The Coral Reef Temperature Anomaly Database (CoRTAD) Version 1 - Global, 4 km, Sea Surface Temperature and Related Thermal Stress Metrics for 1985-2005 (NODC Accession 0044419)

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What does this data set describe?

Title:
The Coral Reef Temperature Anomaly Database (CoRTAD) Version 1 - Global, 4 km, Sea Surface Temperature and Related Thermal Stress Metrics for 1985-2005 (NODC Accession 0044419)
Abstract:
The Coral Reef Temperature Anomaly Database (CoRTAD) is a collection of sea surface temperature (SST) and related thermal stress metrics, developed specifically for coral reef ecosystem applications but relevant to other ecosystems as well. The CoRTAD contains global, approximately 4 km resolution SST data on a weekly time scale from 1985through 2005. In addition to SST, it contains SST anomaly (SSTA, weekly SST minus weekly climatological SST), thermal stress anomaly (TSA, weekly SST minus the maximum weekly climatological SST), SSTA Degree Heating Week (SSTA_DHW, sum of previous 12 weeks when SSTA >= 1 degree C), SSTA Frequency (number of times over previous 52 weeks that SSTA >= 1 degree C), TSA DHW (TSA_DHW, also known as a Degree Heating Week, sum of previous 12 weeks when TSA >= 1 degree C),and TSA Frequency (number of times over previous 52 weeks that TSA >= 1 degree C).The CoRTAD was created at the NOAA National Oceanographic Data Center in partnership with the University of North Carolina - Chapel Hill, with support from the NOAA Coral Reef Conservation Program.
Supplemental_Information:
[Text below adapted from: Selig, Elizabeth R., Kenneth S. Casey, and John F. Bruno (2008), Global patterns of variability in coral reef temperature anomalies: the importance of fine scale spatial and temporal heterogeneity. Global Change Biology, submitted. Hereafter referred to as "SCB2008".] The CoRTAD was developed using data from the Pathfinder Version 5.0 collection produced by the National Oceanic and Atmospheric Administration's (NOAA) National Oceanographic Data Center (NODC) and the University of Miami's Rosenstiel School of Marine and Atmospheric Science (<http://pathfinder.nodc.noaa.gov>). These sea surface temperature data are derived from the Advanced Very High Resolution Radiometer (AVHRR) sensor and are processed to a resolution of approximately 4.6 km at the equator. These data have the highest resolution covering the longest time period of any satellite-based ocean temperature dataset(see Figure 1 of SCB2008). Weekly averages of day and night data with a quality flag of 4 or better were used, which is a commonly accepted cutoff for "good" data (Kilpatrick et al., 2001, Casey and Cornillon, 1999). By using a day-night average, the number of missing pixels was reduced by 25% with virtually no loss in accuracy (see Table 1 of SCB2008). The Pathfinder algorithm eliminates any observation with a Sea Surface Temperature (SST) more than 2 degrees C different from a relatively coarse resolution SST value based on the Reynolds Optimum Interpolation Sea Surface Temperature (OISST version 2.0) value, a long-term, in situ-based data set (Kilpatrick et al., 2001, Reynolds et al., 2002). Observations were added back into the analysis if the SST was greater than the OISST-5 degrees C,, but less than the OISST+5 degrees C. The 5 degrees C threshold is a reasonable selection that allows diurnal warming events (Kawai and Wada, 2007) or other spatially limited warm spots back into the dataset without including unrealistic and erroneously warm values. Values less than the OISST were not included because they may have been biased by cloud contamination and other satellite errors, which tend to result in cooler SST estimates. These processes resulted in a dataset with only 21.2 percent missing data. To create a gap-free dataset for analysis, 3 x 3 pixel median spatial fill was used. A temporal fill was performed using the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) function in Matlab (The Mathworks Inc., 2006) to fill the remaining gaps. This conservative approach was chosen because it provided interpolated SSTs that are bounded by the nearest available values in time. It also used data from only a very limited spatial domain, which is an important consideration given the