The main difference between the 5.0 and 5.1 algorithms is that 5.1 uses a higher resolution data set, the Reynolds Optimum Interpolation 1/4-degree Daily SST Analysis Version 2, as both a first guess SST and in the quality control procedures. The primary effect of this change is to retain more data in the coastal and high gradient regions and in regions where meandering or feature advection is present.
E. 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. 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.
Person who carried out this activity:
(301)713-3272 x197 (voice)
University of Miami Rosenstiel School of Marine and Atmospheric Science, 20010630, AVHRR Pathfinder Oceans: Remote Sensing Group, RSMAS, Miami, FL.
NOAA National Environmental, Satellite, Data, and Information Services (NESDIS)/National Climatic Data Center (NCDC), 19981130, NOAA Polar Orbiter Data User's Guide: NOAA National Climatic Data Center, Asheville, North Carolina.
NASA/Jet Propulsion Laboratory Physical Oceanography, 20031107, NASA/Jet Propulsion Laboratory Physical Oceanography Distributed Active Archive Center (DAAC): NASA/Jet Propulsion Laboratory, Pasadena, CA.
Casey, K.S., and P. Cornillon, 19990630, A comparison of satellite and in situ-based sea surface temperature climatologies: none J. Climate, Volume 12, No. 6, American Meteorological Society, Boston, MA.
Society, American Meteorological , 19990630, Journal of Climate, Vol. 12: None Vol. 12, No. 6, American Meteorological Society, Boston, MA.
Casey, K.S., and P. Cornillon, 20010930, Global and regional sea surface temperature trends: None J. Climate, Volume 14, No. 18, American Meteorological Society, Boston, MA.
Society, American Meteorological , 20010930, Journal of Climate, Vol. 14: None Volume 14, No. 18, American Meteorological Society, Boston, MA.
Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W.Wang, 20020730, An improved in-situ and satellite SST analysis for climate: None J. Climate, Volume 15, No. 13, American Meteorological Society, Boston, MA.
Society, American Meteorological , 20020730, Journal of Climate, Vol. 15: None Volume 15, No.13, American Meteorological Society, Boston, MA.
Kearns, E. J., Hanafin, J. A., Evans, R. H., Minnett, P. J., and Brown, O.B., 20000730, An independent assessment of Pathfinder AVHRR sea surface temperature accuracy using the Marine-Atmosphere Emitted Radiance Interferometer: None Bull. Amer. Met. Soc. 81, No. 7, American Meteorological Society, Boston, Ma.
Society, American Meteorological , 20000730, Bulletin of the American Meteorological Society: None Volume 81, No. 7, American Meteorological Society, Boston, MA.
Barton, I. J., 19950530, Satellite-derived sea surface temperatures: Current status: None J. Geophys. Res., Volume 100, No. C5, American Geophysical Union, Washington, DC.
Union, American Geophysical , 19950530, Journal of Geophysical Research, Vol. 100: None Volume 100, No. C5, American Geophysical Union, Washington, DC.
Kilpatrick, K. A., Podesta, G. P., and Evans, R., 20010515, Overview of the NOAA/NASA Pathfinder algorithm for sea surface temperature and associated matchup database: None Jour. Geophys. Res., Volume 106, No. C5, American Geophysical Union, Washington, DC.
Union, American Geophysical , 20010530, Journal of Geophysical Research, Vol. 106: None Volume 106, No. C5, American Geophysical Union, Washington, DC.
Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent and A. Kaplan, 20030730, Global Analyses of SST, Sea Ice and Night Marine Air Temperature Since the Late 19th Century: None Jour. Geophys. Res., Vol. 108 (No. D14),4407,10.1029/2002JD002670, American Geophysical Union, Washington, DC.This is part of the following larger work.
Union, American Geophysical , 20030730, Journal of Geophysical Research, Vol. 108: None Vol. 108 (No. D14),4407,10.1029/2002JD002670, American Geophysical Union, Washington, DC.
Schluessel, P., W. J. Emery, H. Grassl, and T. Mammen, 19900815, On the skin-bulk temperature difference and its impact on satellite remote sensing of sea surface temperature: None Jour. Geophys. Res., Vol. 95, No. C8, American Geophysical Union, Washington, DC.