variability of coral reef environments. Using these gap-filled data, we then created site-specific climatologies for each reef grid cell to describe long-term temperature patterns over the 21-year dataset (Eqn. 1). The climatology was generated using a harmonic analysis procedure that fits annual and semi-annual signals to the time series of weekly SSTs at each grid cell:climSST(t) = A*cos(2pi*t + B) + C*cos(4pi*t + D) + E (1)where t is time, A and B are coefficients representing the annual phase and amplitude, C and D are the semi-annual phase and amplitude, and E is the long-term temperature mean. Similar approaches have been used for generating climatologies because they are more robust than simple averaging techniques, which can be more susceptible to data gaps from periods of cloudiness (Podesta et al., 1991, Mesias et al., 2007).Sea surface temperatures from AVHRR quantify only the temperature of the 'skin' of the ocean, roughly the first 10 micrometers of the ocean surface (Donlon et al., 2007). Most field surveys of coral cover occur between 1 and 15 m depth. To be useful for coupling with coral reef biological data, these temperature data must be relatively accurate beyond the 'skin' of the ocean. Linear regression was used to examine how data from in situ reef temperature loggers compared with data from the CoRTAD to demonstrate the good accuracy of the CoRTAD temperature data compared to in situ data at a variety of depths and locations around the world (see Table 1 of SCB2008 for details). Temperature anomaly metrics:Several metrics could be used to link coral reef ecosystem health with temperature including trophic structure, diversity or percent coral cover (Newman et al., 2006, Roberts et al., 2002, Bruno and Selig, 2007). However, this analysis focused on coral bleaching and disease because they are key drivers of coral decline and their relationships with temperature patterns are better understood (Aronson and Precht, 2001, Bruno et al., 2007, Glynn, 1993). Analyses were performed on two metrics (see Table 2 of SCB2008): one that is commonly known to lead to bleaching (Liu et al., 2003, Strong et al., 2004, Glynn, 1993), and one that is correlated with increased disease severity (Selig et al., 2006, Bruno et al., 2007). Coral bleaching results when corals lose their symbiotic zooxanthellae (Glynn, 1993, Glynn, 1996). Bleaching is a natural stress response not only to warm temperatures, but also to cool temperatures (Hoegh-Guldberg and Fine, 2004) as well as light and salinity values different from the normal range (Glynn, 1993). Corals can recover from bleaching, but their ability to do so is dependent on the magnitude and duration of the anomaly event (Glynn, 1993). The temperature thresholds that result in coral bleaching vary by location and species (Berkelmans and Willis, 1999). Bleaching is often connected to Thermal Stress Anomalies (TSAs), which are defined as areas where temperatures exceed by 1 degree C or more the climatologically warmest week of the year (Table 2, Glynn, 1993). The temperature anomaly thresholds relevant to disease have been studied in only one pathogen-host system (Selig et al., 2006, Bruno et al., 2007). In that system, changes in disease cases were correlated with Weekly Sea Surface Temperature Anomalies (WSSTAs), temperatures that were 1 degree C greater than the weekly average for that location. The best metric for predicting bleaching or disease may vary according to location, species, and pathogen (Selig et al., 2006, Bruno et al., 2007, Berkelmans, 2002). For example, bleaching on the Great Barrier Reef was best predicted by the maximum anomaly over a 3 day period (Berkelmans et al., 2004), rather than an anomaly metric like the TSA. Although the 7-day averaging approach in the CoRTAD may be too temporally coarse to capture all bleaching events, it is necessary to maintain consistency and minimize gaps in the dataset across broad spatial scales. In addition, the data are less likely to yield false positives for TSAs and will likely capture most WSSTA events, which have a lower temperature threshold. References:Aronson R.B. and W.F. Precht (2001). White-band disease and the changing face of Caribbean coral reefs. Hydrobiologia, 460, 25-38.Berkelmans R. (2002). Time-integrated thermal bleaching thresholds of reefs and their variation on the Great Barrier Reef. Marine Ecology Progress Series, 229, 73-82.Berkelmans R., G. De'ath, S. Kininmonth and W.J. Skirving (2004). A comparison of the 1998 and 2002 coral bleaching events on the Great Barrier Reef: spatial correlation, patterns, and predictions. Coral Reefs, 23, 74-83.Berkelmans R. and B.L. Willis (1999). Seasonal and local spatial patterns in the upper thermal limits of corals on the inshore central Great Barrier Reef. Coral Reefs, 18, 219-228.Bruno J.F. and E.R. Selig (2007). Regional decline of coral cover in the Indo-Pacific: timing, extent, and subregional comparisons. Public Library of Science One, 2, e711.Bruno, J.F., E.R. Selig, K.S. Casey, C.A. Page, B.L. Willis, C.D. Harvell, H. Sweatman, and A. Melendy (2007). Thermal stress and coral cover as drivers of coral disease outbreaks, Public Library of Science Biology, Vol. 5, No. 6, e124.(DOI:10.1371/journal.pbio.0050124)Casey K.S. and P. Cornillon (1999). A comparison of satellite and in situ-based sea surface temperature climatologies. Journal of Climate, 12, 1848-1863.Donlon C., Robinson I., Casey K.S., Vazquez-Cuervo J., Armstrong E., Arino O., Gentemann C., May D., LeBorgne P., Piolle J., Barton I., Beggs H., Poulter D.J.S., Merchant C.J., Bingham A., Heinz S., Harris A., Wick G., Emery B., Minnett P., Evans R., Llewellyn-Jones D., Mutlow C., Reynolds R.W., Kawamura H. and Rayner N. (2007). The global ocean data assimilation experiment high-resolution sea surface temperature pilot project. Bulletin of the American Meteorological Society, 88, 1197-1213.Glynn P.W. (1993). Coral reef bleaching - ecological perspectives. Coral Reefs, 12, 1-17.Glynn P.W. (1996). Coral reef bleaching: facts, hypotheses and implications. Global Change Biology, 2, 495-509. Hoegh-Guldberg O. and Fine M. (2004). Low temperatures cause coral bleaching. Coral Reefs, 23, 444-444.Kawai Y. and Wada A. (2007). Diurnal sea surface temperature variation and its impact on the atmosphere and ocean: A review. Journal of Oceanography, 63, 721-744.Kilpatrick K.A., Podesta G.P. and Evans R. (2001). Overview of the NOAA/NASA advanced very high resolution radiometer Pathfinder algorithm for sea surface temperature and associated matchup database. Journal of Geophysical Research-Oceans, 106, 9179-9197.Liu G., Skirving W. and Strong A.E. (2003). Remote sensing of sea surface temperatures during 2002 Barrier Reef coral bleaching. EOS, 84, 137-144.Mesias J.M., Bisagni J.J. and Brunner A. (2007). A high-resolution satellite-derived sea surface temperature climatology for the western North Atlantic Ocean. Continental Shelf Research, 27, 191-207.Newman M.J.H., Paredes G.A., Sala E. and Jackson J.B.C. (2006). Structure of Caribbean coral reef communities across a large gradient of fish biomass. Ecology Letters, 9, 1216-1227. Podesta G.P., Brown O.B. and Evans R.H. (1991). The annual cycle of satellite-derived sea-surface temperature in the southwestern Atlantic Ocean. Journal of Climate, 4, 457-467.Reynolds R.W., Rayner N.A., Smith T.M., Stokes D.C. and Wang W.Q. (2002). An improved in situ and satellite SST analysis for climate. Journal of Climate, 15, 1609-1625.Roberts C.M., McClean C.J., Veron J.E.N., Hawkins J.P., Allen G.R., McAllister D.E., Mittermeier C.G., Schueler F.W., Spalding M., Wells F., Vynne C. and Werner T.B. (2002). Marine biodiversity hotspots and conservation priorities for tropical reefs. Science, 295, 1280-1284.Selig, E.R., C.D. Harvell, J.F. Bruno, B.L. Willis, C.A. Page, K.S. Casey and H. Sweatman (2006). Analyzing the relationship between ocean temperature anomalies and coral disease outbreaks at broad spatial scales. In; J.T. Phinney, O. Hoegh-Guldberg, J. Kleypas, W. Skirving, and A. Strong (eds.). Coral reefs and climate change: science and management. American Geophysical Union, Washington, DC, Pages 111-128.Selig, E.R., K.S. Casey, and J. Bruno (2008). Global patterns of variability in coral reef temperature anomalies: the importance of fine scale spatial and temporal heterogeneity. Global Change Biology, submitted. Strong A.E., Liu G., Meyer J., Hendee J.C. and Sasko D. (2004). Coral Reef Watch 2002. Bulletin of Marine Science, 75, 259-268.The Mathworks Inc. (2006) Matlab. In. The Mathworks Inc., Natick, MA.