Union, American Geophysical , 19900815, Journal of Geophysical Research, Vol. 95: None Vol. 95, No. C8, American Geophysical Union, Washington, DC.
Minnett, P.J. and B. Ward, 20001230, Measurements of near-surface ocean temperature variability - consequences on the validation of AATSR on Envisat: European Space Agency, The Netherlands.This is part of the following larger work.
Agency, European Space , 20001230, Proceedings of the ERS-ENVISAT Symposium, 2000: European Space Agency, The Netherlands.
Wessel, P., and W. H. F. Smith, National Geophysical Data Center, 20110715, A Global Self-consistent, Hierarchical, High-resolution Shoreline Database (GSHHS): National Geophysical Data Center, Boulder, Colorado, U.S.A.
GSHHS is a high-resolution shoreline data set amalgamated from two databases in the public domain. The data have undergone extensive processing and are free of internal inconsistencies such as erratic points and crossing segments. The shorelines are constructed entirely from hierarchically arranged closed polygons. The data can be used to simplify data searches and data selections, or to study the statistical characteristics of shorelines and land-masses. It comes with access software and routines to facilitate decimation based on a standard line-reduction algorithm. See: 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
Source Pathfinder data used have quality flags 4-7, which indicate good data that are free of clouds. For more information on quality flag attribution, see <http://yyy.rsmas.miami.edu/groups/rrsl/pathfinder/Processing/proc_index.html>. Day and night data of quality flags 4-7 were averaged to create global daily SST files, which were then averaged into 5day temporal bins. These 5day files were used to build up seasonal and annual time series, which were then used to calculate seasonal and annual climatologies, respectively. All SST climatology data layers have corresponding standard deviation and pixel count information to indicate data quality and variability.
The major sources of error in geo-locating AVHRR data are (a) drift in the spacecraft clock (which causes errors in the estimated along-track position), and (b) uncertainty errors in spacecraft and sensor attitude.(a) Clock CorrectionTo minimize error in the along track position estimated by the orbital model, a satellite a clock correction factor is applied to the time code imbedded in each piece. The method used to determine these clock correction factors is presented below. The clock aboard a given satellite drifts continually at a relatively constant rate (e.g., for NOAA-14, ~9msday-1) compared to the reference clock on Earth. Because of this drift, the NOAA/NESDIS Satellite Operation Control Center periodically sends a command to the satellite to reset the on-board clock to a new baseline thereby eliminating the accumulation of a large time offset error between the Earth and satellite clocks. To correct for clock drift between these resets, correction factors were determined from a database of satellite clock time and Earth time offsets collected at the RSMAS High Resolution Picture Transmission (HRPT) receiving station. During HRPT transmission, both the satellite clock (used to create the embedded time code in each piece) and the Earth clock are simultaneously available. The clock correction bias was determined by (1) visual examination of the Earth/satellite clock differences collected in the database to locate the precise magnitude and timing of clock resets performed by the Satellite Operation Control Center and (2) recorded time differences between the identified reset periods were then filtered to remove spurious noise, and regressed against the corresponding satellite time to determine the clock drift correction. These drift corrections were then applied to all data time-stamped during a given reset period. Refer to Sea Surface Temperature Global Area Coverage (GAC) Processing Appendix A: Calibration and Navigation Correction Factors for a list of clock offsets for each NOAA spacecraft (<http://www.rsmas.miami.edu/groups/rrsl/pathfinder/Processing/proc_app_a.html>).(b) Attitude CorrectionsAfter clock correction, a nominal attitude correction is then applied to minimize the uncertainty in regard to the direction in which the spacecraft is pointing. The nominal attitude correction applied was determined by averaging the absolute attitude of the spacecraft over many geographic locations and times along the orbital track. The method used to determine the absolute attitude of the spacecraft involves matching a digital coastal outline to a given image and recording the amount of pitch, yaw, and roll required to make the outline and land coincide. This method has the advantage that it can be performed over small geographical distances and is similar to other techniques which rely on widely separated geographical control points to anchor the navigation. The resultant navigation information, output by the SECTOR procedure for each piece, provides the mapping parameters needed to convert between the satellite perspective of pixel and scan line, and Earth-based latitude and longitude coordinates. Refer to Sea Surface Temperature Global Area Coverage (GAC) Processing Appendix A: Calibration and Navigation Correction Factors for attitude correction factors for each NOAA spacecraft (<http://www.rsmas.miami.edu/groups/rrsl/pathfinder/Processing/proc_app_a.html>).