Resource Description: NODC Accession Number 0044419

  1. How should this data set be cited?

    Elizabeth R. Selig, University of North Carolina (UNC) - Chapel Hill (currently, Conservation International), John F. Bruno, UNC - Chapel Hill, and Kenneth S. Casey, National Oceanic and Atmospheric Administration (NOAA) National Oceanographic Data Center (NODC)., 20080901, The Coral Reef Temperature Anomaly Database (CoRTAD) Version 1 - Global, 4 km, Sea Surface Temperature and Related Thermal Stress Metrics for 1985-2005 (NODC Accession 0044419): not applicable CoRTAD Version 1, NOAA National Oceanographic Data Center, Silver Spring, Maryland.

    Online Links:

  2. What geographic area does the data set cover?

    West_Bounding_Coordinate: -180
    East_Bounding_Coordinate: 180
    North_Bounding_Coordinate: 90
    South_Bounding_Coordinate: -90

  3. What does it look like?

    <http://www.nodc.noaa.gov/SatelliteData/Cortad/currmean.jpg> (JPEG)
    A low resolution browse graphic demonstrating the long term mean SST in the CoRTAD.
    <http://www.nodc.noaa.gov/SatelliteData/Cortad/currmax.jpg> (JPEG)
    A low resolution browse graphic demonstrating the long term maximum SST in the CoRTAD.
    <http://www.nodc.noaa.gov/SatelliteData/Cortad/currmin.jpg> (JPEG)
    A low resolution browse graphic demonstrating the long term minimum SST in the CoRTAD.

  4. Does the data set describe conditions during a particular time period?

    Beginning_Date: 01-Jan-1985
    Beginning_Time: Unknown
    Ending_Date: 31-Dec-2005
    Ending_Time: Unknown
    Currentness_Reference: publication date

  5. What is the general form of this data set?

    Geospatial_Data_Presentation_Form: HDF-SDS version 4

  6. How does the data set represent geographic features?

    1. How are geographic features stored in the data set?

      This is a Raster data set. It contains the following raster data types:

      • Dimensions 4096 x 8192 x 1, type Grid Cell

    2. What coordinate system is used to represent geographic features?

      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.

  7. How does the data set describe geographic features?

    Entity_and_Attribute_Overview:
    The CoRTAD contains global, approximately 4 km resolution SST data on a weekly time scale from 1985through 2005. In addition to SST, it contains SST anomaly (SSTA, weekly SST minus weekly climatological SST), thermal stress anomaly (TSA, weekly SST minus the maximum weekly climatological SST), SSTA Degree Heating Week (SSTA_DHW, sum of previous 12 weeks when SSTA >= 1 degree C), SSTA Frequency (number of times over previous 52 weeks that SSTA >= 1 degree C), TSA DHW (TSA_DHW, also known as a Degree Heating Week, sum of previous 12 weeks when TSA >= 1 degree C),and TSA Frequency (number of times over previous 52 weeks that TSA >= 1 degree C).
    Entity_and_Attribute_Detail_Citation:
    See the CoRTAD web site at <http://www.nodc.noaa.gov/SatelliteData/CoRTAD> for more information.


Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)

  2. Who also contributed to the data set?

    Elizabeth R. Selig (UNC-Chapel Hill, currently with Conservation International), Kenneth S. Casey (NODC), and John F. Bruno (UNC-Chapel Hill)

  3. To whom should users address questions about the data?

    Dr. Kenneth S. Casey
    NOAA National Oceanographic Data Center
    Physical scientist
    NOAA National Oceanographic Data Center, SSMC3, 4th Floor, Room 4853, E/OC1, 1315 East-West Highway
    Silver Spring, Maryland 20910
    U.S.A.

    (301) 713-3272 x133 (voice)
    FAX: (301) 713-3300 (FAX)
    Kenneth.Casey@noaa.gov

    Hours_of_Service: 9:00 AM-4:00 PM, EST
    Contact_Instructions: Phone/FAX/E-mail/letter


Why was the data set created?

To provide sea surface temperature data and related thermal stress parameters with good temporal consistency, high accuracy, and fine spatial resolution. The CoRTAD is intended primarily for climate and ecosystem related applications and studies and was designed specifically to address questions concerning the relationship between coral disease and bleaching and temperature stress.


How was the data set created?

  1. From what previous works were the data drawn?

  2. How were the data generated, processed, and modified?

    Date: Mar-2006 (process 1 of 1)
    Details on the processing of the CoRTAD are provided in:Selig, Elizabeth R., Kenneth S. Casey, and John F. Bruno (2008), Global patterns of variability in coral reef temperature anomalies: the importance of fine scale spatial and temporal heterogeneity. Global Change Biology, submitted.

    Person who carried out this activity:

    Dr. Kenneth S. Casey
    NOAA National Oceanographic Data Center
    Physical scientist
    NOAA National Oceanographic Data Center, SSMC3, 4th Floor, Room 4853, E/OC1, 1315 East-West Highway
    Silver Spring, Maryland 20910
    U.S.A.

    (301) 713-3272 x133 (voice)
    FAX: (301) 713-3300 (FAX)
    Kenneth.Casey@noaa.gov

    Hours_of_Service: 9:00 AM-4:00 PM, EST
    Contact_Instructions: Phone/FAX/E-mail/letter
  3. What similar or related data should the user be aware of?

    University of Miami Rosenstiel School of Marine and Atmospheric Science, 20010630, AVHRR Pathfinder Oceans: Remote Sensing Group, RSMAS, Miami, FL.

    Online Links:

    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.