Refer to the Horizontal Positional Accuracy Report for a discussion of sources of error in geo-locating AVHRR data.
These DAY_NIGHT SST climatologies are very nearly globally complete, especially between 60 N and 60 S latitude. However, the AVHRR instrument used to collect the base SST measurements cannot "see" through clouds, so some data gaps exist. The pathfinder GeoTIFF climatology product for GOM used the most updated 5-day day-night average pathfinder cloud-screened data available as of March 2011. Although the data are represented using 16-bit in the .HDF files, 32-bit GeoTIFF files were developed for these day-night seasonal and annual climatologies to facilitate access and use of these data by the widest variety of users. The original .HDF files are 16-bit files, and pixel values can range from 0 to 65535 (2 to the 16th power). However, realistic pixel values for SST will always be less than 600 or so. SST in degC = 0.075 x pixel value - 3. Temperatures are represented in 0.075 degC increments. Land has a value of 1. The Pathfinder Version 5 land mask was derived from a 1km MODIS data set developed by the USGS Land Processes Distributed Active Archive Center (see <http://www-modis.bu.edu/landcover/userguidelc/> for more information).
Files are run against the program (algorithm) MD5 to verify data integrity which generates a code, called an MD5 checksum. After files are transferred from one place to another, the program can be run on the file again and a new code generated. The old MD5 checksum code should be identical to the new MD5 checksum code. If not, the file was somehow corrupted during transfer (see original MD5 documentation at <http://www.isi.edu/in-notes/rfc1321.txt>). Completed climatology files in GeoTIFF format were compared against climatology layers created using similar methodology in Matlab for validation.
Are there legal restrictions on access or use of the data?
- Access_Constraints: None
- Please acknowledge the use of these data with - The Pathfinder Version 5 SST Data were provided by the NOAA/National Oceanographic Data Center at URL: <http://pathfinder.nodc.noaa.gov>
301-713-3277 or 301-713-3280 (voice)
NOAA makes no warranty regarding these data, expressed or implied, nor does the fact of distribution constitute such a warranty. NOAA and NODC cannot assume liability for any damages caused by any errors or omissions in these data, nor as a result of the failure of these data to function on a particular system.
|Data format:||GeoTIFF files comprising climatologies 1982-2009 (seasons as defined by periods Jan-Mar, Apr-Jun, Jul-Sept, Oct-Dec). The data are represented using 32-bit in the .TIFF files. in format .TIF (version Tag Image File Format Version 2.4) GeoTIFF is a metadata format, which provides geographic information to associate with the image data. Size: 14.6|
|Data format:||Arc Grid files comprising climatologies 1982-2009 (seasons as defined by periods Jan-Mar, Apr-Jun, Jul-Sept, Oct-Dec). Climatologies include annual and seasonal mean, maximum, minimum, standard deviation, and number of observations. Associated browse graphics in .png format are included with these data. in format ARCG Raster GIS file format (Arc grid) developed by ESRI Corporation Size: 30.0|
|Data format:||IN folder SST_means_GOM_color_classified_layers SSTlayers, an ArcGIS 10 (.mxd) file with associated shape files and matching .PNG browse graphics. This file displays SST data as color classified layers and was developed primarily for online displays. These files are of GOM SST annual and seasonal MEANS only. in format MXD (version 10.0) ArcGIS v10.0 Size: 18.6|
Data may be directly downloaded through the NODC website at: <http://www.nodc.noaa.gov/Archive/Search/>. NODC can be contacted directly for custom orders. (When requesting data from the NODC, the desired data set may be referred to by the 7-digit number given in the RESOURCE DESCRIPTION field of this metadata record).
Within 24 hours if downloaded via the Internet
Contact the NODC User Services Group via phone/FAX/E-mail: email@example.com
PC. Mac or other server (Unix, Linux), standard Internet browser. FTP capability
301-713-3280 x127 (voice)