    Online Links:

    NASA/Jet Propulsion Laboratory Physical Oceanography, 20031107, NASA/Jet Propulsion Laboratory Physical Oceanography Distributed Active Archive Center (DAAC): NASA/Jet Propulsion Laboratory, Pasadena, CA.

    Online Links:

    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.

    Online Links:

    Other_Citation_Details: in pp. 1848-1862
    This is part of the following larger work.

    Society, American Meteorological , 19990630, Journal of Climate, Vol. 12: None Vol. 12, No. 6, American Meteorological Society, Boston, MA.

    Online Links:

    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.

    Online Links:

    Other_Citation_Details: pp. 3801-3818
    This is part of the following larger work.

    Society, American Meteorological , 20010930, Journal of Climate, Vol. 14: None Volume 14, No. 18, American Meteorological Society, Boston, MA.

    Online Links:

    Kilpatrick, K. A., Podesta, G. P., and Evans, R., 20010530, 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.

    Online Links:

    Other_Citation_Details: pp. 9179-9197
    This is part of the following larger work.

    Union, American Geophysical , 20010530, Journal of Geophysical Research, Vol. 106: None Volume 106, No. C5, American Geophysical Union, Washington, DC.

    Online Links:


How reliable are the data; what problems remain in the data set?

  1. How well have the observations been checked?

    Details on the accuracy of the CoRTAD are provided in: Selig, Elizabeth R., Kenneth S. Casey, and John F. Bruno (2008), Global patterns of variability in coral reef temperature anomalies: the importance of fine scale spatial and temporal heterogeneity. Global Change Biology, submitted.

  2. How accurate are the geographic locations?

    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 Correction to minimize error in the along track position estimated by the orbital model, a satellite a clock correction factor is applied to the time code embedded 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 recise 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 Corrections After 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>).

  3. How accurate are the heights or depths?

    Refer to the Horizontal Positional Accuracy Report for a discussion of sources of error in geo-locating AVHRR data.

  4. Where are the gaps in the data? What is missing?

    Each pixel over the global ocean is processed to create a gap-free weekly time series. A few pixels over the ocean never have a valid Pathfinder pixel. These locations are not gap filled and are identified in each CoRTAD file by an array called AllBad.

  5. How consistent are the relationships among the observations, including topology?

    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>)


How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?

Access_Constraints: None
Use_Constraints:
Please acknowledge the use of these data with "The Coral Reef Temperature Anomaly Database (CoRTAD) was developed by the NOAA National Oceanographic Data Center and the University of North Carolina - Chapel Hill (Selig, E.R., K.S. Casey, and J. Bruno (2008). Global patterns of variability in coral reef temperature anomalies: the importance of fine scale spatial and temporal heterogeneity. Global Change Biology, submitted.) It was provided by the NOAA National Oceanographic Data Center at URL:<http://www.nodc.noaa.gov/SatelliteData/Cortad/>"

  1. Who distributes the data set? (Distributor 1 of 1)

    NOAA National Oceanographic Data Center
    not applicable
    SSMC3, 4th Floor, E/OC11315 East-West Highway
    Silver Spring, MD 20910
    U.S.A

    301-713-3277 or 301-713-3280 (voice)
    301-713-3301 (FAX)
    nodc.services@noaa.gov

    Hours_of_Service: 8:00 - 6:00 PM, EST
    Contact_Instructions: Phone/FAX/E-mail/letter during business hours
  2. What's the catalog number I need to order this data set?

    Downloadable Data

  3. What legal disclaimers am I supposed to read?

    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.

  4. How can I download or order the data?

  5. Is there some other way to get the data?

    Contact the NODC User Services Group via phone/FAX/E-mail: nodc.services@noaa.gov

  6. What hardware or software do I need in order to use the data set?

    PC, Mac, UNIX or other, standard Internet browser, ability to work with/utilize .HDF files strongly recommended.


Who wrote the metadata?

Dates:
Last modified: 05-Sep-2014
Last Reviewed: 22-Oct-2008
To be reviewed: 20-Aug-2010
Metadata author:
Dr. Kenneth S. Casey
NOAA National Oceanographic Data Center
Physical scientist
NOAA National Oceanographic Data Center, SSMC3, 4th Floor, Room 4853, E/OC1, 1315 East-West Highway
Silver Spring, Maryland 20910
U.S.A.

(301) 713-3272 x133 (voice)
FAX: (301) 713-3300 (FAX)
Kenneth.Casey@noaa.gov

Hours_of_Service: 9:00 AM-4:00 PM, EST
Contact_Instructions: Phone/FAX/E-mail/letter
Metadata standard:
FGDC Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)


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